From 1d833a755bef246749ec217b2c2e8a596efbfdd4 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Sat, 9 Mar 2024 07:59:04 +0100 Subject: [PATCH 01/17] clean TF_WP branch --- bluemira/magnets/cable.py | 633 ++++++++++++++++++ bluemira/magnets/case.py | 357 ++++++++++ bluemira/magnets/conductor.py | 334 +++++++++ bluemira/magnets/fatigue.py | 527 +++++++++++++++ bluemira/magnets/materials.py | 435 ++++++++++++ bluemira/magnets/strand.py | 307 +++++++++ bluemira/magnets/utils.py | 133 ++++ bluemira/magnets/winding_pack.py | 67 ++ .../magnets/central_solenoid_example.ex.py | 48 ++ examples/magnets/tf_example.py | 172 +++++ 10 files changed, 3013 insertions(+) create mode 100644 bluemira/magnets/cable.py create mode 100644 bluemira/magnets/case.py create mode 100644 bluemira/magnets/conductor.py create mode 100644 bluemira/magnets/fatigue.py create mode 100644 bluemira/magnets/materials.py create mode 100644 bluemira/magnets/strand.py create mode 100644 bluemira/magnets/utils.py create mode 100644 bluemira/magnets/winding_pack.py create mode 100644 examples/magnets/central_solenoid_example.ex.py create mode 100644 examples/magnets/tf_example.py diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py new file mode 100644 index 0000000000..ebe98768f4 --- /dev/null +++ b/bluemira/magnets/cable.py @@ -0,0 +1,633 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +"""Cable class""" + +from typing import Callable + +import matplotlib.pyplot as plt +import numpy as np +from scipy.integrate import solve_ivp +from scipy.optimize import minimize_scalar + +from bluemira.magnets.strand import Strand +from bluemira.magnets.utils import parall_r, serie_r + + +class Cable: + def __init__( + self, + dx: float, + sc_strand: Strand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", + ): + """ + Representation of a cable + + Parameters + ---------- + dx: + x-dimension of the cable [m] + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #TODO: discuss if it is necessary to implement also the cooling material + """ + self.name = name + self._dx = dx + self.sc_strand = sc_strand + self.stab_strand = stab_strand + self.void_fraction = void_fraction + self.d_cooling_channel = d_cooling_channel + self.n_sc_strand = int(n_sc_strand) + self._n_stab_strand = int(n_stab_strand) + self.cos_theta = cos_theta + self._check_consistency() + + @property + def n_stab_strand(self): + """Number of stabilizing strands""" + return self._n_stab_strand + + @n_stab_strand.setter + def n_stab_strand(self, value: int): + self._n_stab_strand = int(np.ceil(value)) + + def res(self, **kwargs): + """ + Cable's equivalent resistivity, computed as the parallel between + strands' resistivity + + Parameters + ---------- + Return + ------ + float [Ohm m] + """ + resistances = np.array([ + self.sc_strand.res(**kwargs) / self.area_sc, + self.stab_strand.res(**kwargs) / self.area_stab, + ]) + res_tot = parall_r(resistances) + return res_tot * self.area + + def cp_v(self, **kwargs): + """ + Strand's equivalent Specific Heat, compute the series between strand's components + + Parameters + ---------- + Return + ------ + float [J/K/m] + """ + weighted_specific_heat = np.array([ + self.sc_strand.cp_v(**kwargs) * self.area_sc, + self.stab_strand.cp_v(**kwargs) * self.area_stab, + ]) + return serie_r(weighted_specific_heat) / (self.area_sc + self.area_stab) + + def _check_consistency(self): + """Check consistency and return True if all checks are passed.""" + if self.dx <= self.d_cooling_channel or self.dy <= self.d_cooling_channel: + print("WARNING: inconsistency between dx, dy and d_cooling_channel") + return False + return True + + @property + def area_stab(self): + """Area of the stabilizer""" + return self.stab_strand.area * self.n_stab_strand + + @property + def area_sc(self): + """Area of the superconductor""" + return self.sc_strand.area * self.n_sc_strand + + @property + def area_cc(self): + """Area of the cooling""" + return self.d_cooling_channel ** 2 / 4 * np.pi + + @property + def area(self): + """Area of the cable considering the void fraction""" + return ( + self.area_sc + self.area_stab + ) / self.void_fraction / self.cos_theta + self.area_cc + + @property + def dx(self): + return self._dx + + @dx.setter + def dx(self, value: float): + self._dx = value + + @property + def dy(self): + """y-dimension of the cable [m]""" + return self.area / self.dx + + def ym(self, **kwargs): + return 0 + + def Kx(self, **kwargs): + """Total equivalent stiffness along x-axis""" + return self.ym(**kwargs) * self.dy / self.dx + + def Ky(self, **kwargs): + """Total equivalent stiffness along y-axis""" + return self.ym(**kwargs) * self.dx / self.dy + + def optimize_n_stab_ths( + self, + t0: float, + tf: float, + initial_temperature: float, + target_temperature: float, + B: Callable, + I: Callable, + bounds: np.ndarray = None, + show: bool = False, + ): + """ + Optimize the number of stabilizer strand in the superconducting cable using a + 0-D hot spot criteria + + Parameters + ---------- + t0: float + initial time + tf: float + final time + initial_temperature: float + temperature [K] at initial time + target_temperature: float + target temperature [K] at final time + B : Callable + The magnetic field [T] as time function + I : Callable + The current [A] flowing through the conductor as time function + bounds: np.ndarray + lower and upper limits for the number of strand in the cable + show: bool + if True the behavior of temperature as function of time is plotted + + Returns + ------- + None + + Notes + ----- + The number of stabilizer strands in the cable is directly modified. An + error is raised in case the optimization process did not converge. + """ + return optimize_n_stab_cable( + self, t0, tf, initial_temperature, target_temperature, B, I, bounds, show + ) + + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None, **kwargs): + if ax is None: + _, ax = plt.subplots() + + pc = np.array([xc, yc]) + a = self.dx / 2 + b = self.dy / 2 + + p0 = np.array([-a, -b]) + p1 = np.array([a, -b]) + p2 = np.array([[a, b]]) + p3 = np.array([-a, b]) + + points_ext = np.vstack((p0, p1, p2, p3, p0)) + pc + points_cc = ( + np.array([ + np.array([np.cos(theta), np.sin(theta)]) * self.d_cooling_channel / 2 + for theta in np.linspace(0, np.radians(360), 19) + ]) + + pc + ) + + ax.fill(points_ext[:, 0], points_ext[:, 1], "gold") + ax.fill(points_cc[:, 0], points_cc[:, 1], "r") + + if show: + plt.show() + return ax + + +class SquareCable(Cable): + def __init__( + self, + sc_strand: Strand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", + ): + """ + Representation of a square cable + + Parameters + ---------- + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #todo decide if it is the case to add also the cooling material + """ + dx = 0.1 + super().__init__( + dx=dx, + sc_strand=sc_strand, + stab_strand=stab_strand, + n_sc_strand=n_sc_strand, + n_stab_strand=n_stab_strand, + d_cooling_channel=d_cooling_channel, + void_fraction=void_fraction, + cos_theta=cos_theta, + name=name, + ) + + @property + def dx(self): + return np.sqrt(self.area) + + @property + def dy(self): + """y-dimension of the cable [m]""" + return self.dx + + +class DummySquareCableHTS(SquareCable): + """ + Dummy square cable with young's moduli set to 120 GPa + + Parameters + ---------- + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #todo decide if it is the case to add also the cooling material + """ + + def ym(self, **kwargs): + return 120e9 + + +class DummySquareCableLTS(SquareCable): + """ + Dummy square cable with young's moduli set to 120 GPa + + Parameters + ---------- + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #todo decide if it is the case to add also the cooling material + """ + + def ym(self, **kwargs): + return 0.1e9 + + +class RoundCable(Cable): + def __init__( + self, + sc_strand: Strand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", + ): + """ + Representation of a square cable + + Parameters + ---------- + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #todo decide if it is the case to add also the cooling material + """ + dx = 0.1 + super().__init__( + dx=dx, + sc_strand=sc_strand, + stab_strand=stab_strand, + n_sc_strand=n_sc_strand, + n_stab_strand=n_stab_strand, + d_cooling_channel=d_cooling_channel, + void_fraction=void_fraction, + cos_theta=cos_theta, + name=name, + ) + + @property + def dx(self): + return np.sqrt(self.area * 4 / np.pi) + + @property + def dy(self): + """y-dimension of the cable [m]""" + return self.dx + + +class DummyRoundCableHTS(RoundCable): + """ + Dummy square cable with young's moduli set to 120 GPa + + Parameters + ---------- + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #todo decide if it is the case to add also the cooling material + """ + + def ym(self, **kwargs): + return 120e9 + + +class DummyRoundCableLTS(RoundCable): + """ + Dummy square cable with young's moduli set to 120 GPa + + Parameters + ---------- + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + #todo decide if it is the case to add also the cooling material + """ + + def ym(self, **kwargs): + return 0.1e9 + + +def _heat_balance_model_cable(t, T, B: Callable, I: Callable, cable: Cable): + """ + Calculate the derivative of temperature (dT/dt) for the heat balance problem. + + Parameters + ---------- + t : float + The current time in seconds. + T : float + The current temperature in Celsius. + B : Callable + The magnetic field [T] as time function + I : Callable + The current [A] flowing through the conductor as time function + cable : Cable + the superconducting cable + + Returns + ------- + dTdt : float + The derivative of temperature with respect to time (dT/dt). + """ + # Calculate the rate of heat generation (Joule dissipation) + if isinstance(T, np.ndarray): + T = T[0] + + Q_gen = (I(t) / cable.area) ** 2 * cable.res(B=B(t), T=T) + + # Calculate the rate of heat absorption by conductor components + Q_abs = cable.cp_v(T=T) + + # Calculate the derivative of temperature with respect to time (dT/dt) + dTdt = Q_gen / Q_abs + + return dTdt + + +def _temperature_evolution( + t0: float, + tf: float, + initial_temperature: float, + B: Callable, + I: Callable, + cable: Cable, +): + solution = solve_ivp( + _heat_balance_model_cable, + [t0, tf], + [initial_temperature], + args=(B, I, cable), + dense_output=True, + ) + + if not solution.success: + raise ValueError("Temperature evolution did not converged") + + return solution + + +def optimize_n_stab_cable( + cable: Cable, + t0: float, + tf: float, + initial_temperature: float, + target_temperature: float, + B: Callable, + I: Callable, + bounds: np.ndarray = None, + show: bool = False, +): + """ + Optimize the number of stabilizer strand in a superconducting cable using a 0-D hot spot criteria + + Parameters + ---------- + cable: Cable + the superconducting cable + t0: float + initial time + tf: float + final time + initial_temperature: float + temperature [K] at initial time + target_temperature: float + target temperature [K] at final time + B : Callable + The magnetic field [T] as time function + I : Callable + The current [A] flowing through the conductor as time function + bounds: np.ndarray + lower and upper limits for the number of strand in the cable + show: bool + if True the behavior of temperature as function of time is plotted + + Returns + ------- + None + + Notes + ----- + The number of stabilizer strands in the cable is directly modified. An error is raised in case the optimization + process did not converge. + """ + + def final_temperature_difference( + n_stab: int, + t0: float, + tf: float, + initial_temperature: float, + target_temperature: float, + B: Callable, + I: Callable, + cable: Cable, + ): + cable.n_stab_strand = n_stab + + solution = _temperature_evolution( + t0=t0, tf=tf, initial_temperature=initial_temperature, B=B, I=I, cable=cable + ) + final_T = float(solution.y[0][-1]) + diff = abs(final_T - target_temperature) + return diff + + method = None + if bounds is not None: + method = "bounded" + + result = minimize_scalar( + fun=final_temperature_difference, + args=(t0, tf, initial_temperature, target_temperature, B, I, cable), + bounds=bounds, + method=method, + ) + + if not result.success: + raise ValueError( + "n_stab optimization did not converge. Check your input parameters or initial bracket." + ) + + solution = _temperature_evolution(t0, tf, initial_temperature, B, I, cable) + final_temperature = solution.y[0][-1] + + print(f"Optimal n_stab: {cable.n_stab_strand}") + print(f"Final temperature with optimal n_stab: {final_temperature} Kelvin") + + if show: + _, ax = plt.subplots() + ax.plot(solution.t, solution.y[0], "r") + time_steps = np.linspace(t0, tf, 100) + ax.plot(time_steps, solution.sol(time_steps)[0], "b") + plt.show() + + return result diff --git a/bluemira/magnets/case.py b/bluemira/magnets/case.py new file mode 100644 index 0000000000..c846a13616 --- /dev/null +++ b/bluemira/magnets/case.py @@ -0,0 +1,357 @@ +import math +from typing import List + +import matplotlib.pyplot as plt +import numpy as np +from scipy.optimize import minimize_scalar + +from bluemira.magnets.conductor import Conductor +from bluemira.magnets.materials import Material +from bluemira.magnets.utils import parall_k, serie_k +from bluemira.magnets.winding_pack import WindingPack + + +class CaseTF: + def __init__( + self, + Ri: float, + dy_ps: float, + dy_vault: float, + theta_TF: float, + mat_case: Material, + WPs: List[WindingPack], + name: str = "", + ): + """ + Case structure for TF coils + + Parameters + ---------- + Ri: + external radius of the coil + dy_ps: + radial thickness of the case cap + dy_vault: + radial thickness of the vault + theta_TF: + toroidal angle of a TF coil + mat_case: + material of the case + WPs: + list of winding packs associated with the case + name: + string identifier + """ + self.name = name + self.dy_ps = dy_ps + self.dy_vault = dy_vault + self.theta_TF = theta_TF + self._rad_theta_TF = np.radians(theta_TF) + self.Ri = Ri + self.mat_case = mat_case + self.WPs = WPs + + @property + def dx_i(self): + """Toroidal length of the coil case at its maximum radial position [m]""" + return 2 * self.Ri * np.tan(self._rad_theta_TF / 2) + + @property + def dx_ps(self): + """Average toroidal length of the ps plate [m]""" + return (self.Ri + (self.Ri - self.dy_ps)) * np.tan(self._rad_theta_TF / 2) + + @property + def R_wp_i(self): + """Maximum radial position for each winding pack""" + dy_wp_cumsum = np.cumsum(np.array([0] + [w.dy for w in self.WPs])) + return np.array([self.Ri - self.dy_ps - y for y in dy_wp_cumsum[0:-1]]) + + @property + def R_wp_k(self): + """Minimum radial position for each winding pack""" + return self.R_wp_i - np.array([w.dy for w in self.WPs]) + + @property + def Rk(self): + """Minimum radial position of case""" + return self.R_wp_k[-1] - self.dy_vault + + @property + def dx_k(self): + """Toroidal length of the case at its minimum radial position""" + return 2 * self.Rk * np.tan(self._rad_theta_TF / 2) + + @property + def dx_vault(self): + """Average toroidal length of the vault""" + return (self.R_wp_k[-1] + self.Rk) * np.tan(self._rad_theta_TF / 2) + + @property + def area(self): + return (self.dx_i + self.dx_k) * (self.Ri - self.Rk) / 2 + + @property + def area_jacket(self): + total_wp_area = np.sum([w.conductor.area * w.nx * w.ny for w in self.WPs]) + return self.area - total_wp_area + + @property + def area_wps_jacket(self): + return np.sum([w.conductor.area_jacket * w.nx * w.ny for w in self.WPs]) + + def Kx_ps(self, **kwargs): + """Equivalent radial mechanical stiffness of ps""" + return self.mat_case.ym(**kwargs) * self.dy_ps / self.dx_ps + + def Kx_lat(self, **kwargs): + """Equivalent radial stiffness of the lateral case part connected to each winding pack""" + dx_lat = np.array([ + (self.R_wp_i[i] + self.R_wp_k[i]) / 2 * np.tan(self._rad_theta_TF / 2) + - w.dx / 2 + for i, w in enumerate(self.WPs) + ]) + dy_lat = np.array([w.dy for w in self.WPs]) + return self.mat_case.ym(**kwargs) * dy_lat / dx_lat + + def Kx_vault(self, **kwargs): + """Equivalent radial stiffness of the vault""" + return self.mat_case.ym(**kwargs) * self.dy_vault / self.dx_vault + + def Kx(self, **kwargs): + """Total equivalent radial stiffness of the case""" + temp = [ + serie_k([ + self.Kx_lat(**kwargs)[i], + w.Kx(**kwargs), + self.Kx_lat(**kwargs)[i], + ]) + for i, w in enumerate(self.WPs) + ] + return parall_k([self.Kx_ps(**kwargs), self.Kx_vault(**kwargs)] + temp) + + def Ky_ps(self, **kwargs): + """Equivalent toroidal stiffness of ps""" + return self.mat_case.ym(**kwargs) * self.dx_ps / self.dy_ps + + def Ky_lat(self, **kwargs): + """Equivalent toroidal stiffness of the lateral case part connected to each winding""" + dx_lat = np.array([ + (self.R_wp_i[i] + self.R_wp_k[i]) / 2 * np.tan(self._rad_theta_TF / 2) + - w.dx / 2 + for i, w in enumerate(self.WPs) + ]) + dy_lat = np.array([w.dy for w in self.WPs]) + return self.mat_case.ym(**kwargs) * dx_lat / dy_lat + + def Ky_vault(self, **kwargs): + """Equivalent toroidal stiffness of the vault""" + return self.mat_case.ym(**kwargs) * self.dx_vault / self.dy_vault + + def Ky(self, **kwargs): + """Total equivalent toroidal stiffness of the case""" + temp = [ + parall_k( + [self.Ky_lat(**kwargs)[i], w.Ky(**kwargs), self.Ky_lat(**kwargs)[i]]) + for i, w in enumerate(self.WPs) + ] + return serie_k([self.Ky_ps(**kwargs), self.Ky_vault(**kwargs)] + temp) + + def _tresca_stress(self, pm: float, fz: float, **kwargs): + """Procedure that calculate Tresca principal stress on the case + + Parameters + ---------- + pm: + radial magnetic pressure + fz: + vertical tension acting on the case + Re: + external radius of the TF coil + I: + total current flowing in the case + kwargs: + arguments necessary to calculate the structural properties of the case + + """ + # The maximum principal stress acting on the case nose is the compressive + # hoop stress generated in the equivalent shell from the magnetic pressure. From + # the Shell theory, for an isotropic continuous shell with a thickness ratio: + beta = self.Rk / (self.Rk + self.dy_vault) + # the maximum hoop stress, corrected to account for the presence of the WP, is + # placed at the innermost radius of the case as: + sigma_theta = ( + 2.0 / (1 - beta ** 2) * pm * self.Kx_vault(**kwargs) / self.Kx(**kwargs) + ) + + # In addition to the radial centripetal force, the second in-plane component + # to be accounted is the vertical force acting on the TFC inner-leg. + # t_z = 0.5*np.log(self.Ri / Re) * MU_0_4PI * (360. / self.theta_TF) * I ** 2 + + # As conservative approximation, the vertical force is considered to act only + # on jackets and vault + total_case_area = (self.dx_i + self.dx_k) * (self.Ri - self.Rk) / 2 + total_wp_area = np.sum([w.conductor.area * w.nx * w.ny for w in self.WPs]) + total_wp_jacket_area = np.sum([ + w.conductor.area_jacket * w.nx * w.ny for w in self.WPs + ]) + sigma_z = fz / (total_case_area - total_wp_area + total_wp_jacket_area) + sigma_tot = sigma_theta + sigma_z + return sigma_tot + + def optimize_vault_radial_thickness( + self, + pm: float, + fz: float, + T: float, + B: float, + allowable_sigma: float, + bounds: np.array = None, + ): + def sigma_difference( + dy_vault: float, + pm: float, + fz: float, + T: float, + B: float, + case: CaseTF, + allowable_sigma: float, + ): + case.dy_vault = dy_vault + sigma = case._tresca_stress(pm, fz, T=T, B=B) + diff = abs(sigma - allowable_sigma) + return diff + + method = None + if bounds is not None: + method = "bounded" + + result = minimize_scalar( + fun=sigma_difference, + args=(pm, fz, T, B, self, allowable_sigma), + bounds=bounds, + method=method, + options={"xatol": 1e-4}, + ) + + if not result.success: + raise ValueError("dx_vault optimization did not converge.") + self.dy_vault = result.x + print(f"Optimal dy_vault: {self.dy_vault}") + print(f"Tresca sigma: {self._tresca_stress(pm, fz, T=T, B=B) / 1e6} MPa") + + return result + + def plot(self, ax=None, show: bool = False, homogenized: bool = False): + if ax is None: + _, ax = plt.subplots() + + p0 = np.array([-self.dx_i / 2, self.Ri]) + p1 = np.array([self.dx_i / 2, self.Ri]) + p2 = np.array([self.dx_k / 2, self.Rk]) + p3 = np.array([-self.dx_k / 2, self.Rk]) + + points_ext = np.vstack((p0, p1, p2, p3, p0)) + + ax.plot(points_ext[:, 0], points_ext[:, 1], "r") + for i, w in enumerate(self.WPs): + xc_w = 0 + yc_w = self.R_wp_i[i] - w.dy / 2 + ax = w.plot(xc=xc_w, yc=yc_w, ax=ax, homogenized=homogenized) + + if show: + plt.show() + + return ax + + def rearrange_conductors_in_wp_type1( + self, + n_conductors: int, + cond: Conductor, + R_wp_i: float, + dx_WP: float, + min_gap_x: float, + n_layers_reduction: int, + ): + """ + Rearrange the total number of conductors into the TF coil case considering a specific conductor + + Parameters + ---------- + n_conductors: + number of supercoductors + cond: + type of conductor + R_wp_i: + initial radial distance at which the first winding pack is placed + dx_WP: + toroidal length of the first winding pack + min_gap_x: + minimum toroidal distance between winding pack and tf coils lateral faces + n_layers_reduction: + number of turns to be removed when calculating a new pancake + + Returns + ------- + np.array: number of turns and layers for each "pancake" + + Note + ---- + The final number of allocated superconductors could slightly differ from the one defined + in n_conductors due to the necessity to close the final layer. + """ + WPs = [] + # number of conductors to be allocated + remaining_conductors = n_conductors + # maximum number of internal iterations + i_max = 50 + i = 0 + while i < i_max and remaining_conductors > 0: + i = i + 1 + print(f"iteration: {i}") + print(f"remaining_conductors: {remaining_conductors}") + + # maximum toroidal dimension of the WP most outer pancake + # dx_WP = 2 * (R_wp_i * np.tan(self._rad_theta_TF / 2) - dx0_wp) + + # maximum number of turns on the considered pancake + if i == 1: + n_layers_max = int(math.floor(dx_WP / cond.dx)) + else: + n_layers_max = n_layers_max - n_layers_reduction + + if n_layers_max < 1: + raise ValueError( + f"n_layers_max: {n_layers_max} < 1. There is not enough space to allocate all the conductors" + ) + + dx_WP = n_layers_max * cond.dx + + gap_0 = R_wp_i * np.tan(self._rad_theta_TF / 2) - dx_WP / 2 + gap_1 = min_gap_x + + max_dy = (gap_0 - gap_1) / np.tan(self._rad_theta_TF / 2) + n_turns_max = min( + int(np.floor(max_dy / cond.dy)), + int(np.ceil(remaining_conductors / n_layers_max)), + ) + + if n_turns_max < 1: + raise ValueError( + f"n_turns_max: {n_turns_max} < 1. There is not enough space to allocate all the conductors" + ) + + WPs.append(WindingPack(conductor=cond, nx=n_layers_max, ny=n_turns_max)) + + remaining_conductors = remaining_conductors - (n_layers_max * n_turns_max) + + if remaining_conductors < 0: + print( + f"WARNING: {abs(remaining_conductors)} have been added to complete the last layer." + ) + + R_wp_i = R_wp_i - n_turns_max * cond.dy + # dx_WP = dx_WP - n_layers_reduction * cond.dx + print(f"remaining_conductors: {remaining_conductors}") + + self.WPs = WPs diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py new file mode 100644 index 0000000000..e1f55ee756 --- /dev/null +++ b/bluemira/magnets/conductor.py @@ -0,0 +1,334 @@ +import matplotlib.pyplot as plt +import numpy as np +from scipy.optimize import minimize_scalar + +from bluemira.magnets.cable import Cable +from bluemira.magnets.materials import Material +from bluemira.magnets.utils import ( + parall_k, + parall_r, + serie_k, + serie_r, +) + + +class Conductor: + def __init__( + self, + cable: Cable, + mat_jacket: Material, + mat_ins: Material, + dx_jacket: float, + dy_jacket: float, + dx_ins: float, + dy_ins: float, + name: str = "", + ): + """ + + Parameters + ---------- + cable: + the conductor cable + mat_jacket: + jacket's material + mat_ins: + insulator's material + dx_jacket: + x-thickness of the jacket + dy_jacket: + y-tickness of the jacket + dx_ins: + x-thickness of the insulator + dy_ins: + y-tickness of the insulator + name: + string identifier + """ + self.name = name + self._dx_jacket = dx_jacket + self._dy_jacket = dy_jacket + self._dy_ins = dy_ins + self._dx_ins = dx_ins + self.mat_ins = mat_ins + self.mat_jacket = mat_jacket + self.cable = cable + + @property + def dx(self): + """x-dimension of the conductor [m]""" + return self.dx_ins * 2 + self.dx_jacket * 2 + self.cable.dx + + @property + def dy(self): + """y-dimension of the conductor [m]""" + return self.dy_ins * 2 + self.dy_jacket * 2 + self.cable.dy + + @property + def dx_jacket(self): + return self._dx_jacket + + @dx_jacket.setter + def dx_jacket(self, value): + self._dx_jacket = value + + @property + def dy_jacket(self): + return self._dy_jacket + + @dy_jacket.setter + def dy_jacket(self, value): + self._dy_jacket = value + + @property + def dx_ins(self): + return self._dx_ins + + @dx_ins.setter + def dx_ins(self, value): + self._dx_ins = value + + @property + def dy_ins(self): + return self._dy_ins + + @dy_ins.setter + def dy_ins(self, value): + self._dy_ins = value + + @property + def area(self): + """Area of the conductor [m^2]""" + return self.dx * self.dy + + @property + def area_jacket(self): + """Area of the jacket""" + return (self.dx - 2 * self.dx_ins) * ( + self.dy - 2 * self.dy_ins + ) - self.cable.area + + @property + def area_ins(self): + """Area of the insulator""" + return self.area - self.area_jacket - self.cable.area + + def res(self, **kwargs): + """ + Cable's equivalent resistivity, computed as the parallel + between strands' resistivity + + Parameters + ---------- + Return + ------ + float [Ohm m] + """ + resistances = np.array([ + self.cable.res(**kwargs) / self.cable.area, + self.mat_jacket.res(**kwargs) / self.area_jacket, + ]) + res_tot = parall_r(resistances) + return res_tot * self.area + + def cp_v(self, **kwargs): + """ + Strand's equivalent Specific Heat, compute the series between strand's components + + Parameters + ---------- + Return + ------ + float [J/K/m] + + TODO: decide if also the insulator should be considered + """ + weighted_specific_heat = np.array([ + self.cable.cp_v(**kwargs) * self.cable.area, + self.mat_jacket.cp_v(**kwargs) * self.area_jacket, + ]) + return serie_r(weighted_specific_heat) / self.area + + def Kx_topbot_ins(self, **kwargs): + return self.mat_ins.ym(**kwargs) * self.dy / self.dx_ins + + def Kx_lat_ins(self, **kwargs): + return self.mat_ins.ym(**kwargs) * self.dy_ins / (self.dx - 2 * self.dx_ins) + + def Kx_lat_jacket(self, **kwargs): + return ( + self.mat_jacket.ym(**kwargs) * self.dy_jacket / ( + self.dx - 2 * self.dx_ins) + ) + + def Kx_topbot_jacket(self, **kwargs): + return self.mat_jacket.ym(**kwargs) * self.cable.dy / self.dx_jacket + + def Kx_cable(self, **kwargs): + return self.cable.ym(**kwargs) * self.cable.dy / self.cable.dx + + def Kx(self, **kwargs): + return (serie_k([self.Kx_topbot_ins(**kwargs) / 2, + parall_k([ + 2 * self.Kx_lat_ins(**kwargs), + 2 * self.Kx_lat_jacket(**kwargs), + serie_k([self.Kx_cable(**kwargs), + self.Kx_topbot_jacket(**kwargs) / 2]), + ]), + ]) + ) + + def Ky_topbot_ins(self, **kwargs): + return self.mat_ins.ym(**kwargs) * self.dx / self.dy_ins + + def Ky_lat_ins(self, **kwargs): + return self.mat_ins.ym(**kwargs) * self.dx_ins / (self.dy - 2 * self.dy_ins) + + def Ky_lat_jacket(self, **kwargs): + return ( + self.mat_jacket.ym(**kwargs) * self.dx_jacket / ( + self.dy - 2 * self.dy_ins) + ) + + def Ky_topbot_jacket(self, **kwargs): + return self.mat_jacket.ym(**kwargs) * self.cable.dx / self.dy_jacket + + def Ky_cable(self, **kwargs): + return self.cable.ym(**kwargs) * self.cable.dx / self.cable.dy + + def Ky(self, **kwargs): + return (serie_k([self.Ky_topbot_ins(**kwargs) / 2, + parall_k([ + 2 * self.Ky_lat_ins(**kwargs), + 2 * self.Ky_lat_jacket(**kwargs), + serie_k([self.Ky_cable(**kwargs), + self.Ky_topbot_jacket(**kwargs) / 2]), + ]), + ]) + ) + + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): + if ax is None: + _, ax = plt.subplots() + + pc = np.array([xc, yc]) + a = self.cable.dx / 2 + self.dx_jacket + b = self.cable.dy / 2 + self.dy_jacket + + p0 = np.array([-a, -b]) + p1 = np.array([a, -b]) + p2 = np.array([[a, b]]) + p3 = np.array([-a, b]) + points_ext_jacket = np.vstack((p0, p1, p2, p3, p0)) + pc + + c = a + self.dx_ins + d = b + self.dy_ins + + p0 = np.array([-c, -d]) + p1 = np.array([c, -d]) + p2 = np.array([[c, d]]) + p3 = np.array([-c, d]) + points_ext_ins = np.vstack((p0, p1, p2, p3, p0)) + pc + + ax.fill(points_ext_ins[:, 0], points_ext_ins[:, 1], "red") + ax.fill(points_ext_jacket[:, 0], points_ext_jacket[:, 1], "blue") + + ax = self.cable.plot(xc=xc, yc=yc, show=False, ax=ax) + + if show: + ax.set_aspect("equal") + plt.show() + + return ax + + +class SquareConductor(Conductor): + def __init__( + self, + cable: Cable, + mat_jacket: Material, + mat_ins: Material, + dx_jacket: float, + dx_ins: float, + name: str = "", + ): + dy_jacket = dx_jacket + dy_ins = dx_ins + super().__init__( + cable=cable, + mat_jacket=mat_jacket, + mat_ins=mat_ins, + dx_jacket=dx_jacket, + dy_jacket=dy_jacket, + dx_ins=dx_ins, + dy_ins=dy_ins, + name=name, + ) + + @property + def dy_jacket(self): + return self.dx_jacket + + @property + def dy_ins(self): + return self.dx_ins + + +def _sigma_r_jacket(conductor: Conductor, pressure: float, f_z: float, T: float, + B: float): + saf_jacket = (conductor.cable.dx + 2 * conductor.dx_jacket) / ( + 2 * conductor.dx_jacket + ) + K = parall_k([ + 2 * conductor.Ky_lat_ins(T=T, B=B), + 2 * conductor.Ky_lat_jacket(T=T, B=B), + serie_k([conductor.Ky_cable(T=T, B=B), + conductor.Ky_topbot_jacket(T=T, B=B) / 2]), + ]) + X_jacket = 2 * conductor.Ky_lat_jacket(T=T, B=B) / K + return pressure * X_jacket * saf_jacket + f_z / conductor.area_jacket + + +def optimize_jacket_conductor( + conductor: Conductor, + pressure: float, + fz: float, + T: float, + B: float, + allowable_sigma: float, + bounds: np.array = None, +): + def sigma_difference( + dx_jacket: float, + pressure: float, + fz: float, + T: float, + B: float, + conductor: Conductor, + allowable_sigma: float, + ): + conductor.dx_jacket = dx_jacket + sigma_r = _sigma_r_jacket(conductor, pressure, fz, T, B) + diff = abs(sigma_r - allowable_sigma) + return diff + + method = None + if bounds is not None: + method = "bounded" + + result = minimize_scalar( + fun=sigma_difference, + args=(pressure, fz, T, B, conductor, allowable_sigma), + bounds=bounds, + method=method, + options={"xatol": 1e-4}, + ) + + if not result.success: + raise ValueError("dx_jacket optimization did not converge.") + conductor.dx_jacket = result.x + print(f"Optimal dx_jacket: {conductor.dx_jacket}") + print(f"Averaged sigma_r: {_sigma_r_jacket(conductor, pressure, fz, T, B) / 1e6} " + f"MPa") + + return result diff --git a/bluemira/magnets/fatigue.py b/bluemira/magnets/fatigue.py new file mode 100644 index 0000000000..fe7de2fe8d --- /dev/null +++ b/bluemira/magnets/fatigue.py @@ -0,0 +1,527 @@ +# bluemira is an integrated inter-disciplinary design tool for future fusion +# reactors. It incorporates several modules, some of which rely on other +# codes, to carry out a range of typical conceptual fusion reactor design +# activities. +# +# Copyright (C) 2021-2023 M. Coleman, J. Cook, F. Franza, I.A. Maione, S. McIntosh, +# J. Morris, D. Short +# +# bluemira is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# bluemira is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with bluemira; if not, see . + +""" +Paris Law fatigue model with FE-inspired analytical crack propagation +""" + +import abc +from dataclasses import dataclass + +import numpy as np + +__all__ = [ + "ConductorInfo", + "EllipticalEmbeddedCrack", + "ParisFatigueMaterial", + "ParisFatigueSafetyFactors", + "QuarterEllipticalCornerCrack", + "SemiEllipticalSurfaceCrack", + "calculate_n_pulses", +] + + +@dataclass +class ConductorInfo: + """ + Cable in conduit conductor information for Paris fatigue model + """ + + tk_radial: float # [m] in the loaded direction + width: float # [m] in the loaded direction + max_hoop_stress: float # [Pa] + residual_stress: float # [Pa] + walker_coeff: float + + +@dataclass +class ParisFatigueMaterial: + """ + Material properties for the Paris fatigue model + """ + + C: float # Paris law material constant + m: float # Paris law material exponent + K_ic: float # Fracture toughness [Pa/m^(1/2)] + + +@dataclass +class ParisFatigueSafetyFactors: + """ + Safety factors for the Paris fatigue model + """ + + sf_n_cycle: float + sf_depth_crack: float + sf_width_crack: float + sf_fracture: float + + +def _stress_intensity_factor( + hoop_stress: float, + bend_stress: float, + a: float, + H: float, # noqa: N803 + Q: float, # noqa: N803 + F: float, +) -> float: + """ + Equation 1a of Newman and Raju, 1984 + """ + return (hoop_stress + H * bend_stress) * np.sqrt(np.pi * a / Q) * F + + +def _boundary_correction_factor( + a_d_t, m1: float, m2: float, m3: float, g: float, f_phi: float, f_w: float +) -> float: + """ + Equation 1b of Newman and Raju, 1984 + """ + return (m1 + m2 * a_d_t ** 2 + m3 * a_d_t ** 4) * g * f_phi * f_w + + +def _bending_correction_factor(h1: float, h2: float, p: float, phi: float) -> float: + """ + Equation 1c of Newman and Raju, 1984 + """ + return h1 + (h2 - h1) * np.sin(phi) ** p + + +def _ellipse_shape_factor(ratio: float) -> float: + """ + Equation 2 of Newman and Raju, 1984 + """ + return 1.0 + 1.464 * ratio ** 1.65 + + +def _angular_location_correction(a: float, c: float, phi: float) -> float: + """ + Equation 10 and 13 of Newman and Raju, 1984 + """ + if a <= c: + return ((a / c) ** 2 * np.cos(phi) ** 2 + np.sin(phi) ** 2) ** 0.25 # (10) + return ((c / a) ** 2 * np.sin(phi) ** 2 + np.cos(phi) ** 2) ** 0.25 # (13) + + +def _finite_width_correction(a_d_t: float, c: float, w: float) -> float: + """ + Equation 11 of Newman and Raju, 1984 + """ + return 1.0 / np.sqrt(np.cos(np.sqrt(a_d_t) * np.pi * c / (2 * w))) # (11) + + +class Crack(abc.ABC): + """ + Crack description ABC for the Paris fatigue model + + Parameters + ---------- + depth: + Crack depth in the plate thickness direction + width: + Crack width along the plate length direction + """ + + alpha = None + + def __init__(self, depth: float, width: float): + self.depth = depth # a + self.width = width # c + + @classmethod + def from_area(cls, area: float, aspect_ratio: float): + """ + Instatiate a crack from an area and aspect ratio + """ + depth = np.sqrt(area / (cls.alpha * np.pi * aspect_ratio)) + width = aspect_ratio * depth + return cls(depth, width) + + @property + def area(self) -> float: + """ + Cross-sectional area of the crack + """ + return self.alpha * np.pi * self.depth * self.width + + @abc.abstractmethod + def stress_intensity_factor( + self, + hoop_stress: float, + bend_stress: float, + t: float, + w: float, + a: float, + c: float, + phi: float, + ) -> float: + """ + Calculate the crack stress intensity factor + """ + pass + + +class QuarterEllipticalCornerCrack(Crack): + """ + Quarter-elliptical corner crack + + Parameters + ---------- + depth: + Crack depth in the plate thickness direction + width: + Crack width along the plate length direction + """ + + alpha = 0.25 + + def stress_intensity_factor( + self, + hoop_stress: float, + bend_stress: float, + t: float, + w: float, + a: float, + c: float, + phi: float, + ) -> float: + """ + Calculate quarter-elliptical corner crack stress intensity factor. + + Parameters + ---------- + hoop_stress: + Hoop stress in the plate [Pa] + bend_stress: + Bending stress in the plate [Pa] + t: + Plate thickness [m] + w: + Plate width [m] + a: + Crack depth [m] + c: + Crack width [m] + phi: + Crack angle [rad] + + Returns + ------- + Stress intensity factor + + Notes + ----- + Newman and Raju, 1984, Stress-intensity factor equations for cracks in + three-dimensional finite bodies subjected to tension and bending loads + https://ntrs.nasa.gov/api/citations/19840015857/downloads/19840015857.pdf + """ + a_d_t = a / t + + if a <= c: # a/c <= 1 + ratio = a / c + m1 = 1.08 - 0.03 * ratio # (39) + m2 = -0.44 + 1.06 / (0.3 + ratio) # (40) + m3 = -0.5 + 0.25 * ratio + 14.8 * (1 - ratio) ** 15 # (41) + g1 = 1 + (0.08 + 0.4 * a_d_t ** 2) * (1 - np.sin(phi)) ** 3 # (42) + g2 = 1 + (0.08 + 0.15 * a_d_t ** 2) * (1 - np.cos(phi)) ** 3 # (43) + + g21 = -1.22 - 0.12 * ratio # (24) + g22 = 0.64 - 1.05 * ratio ** 0.75 + 0.47 * ratio ** 1.5 # (46) + h1 = 1.0 - 0.34 * a_d_t - 0.11 * ratio * a_d_t # (22) + h2 = 1.0 + g21 * a_d_t + g22 * a_d_t ** 2 # (23) + else: # a/c > 1 + ratio = c / a + m1 = np.sqrt(ratio) * (1.08 - 0.03 * ratio) # (47) + m2 = 0.375 * ratio ** 2 # (48) + m3 = -0.25 * ratio ** 2 # (49) + g1 = 1 + (0.08 + 0.4 * (c / t) ** 2) * (1 - np.sin(phi)) ** 3 # (50) + g2 = 1 + (0.08 + 0.15 * (c / t) ** 2) * (1 - np.cos(phi)) ** 3 # (51) + + g11 = -0.04 - 0.41 * ratio # (33) + g12 = 0.55 - 1.93 * ratio ** 0.75 + 1.38 * ratio ** 1.5 # (34) + g21 = -2.11 + 0.77 * ratio # (35) + g22 = 0.64 - 0.72 * ratio ** 0.75 + 0.14 * ratio ** 1.5 # (52) + h1 = 1.0 + g11 * a_d_t + g12 * a_d_t ** 2 # (31) + h2 = 1.0 + g21 * a_d_t + g22 * a_d_t ** 2 # (32) + + llambda = c / w * np.sqrt(a_d_t) + f_w = ( # (44) + 1.0 + - 0.2 * llambda + + 9.4 * llambda ** 2 + - 19.4 * llambda ** 3 + + 27.1 * llambda ** 4 + ) + f_phi = _angular_location_correction(a, c, phi) + + p = 0.2 + ratio + 0.6 * a_d_t # (21 & 30) + H = _bending_correction_factor(h1, h2, p, phi) # noqa: N806 + Q = _ellipse_shape_factor(ratio) # noqa: N806 + F = _boundary_correction_factor(a_d_t, m1, m2, m3, g1 * g2, f_phi, f_w) + return _stress_intensity_factor(hoop_stress, bend_stress, a, H, Q, F) + + +class SemiEllipticalSurfaceCrack(Crack): + """ + Semi-elliptical surface crack + + Parameters + ---------- + depth: + Crack depth in the plate thickness direction + width: + Crack width along the plate length direction + """ + + alpha = 0.5 + + def stress_intensity_factor( + self, + hoop_stress: float, + bend_stress: float, + t: float, + w: float, + a: float, + c: float, + phi: float, + ) -> float: + """ + Calculate semi-elliptical surface crack stress intensity factor. + + Parameters + ---------- + hoop_stress: + Hoop stress in the plate [Pa] + bend_stress: + Bending stress in the plate [Pa] + t: + Plate thickness [m] + w: + Plate width [m] + a: + Crack depth [m] + c: + Crack width [m] + phi: + Crack angle [rad] + + Returns + ------- + Stress intensity factor + + Notes + ----- + Newman and Raju, 1984, Stress-intensity factor equations for cracks in + three-dimensional finite bodies subjected to tension and bending loads + https://ntrs.nasa.gov/api/citations/19840015857/downloads/19840015857.pdf + """ + a_d_t = a / t + + if a <= c: # a/c <= 1 + ratio = a / c + m1 = 1.13 - 0.09 * ratio # (16) + m2 = -0.54 + 0.89 / (0.2 + ratio) # (17) + m3 = 0.5 - 1.0 / (0.65 + ratio) + 14.0 * (1 - ratio) ** 24 # (18) + g = 1.0 + (0.1 + 0.35 * a_d_t ** 2) * (1 - np.sin(phi)) ** 2 # (19) + + g21 = -1.22 - 0.12 * ratio # (24) + g22 = 0.55 - 1.05 * ratio ** 0.75 + 0.47 * ratio ** 1.5 # (25) + h1 = 1.0 - 0.34 * a_d_t - 0.11 * ratio * a_d_t # (22) + h2 = 1.0 + g21 * a_d_t + g22 * a_d_t ** 2 # (23) + + else: # a/c > 1 + ratio = c / a + m1 = np.sqrt(ratio) * (1.0 + 0.04 * ratio) # (26) + m2 = 0.2 * ratio ** 4 # (27) + m3 = -0.11 * ratio ** 4 # (28) + g = 1.0 + (0.1 + 0.35 * ratio * a_d_t ** 2) * (1 - np.sin(phi)) ** 2 # (29) + + g11 = -0.04 - 0.41 * ratio # (33) + g12 = 0.55 - 1.93 * ratio ** 0.75 + 1.38 * ratio ** 1.5 # (34) + g21 = -2.11 + 0.77 * ratio # (35) + g22 = 0.55 - 0.72 * ratio ** 0.75 + 0.14 * ratio ** 1.5 # (36) + h1 = 1.0 + g11 * a_d_t + g12 * a_d_t ** 2 # (31) + h2 = 1.0 + g21 * a_d_t + g22 * a_d_t ** 2 # (32) + + f_phi = _angular_location_correction(a, c, phi) + f_w = _finite_width_correction(a_d_t, c, w) + p = 0.2 + ratio + 0.6 * a_d_t # (21 & 30) + H = _bending_correction_factor(h1, h2, p, phi) # noqa: N806 + Q = _ellipse_shape_factor(ratio) # noqa: N806 + F = _boundary_correction_factor(a_d_t, m1, m2, m3, g, f_phi, f_w) + return _stress_intensity_factor(hoop_stress, bend_stress, a, H, Q, F) + + +class EllipticalEmbeddedCrack(Crack): + """ + Full elliptical embedded crack + + Parameters + ---------- + depth: + Crack depth in the plate thickness direction + width: + Crack width along the plate length direction + """ + + alpha = 1.0 + + def stress_intensity_factor( + self, + hoop_stress: float, + bend_stress: float, + t: float, + w: float, + a: float, + c: float, + phi: float, + ) -> float: + """ + Calculate elliptical embedded crack stress intensity factor. + + Parameters + ---------- + hoop_stress: + Hoop stress in the plate [Pa] + bend_stress: + Bending stress in the plate [Pa] + t: + Plate thickness [m] + w: + Plate width [m] + a: + Crack depth [m] + c: + Crack width [m] + phi: + Crack angle [rad] + + Returns + ------- + Stress intensity factor + + Notes + ----- + Newman and Raju, 1984, Stress-intensity factor equations for cracks in + three-dimensional finite bodies subjected to tension and bending loads + https://ntrs.nasa.gov/api/citations/19840015857/downloads/19840015857.pdf + """ + # NOTE: for embedded cracks, t is defined as one-half the plate thickness + t = 0.5 * t + a_d_t = a / t + + if a <= c: # a/c <= 1 + ratio = a / c + m1 = 1.0 # (6) + + else: # a/c > 1 + ratio = c / a + m1 = np.sqrt(ratio) # (12) + + m2 = 0.05 / (0.11 + (a / c) ** 1.5) # (7) + m3 = 0.29 / (0.23 + (a / c) ** 1.5) # (8) + g = 1.0 - (a_d_t ** 4 * np.sqrt(2.6 - 2 * a_d_t)) / (1 + 4 * a / c) * np.abs( + np.cos(phi) + ) # (9) + f_phi = _angular_location_correction(a, c, phi) + f_w = _finite_width_correction(a_d_t, c, w) + + Q = _ellipse_shape_factor(ratio) # noqa: N806 + F = _boundary_correction_factor(a_d_t, m1, m2, m3, g, f_phi, f_w) + return _stress_intensity_factor(hoop_stress, bend_stress, a, 0.0, Q, F) + + +def calculate_n_pulses( + conductor: ConductorInfo, + crack: Crack, + material: ParisFatigueMaterial, + safety: ParisFatigueSafetyFactors, +) -> int: + """ + Calculate the number of plasma pulses possible prior to fatigue. + + Parameters + ---------- + conductor: + Conductor information + crack: + Postulated initiating crack size + material: + Material values + safety: + Safety factors + + Returns + ------- + Number of plasma pulses + + Notes + ----- + Assumes two stress cycles per pulse. + Calculates using the lifecycle method. + """ + mean_stress_ratio = conductor.residual_stress / ( + conductor.max_hoop_stress + conductor.residual_stress + ) + + C_r = material.C * (1 - mean_stress_ratio) ** ( # noqa: N806 + material.m * (conductor.walker_coeff - 1) + ) + + max_crack_depth = conductor.tk_radial / safety.sf_depth_crack + max_crack_width = conductor.width / safety.sf_width_crack + max_stress_intensity = material.K_ic / safety.sf_fracture + + a = crack.depth + c = crack.width + K_max = 0.0 # noqa: N806 + n_cycles = 0 + + delta = 1e-4 # Crack size increment + + while a < max_crack_depth and c < max_crack_width and K_max < max_stress_intensity: + Ka = crack.stress_intensity_factor( # noqa: N806 + conductor.max_hoop_stress, + 0.0, + conductor.tk_radial, + conductor.width, + a, + c, + 0.5 * np.pi, + ) + Kc = crack.stress_intensity_factor( # noqa: N806 + conductor.max_hoop_stress, + 0.0, + conductor.tk_radial, + conductor.width, + a, + c, + 0.0, + ) + K_max = max(Ka, Kc) # noqa: N806 + + a += delta / (Ka / K_max) ** material.m + c += delta / (Kc / K_max) ** material.m + n_cycles += delta / (C_r * K_max ** material.m) + + n_cycles /= safety.sf_n_cycle + + return n_cycles // 2 diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py new file mode 100644 index 0000000000..041c945acd --- /dev/null +++ b/bluemira/magnets/materials.py @@ -0,0 +1,435 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +""" +Material database used for magnets +""" + +import math + + +class OperationalPoint(dict): + def __init__(self, *args, **kwargs): + # Set default values {'T': 0, 'B': 0} + if "T" not in kwargs: + kwargs["T"] = 0 + if "B" not in kwargs: + kwargs["B"] = 0 + super().__init__(*args, **kwargs) + + +class Material: + """Generic Material""" + + def density(self, **kwargs): + """Density""" + return 0 + + def ym(self, **kwargs): + """Young module""" + return 1e-6 + + def res(self, **kwargs): + """Electrical Resistivity""" + return 1e6 + + def cp_v(self, **kwargs): + """Specific heat (constant volume)""" + return 0 + + +class AISI_316LN(Material): + """ + Stainless steel (316 LN, with extra low carbon content, aged) + + Ref + --- + EFDA Material Data Compilation for Superconductor Simulation + P. Bauer, H. Rajainmaki, E. Salpietro + EFDA CSU, Garching, 04/18/07 + + """ + + def density(self, **kwargs): + """Material density [kg/m³]""" + return 7890.0 + + def ym(self, T: float, **kwargs): + """ + Young modulus + + Ref + --- + Data fits from N. Mitchell, “Finite element simulations of elasto-plastic processes” + Cryogenics 45 (2005) 501–515 + + Parameters + ---------- + T: + Operating temperature [K] + + Returns + ------- + float [Pa] + """ + if T > 173: + ym = 200.4 * 1e9 - 8.1221e-2 * (T - 273) * 1e9 + else: + ym = 208.5 * 1e9 + return ym + + def res(self, T: float, **kwargs): + """ + Electrical Resistivity + + Ref + --- + Fit of data quoted by J. Davis in ITER Material Properties Handbook, + ITER Document No. S 74 RE 1 + + Parameters + ---------- + T: + Operating temperature [K] + + Return + ------ + float [Ohm m] + """ + return ( + 76.2063 + (0.071375 * (T - 273.15)) - (2.3109 * 1e-5 * (T - 273.15) ** 2) + ) * 1e-8 + + +class Copper100(Material): + def __init__(self): + """ + Copper (OFHC, high purity RRR~100, annealed) + + Ref + --- + EFDA Material Data Compilation for Superconductor Simulation + P. Bauer, H. Rajainmaki, E. Salpietro + EFDA CSU, Garching, 04/18/07 + + """ + # cp300: Specific heat known data-point at 300 K [J/K/m³] + self.cp300 = 3.454e6 + # gamma: Debye fit parameter for Cp calculation [J/K**2/kg] + self.gamma = 0.011 + # beta: Grueneisen fit parameter for Cp calculation [J/K**4/kg] + self.beta = 0.0011 + # RRR: Residual-resistance ratio (ratio of the resistivity of a material at room temperature and at 0 K) + self.RRR = 100 + + def density(self, **kwargs): + """Material density [kg/m³]""" + return 8960 + + def res(self, T: float, B: float, **kwargs): + """ + Electrical Resistivity + + Ref + --- + NIST MONOGRAPH 177 J.Simon, E.S.Drexler and R.P.Reed, "Properties of + Copper and Copper Alloys at Cryogenic Temperatures", 850 pages, February 1992, U.S. + Government Printing Office, Washington, DC 20402-9325 + + Parameters + ---------- + T: + Operating temperature [K] + B: + Operating magnetic field [T] + + Return + ------ + float [Ohm m] + """ + # print(f"parameters T: {T}, B: {B}") + rho1 = (1.171 * (10 ** -17) * (T ** 4.49)) / ( + 1 + (4.5 * (10 ** -7) * (T ** 3.35) * (math.exp(-((50. / T) ** 6.428)))) + ) + rho2 = ( + (1.69 * (10 ** -8) / self.RRR) + + rho1 + + 0.4531 * ((1.69 * (10 ** -8) * rho1) / ( + self.RRR * rho1 + 1.69 * (10 ** -8))) + ) + A = math.log10(1.553 * (10 ** -8) * B / rho2) + a = ( + -2.662 + + (0.3168 * A) + + (0.6229 * (A ** 2)) + - (0.1839 * (A ** 3)) + + (0.01827 * (A ** 4)) + ) + + return rho2 * (1 + (10 ** a)) + + def cp_v(self, T: float, **kwargs): + """ + Specific heat + + Ref + --- + L. Dresner, “Stability of Superconductors”, Plenum Press, NY, 1995 + + Note + ---- + The specific heat over the whole temperature range is obtained through fitting the function to the + known low temperature and high temperature data. + + Parameters + ---------- + T: + Operating temperature [K] + Return + ------ + float [J/K/m³] + """ + density = self.density(T=T, **kwargs) + c_plow = (self.beta * (T ** 3)) + (self.gamma * T) + return 1 / ((1 / self.cp300) + (1 / (c_plow * density))) + + +class Copper300(Material): + def __init__(self): + """ + Copper (OFHC, high purity RRR~100, annealed) + + Ref + --- + EFDA Material Data Compilation for Superconductor Simulation + P. Bauer, H. Rajainmaki, E. Salpietro + EFDA CSU, Garching, 04/18/07 + + """ + # cp300: Specific heat known data-point at 300 K [J/K/m³] + self.cp300 = 3.454e6 + # gamma: Debye fit parameter for Cp calculation [J/K**2/kg] + self.gamma = 0.011 + # beta: Grueneisen fit parameter for Cp calculation [J/K**4/kg] + self.beta = 0.0011 + # RRR: Residual-resistance ratio (ratio of the resistivity of a material at room temperature and at 0 K) + self.RRR = 300 + + def density(self, **kwargs): + """Material density [kg/m³]""" + return 8960 + + def res(self, T: float, B: float, **kwargs): + """ + Electrical Resistivity + + Ref + --- + NIST MONOGRAPH 177 J.Simon, E.S.Drexler and R.P.Reed, "Properties of + Copper and Copper Alloys at Cryogenic Temperatures", 850 pages, February 1992, U.S. + Government Printing Office, Washington, DC 20402-9325 + + Parameters + ---------- + T: + Operating temperature [K] + B: + Operating magnetic field [T] + + Return + ------ + float [Ohm m] + """ + rho1 = (1.171 * (10 ** -17) * (T ** 4.49)) / ( + 1 + (4.5 * (10 ** -7) * (T ** 3.35) * (math.exp(-((50 / T) ** 6.428)))) + ) + rho2 = ( + (1.69 * (10 ** -8) / self.RRR) + + rho1 + + 0.4531 * ((1.69 * (10 ** -8) * rho1) / ( + self.RRR * rho1 + 1.69 * (10 ** -8))) + ) + A = math.log10(1.553 * (10 ** -8) * B / rho2) + a = ( + -2.662 + + (0.3168 * A) + + (0.6229 * (A ** 2)) + - (0.1839 * (A ** 3)) + + (0.01827 * (A ** 4)) + ) + return rho2 * (1 + (10 ** a)) + + def cp_v(self, T: float, **kwargs): + """ + Specific heat + + Ref + --- + L. Dresner, “Stability of Superconductors”, Plenum Press, NY, 1995 + + Note + ---- + The specific heat over the whole temperature range is obtained through fitting the function to the + known low temperature and high temperature data. + + Parameters + ---------- + T: + Operating temperature [K] + Return + ------ + float [J/K/m³] + """ + density = self.density(T=T, **kwargs) + c_plow = (self.beta * (T ** 3)) + (self.gamma * T) + return 1 / ((1 / self.cp300) + (1 / (c_plow * density))) + + +class Nb3Sn(Material): + def __init__(self): + """ + Nb3Sn superconductor (polycrystalline, normal state) + + Ref + --- + EFDA Material Data Compilation for Superconductor Simulation + P. Bauer, H. Rajainmaki, E. Salpietro + EFDA CSU, Garching, 04/18/07 + + + """ + # cp300: Specific heat known data-point at 300 K [J/K/m³] + self.cp300 = 210 + # gamma: Debye fit parameter for Cp calculation [J/K**2/kg] + self.gamma = 0.1 + # beta: Grueneisen fit parameter for Cp calculation [J/K**4/kg] + self.beta = 0.001 + + def density(self, **kwargs): + """Material density [kg/m³]""" + return 8040 + + def cp_v(self, T: float, **kwargs): + """ + Nb3Sn specific heat + + Ref + --- + ITER DRG1 Annex, Superconducting Material Database, Article 5, N 11 FDR 42 01-07-05 R 0.1, + Thermal, Electrical and Mechanical Properties of Materials at Cryogenic Temperatures + + Note + ---- + Most specific heat data are listed in J/K/kg units. To represent the data in the J/K/m3 units + a density of 8040 kg/m3 was assumed. + + Parameters + ---------- + T: + Operating temperature [K] + + Return + ------ + float [J/K/m³] + """ + density = self.density(T=T, **kwargs) + cp_low_NC = (self.beta * (T ** 3)) + (self.gamma * T) + cp_Nb3Sn = 1 / ((1 / self.cp300) + (1 / cp_low_NC)) + return cp_Nb3Sn * density + + def res(self, T: float, **kwargs): + """ + Electrical Resistivity + + Ref + --- + Data collected by L. Rossi, M. Sorbi, “MATPRO: A Computer Library of Material + Property at Cryogenic Temperature”, INFN/TC-06/02, CARE-Note-2005-018-HHH + + Parameters + ---------- + T: + Operating temperature [K] + + Return + ------ + float [Ohm m] + """ + return (-1e-4 * T ** 2 + 0.0938 * T + 22.601) * 1e-8 + + +class NbTi(Material): + def __init__(self): + """ + NbTi(46.5%) (polycrystalline, normal state) + + Ref + --- + EFDA Material Data Compilation for Superconductor Simulation + P. Bauer, H. Rajainmaki, E. Salpietro + EFDA CSU, Garching, 04/18/07 + + """ + # cp300: Specific heat known data-point at 300 K [J/K/m³] + self.cp300 = 400 + # gamma: Debye fit parameter for Cp calculation [J/K**2/kg] + self.gamma = 0.145 + # beta: Grueneisen fit parameter for Cp calculation [J/K**4/kg] + self.beta = 0.0023 + + def density(self, **kwargs): + """Material density [kg/m³]""" + return 6000 + + def cp_v(self, T: float, **kwargs): + """ + NbTi Specific Heat + + Ref + --- + Elrod S.A. Miller J.R., Dresner L., “The specific heat of NbTi from 0-7T between + 4.2 and 20K”, Advances in Cryogenic Engineering Materials, Vol. 28, 1981 + + Note + ---- + Most specific heat data are listed in J/K/kg units. To represent the data in the J/K/m3 units + a density of 8570 kg/m3 was assumed. + + Parameters + ---------- + T: + Operating temperature [K] + + Return + ------ + float [J/K/m³] + """ + density = self.density(T=T, **kwargs) + cp_low_NC = (self.beta * (T ** 3)) + (self.gamma * T) + cp_NbTi = 1 / ((1 / self.cp300) + (1 / cp_low_NC)) + return cp_NbTi * density + + def res(self, T: float, **kwargs): + """ + Electrical Resistivity + + Ref + --- + data-point discussed in Larbalestier’s article in “Superconducting Material + Properties” is 50 mWm, at 10 K. This value is 2 times higher than those from MATPRO. + We therefore suggest the use of the CRYOCOMP resistivities, which can be fitted with + the following polynomial + + Parameters + ---------- + T: + Operating temperature [K] + + Return + ------ + float [Ohm m] + """ + return (0.0558 * T + 55.668) * 1e-8 diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py new file mode 100644 index 0000000000..a40c0040f1 --- /dev/null +++ b/bluemira/magnets/strand.py @@ -0,0 +1,307 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +"""Strand class""" + +from typing import List, Union + +import numpy as np + +from bluemira.magnets.materials import Copper100, Material, Nb3Sn, NbTi +from bluemira.magnets.utils import parall_r, serie_r + + +class Strand: + """ + Strand class + """ + + def __init__( + self, + materials: List[Material], + percentage: Union[np.array, List[float]], + d_strand: float = 0.82e-3, + ): + """ + Class that represents a strand + + Parameters + ---------- + materials: + list of materials inside the strand + percentage: + percentage of each material (with the same ordering of materials) + d_strand: + strand diameter [m] + """ + self.materials = materials + self.percentage = percentage + self.d_strand = d_strand + + @property + def area(self) -> float: + """Area of the strand cross section""" + return np.pi * self.d_strand ** 2 / 4 + + def ym(self, **kwargs) -> float: + """ + Young modulus for the considered strand + + Parameters + ---------- + kwargs: + Other Parameters that shall be used to calculate the Young moduli + (e.g. Temperature) + + Returns + ------- + float [Pa] + + Note + ---- + Dummy value + """ + return 0.1e9 + + def res(self, **kwargs) -> float: + """ + Strand's equivalent resistivity, compute the parallel between strand's components + + Parameters + ---------- + Return + ------ + float [Ohm m] + """ + resistances = [ + x.res(**kwargs) / self.area / self.percentage[i] + for i, x in enumerate(self.materials) + ] + res_tot = parall_r(resistances) + return res_tot * self.area + + def cp_v(self, **kwargs) -> float: + """ + Strand's equivalent Specific Heat, compute the series between strand's components + + Parameters + ---------- + Return + ------ + float [J/K/m] + """ + specific_heat = [ + x.cp_v(**kwargs) * self.percentage[i] for i, x in enumerate(self.materials) + ] + return serie_r(specific_heat) + + def Ic( + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5 + ) -> float: + """ + Critical current from Jc(B,T,strain) + + Parameters + ---------- + T: + Operating temperature [K] + B: + Operating magnetic field [T] + strain: + total applied measured strain [%] + T_margin: + Strand temperature margin in operation [K] + + Return + ------ + float [A] + """ + return 0 + + +class Wire_Nb3Sn(Strand): + def __init__(self, d_strand: float = 0.82e-3): + """ + Nb3Sn strand. It is made by 50% Copper100 and 50% Nb3Sn + + Parameters + ---------- + d_strand: + strand diameter [m] + """ + copper_100 = Copper100() + mat_Nb3Sn = Nb3Sn() + materials = [copper_100, mat_Nb3Sn] + percentage = [0.5, 0.5] + super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) + + def Ic( + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5 + ) -> float: + """ + Nb3Sn critical current from Jc(B,T,strain). + Parameterization for the ITER Nb3Sn Production. + + Ref + --- + fit from IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, VOL. 19, NO. 3, + JUNE 2009 Luca Bottura and Bernardo Bordini + + note + ---- + fit parameters form WST strand,A. Nijhuis, “TF conductor samples strand + thermo mechanical critical performances tests”, + https://idm.euro-fusion.org/?uid=2M5SMM v1.0. + + Parameters + ---------- + T: + Operating temperature [K] + B: + Operating magnetic field [T] + strain: + total applied measured strain [%] + T_margin: + Strand temperature margin in operation [K] + Return + ------ + float [A] + """ + d_ = self.d_strand * 1e3 + CunonCu = self.percentage[0] / self.percentage[1] + # superconducting area + strand_A = np.pi * d_ ** 2 / (4 * (1 + CunonCu)) + c_ = 1.0 + Ca1 = 50.06 + Ca2 = 0.00 + eps_0a = 0.00312 + eps_m = -0.00059 + Bc20max = 33.24 + Tc0max = 16.34 + p = 0.593 + q = 2.156 + C = 83075 * strand_A # [AT] + T_ = T + T_margin + # Todo: check the sign of eps_m in this equation + int_eps = -strain / 100 # + eps_m + eps_sh = Ca2 * eps_0a / (np.sqrt(Ca1 ** 2 - Ca2 ** 2)) + s_eps = 1 + ( + Ca1 + * ( + np.sqrt(eps_sh ** 2 + eps_0a ** 2) + - np.sqrt((int_eps - eps_sh) ** 2 + eps_0a ** 2) + ) + - Ca2 * int_eps + ) / (1 - Ca1 * eps_0a) + Bc0_eps = Bc20max * s_eps + Tc0_eps = Tc0max * (s_eps) ** (1 / 3) + t = T_ / Tc0_eps + BcT_eps = Bc0_eps * (1 - t ** (1.52)) + b = B / BcT_eps + hT = (1 - t ** (1.52)) * (1 - t ** 2) + fPb = (b ** p) * (1 - b) ** q + return c_ * (C / B) * s_eps * fPb * hT + + def Je(self, Ic: float): + """ + Strand current density + + Parameters + ---------- + Ic: + Critical current [A] + + Return + ------ + float [A/m2] + """ + return Ic / self.area + + +class Wire_NbTi(Strand): + def __init__(self, d_strand: float = 0.82e-3): + """ + NbTi strand + + Parameters + ---------- + d_strand: + strand diameter [m] + """ + copper_100 = Copper100() + mat_NbTi = NbTi() + materials = [copper_100, mat_NbTi] + percentage = [0.5, 0.5] + super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) + + def Ic(self, B: float, T: float, T_margin: float = 1.5, **kwargs): + """ + NbTi critical current + + Ref + --- + Pinning Properties of Commercial Nb-Ti Wires Described by a 2-Components Model, + Luigi Muzzi, Gianluca De Marzi, et al. + + Note + ---- + Fit data from DTT TF strand + + Parameters + ---------- + T: + Operating temperature [K] + B: + Operating magnetic field [T] + T_margin: + Strand temperature margin in operation [K] + Return + ------ + float [A] + """ + d_ = self.d_strand * 1e3 + CunonCu = self.percentage[0] / self.percentage[1] + n = 1.7 + Bc20_T = 15.19 + Tc0_K = 8.907 + t = (T + T_margin) / Tc0_K + tt = 1 - t ** n + b = B / Bc20_T + C0 = 3.00e04 + C1 = 0.45 + a1 = 3.2 + b1 = 2.43 + C2 = 0.55 + a2 = 0.65 + b2 = 2 + g1 = 1.8 + g2 = 1.8 + G = (a1 / (a1 + b1)) ** a1 + GG = (b1 / (a1 + b1)) ** b1 + GGG = G * GG + F = (a2 / (a2 + b2)) ** a2 + FF = (b2 / (a2 + b2)) ** b2 + FFF = F * FF + Jc = ( + C0 * C1 / (B * GGG) * (b / tt) ** a1 * (1 - b / tt) ** b1 * tt ** g1 + + C0 * C2 / (B * FFF) * (b / tt) ** a2 * (1 - b / tt) ** b2 * tt ** g2 + ) + return Jc * np.pi * d_ ** 2 / (4 * (1 + CunonCu)) + + def Je(self, Ic: float): + """ + Strand current density + + Parameters + ---------- + Ic: + Critical current [A] + + Return + ------ + float [A/m2] + """ + return Ic / self.area diff --git a/bluemira/magnets/utils.py b/bluemira/magnets/utils.py new file mode 100644 index 0000000000..82e8512ae8 --- /dev/null +++ b/bluemira/magnets/utils.py @@ -0,0 +1,133 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +"""Utils for magnets""" + +from abc import ABC, abstractmethod +from typing import List, Union + +import numpy as np + + +def serie_r(arr: Union[List, np.array]): + """ + Compute the serie (as for resistance) + + Parameters + ---------- + arr: + list or numpy array containing the elements on which the serie shall + be calculated + + Returns + ------- + Result: float + """ + out = np.sum(arr) + return out + + +def parall_r(arr: Union[List, np.array]): + """ + Compute the parallel (as for resistance) + + Parameters + ---------- + arr: + list or numpy array containing the elements on which the parallel + shall be calculated + + Returns + ------- + Result: float + """ + out = 0 + for i in range(len(arr)): + out += 1 / arr[i] + out = out ** -1 + return out + + +def serie_k(arr: Union[List, np.array]): + """ + Compute the serie (as for spring) + + Parameters + ---------- + arr: + list or numpy array containing the elements on which the serie + shall be calculated + + Returns + ------- + Result: float + """ + out = 0 + for i in range(len(arr)): + out += 1 / arr[i] + out = out ** -1 + return out + + +def parall_k(arr: Union[List, np.array]): + """ + Compute the parallel (as for spring) + + Parameters + ---------- + arr: + list or numpy array containing the elements on which the parallel + shall be calculated + + Returns + ------- + Result: float + """ + out = np.sum(arr) + return out + + +class StructuralModelHooke(ABC): + """Abstract base class for a structural component""" + + @abstractmethod + def Kx(self, *args, **kwargs) -> float: + """Total equivalent stiffness along x-axis""" + pass + + @abstractmethod + def Ky(self, *args, **kwargs) -> float: + """Total equivalent stiffness along y-axis""" + + +def delayed_exp_func(x0: float, tau: float, t_delay: float = 0): + """ + Delayed Exponential function + + x = x0 * exp(-(t-t_delay)/tau) + + Parameters + ---------- + x0: float + initial value + tau: float + characteristic time constant + t_delay: float + delay time + + Returns + ------- + A Callable - exponential function + + """ + + def fun(t): + x = x0 + if t > t_delay: + x = x0 * np.exp(-(t - t_delay) / tau) + return x + + return fun diff --git a/bluemira/magnets/winding_pack.py b/bluemira/magnets/winding_pack.py new file mode 100644 index 0000000000..65320ca4c7 --- /dev/null +++ b/bluemira/magnets/winding_pack.py @@ -0,0 +1,67 @@ +import matplotlib.pyplot as plt +import numpy as np + +from bluemira.magnets.conductor import Conductor +from bluemira.magnets.utils import parall_k, serie_k + + +class WindingPack: + def __init__(self, conductor: Conductor, nx: np.int32, ny: np.int32, name: str = ""): + self.name = name + self.nx = nx + self.ny = ny + self.conductor = conductor + + @property + def dx(self): + return self.conductor.dx * self.nx + + @property + def dy(self): + return self.conductor.dy * self.ny + + def Kx(self, **kwargs): + """Total equivalent stiffness along x-axis""" + return parall_k([serie_k([self.conductor.Kx(**kwargs)] * self.nx)] * self.ny) + + def Ky(self, **kwargs): + """Total equivalent stiffness along x-axis""" + return serie_k([parall_k([self.conductor.Ky(**kwargs)] * self.ny)] * self.nx) + + def plot( + self, + xc: float = 0, + yc: float = 0, + show: bool = False, + ax=None, + homogenized: bool = True, + ): + if ax is None: + _, ax = plt.subplots() + + ax.plot([0], [0]) + + pc = np.array([xc, yc]) + a = self.dx / 2 + b = self.dy / 2 + + p0 = np.array([-a, -b]) + p1 = np.array([a, -b]) + p2 = np.array([a, b]) + p3 = np.array([-a, b]) + + points_ext = np.vstack((p0, p1, p2, p3, p0)) + pc + + ax.fill(points_ext[:, 0], points_ext[:, 1], "gold") + ax.plot(points_ext[:, 0], points_ext[:, 1], "k") + + if not homogenized: + for i in range(self.nx): + for j in range(self.ny): + xc_c = xc - self.dx / 2 + (i + 0.5) * self.conductor.dx + yc_c = yc - self.dy / 2 + (j + 0.5) * self.conductor.dy + self.conductor.plot(xc=xc_c, yc=yc_c, ax=ax) + + if show: + plt.show() + return ax diff --git a/examples/magnets/central_solenoid_example.ex.py b/examples/magnets/central_solenoid_example.ex.py new file mode 100644 index 0000000000..57605c8726 --- /dev/null +++ b/examples/magnets/central_solenoid_example.ex.py @@ -0,0 +1,48 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: tags,-all +# notebook_metadata_filter: -jupytext.text_representation.jupytext_version +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% tags=["remove-cell"] +# bluemira is an integrated inter-disciplinary design tool for future fusion +# reactors. It incorporates several modules, some of which rely on other +# codes, to carry out a range of typical conceptual fusion reactor design +# activities. +# +# Copyright (C) 2021-2023 M. Coleman, J. Cook, F. Franza, I.A. Maione, S. McIntosh, +# J. Morris, D. Short +# +# bluemira is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# bluemira is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with bluemira; if not, see . + +""" +An example central solenoid winding pack design +""" + +# %% [markdown] +# # Simple central solenoid winding pack design example +# ## Introduction +# +# In this example we will make a central solenoid winding pack +# %% +import numpy as np # noqa: F401 diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py new file mode 100644 index 0000000000..7ccb2f3d44 --- /dev/null +++ b/examples/magnets/tf_example.py @@ -0,0 +1,172 @@ +import matplotlib.pyplot as plt +import numpy as np + +from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI +from bluemira.magnets.cable import DummyRoundCableLTS, DummyRoundCableHTS +from bluemira.magnets.case import CaseTF +from bluemira.magnets.conductor import SquareConductor, optimize_jacket_conductor +from bluemira.magnets.materials import Copper300, Material, AISI_316LN +from bluemira.magnets.strand import Strand, Wire_Nb3Sn +from bluemira.magnets.utils import ( + delayed_exp_func, +) +from bluemira.magnets.winding_pack import WindingPack + +""" +Example for TF coils internal structure optimization. + +Note: in this example the conductor operational current is given as input (no check +or optimization that takes into account the conductor critical current). +""" + + +class DummyInsulator(Material): + def res(self, **kwargs): + return 1e6 + + def ym(self, **kwargs): + return 12e9 + + +# plot options +show = True +homogenized = False # if True plot the WP as a homogenized block + +# input values +A = 4.6 +R0 = 2.53 # [m] major machine radius +B0 = 5.0 # [T] magnetic field @R0 +n_TF = 12 # number of TF coils +d = 0.8 +a = R0 / A +ripple = 6e-3 +Ri = R0 - a - d # [m] max external radius of the internal TF leg +Re = (R0 + a) * (1 / ripple) ** ( + 1 / n_TF) # [m] max internal radius of the external TF part +dr_plasma_side = R0 * 2 / 3 * 1e-2 + +R_VV = Ri * 1.05 # Vacuum vessel radius +S_VV = 90e6 # Vacuum vessel steel limit + +I_TF = B0 * R0 / MU_0_2PI / n_TF # total current in each TF coil +B_TF_i = 1.08 * (MU_0_2PI * n_TF * I_TF / Ri) # max magnetic field on the inner TF leg +pm = B_TF_i ** 2 / (2 * MU_0) # magnetic pressure on the inner TF leg +# vertical tension acting on the equatorial section of inner TF leg +# i.e. half of the whole F_Z +t_z = (0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2) + +Iop = 40.0e3 # operational current in each conductor +n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors + +n_spire = np.floor(I_TF / Iop) +L = MU_0 * R0 * (n_TF * n_spire) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1 +E = 1 / 2 * L * n_TF * Iop ** 2 * 1e-9 +V_MAX = (7 * R0 - 3) / 6 * 1.1e3 +Tau_discharge1 = (L * Iop / V_MAX) +Tau_discharge2 = B0 * I_TF * n_TF * (R0 / A) ** 2 / (R_VV * S_VV) +Tau_discharge = max([Tau_discharge1, Tau_discharge2]) + +T_sc = 4.2 # operational temperature of superconducting cable +T_margin = 1.5 # temperature margin +T0 = T_sc + T_margin +t_delay = 3 +t0 = 0 +tf = Tau_discharge +hotspot_target_temperature = 250.0 + +# allowable stress values +safety_factor = 1.5 * 1.3 +S_Y = 1e9 / safety_factor # [Pa] steel allowable limit + +# Current and magnetic field behaviour +I = delayed_exp_func(Iop, Tau_discharge, t_delay) +B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay) + +# define the conductor (with a dummy number of stabilizer strands ) +sc_strand = Wire_Nb3Sn(d_strand=1e-3) +copper300 = Copper300() +insulator = DummyInsulator() +stab_strand = Strand([copper300], [1], d_strand=1e-3) + +Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) +n_sc_strand = int(np.ceil(Iop / Ic_sc)) + +if B_TF_i < 15: + cable = DummyRoundCableLTS(sc_strand, stab_strand, n_sc_strand, 100, 1e-2, + void_fraction=0.7) +else: + cable = DummyRoundCableHTS(sc_strand, stab_strand, n_sc_strand, 100, 1e-2, + void_fraction=0.7) +# c.plot(0,0, show=True) + +ss316 = AISI_316LN() +conductor = SquareConductor( + cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3 +) + +# optimize the number of stabilizer strands using the hot spot criteria +print(f"before optimization: conductor dx_cable = {cable.dx}") +T_for_hts = T0 +result = cable.optimize_n_stab_ths( + t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show +) +print(f"after optimization: conductor dx_cable = {cable.dx}") + +show = False +for i in range(5): + print(f"Internal optimazion - iteration {i}") + # optimize the cable jacket thickness considering 0D stress model for the single cable + print(f"before optimization: conductor dx_jacket = {conductor.dx_jacket}") + t_z_cable_jacket = 0 + if i == 0: + t_z_cable_jacket = t_z / 2 / n_spire + else: + t_z_cable_jacket = t_z * case.area_wps_jacket / ( + case.area_jacket + case.area_wps_jacket) / ( + np.sum([w.nx * w.ny for w in case.WPs])) + result_opt_jacket = optimize_jacket_conductor( + conductor, pm, t_z_cable_jacket, T0, B_TF_i, + S_Y, bounds=[1e-5, 0.2] + ) + print(f"after optimization: conductor dx_jacket = {conductor.dx_jacket}") + + from bluemira.magnets.materials import OperationalPoint + + op = OperationalPoint(B=B_TF_i, T=T0) + + # creation of case + wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case + case = CaseTF( + Ri=Ri, dy_ps=dr_plasma_side, dy_vault=0.6, theta_TF=360 / n_TF, mat_case=ss316, + WPs=[wp1] + ) + + if show: + ax = case.plot(homogenized=False) + ax.set_aspect("equal") + plt.show() + + case.rearrange_conductors_in_wp_type1(n_cond, conductor, case.R_wp_i[0], + case.dx_i * 0.7, 0.05, 4) + + if show: + ax = case.plot(homogenized=homogenized) + ax.set_aspect("equal") + plt.title("Before vault optimization") + plt.show() + + case.optimize_vault_radial_thickness( + pm, t_z, T=T0, B=B_TF_i, allowable_sigma=S_Y, bounds=[1e-2, 1] + ) + if show: + ax = case.plot(homogenized=homogenized) + ax.set_aspect("equal") + plt.title("After vault optimization") + plt.show() + +show = True +if show: + ax = case.plot(homogenized=homogenized) + ax.set_aspect("equal") + plt.title("After vault optimization") + plt.show() From 82c6494898943a153592e580d7ad2224fc098c3c Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Tue, 9 Apr 2024 10:18:51 +0200 Subject: [PATCH 02/17] improving docstrings --- bluemira/magnets/cable.py | 344 +++++++++++++++------------------ bluemira/magnets/conductor.py | 81 +++++++- bluemira/magnets/materials.py | 15 +- bluemira/magnets/strand.py | 29 ++- bluemira/magnets/utils.py | 13 +- examples/magnets/tf_example.py | 40 ++-- 6 files changed, 294 insertions(+), 228 deletions(-) diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py index ebe98768f4..f39a2d1308 100644 --- a/bluemira/magnets/cable.py +++ b/bluemira/magnets/cable.py @@ -13,11 +13,12 @@ from scipy.integrate import solve_ivp from scipy.optimize import minimize_scalar +from bluemira.magnets.materials import Material from bluemira.magnets.strand import Strand from bluemira.magnets.utils import parall_r, serie_r -class Cable: +class Cable(Material): def __init__( self, dx: float, @@ -31,7 +32,8 @@ def __init__( name: str = "", ): """ - Representation of a cable + Representation of a cable. Only the x-dimension of the cable is given as + input. The y-dimension is calculated on the basis of the cable design. Parameters ---------- @@ -78,11 +80,14 @@ def n_stab_strand(self, value: int): def res(self, **kwargs): """ - Cable's equivalent resistivity, computed as the parallel between - strands' resistivity + Computes the cable's equivalent resistivity considering the resistance + of its strands in parallel. Parameters ---------- + **kwargs: dict + Additional parameters for resistance calculations. + Return ------ float [Ohm m] @@ -96,10 +101,14 @@ def res(self, **kwargs): def cp_v(self, **kwargs): """ - Strand's equivalent Specific Heat, compute the series between strand's components + Computes the cable's equivalent specific heat considering the specific heats + of its strands in series. Parameters ---------- + **kwargs: dict + Additional parameters for specific heat calculations. + Return ------ float [J/K/m] @@ -119,17 +128,17 @@ def _check_consistency(self): @property def area_stab(self): - """Area of the stabilizer""" + """Area of the stabilizer region""" return self.stab_strand.area * self.n_stab_strand @property def area_sc(self): - """Area of the superconductor""" + """Area of the superconductor region""" return self.sc_strand.area * self.n_sc_strand @property def area_cc(self): - """Area of the cooling""" + """Area of the cooling channel""" return self.d_cooling_channel ** 2 / 4 * np.pi @property @@ -141,6 +150,7 @@ def area(self): @property def dx(self): + """Cable dimension in the x direction [m]""" return self._dx @dx.setter @@ -149,12 +159,14 @@ def dx(self, value: float): @property def dy(self): - """y-dimension of the cable [m]""" + """Cable dimension in the y direction [m]""" return self.area / self.dx def ym(self, **kwargs): + """Cable Young's moduli""" return 0 + # OD structural properties def Kx(self, **kwargs): """Total equivalent stiffness along x-axis""" return self.ym(**kwargs) * self.dy / self.dx @@ -176,25 +188,25 @@ def optimize_n_stab_ths( ): """ Optimize the number of stabilizer strand in the superconducting cable using a - 0-D hot spot criteria + 0-D hot spot criteria. Parameters ---------- - t0: float + t0: initial time - tf: float + tf: final time - initial_temperature: float + initial_temperature: temperature [K] at initial time - target_temperature: float + target_temperature: target temperature [K] at final time - B : Callable + B : The magnetic field [T] as time function - I : Callable + I : The current [A] flowing through the conductor as time function - bounds: np.ndarray + bounds: lower and upper limits for the number of strand in the cable - show: bool + show: if True the behavior of temperature as function of time is plotted Returns @@ -206,11 +218,130 @@ def optimize_n_stab_ths( The number of stabilizer strands in the cable is directly modified. An error is raised in case the optimization process did not converge. """ - return optimize_n_stab_cable( - self, t0, tf, initial_temperature, target_temperature, B, I, bounds, show + + def _heat_balance_model_cable(t, T, B: Callable, I: Callable, cable: Cable): + """ + Calculate the derivative of temperature (dT/dt) for a 0D heat balance problem. + + Parameters + ---------- + t : float + The current time in seconds. + T : float + The current temperature in Celsius. + B : Callable + The magnetic field [T] as time function + I : Callable + The current [A] flowing through the conductor as time function + cable : Cable + the superconducting cable + + Returns + ------- + dTdt : float + The derivative of temperature with respect to time (dT/dt). + """ + # Calculate the rate of heat generation (Joule dissipation) + if isinstance(T, np.ndarray): + T = T[0] + + Q_gen = (I(t) / cable.area) ** 2 * cable.res(B=B(t), T=T) + + # Calculate the rate of heat absorption by conductor components + Q_abs = cable.cp_v(T=T) + + # Calculate the derivative of temperature with respect to time (dT/dt) + dTdt = Q_gen / Q_abs + + return dTdt + + def _temperature_evolution( + t0: float, + tf: float, + initial_temperature: float, + B: Callable, + I: Callable, + cable: Cable, + ): + solution = solve_ivp( + _heat_balance_model_cable, + [t0, tf], + [initial_temperature], + args=(B, I, cable), + dense_output=True, + ) + + if not solution.success: + raise ValueError("Temperature evolution did not converged") + + return solution + + def final_temperature_difference( + n_stab: int, + t0: float, + tf: float, + initial_temperature: float, + target_temperature: float, + B: Callable, + I: Callable, + ): + self.n_stab_strand = n_stab + + solution = _temperature_evolution( + t0=t0, tf=tf, initial_temperature=initial_temperature, B=B, I=I, + cable=self + ) + final_T = float(solution.y[0][-1]) + diff = abs(final_T - target_temperature) + return diff + + method = None + if bounds is not None: + method = "bounded" + + result = minimize_scalar( + fun=final_temperature_difference, + args=(t0, tf, initial_temperature, target_temperature, B, I), + bounds=bounds, + method=method, ) + if not result.success: + raise ValueError( + "n_stab optimization did not converge. Check your input parameters or initial bracket." + ) + + solution = _temperature_evolution(t0, tf, initial_temperature, B, I, self) + final_temperature = solution.y[0][-1] + + print(f"Optimal n_stab: {self.n_stab_strand}") + print(f"Final temperature with optimal n_stab: {final_temperature} Kelvin") + + if show: + _, ax = plt.subplots() + ax.plot(solution.t, solution.y[0], "r") + time_steps = np.linspace(t0, tf, 100) + ax.plot(time_steps, solution.sol(time_steps)[0], "b") + plt.show() + + return result + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None, **kwargs): + """ + Schematic plot of the cable cross-section. + + Parameters + ---------- + xc: + x coordinate of the cable center in the considered coordinate system + yc: + y coordinate of the cable center in the considered coordinate system + show: + if True, the plot is displayed + ax: + Matplotlib Axis on which the plot shall be displayed. If None, + a new figure is created + """ if ax is None: _, ax = plt.subplots() @@ -291,11 +422,12 @@ def __init__( @property def dx(self): + """Cable dimension in the x direction [m]""" return np.sqrt(self.area) @property def dy(self): - """y-dimension of the cable [m]""" + """Cable dimension in the y direction [m]""" return self.dx @@ -321,8 +453,6 @@ class DummySquareCableHTS(SquareCable): corrective factor that consider the twist of the cable name: cable string identifier - - #todo decide if it is the case to add also the cooling material """ def ym(self, **kwargs): @@ -331,7 +461,7 @@ def ym(self, **kwargs): class DummySquareCableLTS(SquareCable): """ - Dummy square cable with young's moduli set to 120 GPa + Dummy square cable with young's moduli set to 0.1 GPa Parameters ---------- @@ -351,8 +481,6 @@ class DummySquareCableLTS(SquareCable): corrective factor that consider the twist of the cable name: cable string identifier - - #todo decide if it is the case to add also the cooling material """ def ym(self, **kwargs): @@ -372,7 +500,7 @@ def __init__( name: str = "", ): """ - Representation of a square cable + Representation of a round cable Parameters ---------- @@ -410,17 +538,18 @@ def __init__( @property def dx(self): + """Cable dimension in the x direction [m] (i.e. cable's diameter)""" return np.sqrt(self.area * 4 / np.pi) @property def dy(self): - """y-dimension of the cable [m]""" + """Cable dimension in the y direction [m] (i.e. cable's diameter)""" return self.dx class DummyRoundCableHTS(RoundCable): """ - Dummy square cable with young's moduli set to 120 GPa + Dummy round cable with young's moduli set to 120 GPa Parameters ---------- @@ -450,7 +579,7 @@ def ym(self, **kwargs): class DummyRoundCableLTS(RoundCable): """ - Dummy square cable with young's moduli set to 120 GPa + Dummy round cable with young's moduli set to 120 GPa Parameters ---------- @@ -476,158 +605,3 @@ class DummyRoundCableLTS(RoundCable): def ym(self, **kwargs): return 0.1e9 - - -def _heat_balance_model_cable(t, T, B: Callable, I: Callable, cable: Cable): - """ - Calculate the derivative of temperature (dT/dt) for the heat balance problem. - - Parameters - ---------- - t : float - The current time in seconds. - T : float - The current temperature in Celsius. - B : Callable - The magnetic field [T] as time function - I : Callable - The current [A] flowing through the conductor as time function - cable : Cable - the superconducting cable - - Returns - ------- - dTdt : float - The derivative of temperature with respect to time (dT/dt). - """ - # Calculate the rate of heat generation (Joule dissipation) - if isinstance(T, np.ndarray): - T = T[0] - - Q_gen = (I(t) / cable.area) ** 2 * cable.res(B=B(t), T=T) - - # Calculate the rate of heat absorption by conductor components - Q_abs = cable.cp_v(T=T) - - # Calculate the derivative of temperature with respect to time (dT/dt) - dTdt = Q_gen / Q_abs - - return dTdt - - -def _temperature_evolution( - t0: float, - tf: float, - initial_temperature: float, - B: Callable, - I: Callable, - cable: Cable, -): - solution = solve_ivp( - _heat_balance_model_cable, - [t0, tf], - [initial_temperature], - args=(B, I, cable), - dense_output=True, - ) - - if not solution.success: - raise ValueError("Temperature evolution did not converged") - - return solution - - -def optimize_n_stab_cable( - cable: Cable, - t0: float, - tf: float, - initial_temperature: float, - target_temperature: float, - B: Callable, - I: Callable, - bounds: np.ndarray = None, - show: bool = False, -): - """ - Optimize the number of stabilizer strand in a superconducting cable using a 0-D hot spot criteria - - Parameters - ---------- - cable: Cable - the superconducting cable - t0: float - initial time - tf: float - final time - initial_temperature: float - temperature [K] at initial time - target_temperature: float - target temperature [K] at final time - B : Callable - The magnetic field [T] as time function - I : Callable - The current [A] flowing through the conductor as time function - bounds: np.ndarray - lower and upper limits for the number of strand in the cable - show: bool - if True the behavior of temperature as function of time is plotted - - Returns - ------- - None - - Notes - ----- - The number of stabilizer strands in the cable is directly modified. An error is raised in case the optimization - process did not converge. - """ - - def final_temperature_difference( - n_stab: int, - t0: float, - tf: float, - initial_temperature: float, - target_temperature: float, - B: Callable, - I: Callable, - cable: Cable, - ): - cable.n_stab_strand = n_stab - - solution = _temperature_evolution( - t0=t0, tf=tf, initial_temperature=initial_temperature, B=B, I=I, cable=cable - ) - final_T = float(solution.y[0][-1]) - diff = abs(final_T - target_temperature) - return diff - - method = None - if bounds is not None: - method = "bounded" - - result = minimize_scalar( - fun=final_temperature_difference, - args=(t0, tf, initial_temperature, target_temperature, B, I, cable), - bounds=bounds, - method=method, - ) - - if not result.success: - raise ValueError( - "n_stab optimization did not converge. Check your input parameters or initial bracket." - ) - - solution = _temperature_evolution(t0, tf, initial_temperature, B, I, cable) - final_temperature = solution.y[0][-1] - - print(f"Optimal n_stab: {cable.n_stab_strand}") - print(f"Final temperature with optimal n_stab: {final_temperature} Kelvin") - - if show: - _, ax = plt.subplots() - ax.plot(solution.t, solution.y[0], "r") - time_steps = np.linspace(t0, tf, 100) - ax.plot(time_steps, solution.sol(time_steps)[0], "b") - plt.show() - - return result diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index e1f55ee756..39f92c91b3 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -1,3 +1,11 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +"""Conductor class""" + import matplotlib.pyplot as plt import numpy as np from scipy.optimize import minimize_scalar @@ -25,11 +33,13 @@ def __init__( name: str = "", ): """ + A generic conductor consisting of a cable surrounded by a jacket and an + insulator. Parameters ---------- cable: - the conductor cable + the conductor's cable mat_jacket: jacket's material mat_ins: @@ -66,6 +76,7 @@ def dy(self): @property def dx_jacket(self): + """Thickness in the x-direction of the jacket [m]""" return self._dx_jacket @dx_jacket.setter @@ -74,6 +85,7 @@ def dx_jacket(self, value): @property def dy_jacket(self): + """Thickness in the y-direction of the jacket [m]""" return self._dy_jacket @dy_jacket.setter @@ -82,6 +94,7 @@ def dy_jacket(self, value): @property def dx_ins(self): + """Thickness in the x-direction of the insulator [m]""" return self._dx_ins @dx_ins.setter @@ -90,6 +103,7 @@ def dx_ins(self, value): @property def dy_ins(self): + """Thickness in the y-direction of the jacket [m]""" return self._dy_ins @dy_ins.setter @@ -103,26 +117,33 @@ def area(self): @property def area_jacket(self): - """Area of the jacket""" + """Area of the jacket [m^2]""" return (self.dx - 2 * self.dx_ins) * ( self.dy - 2 * self.dy_ins ) - self.cable.area @property def area_ins(self): - """Area of the insulator""" + """Area of the insulator [m^2]""" return self.area - self.area_jacket - self.cable.area def res(self, **kwargs): """ - Cable's equivalent resistivity, computed as the parallel - between strands' resistivity + Computes the conductor's equivalent resistivity considering the resistance + of its strands in parallel. Parameters ---------- + **kwargs: dict + Additional parameters for resistance calculations. + Return ------ float [Ohm m] + + Notes + ----- + The insulator in not considered into the calculation. """ resistances = np.array([ self.cable.res(**kwargs) / self.cable.area, @@ -133,14 +154,22 @@ def res(self, **kwargs): def cp_v(self, **kwargs): """ - Strand's equivalent Specific Heat, compute the series between strand's components + Computes the conductor's equivalent specific heat considering the specific heats + of its components in series. Parameters ---------- + **kwargs: dict + Additional parameters for resistance calculations. + Return ------ float [J/K/m] + Notes + ----- + The insulator in not considered into the calculation. + TODO: decide if also the insulator should be considered """ weighted_specific_heat = np.array([ @@ -149,6 +178,8 @@ def cp_v(self, **kwargs): ]) return serie_r(weighted_specific_heat) / self.area + # TODO: add a description of the 0D structural model, otherwise the following + # function cannot be undestood def Kx_topbot_ins(self, **kwargs): return self.mat_ins.ym(**kwargs) * self.dy / self.dx_ins @@ -165,7 +196,7 @@ def Kx_topbot_jacket(self, **kwargs): return self.mat_jacket.ym(**kwargs) * self.cable.dy / self.dx_jacket def Kx_cable(self, **kwargs): - return self.cable.ym(**kwargs) * self.cable.dy / self.cable.dx + return self.cable.Kx(**kwargs) def Kx(self, **kwargs): return (serie_k([self.Kx_topbot_ins(**kwargs) / 2, @@ -194,7 +225,7 @@ def Ky_topbot_jacket(self, **kwargs): return self.mat_jacket.ym(**kwargs) * self.cable.dx / self.dy_jacket def Ky_cable(self, **kwargs): - return self.cable.ym(**kwargs) * self.cable.dx / self.cable.dy + return self.cable.Ky(**kwargs) def Ky(self, **kwargs): return (serie_k([self.Ky_topbot_ins(**kwargs) / 2, @@ -208,6 +239,21 @@ def Ky(self, **kwargs): ) def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): + """ + Schematic plot of the cable cross-section. + + Parameters + ---------- + xc: + x coordinate of the cable center in the considered coordinate system + yc: + y coordinate of the cable center in the considered coordinate system + show: + if True, the plot is displayed + ax: + Matplotlib Axis on which the plot shall be displayed. If None, + a new figure is created + """ if ax is None: _, ax = plt.subplots() @@ -252,6 +298,25 @@ def __init__( dx_ins: float, name: str = "", ): + """ + Representation of a square conductor + + Parameters + ---------- + cable: + the conductor's cable + mat_jacket: + jacket's material + mat_ins: + insulator's material + dx_jacket: + x(y)-thickness of the jacket + dx_ins: + x(y)-thickness of the insulator + name: + string identifier + + """ dy_jacket = dx_jacket dy_ins = dx_ins super().__init__( diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py index 041c945acd..0ae6c87bc2 100644 --- a/bluemira/magnets/materials.py +++ b/bluemira/magnets/materials.py @@ -150,7 +150,6 @@ def res(self, T: float, B: float, **kwargs): ------ float [Ohm m] """ - # print(f"parameters T: {T}, B: {B}") rho1 = (1.171 * (10 ** -17) * (T ** 4.49)) / ( 1 + (4.5 * (10 ** -7) * (T ** 3.35) * (math.exp(-((50. / T) ** 6.428)))) ) @@ -181,8 +180,8 @@ def cp_v(self, T: float, **kwargs): Note ---- - The specific heat over the whole temperature range is obtained through fitting the function to the - known low temperature and high temperature data. + The specific heat over the whole temperature range is obtained through + fitting the function to the known low temperature and high temperature data. Parameters ---------- @@ -433,3 +432,13 @@ def res(self, T: float, **kwargs): float [Ohm m] """ return (0.0558 * T + 55.668) * 1e-8 + + +class DummyInsulator(Material): + """A dummy insulator""" + + def res(self, **kwargs): + return 1e6 + + def ym(self, **kwargs): + return 12e9 diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index a40c0040f1..3ccda64dee 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -26,7 +26,7 @@ def __init__( d_strand: float = 0.82e-3, ): """ - Class that represents a strand + Class that represents a strand with a circular cross-section. Parameters ---------- @@ -43,7 +43,7 @@ def __init__( @property def area(self) -> float: - """Area of the strand cross section""" + """Area of the strand cross-section""" return np.pi * self.d_strand ** 2 / 4 def ym(self, **kwargs) -> float: @@ -68,10 +68,14 @@ def ym(self, **kwargs) -> float: def res(self, **kwargs) -> float: """ - Strand's equivalent resistivity, compute the parallel between strand's components + Calculates the equivalent resistivity based on the parallel connection + of strand components. Parameters ---------- + **kwargs: dict + Additional parameters for calculating resistivity. + Return ------ float [Ohm m] @@ -85,10 +89,14 @@ def res(self, **kwargs) -> float: def cp_v(self, **kwargs) -> float: """ - Strand's equivalent Specific Heat, compute the series between strand's components + Calculates the equivalent specific heat based on the series connection + of strand components. Parameters ---------- + **kwargs: dict + Additional parameters for calculating specific heat. + Return ------ float [J/K/m] @@ -99,10 +107,11 @@ def cp_v(self, **kwargs) -> float: return serie_r(specific_heat) def Ic( - self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5 + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, + **kwargs ) -> float: """ - Critical current from Jc(B,T,strain) + Critical current Parameters ---------- @@ -114,6 +123,8 @@ def Ic( total applied measured strain [%] T_margin: Strand temperature margin in operation [K] + **kwargs: dict + Additional parameters Return ------ @@ -139,7 +150,8 @@ def __init__(self, d_strand: float = 0.82e-3): super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) def Ic( - self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5 + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, + **kwargs ) -> float: """ Nb3Sn critical current from Jc(B,T,strain). @@ -258,6 +270,7 @@ def Ic(self, B: float, T: float, T_margin: float = 1.5, **kwargs): Operating magnetic field [T] T_margin: Strand temperature margin in operation [K] + Return ------ float [A] @@ -298,7 +311,7 @@ def Je(self, Ic: float): Parameters ---------- Ic: - Critical current [A] + strand current [A] Return ------ diff --git a/bluemira/magnets/utils.py b/bluemira/magnets/utils.py index 82e8512ae8..510b2cc5e8 100644 --- a/bluemira/magnets/utils.py +++ b/bluemira/magnets/utils.py @@ -91,7 +91,7 @@ def parall_k(arr: Union[List, np.array]): class StructuralModelHooke(ABC): - """Abstract base class for a structural component""" + """Abstract base class for a """ @abstractmethod def Kx(self, *args, **kwargs) -> float: @@ -101,6 +101,17 @@ def Kx(self, *args, **kwargs) -> float: @abstractmethod def Ky(self, *args, **kwargs) -> float: """Total equivalent stiffness along y-axis""" + pass + + +class StructuralComponent(ABC): + """Abstract base class for a structural component""" + + @property + @abstractmethod + def structural_model(self, *args, **kwargs): + """Structural model used evaluate stress/strain into the component""" + pass def delayed_exp_func(x0: float, tau: float, t_delay: float = 0): diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index 7ccb2f3d44..da1cf225b3 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -5,7 +5,8 @@ from bluemira.magnets.cable import DummyRoundCableLTS, DummyRoundCableHTS from bluemira.magnets.case import CaseTF from bluemira.magnets.conductor import SquareConductor, optimize_jacket_conductor -from bluemira.magnets.materials import Copper300, Material, AISI_316LN +from bluemira.magnets.materials import (OperationalPoint, Copper300, AISI_316LN, + DummyInsulator) from bluemira.magnets.strand import Strand, Wire_Nb3Sn from bluemira.magnets.utils import ( delayed_exp_func, @@ -19,27 +20,18 @@ or optimization that takes into account the conductor critical current). """ - -class DummyInsulator(Material): - def res(self, **kwargs): - return 1e6 - - def ym(self, **kwargs): - return 12e9 - - # plot options show = True homogenized = False # if True plot the WP as a homogenized block # input values -A = 4.6 -R0 = 2.53 # [m] major machine radius -B0 = 5.0 # [T] magnetic field @R0 -n_TF = 12 # number of TF coils -d = 0.8 -a = R0 / A +B0 = 4.39 # [T] magnetic field @R0 +R0 = 8.6 # [m] major machine radius +A = 2.8 +n_TF = 16 # number of TF coils +d = 1.9 ripple = 6e-3 +a = R0 / A Ri = R0 - a - d # [m] max external radius of the internal TF leg Re = (R0 + a) * (1 / ripple) ** ( 1 / n_TF) # [m] max internal radius of the external TF part @@ -55,7 +47,7 @@ def ym(self, **kwargs): # i.e. half of the whole F_Z t_z = (0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2) -Iop = 40.0e3 # operational current in each conductor +Iop = 70.0e3 # operational current in each conductor n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors n_spire = np.floor(I_TF / Iop) @@ -86,8 +78,9 @@ def ym(self, **kwargs): sc_strand = Wire_Nb3Sn(d_strand=1e-3) copper300 = Copper300() insulator = DummyInsulator() -stab_strand = Strand([copper300], [1], d_strand=1e-3) +stab_strand = Strand([copper300], [1], d_strand=1.0e-3) +# Calculate number of superconducting strands considering the strand critical current Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) n_sc_strand = int(np.ceil(Iop / Ic_sc)) @@ -112,8 +105,13 @@ def ym(self, **kwargs): ) print(f"after optimization: conductor dx_cable = {cable.dx}") +op = OperationalPoint(B=B_TF_i, T=T0) + show = False -for i in range(5): + +# Some iterations to optimize case vault and cable jacket consistently. This should go +# in a kind of "optimization" or "convergence" loop. +for i in range(10): print(f"Internal optimazion - iteration {i}") # optimize the cable jacket thickness considering 0D stress model for the single cable print(f"before optimization: conductor dx_jacket = {conductor.dx_jacket}") @@ -130,10 +128,6 @@ def ym(self, **kwargs): ) print(f"after optimization: conductor dx_jacket = {conductor.dx_jacket}") - from bluemira.magnets.materials import OperationalPoint - - op = OperationalPoint(B=B_TF_i, T=T0) - # creation of case wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case case = CaseTF( From ce1c6a879ffb531a81d5f7c09819bce124557489 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Mon, 15 Apr 2024 11:42:33 +0200 Subject: [PATCH 03/17] improving docstrings --- bluemira/magnets/case.py | 3 ++- bluemira/magnets/materials.py | 4 ++-- examples/magnets/tf_example.py | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/bluemira/magnets/case.py b/bluemira/magnets/case.py index c846a13616..0564ea1c9b 100644 --- a/bluemira/magnets/case.py +++ b/bluemira/magnets/case.py @@ -274,7 +274,8 @@ def rearrange_conductors_in_wp_type1( n_layers_reduction: int, ): """ - Rearrange the total number of conductors into the TF coil case considering a specific conductor + Rearrange the total number of conductors into the TF coil case considering + a specific conductor Parameters ---------- diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py index 0ae6c87bc2..0450b5284d 100644 --- a/bluemira/magnets/materials.py +++ b/bluemira/magnets/materials.py @@ -15,9 +15,9 @@ class OperationalPoint(dict): def __init__(self, *args, **kwargs): # Set default values {'T': 0, 'B': 0} if "T" not in kwargs: - kwargs["T"] = 0 + kwargs["T"] = None if "B" not in kwargs: - kwargs["B"] = 0 + kwargs["B"] = None super().__init__(*args, **kwargs) diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index da1cf225b3..95dd7c328a 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -47,7 +47,7 @@ # i.e. half of the whole F_Z t_z = (0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2) -Iop = 70.0e3 # operational current in each conductor +Iop = 90.0e3 # operational current in each conductor n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors n_spire = np.floor(I_TF / Iop) From 324c770cf1dc3c526e6df55bfa5ae498b46a0590 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 31 May 2024 12:24:28 +0200 Subject: [PATCH 04/17] adjustments to strand.py --- bluemira/magnets/strand.py | 306 ++++++++++++++++--------------------- 1 file changed, 135 insertions(+), 171 deletions(-) diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index 3ccda64dee..4987055f59 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -6,8 +6,6 @@ """Strand class""" -from typing import List, Union - import numpy as np from bluemira.magnets.materials import Copper100, Material, Nb3Sn, NbTi @@ -16,26 +14,26 @@ class Strand: """ - Strand class + Represents a strand with a circular cross-section. """ def __init__( self, - materials: List[Material], - percentage: Union[np.array, List[float]], + materials: list[Material], + percentage: np.array | list[float], d_strand: float = 0.82e-3, ): """ - Class that represents a strand with a circular cross-section. + Initialize a Strand instance. Parameters ---------- materials: - list of materials inside the strand + List of materials inside the strand. percentage: - percentage of each material (with the same ordering of materials) + Percentage of each material (with the same ordering of materials). d_strand: - strand diameter [m] + Strand diameter in meters. """ self.materials = materials self.percentage = percentage @@ -43,42 +41,36 @@ def __init__( @property def area(self) -> float: - """Area of the strand cross-section""" + """Returns the area of the strand cross-section in square meters.""" return np.pi * self.d_strand ** 2 / 4 - def ym(self, **kwargs) -> float: + def E(self, **kwargs) -> float: """ - Young modulus for the considered strand + Returns the Young's modulus for the strand. Parameters ---------- - kwargs: - Other Parameters that shall be used to calculate the Young moduli - (e.g. Temperature) + **kwargs: + Additional parameters (e.g., temperature). Returns ------- - float [Pa] - - Note - ---- - Dummy value + Young's modulus in Pascals. """ return 0.1e9 def res(self, **kwargs) -> float: """ - Calculates the equivalent resistivity based on the parallel connection - of strand components. + Calculates the equivalent resistivity based on the parallel connection of strand components. Parameters ---------- - **kwargs: dict + **kwargs: Additional parameters for calculating resistivity. - Return - ------ - float [Ohm m] + Returns + ------- + Equivalent resistivity in Ohm meters. """ resistances = [ x.res(**kwargs) / self.area / self.percentage[i] @@ -89,17 +81,16 @@ def res(self, **kwargs) -> float: def cp_v(self, **kwargs) -> float: """ - Calculates the equivalent specific heat based on the series connection - of strand components. + Calculates the equivalent specific heat based on the series connection of strand components. Parameters ---------- - **kwargs: dict + **kwargs: Additional parameters for calculating specific heat. - Return - ------ - float [J/K/m] + Returns + ------- + Equivalent specific heat in Joules per Kelvin per meter. """ specific_heat = [ x.cp_v(**kwargs) * self.percentage[i] for i, x in enumerate(self.materials) @@ -107,214 +98,187 @@ def cp_v(self, **kwargs) -> float: return serie_r(specific_heat) def Ic( - self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, - **kwargs + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, **kwargs ) -> float: """ - Critical current + Returns the critical current. Parameters ---------- - T: - Operating temperature [K] B: - Operating magnetic field [T] - strain: - total applied measured strain [%] + Operating magnetic field in Teslas. + T: + Operating temperature in Kelvins. + esps: + Total applied measured strain in percentage. Default is 0.55. T_margin: - Strand temperature margin in operation [K] - **kwargs: dict - Additional parameters + Strand temperature margin in operation in Kelvins. Default is 1.5. + **kwargs: + Additional parameters. - Return - ------ - float [A] + Returns + ------- + Critical current in Amperes. """ return 0 class Wire_Nb3Sn(Strand): + """Represents an Nb3Sn strand made of 50% Copper100 and 50% Nb3Sn.""" + + # superconducting parameters for the calculation of Ic + c_ = 1.0 + Ca1 = 50.06 + Ca2 = 0.00 + eps_0a = 0.00312 + eps_m = -0.00059 + Bc20max = 33.24 + Tc0max = 16.34 + p = 0.593 + q = 2.156 + C = 83075 * 1e6 + def __init__(self, d_strand: float = 0.82e-3): """ - Nb3Sn strand. It is made by 50% Copper100 and 50% Nb3Sn + Initialize a Wire_Nb3Sn instance. Parameters ---------- - d_strand: - strand diameter [m] + d_strand: + Strand diameter in meters. Default is 0.82e-3. """ copper_100 = Copper100() mat_Nb3Sn = Nb3Sn() materials = [copper_100, mat_Nb3Sn] percentage = [0.5, 0.5] super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) + # percentage of copper with respect to superconducting material + self._CunonCu = self.percentage[0] / self.percentage[1] + # area of superconducting material + self._superc_area = np.pi * self.d_strand ** 2 / (4 * (1 + self._CunonCu)) def Ic( - self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, - **kwargs + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, **kwargs ) -> float: """ - Nb3Sn critical current from Jc(B,T,strain). - Parameterization for the ITER Nb3Sn Production. - - Ref - --- - fit from IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, VOL. 19, NO. 3, - JUNE 2009 Luca Bottura and Bernardo Bordini - - note - ---- - fit parameters form WST strand,A. Nijhuis, “TF conductor samples strand - thermo mechanical critical performances tests”, - https://idm.euro-fusion.org/?uid=2M5SMM v1.0. + Returns the strand critical current. Parameters ---------- - T: - Operating temperature [K] B: - Operating magnetic field [T] - strain: - total applied measured strain [%] + Operating magnetic field in Teslas. + T: + Operating temperature in Kelvins. + eps: + Total applied measured strain in percentage. Default is 0.55. T_margin: - Strand temperature margin in operation [K] - Return - ------ - float [A] + Strand temperature margin in operation in Kelvins. Default is 1.5. + + Returns + ------- + Strand current density in Amperes per square meter. """ - d_ = self.d_strand * 1e3 - CunonCu = self.percentage[0] / self.percentage[1] - # superconducting area - strand_A = np.pi * d_ ** 2 / (4 * (1 + CunonCu)) - c_ = 1.0 - Ca1 = 50.06 - Ca2 = 0.00 - eps_0a = 0.00312 - eps_m = -0.00059 - Bc20max = 33.24 - Tc0max = 16.34 - p = 0.593 - q = 2.156 - C = 83075 * strand_A # [AT] T_ = T + T_margin # Todo: check the sign of eps_m in this equation int_eps = -strain / 100 # + eps_m - eps_sh = Ca2 * eps_0a / (np.sqrt(Ca1 ** 2 - Ca2 ** 2)) + eps_sh = self.Ca2 * self.eps_0a / (np.sqrt(self.Ca1 ** 2 - self.Ca2 ** 2)) s_eps = 1 + ( - Ca1 + self.Ca1 * ( - np.sqrt(eps_sh ** 2 + eps_0a ** 2) - - np.sqrt((int_eps - eps_sh) ** 2 + eps_0a ** 2) + np.sqrt(eps_sh ** 2 + self.eps_0a ** 2) + - np.sqrt((int_eps - eps_sh) ** 2 + self.eps_0a ** 2) ) - - Ca2 * int_eps - ) / (1 - Ca1 * eps_0a) - Bc0_eps = Bc20max * s_eps - Tc0_eps = Tc0max * (s_eps) ** (1 / 3) + - self.Ca2 * int_eps + ) / (1 - self.Ca1 * self.eps_0a) + Bc0_eps = self.Bc20max * s_eps + Tc0_eps = self.Tc0max * (s_eps) ** (1 / 3) t = T_ / Tc0_eps BcT_eps = Bc0_eps * (1 - t ** (1.52)) b = B / BcT_eps hT = (1 - t ** (1.52)) * (1 - t ** 2) - fPb = (b ** p) * (1 - b) ** q - return c_ * (C / B) * s_eps * fPb * hT - - def Je(self, Ic: float): - """ - Strand current density - - Parameters - ---------- - Ic: - Critical current [A] - - Return - ------ - float [A/m2] - """ - return Ic / self.area + fPb = (b ** self.p) * (1 - b) ** self.q + return self.c_ * (self.C / B) * s_eps * fPb * hT * self._superc_area class Wire_NbTi(Strand): + """Represents an NbTi strand.""" + + # superconducting parameters for the calculation of Ic + n = 1.7 + Bc20_T = 15.19 + Tc0_K = 8.907 + C0 = 3.00e04 + C1 = 0.45 + a1 = 3.2 + b1 = 2.43 + C2 = 0.55 + a2 = 0.65 + b2 = 2 + g1 = 1.8 + g2 = 1.8 + def __init__(self, d_strand: float = 0.82e-3): """ - NbTi strand + Initialize a Wire_NbTi instance. Parameters ---------- d_strand: - strand diameter [m] + Strand diameter in meters. Default is 0.82e-3. """ copper_100 = Copper100() mat_NbTi = NbTi() materials = [copper_100, mat_NbTi] percentage = [0.5, 0.5] super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) + # percentage of copper with respect to superconducting material + self._CunonCu = self.percentage[0] / self.percentage[1] + # area of superconducting material + self._superc_area = np.pi * self.d_strand ** 2 / (4 * (1 + self._CunonCu)) def Ic(self, B: float, T: float, T_margin: float = 1.5, **kwargs): """ - NbTi critical current - - Ref - --- - Pinning Properties of Commercial Nb-Ti Wires Described by a 2-Components Model, - Luigi Muzzi, Gianluca De Marzi, et al. - - Note - ---- - Fit data from DTT TF strand + NbTi critical current. Parameters ---------- - T: - Operating temperature [K] B: - Operating magnetic field [T] + Operating magnetic field in Teslas. + T: + Operating temperature in Kelvins. T_margin: - Strand temperature margin in operation [K] + Strand temperature margin in operation in Kelvins. Default is 1.5. - Return - ------ - float [A] + Returns + ------- + Critical current in Amperes. + + References + ---------- + - Pinning Properties of Commercial Nb-Ti Wires Described by a 2-Components Model, Luigi Muzzi, Gianluca De Marzi, et al. + - Fit data from DTT TF strand. """ - d_ = self.d_strand * 1e3 - CunonCu = self.percentage[0] / self.percentage[1] - n = 1.7 - Bc20_T = 15.19 - Tc0_K = 8.907 - t = (T + T_margin) / Tc0_K - tt = 1 - t ** n - b = B / Bc20_T - C0 = 3.00e04 - C1 = 0.45 - a1 = 3.2 - b1 = 2.43 - C2 = 0.55 - a2 = 0.65 - b2 = 2 - g1 = 1.8 - g2 = 1.8 - G = (a1 / (a1 + b1)) ** a1 - GG = (b1 / (a1 + b1)) ** b1 + t = (T + T_margin) / self.Tc0_K + b = B / self.Bc20_T + tt = 1 - t ** self.n + G = (self.a1 / (self.a1 + self.b1)) ** self.a1 + GG = (self.b1 / (self.a1 + self.b1)) ** self.b1 GGG = G * GG - F = (a2 / (a2 + b2)) ** a2 - FF = (b2 / (a2 + b2)) ** b2 + F = (self.a2 / (self.a2 + self.b2)) ** self.a2 + FF = (self.b2 / (self.a2 + self.b2)) ** self.b2 FFF = F * FF Jc = ( - C0 * C1 / (B * GGG) * (b / tt) ** a1 * (1 - b / tt) ** b1 * tt ** g1 - + C0 * C2 / (B * FFF) * (b / tt) ** a2 * (1 - b / tt) ** b2 * tt ** g2 - ) - return Jc * np.pi * d_ ** 2 / (4 * (1 + CunonCu)) - - def Je(self, Ic: float): - """ - Strand current density - - Parameters - ---------- - Ic: - strand current [A] - - Return - ------ - float [A/m2] - """ - return Ic / self.area + self.C0 + * self.C1 + / (B * GGG) + * (self.b / tt) ** self.a1 + * (1 - self.b / tt) ** self.b1 + * tt ** self.g1 + + self.C0 + * self.C2 + / (B * FFF) + * (self.b / tt) ** self.a2 + * (1 - self.b / tt) ** self.b2 + * tt ** self.g2 + ) * 1e6 + return Jc * self._superc_area From f4226d2500705271a7632de4258e8ca55878c392 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 31 May 2024 12:32:23 +0200 Subject: [PATCH 05/17] adjustments to strand.py --- bluemira/magnets/strand.py | 28 ++++++++++++---------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index 4987055f59..3719208fe8 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -6,6 +6,8 @@ """Strand class""" +from typing import List, Union + import numpy as np from bluemira.magnets.materials import Copper100, Material, Nb3Sn, NbTi @@ -19,8 +21,8 @@ class Strand: def __init__( self, - materials: list[Material], - percentage: np.array | list[float], + materials: List[Material], + percentage: Union[np.array, List[float]], d_strand: float = 0.82e-3, ): """ @@ -98,7 +100,8 @@ def cp_v(self, **kwargs) -> float: return serie_r(specific_heat) def Ic( - self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, **kwargs + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, + **kwargs ) -> float: """ Returns the critical current. @@ -158,7 +161,8 @@ def __init__(self, d_strand: float = 0.82e-3): self._superc_area = np.pi * self.d_strand ** 2 / (4 * (1 + self._CunonCu)) def Ic( - self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, **kwargs + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, + **kwargs ) -> float: """ Returns the strand critical current. @@ -268,17 +272,9 @@ def Ic(self, B: float, T: float, T_margin: float = 1.5, **kwargs): FF = (self.b2 / (self.a2 + self.b2)) ** self.b2 FFF = F * FF Jc = ( - self.C0 - * self.C1 - / (B * GGG) - * (self.b / tt) ** self.a1 - * (1 - self.b / tt) ** self.b1 - * tt ** self.g1 - + self.C0 - * self.C2 - / (B * FFF) - * (self.b / tt) ** self.a2 - * (1 - self.b / tt) ** self.b2 - * tt ** self.g2 + self.C0 * self.C1 / (B * GGG) * (self.b / tt) ** self.a1 * ( + 1 - self.b / tt) ** self.b1 * tt ** self.g1 + + self.C0 * self.C2 / (B * FFF) * (self.b / tt) ** self.a2 * ( + 1 - self.b / tt) ** self.b2 * tt ** self.g2 ) * 1e6 return Jc * self._superc_area From 303c03e7d71b9a8810a2b5b0552c1b1b9e2dfd04 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 31 May 2024 12:34:11 +0200 Subject: [PATCH 06/17] renamed ym to E --- bluemira/magnets/cable.py | 14 +++++++------- bluemira/magnets/case.py | 12 ++++++------ bluemira/magnets/conductor.py | 16 ++++++++-------- bluemira/magnets/materials.py | 12 ++++++------ 4 files changed, 27 insertions(+), 27 deletions(-) diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py index f39a2d1308..1ac22f74d3 100644 --- a/bluemira/magnets/cable.py +++ b/bluemira/magnets/cable.py @@ -162,18 +162,18 @@ def dy(self): """Cable dimension in the y direction [m]""" return self.area / self.dx - def ym(self, **kwargs): + def E(self, **kwargs): """Cable Young's moduli""" return 0 # OD structural properties def Kx(self, **kwargs): """Total equivalent stiffness along x-axis""" - return self.ym(**kwargs) * self.dy / self.dx + return self.E(**kwargs) * self.dy / self.dx def Ky(self, **kwargs): """Total equivalent stiffness along y-axis""" - return self.ym(**kwargs) * self.dx / self.dy + return self.E(**kwargs) * self.dx / self.dy def optimize_n_stab_ths( self, @@ -455,7 +455,7 @@ class DummySquareCableHTS(SquareCable): cable string identifier """ - def ym(self, **kwargs): + def E(self, **kwargs): return 120e9 @@ -483,7 +483,7 @@ class DummySquareCableLTS(SquareCable): cable string identifier """ - def ym(self, **kwargs): + def E(self, **kwargs): return 0.1e9 @@ -573,7 +573,7 @@ class DummyRoundCableHTS(RoundCable): #todo decide if it is the case to add also the cooling material """ - def ym(self, **kwargs): + def E(self, **kwargs): return 120e9 @@ -603,5 +603,5 @@ class DummyRoundCableLTS(RoundCable): #todo decide if it is the case to add also the cooling material """ - def ym(self, **kwargs): + def E(self, **kwargs): return 0.1e9 diff --git a/bluemira/magnets/case.py b/bluemira/magnets/case.py index 0564ea1c9b..f0c0e78ef8 100644 --- a/bluemira/magnets/case.py +++ b/bluemira/magnets/case.py @@ -102,7 +102,7 @@ def area_wps_jacket(self): def Kx_ps(self, **kwargs): """Equivalent radial mechanical stiffness of ps""" - return self.mat_case.ym(**kwargs) * self.dy_ps / self.dx_ps + return self.mat_case.E(**kwargs) * self.dy_ps / self.dx_ps def Kx_lat(self, **kwargs): """Equivalent radial stiffness of the lateral case part connected to each winding pack""" @@ -112,11 +112,11 @@ def Kx_lat(self, **kwargs): for i, w in enumerate(self.WPs) ]) dy_lat = np.array([w.dy for w in self.WPs]) - return self.mat_case.ym(**kwargs) * dy_lat / dx_lat + return self.mat_case.E(**kwargs) * dy_lat / dx_lat def Kx_vault(self, **kwargs): """Equivalent radial stiffness of the vault""" - return self.mat_case.ym(**kwargs) * self.dy_vault / self.dx_vault + return self.mat_case.E(**kwargs) * self.dy_vault / self.dx_vault def Kx(self, **kwargs): """Total equivalent radial stiffness of the case""" @@ -132,7 +132,7 @@ def Kx(self, **kwargs): def Ky_ps(self, **kwargs): """Equivalent toroidal stiffness of ps""" - return self.mat_case.ym(**kwargs) * self.dx_ps / self.dy_ps + return self.mat_case.E(**kwargs) * self.dx_ps / self.dy_ps def Ky_lat(self, **kwargs): """Equivalent toroidal stiffness of the lateral case part connected to each winding""" @@ -142,11 +142,11 @@ def Ky_lat(self, **kwargs): for i, w in enumerate(self.WPs) ]) dy_lat = np.array([w.dy for w in self.WPs]) - return self.mat_case.ym(**kwargs) * dx_lat / dy_lat + return self.mat_case.E(**kwargs) * dx_lat / dy_lat def Ky_vault(self, **kwargs): """Equivalent toroidal stiffness of the vault""" - return self.mat_case.ym(**kwargs) * self.dx_vault / self.dy_vault + return self.mat_case.E(**kwargs) * self.dx_vault / self.dy_vault def Ky(self, **kwargs): """Total equivalent toroidal stiffness of the case""" diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index 39f92c91b3..cd16322f3d 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -181,19 +181,19 @@ def cp_v(self, **kwargs): # TODO: add a description of the 0D structural model, otherwise the following # function cannot be undestood def Kx_topbot_ins(self, **kwargs): - return self.mat_ins.ym(**kwargs) * self.dy / self.dx_ins + return self.mat_ins.E(**kwargs) * self.dy / self.dx_ins def Kx_lat_ins(self, **kwargs): - return self.mat_ins.ym(**kwargs) * self.dy_ins / (self.dx - 2 * self.dx_ins) + return self.mat_ins.E(**kwargs) * self.dy_ins / (self.dx - 2 * self.dx_ins) def Kx_lat_jacket(self, **kwargs): return ( - self.mat_jacket.ym(**kwargs) * self.dy_jacket / ( + self.mat_jacket.E(**kwargs) * self.dy_jacket / ( self.dx - 2 * self.dx_ins) ) def Kx_topbot_jacket(self, **kwargs): - return self.mat_jacket.ym(**kwargs) * self.cable.dy / self.dx_jacket + return self.mat_jacket.E(**kwargs) * self.cable.dy / self.dx_jacket def Kx_cable(self, **kwargs): return self.cable.Kx(**kwargs) @@ -210,19 +210,19 @@ def Kx(self, **kwargs): ) def Ky_topbot_ins(self, **kwargs): - return self.mat_ins.ym(**kwargs) * self.dx / self.dy_ins + return self.mat_ins.E(**kwargs) * self.dx / self.dy_ins def Ky_lat_ins(self, **kwargs): - return self.mat_ins.ym(**kwargs) * self.dx_ins / (self.dy - 2 * self.dy_ins) + return self.mat_ins.E(**kwargs) * self.dx_ins / (self.dy - 2 * self.dy_ins) def Ky_lat_jacket(self, **kwargs): return ( - self.mat_jacket.ym(**kwargs) * self.dx_jacket / ( + self.mat_jacket.E(**kwargs) * self.dx_jacket / ( self.dy - 2 * self.dy_ins) ) def Ky_topbot_jacket(self, **kwargs): - return self.mat_jacket.ym(**kwargs) * self.cable.dx / self.dy_jacket + return self.mat_jacket.E(**kwargs) * self.cable.dx / self.dy_jacket def Ky_cable(self, **kwargs): return self.cable.Ky(**kwargs) diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py index 0450b5284d..86a419cb99 100644 --- a/bluemira/magnets/materials.py +++ b/bluemira/magnets/materials.py @@ -28,7 +28,7 @@ def density(self, **kwargs): """Density""" return 0 - def ym(self, **kwargs): + def E(self, **kwargs): """Young module""" return 1e-6 @@ -57,7 +57,7 @@ def density(self, **kwargs): """Material density [kg/m³]""" return 7890.0 - def ym(self, T: float, **kwargs): + def E(self, T: float, **kwargs): """ Young modulus @@ -76,10 +76,10 @@ def ym(self, T: float, **kwargs): float [Pa] """ if T > 173: - ym = 200.4 * 1e9 - 8.1221e-2 * (T - 273) * 1e9 + E = 200.4 * 1e9 - 8.1221e-2 * (T - 273) * 1e9 else: - ym = 208.5 * 1e9 - return ym + E = 208.5 * 1e9 + return E def res(self, T: float, **kwargs): """ @@ -440,5 +440,5 @@ class DummyInsulator(Material): def res(self, **kwargs): return 1e6 - def ym(self, **kwargs): + def E(self, **kwargs): return 12e9 From 284a499885a99c91ecaf220948d0c2e839e5cbb4 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 31 May 2024 12:36:07 +0200 Subject: [PATCH 07/17] renamed res to erho --- bluemira/magnets/cable.py | 8 ++++---- bluemira/magnets/conductor.py | 6 +++--- bluemira/magnets/materials.py | 14 +++++++------- bluemira/magnets/strand.py | 4 ++-- 4 files changed, 16 insertions(+), 16 deletions(-) diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py index 1ac22f74d3..3c565c0cac 100644 --- a/bluemira/magnets/cable.py +++ b/bluemira/magnets/cable.py @@ -78,7 +78,7 @@ def n_stab_strand(self): def n_stab_strand(self, value: int): self._n_stab_strand = int(np.ceil(value)) - def res(self, **kwargs): + def erho(self, **kwargs): """ Computes the cable's equivalent resistivity considering the resistance of its strands in parallel. @@ -93,8 +93,8 @@ def res(self, **kwargs): float [Ohm m] """ resistances = np.array([ - self.sc_strand.res(**kwargs) / self.area_sc, - self.stab_strand.res(**kwargs) / self.area_stab, + self.sc_strand.erho(**kwargs) / self.area_sc, + self.stab_strand.erho(**kwargs) / self.area_stab, ]) res_tot = parall_r(resistances) return res_tot * self.area @@ -245,7 +245,7 @@ def _heat_balance_model_cable(t, T, B: Callable, I: Callable, cable: Cable): if isinstance(T, np.ndarray): T = T[0] - Q_gen = (I(t) / cable.area) ** 2 * cable.res(B=B(t), T=T) + Q_gen = (I(t) / cable.area) ** 2 * cable.erho(B=B(t), T=T) # Calculate the rate of heat absorption by conductor components Q_abs = cable.cp_v(T=T) diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index cd16322f3d..a459d86448 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -127,7 +127,7 @@ def area_ins(self): """Area of the insulator [m^2]""" return self.area - self.area_jacket - self.cable.area - def res(self, **kwargs): + def erho(self, **kwargs): """ Computes the conductor's equivalent resistivity considering the resistance of its strands in parallel. @@ -146,8 +146,8 @@ def res(self, **kwargs): The insulator in not considered into the calculation. """ resistances = np.array([ - self.cable.res(**kwargs) / self.cable.area, - self.mat_jacket.res(**kwargs) / self.area_jacket, + self.cable.erho(**kwargs) / self.cable.area, + self.mat_jacket.erho(**kwargs) / self.area_jacket, ]) res_tot = parall_r(resistances) return res_tot * self.area diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py index 86a419cb99..9f0ff2d488 100644 --- a/bluemira/magnets/materials.py +++ b/bluemira/magnets/materials.py @@ -32,7 +32,7 @@ def E(self, **kwargs): """Young module""" return 1e-6 - def res(self, **kwargs): + def erho(self, **kwargs): """Electrical Resistivity""" return 1e6 @@ -81,7 +81,7 @@ def E(self, T: float, **kwargs): E = 208.5 * 1e9 return E - def res(self, T: float, **kwargs): + def erho(self, T: float, **kwargs): """ Electrical Resistivity @@ -129,7 +129,7 @@ def density(self, **kwargs): """Material density [kg/m³]""" return 8960 - def res(self, T: float, B: float, **kwargs): + def erho(self, T: float, B: float, **kwargs): """ Electrical Resistivity @@ -221,7 +221,7 @@ def density(self, **kwargs): """Material density [kg/m³]""" return 8960 - def res(self, T: float, B: float, **kwargs): + def erho(self, T: float, B: float, **kwargs): """ Electrical Resistivity @@ -339,7 +339,7 @@ def cp_v(self, T: float, **kwargs): cp_Nb3Sn = 1 / ((1 / self.cp300) + (1 / cp_low_NC)) return cp_Nb3Sn * density - def res(self, T: float, **kwargs): + def erho(self, T: float, **kwargs): """ Electrical Resistivity @@ -411,7 +411,7 @@ def cp_v(self, T: float, **kwargs): cp_NbTi = 1 / ((1 / self.cp300) + (1 / cp_low_NC)) return cp_NbTi * density - def res(self, T: float, **kwargs): + def erho(self, T: float, **kwargs): """ Electrical Resistivity @@ -437,7 +437,7 @@ def res(self, T: float, **kwargs): class DummyInsulator(Material): """A dummy insulator""" - def res(self, **kwargs): + def erho(self, **kwargs): return 1e6 def E(self, **kwargs): diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index 3719208fe8..d0e6618665 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -61,7 +61,7 @@ def E(self, **kwargs) -> float: """ return 0.1e9 - def res(self, **kwargs) -> float: + def erho(self, **kwargs) -> float: """ Calculates the equivalent resistivity based on the parallel connection of strand components. @@ -75,7 +75,7 @@ def res(self, **kwargs) -> float: Equivalent resistivity in Ohm meters. """ resistances = [ - x.res(**kwargs) / self.area / self.percentage[i] + x.erho(**kwargs) / self.area / self.percentage[i] for i, x in enumerate(self.materials) ] res_tot = parall_r(resistances) From e87fb3eb7fba3fa82b3114fab6a675e76bbc43af Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Mon, 3 Jun 2024 16:15:39 +0200 Subject: [PATCH 08/17] some improvements --- bluemira/magnets/cable.py | 34 +++-- bluemira/magnets/conductor.py | 245 +++++++++++++++++++++++++-------- bluemira/magnets/materials.py | 5 +- bluemira/magnets/strand.py | 2 +- examples/magnets/tf_example.py | 7 +- 5 files changed, 217 insertions(+), 76 deletions(-) diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py index 3c565c0cac..16f60e7f81 100644 --- a/bluemira/magnets/cable.py +++ b/bluemira/magnets/cable.py @@ -56,7 +56,8 @@ def __init__( name: cable string identifier - #TODO: discuss if it is necessary to implement also the cooling material + Notes: + Cooling material not implemented. """ self.name = name self._dx = dx @@ -163,10 +164,10 @@ def dy(self): return self.area / self.dx def E(self, **kwargs): - """Cable Young's moduli""" + """Young's moduli""" return 0 - # OD structural properties + # OD homogenized structural properties def Kx(self, **kwargs): """Total equivalent stiffness along x-axis""" return self.E(**kwargs) * self.dy / self.dx @@ -217,6 +218,7 @@ def optimize_n_stab_ths( ----- The number of stabilizer strands in the cable is directly modified. An error is raised in case the optimization process did not converge. + Cooling material contribution is neglected when applying the hot spot criteria. """ def _heat_balance_model_cable(t, T, B: Callable, I: Callable, cable: Cable): @@ -326,7 +328,7 @@ def final_temperature_difference( return result - def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None, **kwargs): + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): """ Schematic plot of the cable cross-section. @@ -384,7 +386,11 @@ def __init__( name: str = "", ): """ - Representation of a square cable + Representation of a square cable. + + Notes + ----- + No geometrical dimensions are given. They are extrapolated from the cable design parameters. Parameters ---------- @@ -405,7 +411,9 @@ def __init__( name: cable string identifier - #todo decide if it is the case to add also the cooling material + Notes + ----- + Cooling material not implemented """ dx = 0.1 super().__init__( @@ -456,6 +464,7 @@ class DummySquareCableHTS(SquareCable): """ def E(self, **kwargs): + """Young's module""" return 120e9 @@ -484,6 +493,7 @@ class DummySquareCableLTS(SquareCable): """ def E(self, **kwargs): + """Young's module""" return 0.1e9 @@ -521,7 +531,7 @@ def __init__( name: cable string identifier - #todo decide if it is the case to add also the cooling material + """ dx = 0.1 super().__init__( @@ -570,10 +580,13 @@ class DummyRoundCableHTS(RoundCable): name: cable string identifier - #todo decide if it is the case to add also the cooling material + Notes + ----- + Cooling material not implemented """ def E(self, **kwargs): + """Young's module""" return 120e9 @@ -600,8 +613,11 @@ class DummyRoundCableLTS(RoundCable): name: cable string identifier - #todo decide if it is the case to add also the cooling material + Notes + ----- + Cooling material not implemented """ def E(self, **kwargs): + """Young's module""" return 0.1e9 diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index a459d86448..bb6a41e38d 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -6,6 +6,8 @@ """Conductor class""" +from typing import Optional + import matplotlib.pyplot as plt import numpy as np from scipy.optimize import minimize_scalar @@ -238,6 +240,189 @@ def Ky(self, **kwargs): ]) ) + def _tresca_sigma_jacket(self, pressure: float, f_z: float, T: float, + B: float, direction: str = 'x') -> float: + """ + Calculate the radial stress in the jacket when the conductor is subjected to a pressure + along a specified direction and a force perpendicular to the conductor cross-section. + The Tresca criterion is used for this calculation. + + Parameters + ---------- + pressure : + The pressure applied along the specified direction (Pa). + f_z : + The force applied in the z direction, perpendicular to the conductor cross-section (N). + T : + The operating temperature (K). + B : + The operating magnetic field (T). + direction : + The direction along which the pressure is applied ('x' or 'y'). Default is 'x'. + + Returns + ------- + The calculated Tresca stress in the jacket (Pa). + + Raises + ------ + ValueError + If the specified direction is not 'x' or 'y'. + """ + + if direction not in ['x', 'y']: + raise ValueError("Invalid direction: choose either 'x' or 'y'.") + + if direction == 'x': + saf_jacket = (self.cable.dx + 2 * self.dx_jacket) / (2 * self.dx_jacket) + + K = parall_k([ + 2 * self.Ky_lat_ins(T=T, B=B), + 2 * self.Ky_lat_jacket(T=T, B=B), + serie_k([ + self.Ky_cable(T=T, B=B), + self.Ky_topbot_jacket(T=T, B=B) / 2 + ]), + ]) + + X_jacket = 2 * self.Ky_lat_jacket(T=T, B=B) / K + + else: + saf_jacket = (self.cable.dy + 2 * self.dy_jacket) / (2 * self.dy_jacket) + + K = parall_k([ + 2 * self.Kx_lat_ins(T=T, B=B), + 2 * self.Kx_lat_jacket(T=T, B=B), + serie_k([ + self.Kx_cable(T=T, B=B), + self.Kx_topbot_jacket(T=T, B=B) / 2 + ]), + ]) + + X_jacket = 2 * self.Kx_lat_jacket(T=T, B=B) / K + + tresca_stress = pressure * X_jacket * saf_jacket + f_z / self.area_jacket + + return tresca_stress + + def optimize_jacket_conductor( + self, + pressure: float, + f_z: float, + T: float, + B: float, + allowable_sigma: float, + bounds: Optional[np.ndarray] = None, + direction: str = 'x' + ): + """ + Optimize the jacket dimension of a conductor based on allowable stress using the Tresca criterion. + + Parameters + ---------- + pressure : + The pressure applied along the specified direction (Pa). + f_z : + The force applied in the z direction, perpendicular to the conductor cross-section (N). + T : + The operating temperature (K). + B : + The operating magnetic field (T). + allowable_sigma : + The allowable stress (Pa) for the jacket material. + bounds : + Optional bounds for the jacket thickness optimization (default is None). + direction : + The direction along which the pressure is applied ('x' or 'y'). Default is 'x'. + + Returns + ------- + The result of the optimization process containing information about the optimal jacket thickness. + + Raises + ------ + ValueError + If the optimization process did not converge. + + Notes + ----- + This function uses the Tresca yield criterion to optimize the thickness of the jacket surrounding the conductor. + This function directly update the conductor's jacket thickness along the x direction to the optimal value. + """ + + def sigma_difference( + jacket_thickness: float, + pressure: float, + fz: float, + T: float, + B: float, + allowable_sigma: float, + direction: str = 'x' + ): + """ + Fitness function for the optimization problem. It calculates the absolute difference between + the Tresca stress and the allowable stress. + + Parameters + ---------- + jacket_thickness : + The thickness of the jacket in the direction perpendicular to the applied pressure(m). + pressure : + The pressure applied along the specified direction (Pa). + fz : + The force applied in the z direction, perpendicular to the conductor cross-section (N). + T : + The temperature (K) at which the conductor operates. + B : + The magnetic field (T) at which the conductor operates. + allowable_sigma : + The allowable stress (Pa) for the jacket material. + direction : + The direction along which the pressure is applied ('x' or 'y'). Default is 'x'. + + Returns + ------- + The absolute difference between the calculated Tresca stress and the allowable stress (Pa). + + Notes + ----- + This function modifies the conductor's jacket thickness along the specified direction + using the value provided in jacket_thickness. + """ + if direction not in ['x', 'y']: + raise ValueError("Invalid direction: choose either 'x' or 'y'.") + + if direction == 'x': + self.dx_jacket = jacket_thickness + else: + self.dy_jacket = jacket_thickness + + sigma_r = self._tresca_sigma_jacket(pressure, fz, T, B, direction) + diff = abs(sigma_r - allowable_sigma) + return diff + + method = "bounded" if bounds is not None else None + + result = minimize_scalar( + fun=sigma_difference, + args=(pressure, f_z, T, B, allowable_sigma), + bounds=bounds, + method=method, + options={"xatol": 1e-4}, + ) + + if not result.success: + raise ValueError("Optimization of the jacket conductor did not converge.") + self.dx_jacket = result.x + if direction == 'x': + print(f"Optimal dx_jacket: {self.dx_jacket}") + else: + print(f"Optimal dy_jacket: {self.dy_jacket}") + print(f"Averaged sigma in the {direction}-direction: " + f"{self._tresca_sigma_jacket(pressure, f_z, T, B) / 1e6} MPa") + + return result + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): """ Schematic plot of the cable cross-section. @@ -337,63 +522,3 @@ def dy_jacket(self): @property def dy_ins(self): return self.dx_ins - - -def _sigma_r_jacket(conductor: Conductor, pressure: float, f_z: float, T: float, - B: float): - saf_jacket = (conductor.cable.dx + 2 * conductor.dx_jacket) / ( - 2 * conductor.dx_jacket - ) - K = parall_k([ - 2 * conductor.Ky_lat_ins(T=T, B=B), - 2 * conductor.Ky_lat_jacket(T=T, B=B), - serie_k([conductor.Ky_cable(T=T, B=B), - conductor.Ky_topbot_jacket(T=T, B=B) / 2]), - ]) - X_jacket = 2 * conductor.Ky_lat_jacket(T=T, B=B) / K - return pressure * X_jacket * saf_jacket + f_z / conductor.area_jacket - - -def optimize_jacket_conductor( - conductor: Conductor, - pressure: float, - fz: float, - T: float, - B: float, - allowable_sigma: float, - bounds: np.array = None, -): - def sigma_difference( - dx_jacket: float, - pressure: float, - fz: float, - T: float, - B: float, - conductor: Conductor, - allowable_sigma: float, - ): - conductor.dx_jacket = dx_jacket - sigma_r = _sigma_r_jacket(conductor, pressure, fz, T, B) - diff = abs(sigma_r - allowable_sigma) - return diff - - method = None - if bounds is not None: - method = "bounded" - - result = minimize_scalar( - fun=sigma_difference, - args=(pressure, fz, T, B, conductor, allowable_sigma), - bounds=bounds, - method=method, - options={"xatol": 1e-4}, - ) - - if not result.success: - raise ValueError("dx_jacket optimization did not converge.") - conductor.dx_jacket = result.x - print(f"Optimal dx_jacket: {conductor.dx_jacket}") - print(f"Averaged sigma_r: {_sigma_r_jacket(conductor, pressure, fz, T, B) / 1e6} " - f"MPa") - - return result diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py index 9f0ff2d488..bd3e51c155 100644 --- a/bluemira/magnets/materials.py +++ b/bluemira/magnets/materials.py @@ -29,7 +29,7 @@ def density(self, **kwargs): return 0 def E(self, **kwargs): - """Young module""" + """Young's module""" return 1e-6 def erho(self, **kwargs): @@ -59,7 +59,7 @@ def density(self, **kwargs): def E(self, T: float, **kwargs): """ - Young modulus + Young's module Ref --- @@ -441,4 +441,5 @@ def erho(self, **kwargs): return 1e6 def E(self, **kwargs): + """Young's module""" return 12e9 diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index d0e6618665..6c195b1030 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -48,7 +48,7 @@ def area(self) -> float: def E(self, **kwargs) -> float: """ - Returns the Young's modulus for the strand. + Young's modulus. Parameters ---------- diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index 95dd7c328a..12aba3f5a0 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -4,7 +4,7 @@ from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI from bluemira.magnets.cable import DummyRoundCableLTS, DummyRoundCableHTS from bluemira.magnets.case import CaseTF -from bluemira.magnets.conductor import SquareConductor, optimize_jacket_conductor +from bluemira.magnets.conductor import SquareConductor from bluemira.magnets.materials import (OperationalPoint, Copper300, AISI_316LN, DummyInsulator) from bluemira.magnets.strand import Strand, Wire_Nb3Sn @@ -122,9 +122,8 @@ t_z_cable_jacket = t_z * case.area_wps_jacket / ( case.area_jacket + case.area_wps_jacket) / ( np.sum([w.nx * w.ny for w in case.WPs])) - result_opt_jacket = optimize_jacket_conductor( - conductor, pm, t_z_cable_jacket, T0, B_TF_i, - S_Y, bounds=[1e-5, 0.2] + result_opt_jacket = conductor.optimize_jacket_conductor( + pm, t_z_cable_jacket, T0, B_TF_i, S_Y, bounds=[1e-5, 0.2] ) print(f"after optimization: conductor dx_jacket = {conductor.dx_jacket}") From 1d215cdcac5579abd959b0ac05f95a8dc66fcd14 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Tue, 4 Jun 2024 10:47:42 +0200 Subject: [PATCH 09/17] improvements to case and WP --- bluemira/magnets/{case.py => case_tf.py} | 77 ++++++++++++++++++++++- bluemira/magnets/winding_pack.py | 80 +++++++++++++++++++----- examples/magnets/tf_example.py | 9 +-- 3 files changed, 145 insertions(+), 21 deletions(-) rename bluemira/magnets/{case.py => case_tf.py} (81%) diff --git a/bluemira/magnets/case.py b/bluemira/magnets/case_tf.py similarity index 81% rename from bluemira/magnets/case.py rename to bluemira/magnets/case_tf.py index f0c0e78ef8..1b4e1b1ef0 100644 --- a/bluemira/magnets/case.py +++ b/bluemira/magnets/case_tf.py @@ -89,15 +89,18 @@ def dx_vault(self): @property def area(self): + """Total case area (winding packs included)""" return (self.dx_i + self.dx_k) * (self.Ri - self.Rk) / 2 @property def area_jacket(self): + """Total jacket area (total case area - winding packs area)""" total_wp_area = np.sum([w.conductor.area * w.nx * w.ny for w in self.WPs]) return self.area - total_wp_area @property def area_wps_jacket(self): + """Tatal jacket area in the winding packs""" return np.sum([w.conductor.area_jacket * w.nx * w.ny for w in self.WPs]) def Kx_ps(self, **kwargs): @@ -171,7 +174,7 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): I: total current flowing in the case kwargs: - arguments necessary to calculate the structural properties of the case + additional arguments necessary to calculate the structural properties of the case """ # The maximum principal stress acting on the case nose is the compressive @@ -208,6 +211,33 @@ def optimize_vault_radial_thickness( allowable_sigma: float, bounds: np.array = None, ): + """ + Optimize the vault radial thickness of the case + + Parameters + ---------- + pm : + The magnetic pressure applied along the radial direction (Pa). + f_z : + The force applied in the z direction, perpendicular to the case cross-section (N). + T : + The operating temperature (K). + B : + The operating magnetic field (T). + allowable_sigma : + The allowable stress (Pa) for the jacket material. + bounds : + Optional bounds for the jacket thickness optimization (default is None). + + Returns + ------- + The result of the optimization process containing information about the optimal vault thickness. + + Raises + ------ + ValueError + If the optimization process did not converge. + """ def sigma_difference( dy_vault: float, pm: float, @@ -217,6 +247,34 @@ def sigma_difference( case: CaseTF, allowable_sigma: float, ): + """ + Fitness function for the optimization problem. It calculates the absolute difference between + the Tresca stress and the allowable stress. + + Parameters + ---------- + dy_vault : + The thickness of the vault in the direction perpendicular to the applied pressure(m). + pm : + The magnetic pressure applied along the radial direction (Pa). + fz : + The force applied in the z direction, perpendicular to the case cross-section (N). + T : + The temperature (K) at which the conductor operates. + B : + The magnetic field (T) at which the conductor operates. + allowable_sigma : + The allowable stress (Pa) for the vault material. + + Returns + ------- + The absolute difference between the calculated Tresca stress and the allowable stress (Pa). + + Notes + ----- + This function modifies the case's vault thickness + using the value provided in jacket_thickness. + """ case.dy_vault = dy_vault sigma = case._tresca_stress(pm, fz, T=T, B=B) diff = abs(sigma - allowable_sigma) @@ -235,7 +293,7 @@ def sigma_difference( ) if not result.success: - raise ValueError("dx_vault optimization did not converge.") + raise ValueError("dy_vault optimization did not converge.") self.dy_vault = result.x print(f"Optimal dy_vault: {self.dy_vault}") print(f"Tresca sigma: {self._tresca_stress(pm, fz, T=T, B=B) / 1e6} MPa") @@ -243,6 +301,19 @@ def sigma_difference( return result def plot(self, ax=None, show: bool = False, homogenized: bool = False): + """ + Schematic plot of the case cross-section. + + Parameters + ---------- + ax: + Matplotlib Axis on which the plot shall be displayed. If None, + a new figure is created + show: + if True, the plot is displayed + homogenized: + if True, the winding pack is homogenized (default is False) + """ if ax is None: _, ax = plt.subplots() @@ -264,7 +335,7 @@ def plot(self, ax=None, show: bool = False, homogenized: bool = False): return ax - def rearrange_conductors_in_wp_type1( + def rearrange_conductors_in_wp( self, n_conductors: int, cond: Conductor, diff --git a/bluemira/magnets/winding_pack.py b/bluemira/magnets/winding_pack.py index 65320ca4c7..af726e89ae 100644 --- a/bluemira/magnets/winding_pack.py +++ b/bluemira/magnets/winding_pack.py @@ -6,36 +6,88 @@ class WindingPack: - def __init__(self, conductor: Conductor, nx: np.int32, ny: np.int32, name: str = ""): - self.name = name + """ + A class to represent a winding pack which contains multiple conductors arranged in a grid pattern. + """ + + def __init__(self, conductor: Conductor, nx: int, ny: int): + """ + Initializes the WindingPack with the given conductor, number of conductors along x and y axes, and an optional name. + + Parameters + ---------- + conductor : Conductor + An instance of the Conductor class. + nx : int + Number of conductors along the x-axis. + ny : int + Number of conductors along the y-axis. + """ self.nx = nx self.ny = ny self.conductor = conductor @property def dx(self): + """Total width of the winding pack along the x-axis.""" return self.conductor.dx * self.nx @property def dy(self): + """Total height of the winding pack along the y-axis.""" return self.conductor.dy * self.ny - def Kx(self, **kwargs): - """Total equivalent stiffness along x-axis""" + def Kx(self, **kwargs) -> float: + """ + Calculates the total equivalent stiffness of the winding pack along the x-axis. + + Parameters + ---------- + **kwargs + Additional parameters to pass to the stiffness calculation methods. + + Returns + ------- + The total equivalent stiffness along the x-axis. + """ return parall_k([serie_k([self.conductor.Kx(**kwargs)] * self.nx)] * self.ny) - def Ky(self, **kwargs): - """Total equivalent stiffness along x-axis""" + def Ky(self, **kwargs) -> float: + """ + Calculates the total equivalent stiffness of the winding pack along the y-axis. + + Parameters + ---------- + **kwargs + Additional parameters to pass to the stiffness calculation methods. + + Returns + ------- + The total equivalent stiffness along the y-axis. + """ return serie_k([parall_k([self.conductor.Ky(**kwargs)] * self.ny)] * self.nx) - def plot( - self, - xc: float = 0, - yc: float = 0, - show: bool = False, - ax=None, - homogenized: bool = True, - ): + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None, homogenized: bool = True): + """ + Plots the winding pack and its conductors. + + Parameters + ---------- + xc : + The x-coordinate of the center of the winding pack (default is 0). + yc : + The y-coordinate of the center of the winding pack (default is 0). + show : + Whether to show the plot immediately (default is False). + ax : + The axes on which to plot (default is None, which creates a new figure and axes). + homogenized : + Whether to plot the winding pack as a homogenized block or show individual conductors (default is True). + + Returns + ------- + The axes with the plotted winding pack. + """ if ax is None: _, ax = plt.subplots() diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index 12aba3f5a0..62929e2afc 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -3,7 +3,7 @@ from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI from bluemira.magnets.cable import DummyRoundCableLTS, DummyRoundCableHTS -from bluemira.magnets.case import CaseTF +from bluemira.magnets.case_tf import CaseTF from bluemira.magnets.conductor import SquareConductor from bluemira.magnets.materials import (OperationalPoint, Copper300, AISI_316LN, DummyInsulator) @@ -122,8 +122,9 @@ t_z_cable_jacket = t_z * case.area_wps_jacket / ( case.area_jacket + case.area_wps_jacket) / ( np.sum([w.nx * w.ny for w in case.WPs])) - result_opt_jacket = conductor.optimize_jacket_conductor( - pm, t_z_cable_jacket, T0, B_TF_i, S_Y, bounds=[1e-5, 0.2] + conductor.optimize_jacket_conductor( + pm, t_z_cable_jacket, T0, B_TF_i, + S_Y, bounds=[1e-5, 0.2] ) print(f"after optimization: conductor dx_jacket = {conductor.dx_jacket}") @@ -139,7 +140,7 @@ ax.set_aspect("equal") plt.show() - case.rearrange_conductors_in_wp_type1(n_cond, conductor, case.R_wp_i[0], + case.rearrange_conductors_in_wp(n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.7, 0.05, 4) if show: From 194efbc4f875f13d56d937e38596a964e965db22 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Tue, 23 Jul 2024 10:05:46 +0200 Subject: [PATCH 10/17] still quality issues --- bluemira/magnets/__init__.py | 9 ++ bluemira/magnets/case_tf.py | 141 +++++++++++++++++----------- bluemira/magnets/materials.py | 98 ++++++++++++-------- bluemira/magnets/strand.py | 152 ++++++++++++++++++++---------- examples/magnets/tf_example.py | 164 +++++++++++++++++++++------------ 5 files changed, 363 insertions(+), 201 deletions(-) create mode 100644 bluemira/magnets/__init__.py diff --git a/bluemira/magnets/__init__.py b/bluemira/magnets/__init__.py new file mode 100644 index 0000000000..ed025ac93e --- /dev/null +++ b/bluemira/magnets/__init__.py @@ -0,0 +1,9 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +""" +The bluemira magnets module +""" diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index 1b4e1b1ef0..d6fd3ee200 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -1,10 +1,20 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +""" +TF coil case class +""" + import math -from typing import List import matplotlib.pyplot as plt import numpy as np from scipy.optimize import minimize_scalar +from bluemira.base.look_and_feel import bluemira_warn from bluemira.magnets.conductor import Conductor from bluemira.magnets.materials import Material from bluemira.magnets.utils import parall_k, serie_k @@ -12,14 +22,16 @@ class CaseTF: + """TF case class""" + def __init__( self, - Ri: float, + Ri: float, # noqa: N803 dy_ps: float, dy_vault: float, theta_TF: float, mat_case: Material, - WPs: List[WindingPack], + WPs: list[WindingPack], # noqa: N803 name: str = "", ): """ @@ -61,19 +73,25 @@ def dx_ps(self): """Average toroidal length of the ps plate [m]""" return (self.Ri + (self.Ri - self.dy_ps)) * np.tan(self._rad_theta_TF / 2) + def max_Iop(self, B, T, T_margin): # noqa: N803, N802 + """Maximum operational current (equal to the critical current into the + superconductor + """ + return self.WPs[0].conductor.cable.sc_strand.Ic(B=B, T=T, T_margin=T_margin) + @property - def R_wp_i(self): + def R_wp_i(self): # noqa: N802 """Maximum radial position for each winding pack""" dy_wp_cumsum = np.cumsum(np.array([0] + [w.dy for w in self.WPs])) return np.array([self.Ri - self.dy_ps - y for y in dy_wp_cumsum[0:-1]]) @property - def R_wp_k(self): + def R_wp_k(self): # noqa: N802 """Minimum radial position for each winding pack""" return self.R_wp_i - np.array([w.dy for w in self.WPs]) @property - def Rk(self): + def Rk(self): # noqa: N802 """Minimum radial position of case""" return self.R_wp_k[-1] - self.dy_vault @@ -103,12 +121,14 @@ def area_wps_jacket(self): """Tatal jacket area in the winding packs""" return np.sum([w.conductor.area_jacket * w.nx * w.ny for w in self.WPs]) - def Kx_ps(self, **kwargs): + def Kx_ps(self, **kwargs): # noqa: N802 """Equivalent radial mechanical stiffness of ps""" return self.mat_case.E(**kwargs) * self.dy_ps / self.dx_ps - def Kx_lat(self, **kwargs): - """Equivalent radial stiffness of the lateral case part connected to each winding pack""" + def Kx_lat(self, **kwargs): # noqa: N802 + """Equivalent radial stiffness of the lateral case part connected to each + winding pack + """ dx_lat = np.array([ (self.R_wp_i[i] + self.R_wp_k[i]) / 2 * np.tan(self._rad_theta_TF / 2) - w.dx / 2 @@ -117,11 +137,11 @@ def Kx_lat(self, **kwargs): dy_lat = np.array([w.dy for w in self.WPs]) return self.mat_case.E(**kwargs) * dy_lat / dx_lat - def Kx_vault(self, **kwargs): + def Kx_vault(self, **kwargs): # noqa: N802 """Equivalent radial stiffness of the vault""" return self.mat_case.E(**kwargs) * self.dy_vault / self.dx_vault - def Kx(self, **kwargs): + def Kx(self, **kwargs): # noqa: N802 """Total equivalent radial stiffness of the case""" temp = [ serie_k([ @@ -131,14 +151,16 @@ def Kx(self, **kwargs): ]) for i, w in enumerate(self.WPs) ] - return parall_k([self.Kx_ps(**kwargs), self.Kx_vault(**kwargs)] + temp) + return parall_k([self.Kx_ps(**kwargs), self.Kx_vault(**kwargs), *temp]) - def Ky_ps(self, **kwargs): + def Ky_ps(self, **kwargs): # noqa: N802 """Equivalent toroidal stiffness of ps""" return self.mat_case.E(**kwargs) * self.dx_ps / self.dy_ps - def Ky_lat(self, **kwargs): - """Equivalent toroidal stiffness of the lateral case part connected to each winding""" + def Ky_lat(self, **kwargs): # noqa: N802 + """Equivalent toroidal stiffness of the lateral case part connected to each + winding + """ dx_lat = np.array([ (self.R_wp_i[i] + self.R_wp_k[i]) / 2 * np.tan(self._rad_theta_TF / 2) - w.dx / 2 @@ -147,18 +169,21 @@ def Ky_lat(self, **kwargs): dy_lat = np.array([w.dy for w in self.WPs]) return self.mat_case.E(**kwargs) * dx_lat / dy_lat - def Ky_vault(self, **kwargs): + def Ky_vault(self, **kwargs): # noqa: N802 """Equivalent toroidal stiffness of the vault""" return self.mat_case.E(**kwargs) * self.dx_vault / self.dy_vault - def Ky(self, **kwargs): + def Ky(self, **kwargs): # noqa: N802 """Total equivalent toroidal stiffness of the case""" temp = [ - parall_k( - [self.Ky_lat(**kwargs)[i], w.Ky(**kwargs), self.Ky_lat(**kwargs)[i]]) + parall_k([ + self.Ky_lat(**kwargs)[i], + w.Ky(**kwargs), + self.Ky_lat(**kwargs)[i], + ]) for i, w in enumerate(self.WPs) ] - return serie_k([self.Ky_ps(**kwargs), self.Ky_vault(**kwargs)] + temp) + return serie_k([self.Ky_ps(**kwargs), self.Ky_vault(**kwargs), *temp]) def _tresca_stress(self, pm: float, fz: float, **kwargs): """Procedure that calculate Tresca principal stress on the case @@ -174,7 +199,8 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): I: total current flowing in the case kwargs: - additional arguments necessary to calculate the structural properties of the case + additional arguments necessary to calculate the structural properties + of the case """ # The maximum principal stress acting on the case nose is the compressive @@ -199,14 +225,13 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): w.conductor.area_jacket * w.nx * w.ny for w in self.WPs ]) sigma_z = fz / (total_case_area - total_wp_area + total_wp_jacket_area) - sigma_tot = sigma_theta + sigma_z - return sigma_tot + return sigma_theta + sigma_z def optimize_vault_radial_thickness( self, pm: float, fz: float, - T: float, + T: float, # noqa: N803 B: float, allowable_sigma: float, bounds: np.array = None, @@ -219,7 +244,8 @@ def optimize_vault_radial_thickness( pm : The magnetic pressure applied along the radial direction (Pa). f_z : - The force applied in the z direction, perpendicular to the case cross-section (N). + The force applied in the z direction, perpendicular to the case + cross-section (N). T : The operating temperature (K). B : @@ -231,34 +257,39 @@ def optimize_vault_radial_thickness( Returns ------- - The result of the optimization process containing information about the optimal vault thickness. + The result of the optimization process containing information about the + optimal vault thickness. Raises ------ ValueError If the optimization process did not converge. """ + def sigma_difference( dy_vault: float, pm: float, fz: float, - T: float, + T: float, # noqa: N803 B: float, case: CaseTF, allowable_sigma: float, ): """ - Fitness function for the optimization problem. It calculates the absolute difference between + Fitness function for the optimization problem. It calculates the absolute + difference between the Tresca stress and the allowable stress. Parameters ---------- dy_vault : - The thickness of the vault in the direction perpendicular to the applied pressure(m). + The thickness of the vault in the direction perpendicular to the + applied pressure(m). pm : The magnetic pressure applied along the radial direction (Pa). fz : - The force applied in the z direction, perpendicular to the case cross-section (N). + The force applied in the z direction, perpendicular to the case + cross-section (N). T : The temperature (K) at which the conductor operates. B : @@ -268,7 +299,8 @@ def sigma_difference( Returns ------- - The absolute difference between the calculated Tresca stress and the allowable stress (Pa). + The absolute difference between the calculated Tresca stress and the + allowable stress (Pa). Notes ----- @@ -277,8 +309,7 @@ def sigma_difference( """ case.dy_vault = dy_vault sigma = case._tresca_stress(pm, fz, T=T, B=B) - diff = abs(sigma - allowable_sigma) - return diff + return abs(sigma - allowable_sigma) method = None if bounds is not None: @@ -295,12 +326,12 @@ def sigma_difference( if not result.success: raise ValueError("dy_vault optimization did not converge.") self.dy_vault = result.x - print(f"Optimal dy_vault: {self.dy_vault}") - print(f"Tresca sigma: {self._tresca_stress(pm, fz, T=T, B=B) / 1e6} MPa") + # print(f"Optimal dy_vault: {self.dy_vault}") + # print(f"Tresca sigma: {self._tresca_stress(pm, fz, T=T, B=B) / 1e6} MPa") return result - def plot(self, ax=None, show: bool = False, homogenized: bool = False): + def plot(self, ax=None, *, show: bool = False, homogenized: bool = False): """ Schematic plot of the case cross-section. @@ -339,8 +370,8 @@ def rearrange_conductors_in_wp( self, n_conductors: int, cond: Conductor, - R_wp_i: float, - dx_WP: float, + R_wp_i: float, # noqa: N803 + dx_WP: float, # noqa: N803 min_gap_x: float, n_layers_reduction: int, ): @@ -369,19 +400,20 @@ def rearrange_conductors_in_wp( Note ---- - The final number of allocated superconductors could slightly differ from the one defined - in n_conductors due to the necessity to close the final layer. + The final number of allocated superconductors could slightly differ from + the one defined in n_conductors due to the necessity to close the final + layer. """ - WPs = [] + WPs = [] # noqa: N806 # number of conductors to be allocated remaining_conductors = n_conductors # maximum number of internal iterations i_max = 50 i = 0 while i < i_max and remaining_conductors > 0: - i = i + 1 - print(f"iteration: {i}") - print(f"remaining_conductors: {remaining_conductors}") + i += 1 + # print(f"Rearrange conductors in WP - iteration: {i}") + # print(f"remaining_conductors: {remaining_conductors}") # maximum toroidal dimension of the WP most outer pancake # dx_WP = 2 * (R_wp_i * np.tan(self._rad_theta_TF / 2) - dx0_wp) @@ -390,14 +422,15 @@ def rearrange_conductors_in_wp( if i == 1: n_layers_max = int(math.floor(dx_WP / cond.dx)) else: - n_layers_max = n_layers_max - n_layers_reduction + n_layers_max -= n_layers_reduction if n_layers_max < 1: raise ValueError( - f"n_layers_max: {n_layers_max} < 1. There is not enough space to allocate all the conductors" + f"n_layers_max: {n_layers_max} < 1. There is not enough space to " + f"allocate all the conductors" ) - dx_WP = n_layers_max * cond.dx + dx_WP = n_layers_max * cond.dx # noqa: N806 gap_0 = R_wp_i * np.tan(self._rad_theta_TF / 2) - dx_WP / 2 gap_1 = min_gap_x @@ -410,20 +443,22 @@ def rearrange_conductors_in_wp( if n_turns_max < 1: raise ValueError( - f"n_turns_max: {n_turns_max} < 1. There is not enough space to allocate all the conductors" + f"n_turns_max: {n_turns_max} < 1. There is not enough space to " + f"allocate all the conductors" ) WPs.append(WindingPack(conductor=cond, nx=n_layers_max, ny=n_turns_max)) - remaining_conductors = remaining_conductors - (n_layers_max * n_turns_max) + remaining_conductors -= n_layers_max * n_turns_max if remaining_conductors < 0: - print( - f"WARNING: {abs(remaining_conductors)} have been added to complete the last layer." + bluemira_warn( + f"{abs(remaining_conductors)} have been added" + f"to complete the last layer." ) - R_wp_i = R_wp_i - n_turns_max * cond.dy + R_wp_i -= n_turns_max * cond.dy # noqa: N806 # dx_WP = dx_WP - n_layers_reduction * cond.dx - print(f"remaining_conductors: {remaining_conductors}") + # print(f"remaining_conductors: {remaining_conductors}") self.WPs = WPs diff --git a/bluemira/magnets/materials.py b/bluemira/magnets/materials.py index bd3e51c155..13a57b0658 100644 --- a/bluemira/magnets/materials.py +++ b/bluemira/magnets/materials.py @@ -9,39 +9,54 @@ """ import math +from abc import ABC, abstractmethod -class OperationalPoint(dict): - def __init__(self, *args, **kwargs): - # Set default values {'T': 0, 'B': 0} - if "T" not in kwargs: - kwargs["T"] = None - if "B" not in kwargs: - kwargs["B"] = None - super().__init__(*args, **kwargs) +class ABCMaterial(ABC): + """Abstract Material class""" + @abstractmethod + def density(self, **kwargs): + """Density""" + pass + + @abstractmethod + def E(self, **kwargs): # noqa: N802 + """Young's module""" + pass + + @abstractmethod + def erho(self, **kwargs): + """Electrical Resistivity""" + pass + + @abstractmethod + def cp_v(self, **kwargs): + """Specific heat (constant volume)""" + pass -class Material: + +class Material(ABCMaterial): """Generic Material""" - def density(self, **kwargs): + def density(self, **kwargs): # noqa: ARG002 """Density""" return 0 - def E(self, **kwargs): + def E(self, **kwargs): # noqa: N802, ARG002 """Young's module""" return 1e-6 - def erho(self, **kwargs): + def erho(self, **kwargs): # noqa: ARG002 """Electrical Resistivity""" return 1e6 - def cp_v(self, **kwargs): + def cp_v(self, **kwargs): # noqa: ARG002 """Specific heat (constant volume)""" return 0 -class AISI_316LN(Material): +class AISI_316LN(Material): # noqa: N801 """ Stainless steel (316 LN, with extra low carbon content, aged) @@ -53,18 +68,18 @@ class AISI_316LN(Material): """ - def density(self, **kwargs): + def density(self, **kwargs): # noqa: ARG002 """Material density [kg/m³]""" return 7890.0 - def E(self, T: float, **kwargs): + def E(self, T: float, **kwargs): # noqa: N802, N803, ARG002 """ Young's module Ref --- - Data fits from N. Mitchell, “Finite element simulations of elasto-plastic processes” - Cryogenics 45 (2005) 501–515 + Data fits from N. Mitchell, “Finite element simulations of elasto-plastic + processes” Cryogenics 45 (2005) 501–515 Parameters ---------- @@ -81,7 +96,7 @@ def E(self, T: float, **kwargs): E = 208.5 * 1e9 return E - def erho(self, T: float, **kwargs): + def erho(self, T: float, **kwargs): # noqa: N803 """ Electrical Resistivity @@ -122,7 +137,8 @@ def __init__(self): self.gamma = 0.011 # beta: Grueneisen fit parameter for Cp calculation [J/K**4/kg] self.beta = 0.0011 - # RRR: Residual-resistance ratio (ratio of the resistivity of a material at room temperature and at 0 K) + # RRR: Residual-resistance ratio (ratio of the resistivity of a material at + # room temperature and at 0 K) self.RRR = 100 def density(self, **kwargs): @@ -136,8 +152,8 @@ def erho(self, T: float, B: float, **kwargs): Ref --- NIST MONOGRAPH 177 J.Simon, E.S.Drexler and R.P.Reed, "Properties of - Copper and Copper Alloys at Cryogenic Temperatures", 850 pages, February 1992, U.S. - Government Printing Office, Washington, DC 20402-9325 + Copper and Copper Alloys at Cryogenic Temperatures", 850 pages, February 1992, + U.S. Government Printing Office, Washington, DC 20402-9325 Parameters ---------- @@ -151,13 +167,13 @@ def erho(self, T: float, B: float, **kwargs): float [Ohm m] """ rho1 = (1.171 * (10 ** -17) * (T ** 4.49)) / ( - 1 + (4.5 * (10 ** -7) * (T ** 3.35) * (math.exp(-((50. / T) ** 6.428)))) + 1 + (4.5 * (10 ** -7) * (T ** 3.35) * (math.exp(-((50.0 / T) ** 6.428)))) ) rho2 = ( (1.69 * (10 ** -8) / self.RRR) + rho1 + 0.4531 * ((1.69 * (10 ** -8) * rho1) / ( - self.RRR * rho1 + 1.69 * (10 ** -8))) + self.RRR * rho1 + 1.69 * (10 ** -8))) ) A = math.log10(1.553 * (10 ** -8) * B / rho2) a = ( @@ -214,7 +230,8 @@ def __init__(self): self.gamma = 0.011 # beta: Grueneisen fit parameter for Cp calculation [J/K**4/kg] self.beta = 0.0011 - # RRR: Residual-resistance ratio (ratio of the resistivity of a material at room temperature and at 0 K) + # RRR: Residual-resistance ratio (ratio of the resistivity of a material at + # room temperature and at 0 K) self.RRR = 300 def density(self, **kwargs): @@ -228,8 +245,8 @@ def erho(self, T: float, B: float, **kwargs): Ref --- NIST MONOGRAPH 177 J.Simon, E.S.Drexler and R.P.Reed, "Properties of - Copper and Copper Alloys at Cryogenic Temperatures", 850 pages, February 1992, U.S. - Government Printing Office, Washington, DC 20402-9325 + Copper and Copper Alloys at Cryogenic Temperatures", 850 pages, February 1992, + U.S. Government Printing Office, Washington, DC 20402-9325 Parameters ---------- @@ -249,7 +266,7 @@ def erho(self, T: float, B: float, **kwargs): (1.69 * (10 ** -8) / self.RRR) + rho1 + 0.4531 * ((1.69 * (10 ** -8) * rho1) / ( - self.RRR * rho1 + 1.69 * (10 ** -8))) + self.RRR * rho1 + 1.69 * (10 ** -8))) ) A = math.log10(1.553 * (10 ** -8) * B / rho2) a = ( @@ -271,8 +288,8 @@ def cp_v(self, T: float, **kwargs): Note ---- - The specific heat over the whole temperature range is obtained through fitting the function to the - known low temperature and high temperature data. + The specific heat over the whole temperature range is obtained through + fitting the function to the known low temperature and high temperature data. Parameters ---------- @@ -317,13 +334,14 @@ def cp_v(self, T: float, **kwargs): Ref --- - ITER DRG1 Annex, Superconducting Material Database, Article 5, N 11 FDR 42 01-07-05 R 0.1, - Thermal, Electrical and Mechanical Properties of Materials at Cryogenic Temperatures + ITER DRG1 Annex, Superconducting Material Database, Article 5, N 11 FDR + 42 01-07-05 R 0.1, Thermal, Electrical and Mechanical Properties of Materials + at Cryogenic Temperatures Note ---- - Most specific heat data are listed in J/K/kg units. To represent the data in the J/K/m3 units - a density of 8040 kg/m3 was assumed. + Most specific heat data are listed in J/K/kg units. To represent the data + in the J/K/m3 units a density of 8040 kg/m3 was assumed. Parameters ---------- @@ -394,8 +412,8 @@ def cp_v(self, T: float, **kwargs): Note ---- - Most specific heat data are listed in J/K/kg units. To represent the data in the J/K/m3 units - a density of 8570 kg/m3 was assumed. + Most specific heat data are listed in J/K/kg units. To represent the data + in the J/K/m3 units a density of 8570 kg/m3 was assumed. Parameters ---------- @@ -417,10 +435,10 @@ def erho(self, T: float, **kwargs): Ref --- - data-point discussed in Larbalestier’s article in “Superconducting Material - Properties” is 50 mWm, at 10 K. This value is 2 times higher than those from MATPRO. - We therefore suggest the use of the CRYOCOMP resistivities, which can be fitted with - the following polynomial + data-point discussed in Larbalestier’s article in 'Superconducting Material + Properties' is 50 mWm, at 10 K. This value is 2 times higher than those from + MATPRO. We therefore suggest the use of the CRYOCOMP resistivities, which can + be fitted with the following polynomial Parameters ---------- diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index 6c195b1030..0e904b0877 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -6,8 +6,6 @@ """Strand class""" -from typing import List, Union - import numpy as np from bluemira.magnets.materials import Copper100, Material, Nb3Sn, NbTi @@ -21,8 +19,8 @@ class Strand: def __init__( self, - materials: List[Material], - percentage: Union[np.array, List[float]], + materials: list[Material], + percentage: np.array, d_strand: float = 0.82e-3, ): """ @@ -83,7 +81,8 @@ def erho(self, **kwargs) -> float: def cp_v(self, **kwargs) -> float: """ - Calculates the equivalent specific heat based on the series connection of strand components. + Calculates the equivalent specific heat based on the series connection of + strand components. Parameters ---------- @@ -99,6 +98,63 @@ def cp_v(self, **kwargs) -> float: ] return serie_r(specific_heat) + +class SuperconductingStrand(Strand): + """ + Represents a superconducting strand with a circular cross-section. + """ + + def __init__( + self, + materials: list[Material], + percentage: np.array, + d_strand: float = 0.82e-3, + ): + """ + Initialize a Strand instance. + + Parameters + ---------- + materials: + List of materials inside the strand. First material is the + superconducting material. + percentage: + Percentage of each material (with the same ordering of materials). + d_strand: + Strand diameter in meters. + """ + super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) + + @property + def sc_area(self): + return self.area * self.percentage[0] + + def Jc( + self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, + **kwargs + ) -> float: + """ + Returns the critical current density. + + Parameters + ---------- + B: + Operating magnetic field in Teslas. + T: + Operating temperature in Kelvins. + esps: + Total applied measured strain in percentage. Default is 0.55. + T_margin: + Strand temperature margin in operation in Kelvins. Default is 1.5. + **kwargs: + Additional parameters. + + Returns + ------- + Critical current density in Amperes/m². + """ + return 0 + def Ic( self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, **kwargs @@ -121,12 +177,12 @@ def Ic( Returns ------- - Critical current in Amperes. + Critical current density in Amperes. """ - return 0 + return self.Jc(B=B, T=T, strain=strain, T_margin=T_margin) * self.sc_area -class Wire_Nb3Sn(Strand): +class WireNb3Sn(SuperconductingStrand): """Represents an Nb3Sn strand made of 50% Copper100 and 50% Nb3Sn.""" # superconducting parameters for the calculation of Ic @@ -143,7 +199,7 @@ class Wire_Nb3Sn(Strand): def __init__(self, d_strand: float = 0.82e-3): """ - Initialize a Wire_Nb3Sn instance. + Initialize a WireNb3Sn instance. Parameters ---------- @@ -151,21 +207,18 @@ def __init__(self, d_strand: float = 0.82e-3): Strand diameter in meters. Default is 0.82e-3. """ copper_100 = Copper100() - mat_Nb3Sn = Nb3Sn() - materials = [copper_100, mat_Nb3Sn] + mat_Nb3Sn = Nb3Sn() # noqa: N806 + # materials: first material is the SC, then the other materials + materials = [mat_Nb3Sn, copper_100] percentage = [0.5, 0.5] super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) - # percentage of copper with respect to superconducting material - self._CunonCu = self.percentage[0] / self.percentage[1] - # area of superconducting material - self._superc_area = np.pi * self.d_strand ** 2 / (4 * (1 + self._CunonCu)) - def Ic( + def Jc( self, B: float, T: float, strain: float = 0.55, T_margin: float = 1.5, **kwargs ) -> float: """ - Returns the strand critical current. + Returns the strand critical current density. Parameters ---------- @@ -182,9 +235,8 @@ def Ic( ------- Strand current density in Amperes per square meter. """ - T_ = T + T_margin - # Todo: check the sign of eps_m in this equation - int_eps = -strain / 100 # + eps_m + T_ = T + T_margin # noqa: N806 + int_eps = -strain / 100 eps_sh = self.Ca2 * self.eps_0a / (np.sqrt(self.Ca1 ** 2 - self.Ca2 ** 2)) s_eps = 1 + ( self.Ca1 @@ -195,16 +247,16 @@ def Ic( - self.Ca2 * int_eps ) / (1 - self.Ca1 * self.eps_0a) Bc0_eps = self.Bc20max * s_eps - Tc0_eps = self.Tc0max * (s_eps) ** (1 / 3) + Tc0_eps = self.Tc0max * (s_eps) ** (1 / 3) # noqa: N806 t = T_ / Tc0_eps BcT_eps = Bc0_eps * (1 - t ** (1.52)) b = B / BcT_eps - hT = (1 - t ** (1.52)) * (1 - t ** 2) - fPb = (b ** self.p) * (1 - b) ** self.q - return self.c_ * (self.C / B) * s_eps * fPb * hT * self._superc_area + hT = (1 - t ** (1.52)) * (1 - t ** 2) # noqa: N806 + fPb = (b ** self.p) * (1 - b) ** self.q # noqa: N806 + return self.c_ * (self.C / B) * s_eps * fPb * hT -class Wire_NbTi(Strand): +class Wire_NbTi(SuperconductingStrand): # noqa: N801 """Represents an NbTi strand.""" # superconducting parameters for the calculation of Ic @@ -231,18 +283,15 @@ def __init__(self, d_strand: float = 0.82e-3): Strand diameter in meters. Default is 0.82e-3. """ copper_100 = Copper100() - mat_NbTi = NbTi() - materials = [copper_100, mat_NbTi] + mat_NbTi = NbTi() # noqa: N806 + materials = [mat_NbTi, copper_100] percentage = [0.5, 0.5] super().__init__(materials=materials, percentage=percentage, d_strand=d_strand) - # percentage of copper with respect to superconducting material - self._CunonCu = self.percentage[0] / self.percentage[1] - # area of superconducting material - self._superc_area = np.pi * self.d_strand ** 2 / (4 * (1 + self._CunonCu)) - def Ic(self, B: float, T: float, T_margin: float = 1.5, **kwargs): + def Jc(self, B: float, T: float, T_margin: float = 1.5, **kwargs): # noqa: + # ARG002, N803 """ - NbTi critical current. + NbTi critical current density. Parameters ---------- @@ -255,26 +304,35 @@ def Ic(self, B: float, T: float, T_margin: float = 1.5, **kwargs): Returns ------- - Critical current in Amperes. + Critical current in Amperes per square meter. References ---------- - - Pinning Properties of Commercial Nb-Ti Wires Described by a 2-Components Model, Luigi Muzzi, Gianluca De Marzi, et al. + - Pinning Properties of Commercial Nb-Ti Wires Described by a 2-Components Model, + Luigi Muzzi, Gianluca De Marzi, et al. - Fit data from DTT TF strand. """ t = (T + T_margin) / self.Tc0_K - b = B / self.Bc20_T + # b = B / self.Bc20_T tt = 1 - t ** self.n - G = (self.a1 / (self.a1 + self.b1)) ** self.a1 - GG = (self.b1 / (self.a1 + self.b1)) ** self.b1 - GGG = G * GG + G = (self.a1 / (self.a1 + self.b1)) ** self.a1 # noqa: N806 + GG = (self.b1 / (self.a1 + self.b1)) ** self.b1 # noqa: N806 + GGG = G * GG # noqa: N806 F = (self.a2 / (self.a2 + self.b2)) ** self.a2 - FF = (self.b2 / (self.a2 + self.b2)) ** self.b2 - FFF = F * FF - Jc = ( - self.C0 * self.C1 / (B * GGG) * (self.b / tt) ** self.a1 * ( - 1 - self.b / tt) ** self.b1 * tt ** self.g1 - + self.C0 * self.C2 / (B * FFF) * (self.b / tt) ** self.a2 * ( - 1 - self.b / tt) ** self.b2 * tt ** self.g2 + FF = (self.b2 / (self.a2 + self.b2)) ** self.b2 # noqa: N806 + FFF = F * FF # noqa: N806 + Jc = ( # noqa: N806 + self.C0 + * self.C1 + / (B * GGG) + * (self.b / tt) ** self.a1 + * (1 - self.b / tt) ** self.b1 + * tt ** self.g1 + + self.C0 + * self.C2 + / (B * FFF) + * (self.b / tt) ** self.a2 + * (1 - self.b / tt) ** self.b2 + * tt ** self.g2 ) * 1e6 - return Jc * self._superc_area + return Jc # noqa: RET504 diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index 62929e2afc..ea33f016dc 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -1,25 +1,29 @@ +# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza +# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh +# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short +# +# SPDX-License-Identifier: LGPL-2.1-or-later + +""" +Example for TF coils internal structure optimization. + +Note: in this example the conductor operational current is given as input. +""" + import matplotlib.pyplot as plt import numpy as np from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI -from bluemira.magnets.cable import DummyRoundCableLTS, DummyRoundCableHTS +from bluemira.magnets.cable import DummyRoundCableHTS, DummyRoundCableLTS from bluemira.magnets.case_tf import CaseTF from bluemira.magnets.conductor import SquareConductor -from bluemira.magnets.materials import (OperationalPoint, Copper300, AISI_316LN, - DummyInsulator) -from bluemira.magnets.strand import Strand, Wire_Nb3Sn +from bluemira.magnets.materials import AISI_316LN, Copper300, DummyInsulator +from bluemira.magnets.strand import Strand, WireNb3Sn from bluemira.magnets.utils import ( delayed_exp_func, ) from bluemira.magnets.winding_pack import WindingPack -""" -Example for TF coils internal structure optimization. - -Note: in this example the conductor operational current is given as input (no check -or optimization that takes into account the conductor critical current). -""" - # plot options show = True homogenized = False # if True plot the WP as a homogenized block @@ -34,7 +38,8 @@ a = R0 / A Ri = R0 - a - d # [m] max external radius of the internal TF leg Re = (R0 + a) * (1 / ripple) ** ( - 1 / n_TF) # [m] max internal radius of the external TF part + 1 / n_TF +) # [m] max internal radius of the external TF part dr_plasma_side = R0 * 2 / 3 * 1e-2 R_VV = Ri * 1.05 # Vacuum vessel radius @@ -45,16 +50,16 @@ pm = B_TF_i ** 2 / (2 * MU_0) # magnetic pressure on the inner TF leg # vertical tension acting on the equatorial section of inner TF leg # i.e. half of the whole F_Z -t_z = (0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2) +t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2 Iop = 90.0e3 # operational current in each conductor -n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors +# n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors -n_spire = np.floor(I_TF / Iop) -L = MU_0 * R0 * (n_TF * n_spire) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1 +n_cond = np.floor(I_TF / Iop) +L = MU_0 * R0 * (n_TF * n_cond) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1 E = 1 / 2 * L * n_TF * Iop ** 2 * 1e-9 V_MAX = (7 * R0 - 3) / 6 * 1.1e3 -Tau_discharge1 = (L * Iop / V_MAX) +Tau_discharge1 = L * Iop / V_MAX Tau_discharge2 = B0 * I_TF * n_TF * (R0 / A) ** 2 / (R_VV * S_VV) Tau_discharge = max([Tau_discharge1, Tau_discharge2]) @@ -71,11 +76,11 @@ S_Y = 1e9 / safety_factor # [Pa] steel allowable limit # Current and magnetic field behaviour -I = delayed_exp_func(Iop, Tau_discharge, t_delay) +I = delayed_exp_func(Iop, Tau_discharge, t_delay) # noqa: E741 B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay) # define the conductor (with a dummy number of stabilizer strands ) -sc_strand = Wire_Nb3Sn(d_strand=1e-3) +sc_strand = WireNb3Sn(d_strand=1e-3) copper300 = Copper300() insulator = DummyInsulator() stab_strand = Strand([copper300], [1], d_strand=1.0e-3) @@ -84,12 +89,16 @@ Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) n_sc_strand = int(np.ceil(Iop / Ic_sc)) -if B_TF_i < 15: - cable = DummyRoundCableLTS(sc_strand, stab_strand, n_sc_strand, 100, 1e-2, - void_fraction=0.7) +B_ref = 15 + +if B_TF_i < B_ref: + cable = DummyRoundCableLTS( + sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 + ) else: - cable = DummyRoundCableHTS(sc_strand, stab_strand, n_sc_strand, 100, 1e-2, - void_fraction=0.7) + cable = DummyRoundCableHTS( + sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 + ) # c.plot(0,0, show=True) ss316 = AISI_316LN() @@ -105,62 +114,95 @@ ) print(f"after optimization: conductor dx_cable = {cable.dx}") -op = OperationalPoint(B=B_TF_i, T=T0) +# creation of case +wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case +case = CaseTF( + Ri=Ri, + dy_ps=dr_plasma_side, + dy_vault=0.6, + theta_TF=360 / n_TF, + mat_case=ss316, + WPs=[wp1], +) show = False # Some iterations to optimize case vault and cable jacket consistently. This should go # in a kind of "optimization" or "convergence" loop. -for i in range(10): +max_niter = 10 +eps = 1e-3 +i = 0 +tot_err = 100 * eps + +while i < max_niter and tot_err > eps: + i += 1 print(f"Internal optimazion - iteration {i}") - # optimize the cable jacket thickness considering 0D stress model for the single cable print(f"before optimization: conductor dx_jacket = {conductor.dx_jacket}") - t_z_cable_jacket = 0 - if i == 0: - t_z_cable_jacket = t_z / 2 / n_spire - else: - t_z_cable_jacket = t_z * case.area_wps_jacket / ( - case.area_jacket + case.area_wps_jacket) / ( - np.sum([w.nx * w.ny for w in case.WPs])) + cond_dx_jacket0 = conductor.dx_jacket + t_z_cable_jacket = ( + t_z + * case.area_wps_jacket + / (case.area_jacket + case.area_wps_jacket) + / (np.sum([w.nx * w.ny for w in case.WPs])) + ) conductor.optimize_jacket_conductor( - pm, t_z_cable_jacket, T0, B_TF_i, - S_Y, bounds=[1e-5, 0.2] + pm, t_z_cable_jacket, T0, B_TF_i, S_Y, bounds=[1e-5, 0.2] ) print(f"after optimization: conductor dx_jacket = {conductor.dx_jacket}") + delta_conductor_dx_jacket = abs(conductor.dx_jacket - cond_dx_jacket0) + err_dy_jacket = delta_conductor_dx_jacket / conductor.dy_jacket - # creation of case - wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case - case = CaseTF( - Ri=Ri, dy_ps=dr_plasma_side, dy_vault=0.6, theta_TF=360 / n_TF, mat_case=ss316, - WPs=[wp1] + case.rearrange_conductors_in_wp( + n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.7, 0.05, 4 ) - if show: - ax = case.plot(homogenized=False) - ax.set_aspect("equal") - plt.show() - - case.rearrange_conductors_in_wp(n_cond, conductor, case.R_wp_i[0], - case.dx_i * 0.7, 0.05, 4) - - if show: - ax = case.plot(homogenized=homogenized) - ax.set_aspect("equal") - plt.title("Before vault optimization") - plt.show() - + case_dy_vault0 = case.dy_vault + print(f"before optimization: case dy_vault = {case.dy_vault}") case.optimize_vault_radial_thickness( pm, t_z, T=T0, B=B_TF_i, allowable_sigma=S_Y, bounds=[1e-2, 1] ) - if show: - ax = case.plot(homogenized=homogenized) - ax.set_aspect("equal") - plt.title("After vault optimization") - plt.show() + print(f"after optimization: case dy_vault = {case.dy_vault}") + delta_case_dy_vault = abs(case.dy_vault - case_dy_vault0) + err_dy_vault = delta_case_dy_vault / case.dy_vault + tot_err = err_dy_vault + err_dy_jacket + + print(f"err_dy_jacket = {err_dy_jacket}") + print(f"err_dy_vault = {err_dy_vault}") + print(f"tot_err = {tot_err}") + show = True if show: + scalex = np.array([2, 1]) + scaley = np.array([1, 1.2]) + ax = case.plot(homogenized=homogenized) ax.set_aspect("equal") - plt.title("After vault optimization") + + # Fix the x and y limits + ax.set_xlim(-scalex[0] * case.dx_i, scalex[1] * case.dx_i) + ax.set_ylim(scaley[0] * 0, scaley[1] * case.Ri) + + deltax = [-case.dx_i / 2, case.dx_i / 2] + + ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Ri, case.Ri], "k:") + + for i in range(len(case.WPs)): + ax.plot( + [-scalex[0] * case.dx_i, -case.dx_i / 2], + [case.R_wp_i[i], case.R_wp_i[i]], + "k:", + ) + + ax.plot( + [-scalex[0] * case.dx_i, -case.dx_i / 2], + [case.R_wp_k[-1], case.R_wp_k[-1]], + "k:", + ) + ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Rk, case.Rk], "k:") + + ax.set_title("Equatorial cross section of the TF WP") + ax.set_xlabel("Toroidal direction [m]") + ax.set_ylabel("Radial direction [m]") + plt.show() From fa7a602992a9379c6b46c371971bb47a1938bdd4 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Thu, 10 Oct 2024 14:48:13 +0200 Subject: [PATCH 11/17] improvements to magnets classes --- bluemira/magnets/cable.py | 400 +++++++++++++++++++++++++-------- bluemira/magnets/case_tf.py | 97 ++++---- bluemira/magnets/conductor.py | 6 +- bluemira/magnets/strand.py | 113 ++++++++-- examples/magnets/tf_example.py | 64 +++++- 5 files changed, 513 insertions(+), 167 deletions(-) diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py index 16f60e7f81..1b5fdb1f23 100644 --- a/bluemira/magnets/cable.py +++ b/bluemira/magnets/cable.py @@ -6,6 +6,7 @@ """Cable class""" +from abc import ABC, abstractmethod from typing import Callable import matplotlib.pyplot as plt @@ -14,15 +15,20 @@ from scipy.optimize import minimize_scalar from bluemira.magnets.materials import Material -from bluemira.magnets.strand import Strand +from bluemira.magnets.strand import Strand, SuperconductingStrand from bluemira.magnets.utils import parall_r, serie_r +from bluemira.base.look_and_feel import bluemira_error, bluemira_warn +from bluemira.geometry.tools import make_circle, make_polygon +from bluemira.geometry.face import BluemiraFace +from bluemira import display +from bluemira.display.plotter import PlotOptions -class Cable(Material): + +class ABCCable(Material, ABC): def __init__( self, - dx: float, - sc_strand: Strand, + sc_strand: SuperconductingStrand, stab_strand: Strand, n_sc_strand: int, n_stab_strand: int, @@ -35,10 +41,12 @@ def __init__( Representation of a cable. Only the x-dimension of the cable is given as input. The y-dimension is calculated on the basis of the cable design. + Notes + ----- + Cooling material not implemented. + Parameters ---------- - dx: - x-dimension of the cable [m] sc_strand: strand of the superconductor stab_strand: @@ -55,20 +63,47 @@ def __init__( corrective factor that consider the twist of the cable name: cable string identifier - - Notes: - Cooling material not implemented. """ + # initialize private variables + self._d_cooling_channel = None + self._void_fraction = None + self._n_sc_strand = None + self._n_stab_strand = None + self._cos_theta = None + self._shape = None + + # assign self.name = name - self._dx = dx self.sc_strand = sc_strand self.stab_strand = stab_strand self.void_fraction = void_fraction self.d_cooling_channel = d_cooling_channel - self.n_sc_strand = int(n_sc_strand) - self._n_stab_strand = int(n_stab_strand) + self.n_sc_strand = n_sc_strand + self.n_stab_strand = n_stab_strand self.cos_theta = cos_theta - self._check_consistency() + + @property + @abstractmethod + def dx(self): + pass + + @property + @abstractmethod + def dy(self): + pass + + @property + def n_sc_strand(self): + """Number of stabilizing strands""" + return self._n_sc_strand + + @n_sc_strand.setter + def n_sc_strand(self, value: int): + if value < 0: + msg = f"The number of superconducting strands must be positive, got {value}" + bluemira_error(msg) + raise ValueError(msg) + self._n_sc_strand = int(np.ceil(value)) @property def n_stab_strand(self): @@ -77,8 +112,51 @@ def n_stab_strand(self): @n_stab_strand.setter def n_stab_strand(self, value: int): + if value < 0: + msg = f"The number of stabilizing strands must be positive, got {value}" + bluemira_error(msg) + raise ValueError(msg) self._n_stab_strand = int(np.ceil(value)) + @property + def d_cooling_channel(self): + return self._d_cooling_channel + + @d_cooling_channel.setter + def d_cooling_channel(self, value: float): + if value < 0: + msg = f"diameter of the cooling channel must be positive, got {value}" + bluemira_error(msg) + raise ValueError(msg) + + self._d_cooling_channel = value + + @property + def void_fraction(self): + return self._void_fraction + + @void_fraction.setter + def void_fraction(self, value: float): + if value < 0 or value > 1: + msg = f"void_fraction must be between 0 and 1, got {value}" + bluemira_error(msg) + raise ValueError(msg) + + self._void_fraction = value + + @property + def cos_theta(self): + return self._cos_theta + + @cos_theta.setter + def cos_theta(self, value: float): + if value <= 0 or value > 1: + msg = f"cos theta must be in the interval ]0, 1], got {value}" + bluemira_error(msg) + raise ValueError(msg) + + self._cos_theta = value + def erho(self, **kwargs): """ Computes the cable's equivalent resistivity considering the resistance @@ -120,13 +198,6 @@ def cp_v(self, **kwargs): ]) return serie_r(weighted_specific_heat) / (self.area_sc + self.area_stab) - def _check_consistency(self): - """Check consistency and return True if all checks are passed.""" - if self.dx <= self.d_cooling_channel or self.dy <= self.d_cooling_channel: - print("WARNING: inconsistency between dx, dy and d_cooling_channel") - return False - return True - @property def area_stab(self): """Area of the stabilizer region""" @@ -149,32 +220,45 @@ def area(self): self.area_sc + self.area_stab ) / self.void_fraction / self.cos_theta + self.area_cc - @property - def dx(self): - """Cable dimension in the x direction [m]""" - return self._dx - - @dx.setter - def dx(self, value: float): - self._dx = value - - @property - def dy(self): - """Cable dimension in the y direction [m]""" - return self.area / self.dx - def E(self, **kwargs): """Young's moduli""" return 0 - # OD homogenized structural properties - def Kx(self, **kwargs): - """Total equivalent stiffness along x-axis""" - return self.E(**kwargs) * self.dy / self.dx + def _heat_balance_model_cable(self, t: float, T: float, B: Callable, I: Callable): + """ + Calculate the derivative of temperature (dT/dt) for a 0D heat balance problem. - def Ky(self, **kwargs): - """Total equivalent stiffness along y-axis""" - return self.E(**kwargs) * self.dx / self.dy + Parameters + ---------- + t : float + The current time in seconds. + T : float + The current temperature in Celsius. + B : Callable + The magnetic field [T] as time function + I : Callable + The current [A] flowing through the conductor as time function + cable : Cable + the superconducting cable + + Returns + ------- + dTdt : float + The derivative of temperature with respect to time (dT/dt). + """ + # Calculate the rate of heat generation (Joule dissipation) + if isinstance(T, np.ndarray): + T = T[0] + + Q_gen = (I(t) / self.area) ** 2 * self.erho(B=B(t), T=T) + + # Calculate the rate of heat absorption by conductor components + Q_abs = self.cp_v(T=T) + + # Calculate the derivative of temperature with respect to time (dT/dt) + dTdt = Q_gen / Q_abs + + return dTdt def optimize_n_stab_ths( self, @@ -221,55 +305,18 @@ def optimize_n_stab_ths( Cooling material contribution is neglected when applying the hot spot criteria. """ - def _heat_balance_model_cable(t, T, B: Callable, I: Callable, cable: Cable): - """ - Calculate the derivative of temperature (dT/dt) for a 0D heat balance problem. - - Parameters - ---------- - t : float - The current time in seconds. - T : float - The current temperature in Celsius. - B : Callable - The magnetic field [T] as time function - I : Callable - The current [A] flowing through the conductor as time function - cable : Cable - the superconducting cable - - Returns - ------- - dTdt : float - The derivative of temperature with respect to time (dT/dt). - """ - # Calculate the rate of heat generation (Joule dissipation) - if isinstance(T, np.ndarray): - T = T[0] - - Q_gen = (I(t) / cable.area) ** 2 * cable.erho(B=B(t), T=T) - - # Calculate the rate of heat absorption by conductor components - Q_abs = cable.cp_v(T=T) - - # Calculate the derivative of temperature with respect to time (dT/dt) - dTdt = Q_gen / Q_abs - - return dTdt - def _temperature_evolution( t0: float, tf: float, initial_temperature: float, B: Callable, I: Callable, - cable: Cable, ): solution = solve_ivp( - _heat_balance_model_cable, + self._heat_balance_model_cable, [t0, tf], [initial_temperature], - args=(B, I, cable), + args=(B, I), dense_output=True, ) @@ -291,7 +338,6 @@ def final_temperature_difference( solution = _temperature_evolution( t0=t0, tf=tf, initial_temperature=initial_temperature, B=B, I=I, - cable=self ) final_T = float(solution.y[0][-1]) diff = abs(final_T - target_temperature) @@ -313,7 +359,7 @@ def final_temperature_difference( "n_stab optimization did not converge. Check your input parameters or initial bracket." ) - solution = _temperature_evolution(t0, tf, initial_temperature, B, I, self) + solution = _temperature_evolution(t0, tf, initial_temperature, B, I) final_temperature = solution.y[0][-1] print(f"Optimal n_stab: {self.n_stab_strand}") @@ -321,13 +367,39 @@ def final_temperature_difference( if show: _, ax = plt.subplots() - ax.plot(solution.t, solution.y[0], "r") + ax.plot(solution.t, solution.y[0], "r*") time_steps = np.linspace(t0, tf, 100) ax.plot(time_steps, solution.sol(time_steps)[0], "b") + plt.grid(True) + plt.xlabel("Time [s]") + plt.ylabel("Temperature [K]") + plt.title("Quench temperature evoltuion") + + # *** Additional info *** + additional_info = [f"Hot spot temp. = {target_temperature} [K]", + f"Initial temp. = {initial_temperature} [K]", + f"Sc. strand = {self.sc_strand.__class__.__name__}", + f"n. sc. strand = {self.n_sc_strand}", + f"Stab. strand = {self.stab_strand.__class__.__name__}", + f"n. stab. strand = {self.n_stab_strand}"] + + additional_info = '\n'.join(additional_info) + plt.text(50, 80, additional_info) plt.show() return result + # OD homogenized structural properties + @abstractmethod + def Kx(self, **kwargs): + """Total equivalent stiffness along x-axis""" + pass + + @abstractmethod + def Ky(self, **kwargs): + """Total equivalent stiffness along y-axis""" + pass + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): """ Schematic plot of the cable cross-section. @@ -373,10 +445,98 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): return ax -class SquareCable(Cable): +class RectangularCable(ABCCable): + def __init__( + self, + dx: float, + sc_strand: SuperconductingStrand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", + ): + """ + Representation of a cable. Only the x-dimension of the cable is given as + input. The y-dimension is calculated on the basis of the cable design. + + Notes + ----- + Cooling material not implemented. + + Parameters + ---------- + dx: + x-dimension of the cable [m] + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + """ + super().__init__( + sc_strand=sc_strand, + stab_strand=stab_strand, + n_sc_strand=n_sc_strand, + n_stab_strand=n_stab_strand, + d_cooling_channel=d_cooling_channel, + void_fraction=void_fraction, + cos_theta=cos_theta, + name=name, + ) + + # initialize private variables + self._dx = None + + # assign + self.dx = dx + + + @property + def dx(self): + """Cable dimension in the x direction [m]""" + return self._dx + + @dx.setter + def dx(self, value: float): + if value < 0: + msg = "dx must be positive" + bluemira_error(msg) + raise ValueError(msg) + self._dx = value + + @property + def dy(self): + """Cable dimension in the y direction [m]""" + return self.area / self.dx + + # OD homogenized structural properties + def Kx(self, **kwargs): + """Total equivalent stiffness along x-axis""" + return self.E(**kwargs) * self.dy / self.dx + + def Ky(self, **kwargs): + """Total equivalent stiffness along y-axis""" + return self.E(**kwargs) * self.dx / self.dy + + +class SquareCable(ABCCable): def __init__( self, - sc_strand: Strand, + sc_strand: SuperconductingStrand, stab_strand: Strand, n_sc_strand: int, n_stab_strand: int, @@ -415,9 +575,7 @@ def __init__( ----- Cooling material not implemented """ - dx = 0.1 super().__init__( - dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, @@ -438,6 +596,15 @@ def dy(self): """Cable dimension in the y direction [m]""" return self.dx + # OD homogenized structural properties + def Kx(self, **kwargs): + """Total equivalent stiffness along x-axis""" + return self.E(**kwargs) * self.dy / self.dx + + def Ky(self, **kwargs): + """Total equivalent stiffness along y-axis""" + return self.E(**kwargs) * self.dx / self.dy + class DummySquareCableHTS(SquareCable): """ @@ -497,10 +664,10 @@ def E(self, **kwargs): return 0.1e9 -class RoundCable(Cable): +class RoundCable(ABCCable): def __init__( self, - sc_strand: Strand, + sc_strand: SuperconductingStrand, stab_strand: Strand, n_sc_strand: int, n_stab_strand: int, @@ -533,9 +700,7 @@ def __init__( """ - dx = 0.1 super().__init__( - dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, @@ -556,6 +721,59 @@ def dy(self): """Cable dimension in the y direction [m] (i.e. cable's diameter)""" return self.dx + # OD homogenized structural properties + # Todo: check if the rectangular approximation is fine also for this case + def Kx(self, **kwargs): + """Total equivalent stiffness along x-axis""" + return self.E(**kwargs) * self.dy / self.dx + + def Ky(self, **kwargs): + """Total equivalent stiffness along y-axis""" + return self.E(**kwargs) * self.dx / self.dy + + def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): + """ + Schematic plot of the cable cross-section. + + Parameters + ---------- + xc: + x coordinate of the cable center in the considered coordinate system + yc: + y coordinate of the cable center in the considered coordinate system + show: + if True, the plot is displayed + ax: + Matplotlib Axis on which the plot shall be displayed. If None, + a new figure is created + """ + if ax is None: + _, ax = plt.subplots() + + pc = np.array([xc, yc]) + + points_ext = ( + np.array([ + np.array([np.cos(theta), np.sin(theta)]) * self.dx / 2 + for theta in np.linspace(0, np.radians(360), 19) + ]) + + pc + ) + + points_cc = ( + np.array([ + np.array([np.cos(theta), np.sin(theta)]) * self.d_cooling_channel / 2 + for theta in np.linspace(0, np.radians(360), 19) + ]) + + pc + ) + + ax.fill(points_ext[:, 0], points_ext[:, 1], "gold") + ax.fill(points_cc[:, 0], points_cc[:, 1], "r") + + if show: + plt.show() + return ax class DummyRoundCableHTS(RoundCable): """ diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index d6fd3ee200..e8023d569d 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -266,58 +266,13 @@ def optimize_vault_radial_thickness( If the optimization process did not converge. """ - def sigma_difference( - dy_vault: float, - pm: float, - fz: float, - T: float, # noqa: N803 - B: float, - case: CaseTF, - allowable_sigma: float, - ): - """ - Fitness function for the optimization problem. It calculates the absolute - difference between - the Tresca stress and the allowable stress. - - Parameters - ---------- - dy_vault : - The thickness of the vault in the direction perpendicular to the - applied pressure(m). - pm : - The magnetic pressure applied along the radial direction (Pa). - fz : - The force applied in the z direction, perpendicular to the case - cross-section (N). - T : - The temperature (K) at which the conductor operates. - B : - The magnetic field (T) at which the conductor operates. - allowable_sigma : - The allowable stress (Pa) for the vault material. - - Returns - ------- - The absolute difference between the calculated Tresca stress and the - allowable stress (Pa). - - Notes - ----- - This function modifies the case's vault thickness - using the value provided in jacket_thickness. - """ - case.dy_vault = dy_vault - sigma = case._tresca_stress(pm, fz, T=T, B=B) - return abs(sigma - allowable_sigma) - method = None if bounds is not None: method = "bounded" result = minimize_scalar( - fun=sigma_difference, - args=(pm, fz, T, B, self, allowable_sigma), + fun=self._sigma_difference, + args=(pm, fz, T, B, allowable_sigma), bounds=bounds, method=method, options={"xatol": 1e-4}, @@ -331,6 +286,52 @@ def sigma_difference( return result + def _sigma_difference( + self, + dy_vault: float, + pm: float, + fz: float, + T: float, + B: float, + allowable_sigma: float, + ): + """ + Fitness function for the optimization problem. It calculates the absolute + difference between + the Tresca stress and the allowable stress. + + Parameters + ---------- + dy_vault : + The thickness of the vault in the direction perpendicular to the + applied pressure(m). + pm : + The magnetic pressure applied along the radial direction (Pa). + fz : + The force applied in the z direction, perpendicular to the case + cross-section (N). + T : + The temperature (K) at which the conductor operates. + B : + The magnetic field (T) at which the conductor operates. + allowable_sigma : + The allowable stress (Pa) for the vault material. + + Returns + ------- + The absolute difference between the calculated Tresca stress and the + allowable stress (Pa). + + Notes + ----- + This function modifies the case's vault thickness + using the value provided in jacket_thickness. + """ + self.dy_vault = dy_vault + sigma = self._tresca_stress(pm, fz, T=T, B=B) + return abs(sigma - allowable_sigma) + + def plot(self, ax=None, *, show: bool = False, homogenized: bool = False): """ Schematic plot of the case cross-section. @@ -454,7 +455,7 @@ def rearrange_conductors_in_wp( if remaining_conductors < 0: bluemira_warn( f"{abs(remaining_conductors)} have been added" - f"to complete the last layer." + f" to complete the last layer." ) R_wp_i -= n_turns_max * cond.dy # noqa: N806 diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index bb6a41e38d..27f09aee83 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -12,7 +12,7 @@ import numpy as np from scipy.optimize import minimize_scalar -from bluemira.magnets.cable import Cable +from bluemira.magnets.cable import ABCCable from bluemira.magnets.materials import Material from bluemira.magnets.utils import ( parall_k, @@ -25,7 +25,7 @@ class Conductor: def __init__( self, - cable: Cable, + cable: ABCCable, mat_jacket: Material, mat_ins: Material, dx_jacket: float, @@ -476,7 +476,7 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): class SquareConductor(Conductor): def __init__( self, - cable: Cable, + cable: ABCCable, mat_jacket: Material, mat_ins: Material, dx_jacket: float, diff --git a/bluemira/magnets/strand.py b/bluemira/magnets/strand.py index 0e904b0877..daaec4c1c3 100644 --- a/bluemira/magnets/strand.py +++ b/bluemira/magnets/strand.py @@ -7,20 +7,26 @@ """Strand class""" import numpy as np - +import matplotlib.pyplot as plt +from bluemira.base.look_and_feel import bluemira_error from bluemira.magnets.materials import Copper100, Material, Nb3Sn, NbTi from bluemira.magnets.utils import parall_r, serie_r +from bluemira.geometry.tools import make_circle +from bluemira.geometry.face import BluemiraFace +from bluemira import display +from bluemira.display.plotter import PlotOptions class Strand: """ Represents a strand with a circular cross-section. """ + # Todo: discuss if it could be worth to consider the Strand as a PhysicalComponent def __init__( self, materials: list[Material], - percentage: np.array, + percentage: np.ndarray, d_strand: float = 0.82e-3, ): """ @@ -31,27 +37,70 @@ def __init__( materials: List of materials inside the strand. percentage: - Percentage of each material (with the same ordering of materials). + Percentage of each material (with the same ordering of materials). The sum of all percentages + must be equal to 1. d_strand: Strand diameter in meters. """ + self._materials = None + self._percentage = None + self._d_strand = d_strand + self._shape = None + self.materials = materials self.percentage = percentage self.d_strand = d_strand + @property + def materials(self): + return self._materials + + @materials.setter + def materials(self, value: list[Material]): + if len(value) < 1: + msg = f"At least one material must be provided." + bluemira_error(msg) + raise ValueError(msg) + self._materials = value + + @property + def percentage(self): + return self._percentage + + @percentage.setter + def percentage(self, value: np.ndarray): + if len(value) != len(self.materials): + msg = f"Percentage and Materials must have the same length." + bluemira_error(msg) + raise ValueError(msg) + + if all(v>0 for v in value) and sum(value) != 1.0: + msg = f"Percentages must be positive and their sum must be equal to 1.0." + bluemira_error(msg) + raise ValueError(msg) + + self._percentage = value + + @property + def d_strand(self): + return self._d_strand + + @d_strand.setter + def d_strand(self, value: float): + if value < 0.0: + msg = f"Strand diameter must be positive." + bluemira_error(msg) + raise ValueError(msg) + self._d_strand = value + @property def area(self) -> float: """Returns the area of the strand cross-section in square meters.""" return np.pi * self.d_strand ** 2 / 4 - def E(self, **kwargs) -> float: + def E(self) -> float: """ - Young's modulus. - - Parameters - ---------- - **kwargs: - Additional parameters (e.g., temperature). + Young's modulus (dummy value) Returns ------- @@ -98,6 +147,20 @@ def cp_v(self, **kwargs) -> float: ] return serie_r(specific_heat) + def _create_shape(self): + self._shape = BluemiraFace([make_circle(self.d_strand)]) + + @property + def shape(self): + return self._create_shape() + + def plot(self, ax=None, *, show: bool = True, **kwargs,): + # Todo: plot approach to be discussed (also in view of using PhysicalComponents). + plot_options = PlotOptions() + plot_options.view = "xy" + ax = display.plot_2d(self.shape, options = plot_options, ax=ax, show=show, **kwargs) + return ax + class SuperconductingStrand(Strand): """ @@ -182,6 +245,26 @@ def Ic( return self.Jc(B=B, T=T, strain=strain, T_margin=T_margin) * self.sc_area + def plot_Ic_B(self, B: np.ndarray, T: float, strain: float = 0.55, T_margin: float = 1.5, ax = None, show: bool = True): + """ + Plot the critical current in a range of B + """ + if ax is None: + fig, ax = plt.subplots() + + Ic_sc = self.Ic(B=B , T=T, T_margin=T_margin) + ax.plot(B, Ic_sc) + # Adding the plot title and axis labels + plt.title(f"Critical current for {self.__class__.__name__}\n" + f"T = {T} [K], Tmargin = {T_margin} [K], strain = {strain}") # Title + plt.xlabel('B [T]') # X-axis label + plt.ylabel('Ic [A]') # Y-axis label + # Enabling the grid + plt.grid(True) + if show: + plt.show() + return ax + class WireNb3Sn(SuperconductingStrand): """Represents an Nb3Sn strand made of 50% Copper100 and 50% Nb3Sn.""" @@ -313,7 +396,7 @@ def Jc(self, B: float, T: float, T_margin: float = 1.5, **kwargs): # noqa: - Fit data from DTT TF strand. """ t = (T + T_margin) / self.Tc0_K - # b = B / self.Bc20_T + b = B / self.Bc20_T tt = 1 - t ** self.n G = (self.a1 / (self.a1 + self.b1)) ** self.a1 # noqa: N806 GG = (self.b1 / (self.a1 + self.b1)) ** self.b1 # noqa: N806 @@ -325,14 +408,14 @@ def Jc(self, B: float, T: float, T_margin: float = 1.5, **kwargs): # noqa: self.C0 * self.C1 / (B * GGG) - * (self.b / tt) ** self.a1 - * (1 - self.b / tt) ** self.b1 + * (b / tt) ** self.a1 + * (1 - b / tt) ** self.b1 * tt ** self.g1 + self.C0 * self.C2 / (B * FFF) - * (self.b / tt) ** self.a2 - * (1 - self.b / tt) ** self.b2 + * (b / tt) ** self.a2 + * (1 - b / tt) ** self.b2 * tt ** self.g2 ) * 1e6 return Jc # noqa: RET504 diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index ea33f016dc..724f7fd34e 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -14,6 +14,7 @@ import numpy as np from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI +from bluemira.base.look_and_feel import bluemira_print from bluemira.magnets.cable import DummyRoundCableHTS, DummyRoundCableLTS from bluemira.magnets.case_tf import CaseTF from bluemira.magnets.conductor import SquareConductor @@ -26,7 +27,7 @@ # plot options show = True -homogenized = False # if True plot the WP as a homogenized block +homogenized = True # if True plot the WP as a homogenized block # input values B0 = 4.39 # [T] magnetic field @R0 @@ -52,7 +53,7 @@ # i.e. half of the whole F_Z t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2 -Iop = 90.0e3 # operational current in each conductor +Iop = 9.0e3 # operational current in each conductor # n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors n_cond = np.floor(I_TF / Iop) @@ -79,18 +80,59 @@ I = delayed_exp_func(Iop, Tau_discharge, t_delay) # noqa: E741 B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay) -# define the conductor (with a dummy number of stabilizer strands ) +# define materials (the conductor is defined with a dummy number of stabilizer strands ) +ss316 = AISI_316LN() sc_strand = WireNb3Sn(d_strand=1e-3) copper300 = Copper300() insulator = DummyInsulator() stab_strand = Strand([copper300], [1], d_strand=1.0e-3) -# Calculate number of superconducting strands considering the strand critical current +if True: + # plot the critical current in a range of B between [10,16] + Bt_arr = np.linspace(10,16,100) + sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin) + + ##### Plot number of conductor vs Iop##################### + # Define the range of operating current (Iop) values + Iop_range = np.linspace(30, 100, 100)*1e3 # 5 equally spaced values between 30 and 100 A + Ic_sc_arr = sc_strand.Ic(B=Bt_arr, T=T_sc, T_margin=T_margin) + + # Create a colormap to assign colors to different Iop values + import matplotlib.cm as cm # Import colormap + colors = cm.viridis(np.linspace(0, 1, len(Iop_range))) # Use the 'viridis' colormap + + # Create a figure and axis + fig, ax = plt.subplots() + + # Plot the number of superconducting strands as a function of B for different Iop values + for Iop_ref, color in zip(Iop_range, colors): + n_sc_strand = Iop_ref / Ic_sc_arr # Calculate number of strands + ax.plot(Bt_arr, n_sc_strand, color=color, label=f'Iop = {Iop} A') + # Add plot title, axis labels, and grid + ax.set_title('Number of strands as function of B and Iop') # Title + ax.set_xlabel('B [T]') # X-axis label + ax.set_ylabel('Nc strands') # Y-axis label + ax.grid(True) + + # Create a ScalarMappable to map colors to the colorbar + sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=Iop_range.min(), vmax=Iop_range.max())) + sm.set_array([]) # Dummy array for the ScalarMappable + + # Add the colorbar to the figure + cbar = fig.colorbar(sm, ax=ax) + cbar.set_label('Iop [A]') # Label the colorbar + + # Show the plot + plt.show() + +####################################################################### +# Calculate number of superconducting strands considering the strand critical current at B_TF_i and T_sc + T_margin Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) n_sc_strand = int(np.ceil(Iop / Ic_sc)) -B_ref = 15 +########################################################### +B_ref = 15 if B_TF_i < B_ref: cable = DummyRoundCableLTS( sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 @@ -99,20 +141,22 @@ cable = DummyRoundCableHTS( sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 ) -# c.plot(0,0, show=True) +cable.plot(0,0, show=True) -ss316 = AISI_316LN() + + +########################################################### conductor = SquareConductor( cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3 ) # optimize the number of stabilizer strands using the hot spot criteria -print(f"before optimization: conductor dx_cable = {cable.dx}") +bluemira_print(f"before optimization: conductor dx_cable = {cable.dx}") T_for_hts = T0 result = cable.optimize_n_stab_ths( t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show ) -print(f"after optimization: conductor dx_cable = {cable.dx}") +bluemira_print(f"after optimization: conductor dx_cable = {cable.dx}") # creation of case wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case @@ -153,7 +197,7 @@ err_dy_jacket = delta_conductor_dx_jacket / conductor.dy_jacket case.rearrange_conductors_in_wp( - n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.7, 0.05, 4 + n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.8, 0.05, 4 ) case_dy_vault0 = case.dy_vault From aea8d4ab23140431eb757dff3246911ff1860a7a Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Thu, 10 Oct 2024 15:38:14 +0200 Subject: [PATCH 12/17] implementing different WP layouts --- bluemira/magnets/case_tf.py | 30 +++++++++++++++++++++++++++++- bluemira/magnets/winding_pack.py | 10 ++++++++++ examples/magnets/tf_example.py | 7 ++++--- 3 files changed, 43 insertions(+), 4 deletions(-) diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index e8023d569d..444580d98a 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -73,6 +73,16 @@ def dx_ps(self): """Average toroidal length of the ps plate [m]""" return (self.Ri + (self.Ri - self.dy_ps)) * np.tan(self._rad_theta_TF / 2) + @property + def n_conductors(self): + """Total number of conductors in the winding pack.""" + return sum([w.n_conductors for w in self.WPs]) + + @property + def jacket_area(self): + """Total jacket area in the winding pack.""" + return sum([w.jacket_area for w in self.WPs]) + def max_Iop(self, B, T, T_margin): # noqa: N803, N802 """Maximum operational current (equal to the critical current into the superconductor @@ -375,6 +385,7 @@ def rearrange_conductors_in_wp( dx_WP: float, # noqa: N803 min_gap_x: float, n_layers_reduction: int, + layout: str = "auto", ): """ Rearrange the total number of conductors into the TF coil case considering @@ -394,6 +405,12 @@ def rearrange_conductors_in_wp( minimum toroidal distance between winding pack and tf coils lateral faces n_layers_reduction: number of turns to be removed when calculating a new pancake + layout: + type of layout for the conductor placement. Available types are "single", "layer", "pancake". + - "auto" means that the conductors are placed in order + to only verify the geometrical constraints + - "layer" adds the odd constrain on ny (ny % 2 = 0). + - "pancake" adds the even constrain on nx (nx % 2 = 0) Returns ------- @@ -419,9 +436,13 @@ def rearrange_conductors_in_wp( # maximum toroidal dimension of the WP most outer pancake # dx_WP = 2 * (R_wp_i * np.tan(self._rad_theta_TF / 2) - dx0_wp) - # maximum number of turns on the considered pancake + # maximum number of turns on the considered WP if i == 1: n_layers_max = int(math.floor(dx_WP / cond.dx)) + if layout == "pancake": + n_layers_max = int(math.floor(dx_WP / cond.dx / 2.) * 2) + if n_layers_max == 0: + n_layers_max = 2 else: n_layers_max -= n_layers_reduction @@ -441,6 +462,13 @@ def rearrange_conductors_in_wp( int(np.floor(max_dy / cond.dy)), int(np.ceil(remaining_conductors / n_layers_max)), ) + if layout == "layer": + n_turns_max = min( + int(np.floor(max_dy / cond.dy / 2.) * 2), + int(np.ceil(remaining_conductors / n_layers_max / 2.) * 2), + ) + if n_turns_max == 0: + n_turns_max = 2 if n_turns_max < 1: raise ValueError( diff --git a/bluemira/magnets/winding_pack.py b/bluemira/magnets/winding_pack.py index af726e89ae..7a77cd300f 100644 --- a/bluemira/magnets/winding_pack.py +++ b/bluemira/magnets/winding_pack.py @@ -37,6 +37,16 @@ def dy(self): """Total height of the winding pack along the y-axis.""" return self.conductor.dy * self.ny + @property + def n_conductors(self): + """Total number of conductors in the winding pack.""" + return self.nx * self.ny + + @property + def jacket_area(self): + """Total jacket area in the winding pack.""" + return self.conductor.area_jacket * self.n_conductors + def Kx(self, **kwargs) -> float: """ Calculates the total equivalent stiffness of the winding pack along the x-axis. diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py index 724f7fd34e..054a8befda 100644 --- a/examples/magnets/tf_example.py +++ b/examples/magnets/tf_example.py @@ -53,7 +53,7 @@ # i.e. half of the whole F_Z t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2 -Iop = 9.0e3 # operational current in each conductor +Iop = 90.0e3 # operational current in each conductor # n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors n_cond = np.floor(I_TF / Iop) @@ -177,7 +177,7 @@ eps = 1e-3 i = 0 tot_err = 100 * eps - +layout = "layer" while i < max_niter and tot_err > eps: i += 1 print(f"Internal optimazion - iteration {i}") @@ -197,7 +197,7 @@ err_dy_jacket = delta_conductor_dx_jacket / conductor.dy_jacket case.rearrange_conductors_in_wp( - n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.8, 0.05, 4 + n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.8, 0.05, 4, layout = layout ) case_dy_vault0 = case.dy_vault @@ -216,6 +216,7 @@ show = True +homogenized = False if show: scalex = np.array([2, 1]) scaley = np.array([1, 1.2]) From b4628d7f4aca465caad07a4578c24d73809ca131 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Wed, 16 Oct 2024 11:55:19 +0200 Subject: [PATCH 13/17] improved wp optimization and example --- bluemira/magnets/cable.py | 310 ++++--- bluemira/magnets/case_tf.py | 142 +++- bluemira/magnets/conductor.py | 149 ++-- .../magnets/example_tf_wp_optimization.ipynb | 801 ++++++++++++++++++ .../magnets/example_tf_wp_optimization.py | 328 +++++++ 5 files changed, 1510 insertions(+), 220 deletions(-) create mode 100644 examples/magnets/example_tf_wp_optimization.ipynb create mode 100644 examples/magnets/example_tf_wp_optimization.py diff --git a/bluemira/magnets/cable.py b/bluemira/magnets/cable.py index 1b5fdb1f23..805ee5f8bb 100644 --- a/bluemira/magnets/cable.py +++ b/bluemira/magnets/cable.py @@ -7,35 +7,30 @@ """Cable class""" from abc import ABC, abstractmethod -from typing import Callable +from collections.abc import Callable import matplotlib.pyplot as plt import numpy as np from scipy.integrate import solve_ivp from scipy.optimize import minimize_scalar +from bluemira.base.look_and_feel import bluemira_error, bluemira_print from bluemira.magnets.materials import Material from bluemira.magnets.strand import Strand, SuperconductingStrand from bluemira.magnets.utils import parall_r, serie_r -from bluemira.base.look_and_feel import bluemira_error, bluemira_warn -from bluemira.geometry.tools import make_circle, make_polygon -from bluemira.geometry.face import BluemiraFace -from bluemira import display -from bluemira.display.plotter import PlotOptions - class ABCCable(Material, ABC): def __init__( - self, - sc_strand: SuperconductingStrand, - stab_strand: Strand, - n_sc_strand: int, - n_stab_strand: int, - d_cooling_channel: float, - void_fraction: float = 0.725, - cos_theta: float = 0.97, - name: str = "", + self, + sc_strand: SuperconductingStrand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", ): """ Representation of a cable. Only the x-dimension of the cable is given as @@ -92,6 +87,10 @@ def dx(self): def dy(self): pass + @property + def aspect_ratio(self): + return self.dx / self.dy + @property def n_sc_strand(self): """Number of stabilizing strands""" @@ -211,13 +210,13 @@ def area_sc(self): @property def area_cc(self): """Area of the cooling channel""" - return self.d_cooling_channel ** 2 / 4 * np.pi + return self.d_cooling_channel**2 / 4 * np.pi @property def area(self): """Area of the cable considering the void fraction""" return ( - self.area_sc + self.area_stab + self.area_sc + self.area_stab ) / self.void_fraction / self.cos_theta + self.area_cc def E(self, **kwargs): @@ -261,15 +260,15 @@ def _heat_balance_model_cable(self, t: float, T: float, B: Callable, I: Callable return dTdt def optimize_n_stab_ths( - self, - t0: float, - tf: float, - initial_temperature: float, - target_temperature: float, - B: Callable, - I: Callable, - bounds: np.ndarray = None, - show: bool = False, + self, + t0: float, + tf: float, + initial_temperature: float, + target_temperature: float, + B: Callable, + I: Callable, + bounds: np.ndarray = None, + show: bool = False, ): """ Optimize the number of stabilizer strand in the superconducting cable using a @@ -306,11 +305,11 @@ def optimize_n_stab_ths( """ def _temperature_evolution( - t0: float, - tf: float, - initial_temperature: float, - B: Callable, - I: Callable, + t0: float, + tf: float, + initial_temperature: float, + B: Callable, + I: Callable, ): solution = solve_ivp( self._heat_balance_model_cable, @@ -326,18 +325,22 @@ def _temperature_evolution( return solution def final_temperature_difference( - n_stab: int, - t0: float, - tf: float, - initial_temperature: float, - target_temperature: float, - B: Callable, - I: Callable, + n_stab: int, + t0: float, + tf: float, + initial_temperature: float, + target_temperature: float, + B: Callable, + I: Callable, ): self.n_stab_strand = n_stab solution = _temperature_evolution( - t0=t0, tf=tf, initial_temperature=initial_temperature, B=B, I=I, + t0=t0, + tf=tf, + initial_temperature=initial_temperature, + B=B, + I=I, ) final_T = float(solution.y[0][-1]) diff = abs(final_T - target_temperature) @@ -362,29 +365,70 @@ def final_temperature_difference( solution = _temperature_evolution(t0, tf, initial_temperature, B, I) final_temperature = solution.y[0][-1] - print(f"Optimal n_stab: {self.n_stab_strand}") - print(f"Final temperature with optimal n_stab: {final_temperature} Kelvin") + bluemira_print(f"Optimal n_stab: {self.n_stab_strand}") + bluemira_print( + f"Final temperature with optimal n_stab: {final_temperature} Kelvin" + ) if show: + # _, ax = plt.subplots() + # ax.plot(solution.t, solution.y[0], "r*") + # time_steps = np.linspace(t0, tf, 100) + # ax.plot(time_steps, solution.sol(time_steps)[0], "b") + # plt.grid(True) + # plt.xlabel("Time [s]") + # plt.ylabel("Temperature [K]") + # plt.title("Quench temperature evoltuion") + # + # # *** Additional info *** + # additional_info = [f"Hot spot temp. = {target_temperature} [K]", + # f"Initial temp. = {initial_temperature} [K]", + # f"Sc. strand = {self.sc_strand.__class__.__name__}", + # f"n. sc. strand = {self.n_sc_strand}", + # f"Stab. strand = {self.stab_strand.__class__.__name__}", + # f"n. stab. strand = {self.n_stab_strand}"] + # + # additional_info = '\n'.join(additional_info) + # plt.text(50, 80, additional_info) + # plt.show() + _, ax = plt.subplots() + + # Plot the main solution ax.plot(solution.t, solution.y[0], "r*") time_steps = np.linspace(t0, tf, 100) ax.plot(time_steps, solution.sol(time_steps)[0], "b") plt.grid(True) plt.xlabel("Time [s]") plt.ylabel("Temperature [K]") - plt.title("Quench temperature evoltuion") - - # *** Additional info *** - additional_info = [f"Hot spot temp. = {target_temperature} [K]", - f"Initial temp. = {initial_temperature} [K]", - f"Sc. strand = {self.sc_strand.__class__.__name__}", - f"n. sc. strand = {self.n_sc_strand}", - f"Stab. strand = {self.stab_strand.__class__.__name__}", - f"n. stab. strand = {self.n_stab_strand}"] - - additional_info = '\n'.join(additional_info) - plt.text(50, 80, additional_info) + plt.title("Quench temperature evolution") + + # Create secondary axis on the right (no ticks or labels needed) + ax2 = ax.twinx() # This creates a new y-axis that shares the same x-axis + ax2.set_yticks([]) # Remove y-axis ticks + ax2.set_ylabel("") # Remove y-axis label + + # Plot additional info next to the right y-axis + additional_info = [ + f"Hot spot temp. = {target_temperature} [K]", + f"Initial temp. = {initial_temperature} [K]", + f"Sc. strand = {self.sc_strand.__class__.__name__}", + f"n. sc. strand = {self.n_sc_strand}", + f"Stab. strand = {self.stab_strand.__class__.__name__}", + f"n. stab. strand = {self.n_stab_strand}", + ] + additional_info = "\n".join(additional_info) + + # Set text position right after the right y-axis + ax2.text( + 1.05, + 0.5, + additional_info, + transform=ax2.transAxes, + verticalalignment="center", + fontsize=10, + ) + plt.show() return result @@ -430,15 +474,16 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): points_ext = np.vstack((p0, p1, p2, p3, p0)) + pc points_cc = ( - np.array([ - np.array([np.cos(theta), np.sin(theta)]) * self.d_cooling_channel / 2 - for theta in np.linspace(0, np.radians(360), 19) - ]) - + pc + np.array([ + np.array([np.cos(theta), np.sin(theta)]) * self.d_cooling_channel / 2 + for theta in np.linspace(0, np.radians(360), 19) + ]) + + pc ) ax.fill(points_ext[:, 0], points_ext[:, 1], "gold") ax.fill(points_cc[:, 0], points_cc[:, 1], "r") + ax.set_aspect("equal") if show: plt.show() @@ -447,16 +492,16 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): class RectangularCable(ABCCable): def __init__( - self, - dx: float, - sc_strand: SuperconductingStrand, - stab_strand: Strand, - n_sc_strand: int, - n_stab_strand: int, - d_cooling_channel: float, - void_fraction: float = 0.725, - cos_theta: float = 0.97, - name: str = "", + self, + dx: float, + sc_strand: SuperconductingStrand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", ): """ Representation of a cable. Only the x-dimension of the cable is given as @@ -504,7 +549,6 @@ def __init__( # assign self.dx = dx - @property def dx(self): """Cable dimension in the x direction [m]""" @@ -523,6 +567,12 @@ def dy(self): """Cable dimension in the y direction [m]""" return self.area / self.dx + # Todo: decide if this function shall be a setter. + # Defined as "normal" function to underline that it modifies dx. + def set_aspect_ratio(self, value: float) -> None: + """Modify dx in order to get the given aspect ratio""" + self.dx = np.sqrt(value * self.area) + # OD homogenized structural properties def Kx(self, **kwargs): """Total equivalent stiffness along x-axis""" @@ -533,17 +583,80 @@ def Ky(self, **kwargs): return self.E(**kwargs) * self.dx / self.dy +class DummyRectangularCableHTS(RectangularCable): + """ + Dummy rectangular cable with young's moduli set to 120 GPa. + + Parameters + ---------- + dx: + x-dimension of the cable [m] + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + + """ + + def E(self, **kwargs): + """Young's module""" + return 120e9 + + +class DummyRectangularCableLTS(RectangularCable): + """ + Dummy square cable with young's moduli set to 0.1 GPa + + Parameters + ---------- + dx: + x-dimension of the cable [m] + sc_strand: + strand of the superconductor + stab_strand: + strand of the stabilizer + d_cooling_channel: + diameter of the cooling channel + n_sc_strand: + number of superconducting strands + n_stab_strand: + number of stabilizer strands + void_fraction: + void fraction defined as material_volume/total_volume + cos_theta: + corrective factor that consider the twist of the cable + name: + cable string identifier + """ + + def E(self, **kwargs): + """Young's module""" + return 0.1e9 + + class SquareCable(ABCCable): def __init__( - self, - sc_strand: SuperconductingStrand, - stab_strand: Strand, - n_sc_strand: int, - n_stab_strand: int, - d_cooling_channel: float, - void_fraction: float = 0.725, - cos_theta: float = 0.97, - name: str = "", + self, + sc_strand: SuperconductingStrand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", ): """ Representation of a square cable. @@ -666,15 +779,15 @@ def E(self, **kwargs): class RoundCable(ABCCable): def __init__( - self, - sc_strand: SuperconductingStrand, - stab_strand: Strand, - n_sc_strand: int, - n_stab_strand: int, - d_cooling_channel: float, - void_fraction: float = 0.725, - cos_theta: float = 0.97, - name: str = "", + self, + sc_strand: SuperconductingStrand, + stab_strand: Strand, + n_sc_strand: int, + n_stab_strand: int, + d_cooling_channel: float, + void_fraction: float = 0.725, + cos_theta: float = 0.97, + name: str = "", ): """ Representation of a round cable @@ -753,19 +866,19 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): pc = np.array([xc, yc]) points_ext = ( - np.array([ - np.array([np.cos(theta), np.sin(theta)]) * self.dx / 2 - for theta in np.linspace(0, np.radians(360), 19) - ]) - + pc + np.array([ + np.array([np.cos(theta), np.sin(theta)]) * self.dx / 2 + for theta in np.linspace(0, np.radians(360), 19) + ]) + + pc ) points_cc = ( - np.array([ - np.array([np.cos(theta), np.sin(theta)]) * self.d_cooling_channel / 2 - for theta in np.linspace(0, np.radians(360), 19) - ]) - + pc + np.array([ + np.array([np.cos(theta), np.sin(theta)]) * self.d_cooling_channel / 2 + for theta in np.linspace(0, np.radians(360), 19) + ]) + + pc ) ax.fill(points_ext[:, 0], points_ext[:, 1], "gold") @@ -775,6 +888,7 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): plt.show() return ax + class DummyRoundCableHTS(RoundCable): """ Dummy round cable with young's moduli set to 120 GPa diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index 444580d98a..fc7a6aff50 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -25,14 +25,14 @@ class CaseTF: """TF case class""" def __init__( - self, - Ri: float, # noqa: N803 - dy_ps: float, - dy_vault: float, - theta_TF: float, - mat_case: Material, - WPs: list[WindingPack], # noqa: N803 - name: str = "", + self, + Ri: float, # noqa: N803 + dy_ps: float, + dy_vault: float, + theta_TF: float, + mat_case: Material, + WPs: list[WindingPack], # noqa: N803 + name: str = "", ): """ Case structure for TF coils @@ -78,11 +78,6 @@ def n_conductors(self): """Total number of conductors in the winding pack.""" return sum([w.n_conductors for w in self.WPs]) - @property - def jacket_area(self): - """Total jacket area in the winding pack.""" - return sum([w.jacket_area for w in self.WPs]) - def max_Iop(self, B, T, T_margin): # noqa: N803, N802 """Maximum operational current (equal to the critical current into the superconductor @@ -220,7 +215,7 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): # the maximum hoop stress, corrected to account for the presence of the WP, is # placed at the innermost radius of the case as: sigma_theta = ( - 2.0 / (1 - beta ** 2) * pm * self.Kx_vault(**kwargs) / self.Kx(**kwargs) + 2.0 / (1 - beta**2) * pm * self.Kx_vault(**kwargs) / self.Kx(**kwargs) ) # In addition to the radial centripetal force, the second in-plane component @@ -238,13 +233,13 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): return sigma_theta + sigma_z def optimize_vault_radial_thickness( - self, - pm: float, - fz: float, - T: float, # noqa: N803 - B: float, - allowable_sigma: float, - bounds: np.array = None, + self, + pm: float, + fz: float, + T: float, # noqa: N803 + B: float, + allowable_sigma: float, + bounds: np.array = None, ): """ Optimize the vault radial thickness of the case @@ -275,7 +270,6 @@ def optimize_vault_radial_thickness( ValueError If the optimization process did not converge. """ - method = None if bounds is not None: method = "bounded" @@ -297,13 +291,13 @@ def optimize_vault_radial_thickness( return result def _sigma_difference( - self, - dy_vault: float, - pm: float, - fz: float, - T: float, - B: float, - allowable_sigma: float, + self, + dy_vault: float, + pm: float, + fz: float, + T: float, + B: float, + allowable_sigma: float, ): """ Fitness function for the optimization problem. It calculates the absolute @@ -341,6 +335,69 @@ def _sigma_difference( sigma = self._tresca_stress(pm, fz, T=T, B=B) return abs(sigma - allowable_sigma) + def optimize_jacket_and_vault( + self, + pm: float, + fz: float, + T: float, # noqa: N803 + B: float, + allowable_sigma: float, + bounds: np.array = None, + layout: str = "auto", + wp_reduction_factor: float = 0.8, + min_gap_x: float = 0.05, + n_layers_reduction: int = 4, + max_niter: int = 10, + eps: float = 1e-8, + ): + conductor = self.WPs[0].conductor + tot_err = 100 * eps + i = 0 + while i < max_niter and tot_err > eps: + i += 1 + print(f"Internal optimazion - iteration {i}") + print( + f"before optimization: conductor jacket area = {conductor.area_jacket}" + ) + cond_area_jacket0 = conductor.area_jacket + t_z_cable_jacket = ( + fz + * self.area_wps_jacket + / (self.area_jacket + self.area_wps_jacket) + / self.n_conductors + ) + conductor.optimize_jacket_conductor( + pm, t_z_cable_jacket, T, B, allowable_sigma, bounds + ) + print(t_z_cable_jacket) + print(f"after optimization: conductor jacket area = {conductor.area_jacket}") + err_conductor_area_jacket = ( + abs(conductor.area_jacket - cond_area_jacket0) / cond_area_jacket0 + ) + + self.rearrange_conductors_in_wp( + self.n_conductors, + conductor, + self.R_wp_i[0], + wp_reduction_factor, + min_gap_x, + n_layers_reduction, + layout=layout, + ) + case_dy_vault0 = self.dy_vault + print(f"before optimization: case dy_vault = {self.dy_vault}") + self.optimize_vault_radial_thickness( + pm=pm, fz=fz, T=T, B=B, allowable_sigma=allowable_sigma, bounds=bounds + ) + + print(f"after optimization: case dy_vault = {self.dy_vault}") + delta_case_dy_vault = abs(self.dy_vault - case_dy_vault0) + err_dy_vault = delta_case_dy_vault / self.dy_vault + tot_err = err_dy_vault + err_conductor_area_jacket + + print(f"err_dy_jacket = {err_conductor_area_jacket}") + print(f"err_dy_vault = {err_dy_vault}") + print(f"tot_err = {tot_err}") def plot(self, ax=None, *, show: bool = False, homogenized: bool = False): """ @@ -358,6 +415,7 @@ def plot(self, ax=None, *, show: bool = False, homogenized: bool = False): """ if ax is None: _, ax = plt.subplots() + ax.set_aspect("equal", adjustable="box") p0 = np.array([-self.dx_i / 2, self.Ri]) p1 = np.array([self.dx_i / 2, self.Ri]) @@ -378,14 +436,14 @@ def plot(self, ax=None, *, show: bool = False, homogenized: bool = False): return ax def rearrange_conductors_in_wp( - self, - n_conductors: int, - cond: Conductor, - R_wp_i: float, # noqa: N803 - dx_WP: float, # noqa: N803 - min_gap_x: float, - n_layers_reduction: int, - layout: str = "auto", + self, + n_conductors: int, + cond: Conductor, + R_wp_i: float, # noqa: N803 + wp_reduction_factor: float, + min_gap_x: float, + n_layers_reduction: int, + layout: str = "auto", ): """ Rearrange the total number of conductors into the TF coil case considering @@ -422,6 +480,8 @@ def rearrange_conductors_in_wp( the one defined in n_conductors due to the necessity to close the final layer. """ + dx_WP = self.dx_i * wp_reduction_factor + WPs = [] # noqa: N806 # number of conductors to be allocated remaining_conductors = n_conductors @@ -440,7 +500,7 @@ def rearrange_conductors_in_wp( if i == 1: n_layers_max = int(math.floor(dx_WP / cond.dx)) if layout == "pancake": - n_layers_max = int(math.floor(dx_WP / cond.dx / 2.) * 2) + n_layers_max = int(math.floor(dx_WP / cond.dx / 2.0) * 2) if n_layers_max == 0: n_layers_max = 2 else: @@ -464,8 +524,8 @@ def rearrange_conductors_in_wp( ) if layout == "layer": n_turns_max = min( - int(np.floor(max_dy / cond.dy / 2.) * 2), - int(np.ceil(remaining_conductors / n_layers_max / 2.) * 2), + int(np.floor(max_dy / cond.dy / 2.0) * 2), + int(np.ceil(remaining_conductors / n_layers_max / 2.0) * 2), ) if n_turns_max == 0: n_turns_max = 2 @@ -482,7 +542,7 @@ def rearrange_conductors_in_wp( if remaining_conductors < 0: bluemira_warn( - f"{abs(remaining_conductors)} have been added" + f"{abs(remaining_conductors)}/{n_layers_max * n_turns_max} have been added" f" to complete the last layer." ) diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index 27f09aee83..73e7b13da5 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -6,8 +6,6 @@ """Conductor class""" -from typing import Optional - import matplotlib.pyplot as plt import numpy as np from scipy.optimize import minimize_scalar @@ -24,15 +22,15 @@ class Conductor: def __init__( - self, - cable: ABCCable, - mat_jacket: Material, - mat_ins: Material, - dx_jacket: float, - dy_jacket: float, - dx_ins: float, - dy_ins: float, - name: str = "", + self, + cable: ABCCable, + mat_jacket: Material, + mat_ins: Material, + dx_jacket: float, + dy_jacket: float, + dx_ins: float, + dy_ins: float, + name: str = "", ): """ A generic conductor consisting of a cable surrounded by a jacket and an @@ -121,7 +119,7 @@ def area(self): def area_jacket(self): """Area of the jacket [m^2]""" return (self.dx - 2 * self.dx_ins) * ( - self.dy - 2 * self.dy_ins + self.dy - 2 * self.dy_ins ) - self.cable.area @property @@ -189,10 +187,7 @@ def Kx_lat_ins(self, **kwargs): return self.mat_ins.E(**kwargs) * self.dy_ins / (self.dx - 2 * self.dx_ins) def Kx_lat_jacket(self, **kwargs): - return ( - self.mat_jacket.E(**kwargs) * self.dy_jacket / ( - self.dx - 2 * self.dx_ins) - ) + return self.mat_jacket.E(**kwargs) * self.dy_jacket / (self.dx - 2 * self.dx_ins) def Kx_topbot_jacket(self, **kwargs): return self.mat_jacket.E(**kwargs) * self.cable.dy / self.dx_jacket @@ -201,15 +196,14 @@ def Kx_cable(self, **kwargs): return self.cable.Kx(**kwargs) def Kx(self, **kwargs): - return (serie_k([self.Kx_topbot_ins(**kwargs) / 2, - parall_k([ - 2 * self.Kx_lat_ins(**kwargs), - 2 * self.Kx_lat_jacket(**kwargs), - serie_k([self.Kx_cable(**kwargs), - self.Kx_topbot_jacket(**kwargs) / 2]), - ]), - ]) - ) + return serie_k([ + self.Kx_topbot_ins(**kwargs) / 2, + parall_k([ + 2 * self.Kx_lat_ins(**kwargs), + 2 * self.Kx_lat_jacket(**kwargs), + serie_k([self.Kx_cable(**kwargs), self.Kx_topbot_jacket(**kwargs) / 2]), + ]), + ]) def Ky_topbot_ins(self, **kwargs): return self.mat_ins.E(**kwargs) * self.dx / self.dy_ins @@ -218,10 +212,7 @@ def Ky_lat_ins(self, **kwargs): return self.mat_ins.E(**kwargs) * self.dx_ins / (self.dy - 2 * self.dy_ins) def Ky_lat_jacket(self, **kwargs): - return ( - self.mat_jacket.E(**kwargs) * self.dx_jacket / ( - self.dy - 2 * self.dy_ins) - ) + return self.mat_jacket.E(**kwargs) * self.dx_jacket / (self.dy - 2 * self.dy_ins) def Ky_topbot_jacket(self, **kwargs): return self.mat_jacket.E(**kwargs) * self.cable.dx / self.dy_jacket @@ -230,18 +221,18 @@ def Ky_cable(self, **kwargs): return self.cable.Ky(**kwargs) def Ky(self, **kwargs): - return (serie_k([self.Ky_topbot_ins(**kwargs) / 2, - parall_k([ - 2 * self.Ky_lat_ins(**kwargs), - 2 * self.Ky_lat_jacket(**kwargs), - serie_k([self.Ky_cable(**kwargs), - self.Ky_topbot_jacket(**kwargs) / 2]), - ]), - ]) - ) - - def _tresca_sigma_jacket(self, pressure: float, f_z: float, T: float, - B: float, direction: str = 'x') -> float: + return serie_k([ + self.Ky_topbot_ins(**kwargs) / 2, + parall_k([ + 2 * self.Ky_lat_ins(**kwargs), + 2 * self.Ky_lat_jacket(**kwargs), + serie_k([self.Ky_cable(**kwargs), self.Ky_topbot_jacket(**kwargs) / 2]), + ]), + ]) + + def _tresca_sigma_jacket( + self, pressure: float, f_z: float, T: float, B: float, direction: str = "x" + ) -> float: """ Calculate the radial stress in the jacket when the conductor is subjected to a pressure along a specified direction and a force perpendicular to the conductor cross-section. @@ -269,20 +260,16 @@ def _tresca_sigma_jacket(self, pressure: float, f_z: float, T: float, ValueError If the specified direction is not 'x' or 'y'. """ - - if direction not in ['x', 'y']: + if direction not in ["x", "y"]: raise ValueError("Invalid direction: choose either 'x' or 'y'.") - if direction == 'x': + if direction == "x": saf_jacket = (self.cable.dx + 2 * self.dx_jacket) / (2 * self.dx_jacket) K = parall_k([ 2 * self.Ky_lat_ins(T=T, B=B), 2 * self.Ky_lat_jacket(T=T, B=B), - serie_k([ - self.Ky_cable(T=T, B=B), - self.Ky_topbot_jacket(T=T, B=B) / 2 - ]), + serie_k([self.Ky_cable(T=T, B=B), self.Ky_topbot_jacket(T=T, B=B) / 2]), ]) X_jacket = 2 * self.Ky_lat_jacket(T=T, B=B) / K @@ -293,10 +280,7 @@ def _tresca_sigma_jacket(self, pressure: float, f_z: float, T: float, K = parall_k([ 2 * self.Kx_lat_ins(T=T, B=B), 2 * self.Kx_lat_jacket(T=T, B=B), - serie_k([ - self.Kx_cable(T=T, B=B), - self.Kx_topbot_jacket(T=T, B=B) / 2 - ]), + serie_k([self.Kx_cable(T=T, B=B), self.Kx_topbot_jacket(T=T, B=B) / 2]), ]) X_jacket = 2 * self.Kx_lat_jacket(T=T, B=B) / K @@ -306,14 +290,14 @@ def _tresca_sigma_jacket(self, pressure: float, f_z: float, T: float, return tresca_stress def optimize_jacket_conductor( - self, - pressure: float, - f_z: float, - T: float, - B: float, - allowable_sigma: float, - bounds: Optional[np.ndarray] = None, - direction: str = 'x' + self, + pressure: float, + f_z: float, + T: float, + B: float, + allowable_sigma: float, + bounds: np.ndarray | None = None, + direction: str = "x", ): """ Optimize the jacket dimension of a conductor based on allowable stress using the Tresca criterion. @@ -351,13 +335,13 @@ def optimize_jacket_conductor( """ def sigma_difference( - jacket_thickness: float, - pressure: float, - fz: float, - T: float, - B: float, - allowable_sigma: float, - direction: str = 'x' + jacket_thickness: float, + pressure: float, + fz: float, + T: float, + B: float, + allowable_sigma: float, + direction: str = "x", ): """ Fitness function for the optimization problem. It calculates the absolute difference between @@ -389,10 +373,10 @@ def sigma_difference( This function modifies the conductor's jacket thickness along the specified direction using the value provided in jacket_thickness. """ - if direction not in ['x', 'y']: + if direction not in ["x", "y"]: raise ValueError("Invalid direction: choose either 'x' or 'y'.") - if direction == 'x': + if direction == "x": self.dx_jacket = jacket_thickness else: self.dy_jacket = jacket_thickness @@ -414,12 +398,14 @@ def sigma_difference( if not result.success: raise ValueError("Optimization of the jacket conductor did not converge.") self.dx_jacket = result.x - if direction == 'x': + if direction == "x": print(f"Optimal dx_jacket: {self.dx_jacket}") else: print(f"Optimal dy_jacket: {self.dy_jacket}") - print(f"Averaged sigma in the {direction}-direction: " - f"{self._tresca_sigma_jacket(pressure, f_z, T, B) / 1e6} MPa") + print( + f"Averaged sigma in the {direction}-direction: " + f"{self._tresca_sigma_jacket(pressure, f_z, T, B) / 1e6} MPa" + ) return result @@ -473,18 +459,19 @@ def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None): return ax -class SquareConductor(Conductor): +class SymmetricConductor(Conductor): def __init__( - self, - cable: ABCCable, - mat_jacket: Material, - mat_ins: Material, - dx_jacket: float, - dx_ins: float, - name: str = "", + self, + cable: ABCCable, + mat_jacket: Material, + mat_ins: Material, + dx_jacket: float, + dx_ins: float, + name: str = "", ): """ - Representation of a square conductor + Representation of a symmetric conductor in which both jacket and insulator mantain + a constant thickness (i.e. dy_jacket = dx_jacket and dy_ins = dx_ins). Parameters ---------- diff --git a/examples/magnets/example_tf_wp_optimization.ipynb b/examples/magnets/example_tf_wp_optimization.ipynb new file mode 100644 index 0000000000..ca49488af6 --- /dev/null +++ b/examples/magnets/example_tf_wp_optimization.ipynb @@ -0,0 +1,801 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# This is an example that shows the application of the WP module for the TF coils", + "id": "9cf6ac4ba2d3f462" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Some import", + "id": "8dd4426f35141a5a" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.064717Z", + "start_time": "2024-10-16T09:49:07.550948Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI\n", + "from bluemira.base.look_and_feel import bluemira_print\n", + "from bluemira.magnets.cable import DummyRoundCableHTS, DummyRoundCableLTS, DummyRectangularCableLTS, \\\n", + " DummyRectangularCableHTS\n", + "from bluemira.magnets.case_tf import CaseTF\n", + "from bluemira.magnets.conductor import SymmetricConductor\n", + "from bluemira.magnets.materials import AISI_316LN, Copper300, DummyInsulator\n", + "from bluemira.magnets.strand import Strand, WireNb3Sn\n", + "from bluemira.magnets.utils import (\n", + " delayed_exp_func,\n", + ")\n", + "from bluemira.magnets.winding_pack import WindingPack" + ], + "id": "54d702a3b1f86263", + "outputs": [], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Plot options", + "id": "a709656b9dc95948" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.070855Z", + "start_time": "2024-10-16T09:49:09.068361Z" + } + }, + "cell_type": "code", + "source": [ + "show = True\n", + "homogenized = False" + ], + "id": "78e8b10c23faa689", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "\n", + "## Input (and derived) values\n", + "*Note: these values shall be provided internally by bluemira code (as reactor settings or as information coming from the TF coils builder)

\n", + "\n", + "**Machine (generic)**" + ], + "id": "b13898853001e74c" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.345603Z", + "start_time": "2024-10-16T09:49:09.342989Z" + } + }, + "cell_type": "code", + "source": [ + "R0 = 8.6 # [m] major machine radius\n", + "B0 = 4.39 # [T] magnetic field @R0\n", + "A = 2.8 # machine aspect ratio\n", + "n_TF = 16 # number of TF coils\n", + "ripple = 6e-3 # requirement on the maximum plasma ripple\n", + "\n", + "a = R0 / A # minor radius" + ], + "id": "60966584009a130", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "**Inputs for the TF coils**", + "id": "25080dfa3f05156c" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.398854Z", + "start_time": "2024-10-16T09:49:09.393778Z" + } + }, + "cell_type": "code", + "source": [ + "d = 1.9 # additional distance to calculate the max external radius of the inner TF leg\n", + "Iop = 90.0e3 # operational current in each conductor\n", + "dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP\n", + "T_sc = 4.2 # operational temperature of superconducting cable\n", + "T_margin = 1.5 # temperature margin\n", + "t_delay = 3 #[s]\n", + "t0 = 0 #[s]\n", + "hotspot_target_temperature = 250.0 #[K]\n", + "\n", + "\n", + "Ri = R0 - a - d # [m] max external radius of the internal TF leg\n", + "Re = (R0 + a) * (1 / ripple) ** (1 / n_TF) # [m] max internal radius of the external TF leg\n", + "I_TF = B0 * R0 / MU_0_2PI / n_TF # total current in each TF coil\n", + "\n", + "# magnetic field generated by the TF coils with a correction factor that takes into account the ripple\n", + "B_TF_r = lambda I_TF, n_TF, R: 1.08 * (MU_0_2PI * n_TF * I_TF / R)\n", + "B_TF_i = B_TF_r(I_TF, n_TF, Ri) # max magnetic field on the inner TF leg\n", + "pm = B_TF_i ** 2 / (2 * MU_0) # magnetic pressure on the inner TF leg\n", + "\n", + "# vertical tension acting on the equatorial section of inner TF leg\n", + "# i.e. half of the whole F_Z\n", + "t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2\n", + "\n", + "T0 = T_sc + T_margin\n", + "n_cond = np.floor(I_TF / Iop) # minimum number of conductors\n", + "bluemira_print(f\"Total number of conductor: {n_cond}\")" + ], + "id": "f505e9d0370d72c3", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Total number of conductor: 131.0 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "***Additional data***", + "id": "9cd6a2fe5da403c8" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.450198Z", + "start_time": "2024-10-16T09:49:09.447253Z" + } + }, + "cell_type": "code", + "source": [ + "R_VV = Ri * 1.05 # Vacuum vessel radius\n", + "S_VV = 90e6 # Vacuum vessel steel limit\n", + "\n", + "# allowable stress values\n", + "safety_factor = 1.5 * 1.3\n", + "S_Y = 1e9 / safety_factor # [Pa] steel allowable limit\n" + ], + "id": "ab9069aa29c550df", + "outputs": [], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Calculation of the maximum discharge time for the TF coils", + "id": "af95628875cc373" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.504967Z", + "start_time": "2024-10-16T09:49:09.499250Z" + } + }, + "cell_type": "code", + "source": [ + "# inductance (here approximated... better estimation in bluemira)\n", + "L = MU_0 * R0 * (n_TF * n_cond) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1\n", + "# Magnetic energy\n", + "E = 1 / 2 * L * n_TF * Iop ** 2 * 1e-9\n", + "# Maximum tension... (empirical formula from Lorenzo... find a generic equation)\n", + "V_MAX = (7 * R0 - 3) / 6 * 1.1e3\n", + "# Discharge characteristic times\n", + "Tau_discharge1 = L * Iop / V_MAX\n", + "Tau_discharge2 = B0 * I_TF * n_TF * (R0 / A) ** 2 / (R_VV * S_VV)\n", + "# Discharge characteristic time to be considered in the following\n", + "Tau_discharge = max([Tau_discharge1, Tau_discharge2])\n", + "tf = Tau_discharge\n", + "bluemira_print(f\"Maximum TF discharge time: {tf}\")" + ], + "id": "def20d4679fa5b1a", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Maximum TF discharge time: 22.798740257170234 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Current and magnetic field behaviour during discharge\n", + "id": "4efb3a18c4418ba1" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.785935Z", + "start_time": "2024-10-16T09:49:09.560759Z" + } + }, + "cell_type": "code", + "source": [ + "I = delayed_exp_func(Iop, Tau_discharge, t_delay)\n", + "B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay)\n", + "\n", + "# Create a time array from 0 to 3*Tau_discharge\n", + "t = np.linspace(0, 3 * Tau_discharge, 500)\n", + "I_data = np.array([I(t_i) for t_i in t])\n", + "B_data = np.array([B(t_i) for t_i in t])\n", + "\n", + "# Create a figure and axis\n", + "fig, ax1 = plt.subplots()\n", + "\n", + "# Plot I on the left y-axis\n", + "ax1.plot(t, I_data, 'b-o', label='Current (I)', markevery=30) \n", + "ax1.set_xlabel('Time [s]')\n", + "ax1.set_ylabel('Current (I) [A]', color='b')\n", + "ax1.tick_params('y', colors='b')\n", + "\n", + "# Create a twin y-axis for the magnetic field B\n", + "ax2 = ax1.twinx()\n", + "ax2.plot(t, B_data, 'r:s', label='Magnetic Field (B)', markevery=30)\n", + "ax2.set_ylabel('Magnetic Field (B) [T]', color='r')\n", + "ax2.tick_params('y', colors='r')\n", + "\n", + "# Add grid and title\n", + "ax1.grid(True)\n", + "plt.title('Current (I) and Magnetic Field (B) vs Time')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ], + "id": "af2bd8eb6ddb0b38", + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Define materials (at the beginning the conductor is defined with a dummy number of stabilizer strands)", + "id": "bf707d2cee6aa9a9" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:09.923961Z", + "start_time": "2024-10-16T09:49:09.797051Z" + } + }, + "cell_type": "code", + "source": [ + "ss316 = AISI_316LN()\n", + "sc_strand = WireNb3Sn(d_strand=1e-3)\n", + "copper300 = Copper300()\n", + "insulator = DummyInsulator()\n", + "stab_strand = Strand([copper300], [1], d_strand=1.0e-3)\n", + "\n", + "# plot the critical current in a range of B between [10,16]\n", + "Bt_arr = np.linspace(10, 16, 100)\n", + "sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin)\n" + ], + "id": "fdb040f8a7113646", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "#### Plot number of conductor vs Iop", + "id": "9dec0e6bd1443511" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:10.206762Z", + "start_time": "2024-10-16T09:49:09.960678Z" + } + }, + "cell_type": "code", + "source": [ + "# Define the range of operating current (Iop) values\n", + "Iop_range = np.linspace(30, 100, 100) * 1e3 # 5 equally spaced values between 30 and 100 A\n", + "Ic_sc_arr = sc_strand.Ic(B=Bt_arr, T=T_sc, T_margin=T_margin)\n", + "\n", + "# Create a colormap to assign colors to different Iop values\n", + "import matplotlib.cm as cm # Import colormap\n", + "\n", + "colors = cm.viridis(np.linspace(0, 1, len(Iop_range))) # Use the 'viridis' colormap\n", + "\n", + "# Create a figure and axis\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Plot the number of superconducting strands as a function of B for different Iop values\n", + "for Iop_ref, color in zip(Iop_range, colors):\n", + " n_sc_strand = Iop_ref / Ic_sc_arr # Calculate number of strands\n", + " ax.plot(Bt_arr, n_sc_strand, color=color, label=f'Iop = {Iop} A')\n", + " # Add plot title, axis labels, and grid\n", + "ax.set_title('Number of strands as function of B and Iop') # Title\n", + "ax.set_xlabel('B [T]') # X-axis label\n", + "ax.set_ylabel('Nc strands') # Y-axis label\n", + "ax.grid(True)\n", + "\n", + "# Create a ScalarMappable to map colors to the colorbar\n", + "sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=Iop_range.min(), vmax=Iop_range.max()))\n", + "sm.set_array([]) # Dummy array for the ScalarMappable\n", + "\n", + "# Add the colorbar to the figure\n", + "cbar = fig.colorbar(sm, ax=ax)\n", + "cbar.set_label('Iop [A]') # Label the colorbar\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ], + "id": "151dbed8d8d5586a", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "**Calculate number of superconducting strands considering the strand critical current at B_TF_i and T_sc + T_margin**", + "id": "2eb939b1d3c9a1f5" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:10.287787Z", + "start_time": "2024-10-16T09:49:10.224405Z" + } + }, + "cell_type": "code", + "source": [ + "Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin)\n", + "n_sc_strand = int(np.ceil(Iop / Ic_sc))\n", + "\n", + "###########################################################\n", + "dx = 0.05 # cable length... just a dummy value\n", + "B_ref = 15 #[T] Reference B field value (limit for LTS)\n", + "\n", + "if B_TF_i < B_ref:\n", + " cable = DummyRectangularCableLTS(\n", + " dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7\n", + " )\n", + "else:\n", + " cable = DummyRectangularCableHTS(\n", + " dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7\n", + " )\n", + "cable.plot(0, 0, show=True)\n", + "bluemira_print(f\"cable area: {cable.area}\")" + ], + "id": "b6f5325528f9737f", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| cable area: 0.0005874870798078997 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + } + ], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "", + "id": "b2cee1da26df8bfe" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "***Change cable aspect ratio***", + "id": "1c58312b42bad5bd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:10.421743Z", + "start_time": "2024-10-16T09:49:10.326368Z" + } + }, + "cell_type": "code", + "source": [ + "cable.set_aspect_ratio(2) # This adjusts the cable dimensions while maintaining the total cross-sectional area.\n", + "cable.plot(0, 0, show=True)\n", + "bluemira_print(f\"cable area: {cable.area}\")" + ], + "id": "739bf1efd26b1cc7", + "outputs": [ + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| cable area: 0.0005874870798078997 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + } + ], + "execution_count": 11 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:10.530934Z", + "start_time": "2024-10-16T09:49:10.528368Z" + } + }, + "cell_type": "code", + "source": [ + "###########################################################\n", + "# Create a conductor with the specified cable\n", + "conductor = SymmetricConductor(\n", + " cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3\n", + ")" + ], + "id": "6c93975bf7e99f07", + "outputs": [], + "execution_count": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:10.936861Z", + "start_time": "2024-10-16T09:49:10.603105Z" + } + }, + "cell_type": "code", + "source": [ + "# optimize the number of stabilizer strands using the hot spot criteria.\n", + "# Note: optimize_n_stab_ths changes adjust cable.dy while maintaining cable.dx. It could be possible to add a parameter\n", + "# maintain constant the aspect ratio if necessary.\n", + "bluemira_print(f\"before optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", + "T_for_hts = T0\n", + "result = cable.optimize_n_stab_ths(\n", + " t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show\n", + ")\n", + "bluemira_print(f\"after optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", + "\n", + "cable.set_aspect_ratio(2)\n", + "bluemira_print(f\"Adjust aspect ratio: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")" + ], + "id": "21515b4b0918bd75", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| before optimization: conductor dx_cable = 0.03427789607919073, aspect |\n", + "| ratio = 2.0 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Optimal n_stab: 651 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Final temperature with optimal n_stab: 250.1764990506607 Kelvin |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| after optimization: conductor dx_cable = 0.03427789607919073, aspect |\n", + "| ratio = 0.8772778305553156 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Adjust aspect ratio: conductor dx_cable = 0.05175598500172694, aspect |\n", + "| ratio = 1.9999999999999998 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + } + ], + "execution_count": 13 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:11.406604Z", + "start_time": "2024-10-16T09:49:11.012129Z" + } + }, + "cell_type": "code", + "source": [ + "# case parameters\n", + "layout = \"auto\" #\"layer\" or \"pancake\"\n", + "wp_reduction_factor = 0.9\n", + "min_gap_x = 0.05\n", + "n_layers_reduction = 4\n", + "\n", + "\n", + "# creation of the case\n", + "wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case\n", + "case = CaseTF(\n", + " Ri=Ri,\n", + " dy_ps=dr_plasma_side,\n", + " dy_vault=0.6,\n", + " theta_TF=360 / n_TF,\n", + " mat_case=ss316,\n", + " WPs=[wp1],\n", + ")\n", + "\n", + "# arrangement of conductors into the winding pack and case\n", + "case.rearrange_conductors_in_wp(n_conductors=n_cond, cond=conductor, R_wp_i=case.R_wp_i, wp_reduction_factor=wp_reduction_factor, min_gap_x=min_gap_x,\n", + " n_layers_reduction=n_layers_reduction, layout=layout)\n", + "\n", + "case.plot(show=True, homogenized=False)" + ], + "id": "b0c52e83717e11c7", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 11.0/91 have been added to complete the last layer. |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Optimize", + "id": "6a8bfbbae888a422" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T09:49:12.042018Z", + "start_time": "2024-10-16T09:49:11.421514Z" + } + }, + "cell_type": "code", + "source": [ + "# Optimization parameters\n", + "bounds = [1e-5, 0.2]\n", + "max_niter = 10\n", + "err = 1e-3\n", + "\n", + "case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y,bounds, layout, wp_reduction_factor, min_gap_x, n_layers_reduction, max_niter, err)\n", + "\n", + "if show:\n", + " scalex = np.array([2, 1])\n", + " scaley = np.array([1, 1.2])\n", + "\n", + " ax = case.plot(homogenized=homogenized)\n", + " ax.set_aspect(\"equal\")\n", + "\n", + " # Fix the x and y limits\n", + " ax.set_xlim(-scalex[0] * case.dx_i, scalex[1] * case.dx_i)\n", + " ax.set_ylim(scaley[0] * 0, scaley[1] * case.Ri)\n", + "\n", + " deltax = [-case.dx_i / 2, case.dx_i / 2]\n", + "\n", + " ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Ri, case.Ri], \"k:\")\n", + "\n", + " for i in range(len(case.WPs)):\n", + " ax.plot(\n", + " [-scalex[0] * case.dx_i, -case.dx_i / 2],\n", + " [case.R_wp_i[i], case.R_wp_i[i]],\n", + " \"k:\",\n", + " )\n", + "\n", + " ax.plot(\n", + " [-scalex[0] * case.dx_i, -case.dx_i / 2],\n", + " [case.R_wp_k[-1], case.R_wp_k[-1]],\n", + " \"k:\",\n", + " )\n", + " ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Rk, case.Rk], \"k:\")\n", + "\n", + " ax.set_title(\"Equatorial cross section of the TF WP\")\n", + " ax.set_xlabel(\"Toroidal direction [m]\")\n", + " ax.set_ylabel(\"Radial direction [m]\")\n", + "\n", + " plt.show()" + ], + "id": "877e890c0426d693", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 9/75 have been added to complete the last layer. |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 11/105 have been added to complete the last layer. |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Internal optimazion - iteration 1\n", + "before optimization: conductor jacket area = 0.0019526795500518083\n", + "Optimal dx_jacket: 0.00614102271010192\n", + "Averaged sigma in the x-direction: 511.53502071229445 MPa\n", + "279152.14189880114\n", + "after optimization: conductor jacket area = 0.0011043526775418486\n", + "before optimization: case dy_vault = 0.6\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 0.4344424421751392\n", + "err_dy_vault = 2.0008402940399646\n", + "tot_err = 2.435282736215104\n", + "Internal optimazion - iteration 2\n", + "before optimization: conductor jacket area = 0.0011043526775418486\n", + "Optimal dx_jacket: 0.006497507079772735\n", + "Averaged sigma in the x-direction: 513.8286085340883 MPa\n", + "313733.27858232206\n", + "after optimization: conductor jacket area = 0.001177725029914784\n", + "before optimization: case dy_vault = 0.19994399608392133\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 0.06643923980539727\n", + "err_dy_vault = 0.0\n", + "tot_err = 0.06643923980539727\n", + "Internal optimazion - iteration 3\n", + "before optimization: conductor jacket area = 0.001177725029914784\n", + "Optimal dx_jacket: 0.006497734919877527\n", + "Averaged sigma in the x-direction: 513.8370469915408 MPa\n", + "313763.85513845686\n", + "after optimization: conductor jacket area = 0.0011777722495311196\n", + "before optimization: case dy_vault = 0.19994399608392133\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 4.009392272059603e-05\n", + "err_dy_vault = 0.0\n", + "tot_err = 4.009392272059603e-05\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 15 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/magnets/example_tf_wp_optimization.py b/examples/magnets/example_tf_wp_optimization.py new file mode 100644 index 0000000000..2c391968da --- /dev/null +++ b/examples/magnets/example_tf_wp_optimization.py @@ -0,0 +1,328 @@ +# %% md +# # This is an example that shows the application of the WP module for the TF coils +# %% md +# ## Some import +# %% +import matplotlib.pyplot as plt +import numpy as np + +from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI +from bluemira.base.look_and_feel import bluemira_print +from bluemira.magnets.cable import ( + DummyRectangularCableHTS, + DummyRectangularCableLTS, +) +from bluemira.magnets.case_tf import CaseTF +from bluemira.magnets.conductor import SymmetricConductor +from bluemira.magnets.materials import AISI_316LN, Copper300, DummyInsulator +from bluemira.magnets.strand import Strand, WireNb3Sn +from bluemira.magnets.utils import ( + delayed_exp_func, +) +from bluemira.magnets.winding_pack import WindingPack + +# %% md +# ## Plot options +# %% +show = True +homogenized = False +# %% md +# +# ## Input (and derived) values +# *Note: these values shall be provided internally by bluemira code (as reactor settings or as information coming from the TF coils builder)

+# +# **Machine (generic)** +# %% +R0 = 8.6 # [m] major machine radius +B0 = 4.39 # [T] magnetic field @R0 +A = 2.8 # machine aspect ratio +n_TF = 16 # number of TF coils +ripple = 6e-3 # requirement on the maximum plasma ripple + +a = R0 / A # minor radius +# %% md +# **Inputs for the TF coils** +# %% +d = 1.9 # additional distance to calculate the max external radius of the inner TF leg +Iop = 90.0e3 # operational current in each conductor +dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP +T_sc = 4.2 # operational temperature of superconducting cable +T_margin = 1.5 # temperature margin +t_delay = 3 # [s] +t0 = 0 # [s] +hotspot_target_temperature = 250.0 # [K] + + +Ri = R0 - a - d # [m] max external radius of the internal TF leg +Re = (R0 + a) * (1 / ripple) ** ( + 1 / n_TF +) # [m] max internal radius of the external TF leg +I_TF = B0 * R0 / MU_0_2PI / n_TF # total current in each TF coil + +# magnetic field generated by the TF coils with a correction factor that takes into account the ripple +B_TF_r = lambda I_TF, n_TF, R: 1.08 * (MU_0_2PI * n_TF * I_TF / R) +B_TF_i = B_TF_r(I_TF, n_TF, Ri) # max magnetic field on the inner TF leg +pm = B_TF_i**2 / (2 * MU_0) # magnetic pressure on the inner TF leg + +# vertical tension acting on the equatorial section of inner TF leg +# i.e. half of the whole F_Z +t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF**2 + +T0 = T_sc + T_margin +n_cond = np.floor(I_TF / Iop) # minimum number of conductors +bluemira_print(f"Total number of conductor: {n_cond}") +# %% md +# ***Additional data*** +# %% +R_VV = Ri * 1.05 # Vacuum vessel radius +S_VV = 90e6 # Vacuum vessel steel limit + +# allowable stress values +safety_factor = 1.5 * 1.3 +S_Y = 1e9 / safety_factor # [Pa] steel allowable limit + +# %% md +# ## Calculation of the maximum discharge time for the TF coils +# %% +# inductance (here approximated... better estimation in bluemira) +L = MU_0 * R0 * (n_TF * n_cond) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1 +# Magnetic energy +E = 1 / 2 * L * n_TF * Iop**2 * 1e-9 +# Maximum tension... (empirical formula from Lorenzo... find a generic equation) +V_MAX = (7 * R0 - 3) / 6 * 1.1e3 +# Discharge characteristic times +Tau_discharge1 = L * Iop / V_MAX +Tau_discharge2 = B0 * I_TF * n_TF * (R0 / A) ** 2 / (R_VV * S_VV) +# Discharge characteristic time to be considered in the following +Tau_discharge = max([Tau_discharge1, Tau_discharge2]) +tf = Tau_discharge +bluemira_print(f"Maximum TF discharge time: {tf}") +# %% md +# ## Current and magnetic field behaviour during discharge +# +# %% +I = delayed_exp_func(Iop, Tau_discharge, t_delay) +B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay) + +# Create a time array from 0 to 3*Tau_discharge +t = np.linspace(0, 3 * Tau_discharge, 500) +I_data = np.array([I(t_i) for t_i in t]) +B_data = np.array([B(t_i) for t_i in t]) + +# Create a figure and axis +fig, ax1 = plt.subplots() + +# Plot I on the left y-axis +ax1.plot(t, I_data, "b-o", label="Current (I)", markevery=30) +ax1.set_xlabel("Time [s]") +ax1.set_ylabel("Current (I) [A]", color="b") +ax1.tick_params("y", colors="b") + +# Create a twin y-axis for the magnetic field B +ax2 = ax1.twinx() +ax2.plot(t, B_data, "r:s", label="Magnetic Field (B)", markevery=30) +ax2.set_ylabel("Magnetic Field (B) [T]", color="r") +ax2.tick_params("y", colors="r") + +# Add grid and title +ax1.grid(True) +plt.title("Current (I) and Magnetic Field (B) vs Time") + +# Show the plot +plt.show() +# %% md +# ### Define materials (at the beginning the conductor is defined with a dummy number of stabilizer strands) +# %% +ss316 = AISI_316LN() +sc_strand = WireNb3Sn(d_strand=1e-3) +copper300 = Copper300() +insulator = DummyInsulator() +stab_strand = Strand([copper300], [1], d_strand=1.0e-3) + +# plot the critical current in a range of B between [10,16] +Bt_arr = np.linspace(10, 16, 100) +sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin) + +# %% md +# #### Plot number of conductor vs Iop +# %% +# Define the range of operating current (Iop) values +Iop_range = ( + np.linspace(30, 100, 100) * 1e3 +) # 5 equally spaced values between 30 and 100 A +Ic_sc_arr = sc_strand.Ic(B=Bt_arr, T=T_sc, T_margin=T_margin) + +# Create a colormap to assign colors to different Iop values +from matplotlib import cm # Import colormap + +colors = cm.viridis(np.linspace(0, 1, len(Iop_range))) # Use the 'viridis' colormap + +# Create a figure and axis +fig, ax = plt.subplots() + +# Plot the number of superconducting strands as a function of B for different Iop values +for Iop_ref, color in zip(Iop_range, colors, strict=False): + n_sc_strand = Iop_ref / Ic_sc_arr # Calculate number of strands + ax.plot(Bt_arr, n_sc_strand, color=color, label=f"Iop = {Iop} A") + # Add plot title, axis labels, and grid +ax.set_title("Number of strands as function of B and Iop") # Title +ax.set_xlabel("B [T]") # X-axis label +ax.set_ylabel("Nc strands") # Y-axis label +ax.grid(True) + +# Create a ScalarMappable to map colors to the colorbar +sm = plt.cm.ScalarMappable( + cmap="viridis", norm=plt.Normalize(vmin=Iop_range.min(), vmax=Iop_range.max()) +) +sm.set_array([]) # Dummy array for the ScalarMappable + +# Add the colorbar to the figure +cbar = fig.colorbar(sm, ax=ax) +cbar.set_label("Iop [A]") # Label the colorbar + +# Show the plot +plt.show() + +# %% md +# **Calculate number of superconducting strands considering the strand critical current at B_TF_i and T_sc + T_margin** +# %% +Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) +n_sc_strand = int(np.ceil(Iop / Ic_sc)) + +########################################################### +dx = 0.05 # cable length... just a dummy value +B_ref = 15 # [T] Reference B field value (limit for LTS) + +if B_TF_i < B_ref: + cable = DummyRectangularCableLTS( + dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 + ) +else: + cable = DummyRectangularCableHTS( + dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 + ) +cable.plot(0, 0, show=True) +bluemira_print(f"cable area: {cable.area}") +# %% md +# +# %% md +# ***Change cable aspect ratio*** +# %% +cable.set_aspect_ratio( + 2 +) # This adjusts the cable dimensions while maintaining the total cross-sectional area. +cable.plot(0, 0, show=True) +bluemira_print(f"cable area: {cable.area}") +# %% +########################################################### +# Create a conductor with the specified cable +conductor = SymmetricConductor( + cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3 +) +# %% +# optimize the number of stabilizer strands using the hot spot criteria. +# Note: optimize_n_stab_ths changes adjust cable.dy while maintaining cable.dx. It could be possible to add a parameter +# maintain constant the aspect ratio if necessary. +bluemira_print( + f"before optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}" +) +T_for_hts = T0 +result = cable.optimize_n_stab_ths( + t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show +) +bluemira_print( + f"after optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}" +) + +cable.set_aspect_ratio(2) +bluemira_print( + f"Adjust aspect ratio: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}" +) +# %% +# case parameters +layout = "auto" # "layer" or "pancake" +wp_reduction_factor = 0.9 +min_gap_x = 0.05 +n_layers_reduction = 4 + + +# creation of the case +wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case +case = CaseTF( + Ri=Ri, + dy_ps=dr_plasma_side, + dy_vault=0.6, + theta_TF=360 / n_TF, + mat_case=ss316, + WPs=[wp1], +) + +# arrangement of conductors into the winding pack and case +case.rearrange_conductors_in_wp( + n_conductors=n_cond, + cond=conductor, + R_wp_i=case.R_wp_i, + wp_reduction_factor=wp_reduction_factor, + min_gap_x=min_gap_x, + n_layers_reduction=n_layers_reduction, + layout=layout, +) + +case.plot(show=True, homogenized=False) +# %% md +# ## Optimize +# %% +# Optimization parameters +bounds = [1e-5, 0.2] +max_niter = 10 +err = 1e-3 + +case.optimize_jacket_and_vault( + pm, + t_z, + T0, + B_TF_i, + S_Y, + bounds, + layout, + wp_reduction_factor, + min_gap_x, + n_layers_reduction, + max_niter, + err, +) + +if show: + scalex = np.array([2, 1]) + scaley = np.array([1, 1.2]) + + ax = case.plot(homogenized=homogenized) + ax.set_aspect("equal") + + # Fix the x and y limits + ax.set_xlim(-scalex[0] * case.dx_i, scalex[1] * case.dx_i) + ax.set_ylim(scaley[0] * 0, scaley[1] * case.Ri) + + deltax = [-case.dx_i / 2, case.dx_i / 2] + + ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Ri, case.Ri], "k:") + + for i in range(len(case.WPs)): + ax.plot( + [-scalex[0] * case.dx_i, -case.dx_i / 2], + [case.R_wp_i[i], case.R_wp_i[i]], + "k:", + ) + + ax.plot( + [-scalex[0] * case.dx_i, -case.dx_i / 2], + [case.R_wp_k[-1], case.R_wp_k[-1]], + "k:", + ) + ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Rk, case.Rk], "k:") + + ax.set_title("Equatorial cross section of the TF WP") + ax.set_xlabel("Toroidal direction [m]") + ax.set_ylabel("Radial direction [m]") + + plt.show() From 6717818f1bb6a56cb8b1e790062121c25f00764c Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Wed, 16 Oct 2024 15:39:29 +0200 Subject: [PATCH 14/17] improved wp optimization and example --- bluemira/magnets/case_tf.py | 7 +- .../magnets/example_tf_wp_optimization.ipynb | 325 ++++++++++++------ .../magnets/example_tf_wp_optimization.py | 228 ++++++------ examples/magnets/tf_example.py | 253 -------------- 4 files changed, 320 insertions(+), 493 deletions(-) delete mode 100644 examples/magnets/tf_example.py diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index fc7a6aff50..8d06d7eee1 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -349,7 +349,10 @@ def optimize_jacket_and_vault( n_layers_reduction: int = 4, max_niter: int = 10, eps: float = 1e-8, + n_conds: int = None, ): + if n_conds is None: + n_conds = self.n_conductors conductor = self.WPs[0].conductor tot_err = 100 * eps i = 0 @@ -376,7 +379,7 @@ def optimize_jacket_and_vault( ) self.rearrange_conductors_in_wp( - self.n_conductors, + n_conds, conductor, self.R_wp_i[0], wp_reduction_factor, @@ -543,7 +546,7 @@ def rearrange_conductors_in_wp( if remaining_conductors < 0: bluemira_warn( f"{abs(remaining_conductors)}/{n_layers_max * n_turns_max} have been added" - f" to complete the last layer." + f" to complete the last winding pack (nx={n_layers_max}, ny={n_turns_max})." ) R_wp_i -= n_turns_max * cond.dy # noqa: N806 diff --git a/examples/magnets/example_tf_wp_optimization.ipynb b/examples/magnets/example_tf_wp_optimization.ipynb index ca49488af6..132a21d36b 100644 --- a/examples/magnets/example_tf_wp_optimization.ipynb +++ b/examples/magnets/example_tf_wp_optimization.ipynb @@ -15,8 +15,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.064717Z", - "start_time": "2024-10-16T09:49:07.550948Z" + "end_time": "2024-10-16T13:38:19.043849Z", + "start_time": "2024-10-16T13:38:17.621479Z" } }, "cell_type": "code", @@ -50,12 +50,15 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.070855Z", - "start_time": "2024-10-16T09:49:09.068361Z" + "end_time": "2024-10-16T13:38:19.049963Z", + "start_time": "2024-10-16T13:38:19.047456Z" } }, "cell_type": "code", "source": [ + "# Enable interactive mode\n", + "# %matplotlib notebook\n", + "\n", "show = True\n", "homogenized = False" ], @@ -78,8 +81,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.345603Z", - "start_time": "2024-10-16T09:49:09.342989Z" + "end_time": "2024-10-16T13:38:19.323619Z", + "start_time": "2024-10-16T13:38:19.321327Z" } }, "cell_type": "code", @@ -105,22 +108,22 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.398854Z", - "start_time": "2024-10-16T09:49:09.393778Z" + "end_time": "2024-10-16T13:38:19.375915Z", + "start_time": "2024-10-16T13:38:19.371526Z" } }, "cell_type": "code", "source": [ "d = 1.9 # additional distance to calculate the max external radius of the inner TF leg\n", - "Iop = 90.0e3 # operational current in each conductor\n", + "Iop = 60.0e3 # operational current in each conductor\n", "dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP\n", "T_sc = 4.2 # operational temperature of superconducting cable\n", "T_margin = 1.5 # temperature margin\n", + "T0 = T_sc + T_margin # temperature considered for the superconducting cable\n", "t_delay = 3 #[s]\n", "t0 = 0 #[s]\n", "hotspot_target_temperature = 250.0 #[K]\n", "\n", - "\n", "Ri = R0 - a - d # [m] max external radius of the internal TF leg\n", "Re = (R0 + a) * (1 / ripple) ** (1 / n_TF) # [m] max internal radius of the external TF leg\n", "I_TF = B0 * R0 / MU_0_2PI / n_TF # total current in each TF coil\n", @@ -134,7 +137,6 @@ "# i.e. half of the whole F_Z\n", "t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2\n", "\n", - "T0 = T_sc + T_margin\n", "n_cond = np.floor(I_TF / Iop) # minimum number of conductors\n", "bluemira_print(f\"Total number of conductor: {n_cond}\")" ], @@ -145,7 +147,7 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Total number of conductor: 131.0 |\n", + "| Total number of conductor: 196.0 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } @@ -161,8 +163,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.450198Z", - "start_time": "2024-10-16T09:49:09.447253Z" + "end_time": "2024-10-16T13:38:19.429256Z", + "start_time": "2024-10-16T13:38:19.425691Z" } }, "cell_type": "code", @@ -187,8 +189,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.504967Z", - "start_time": "2024-10-16T09:49:09.499250Z" + "end_time": "2024-10-16T13:38:19.485718Z", + "start_time": "2024-10-16T13:38:19.479705Z" } }, "cell_type": "code", @@ -230,8 +232,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.785935Z", - "start_time": "2024-10-16T09:49:09.560759Z" + "end_time": "2024-10-16T13:38:19.806913Z", + "start_time": "2024-10-16T13:38:19.543626Z" } }, "cell_type": "code", @@ -273,7 +275,7 @@ "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data" @@ -290,8 +292,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:09.923961Z", - "start_time": "2024-10-16T09:49:09.797051Z" + "end_time": "2024-10-16T13:38:19.960778Z", + "start_time": "2024-10-16T13:38:19.822775Z" } }, "cell_type": "code", @@ -340,8 +342,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:10.206762Z", - "start_time": "2024-10-16T09:49:09.960678Z" + "end_time": "2024-10-16T13:38:20.248597Z", + "start_time": "2024-10-16T13:38:20.000628Z" } }, "cell_type": "code", @@ -403,8 +405,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:10.287787Z", - "start_time": "2024-10-16T09:49:10.224405Z" + "end_time": "2024-10-16T13:38:20.334096Z", + "start_time": "2024-10-16T13:38:20.268564Z" } }, "cell_type": "code", @@ -418,11 +420,12 @@ "\n", "if B_TF_i < B_ref:\n", " cable = DummyRectangularCableLTS(\n", - " dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7\n", + " dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1, d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97\n", " )\n", "else:\n", " cable = DummyRectangularCableHTS(\n", - " dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7\n", + " dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1,\n", + " d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97\n", " )\n", "cable.plot(0, 0, show=True)\n", "bluemira_print(f\"cable area: {cable.area}\")" @@ -434,7 +437,7 @@ "text/plain": [ "
" ], - "image/png": 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}, "metadata": {}, "output_type": "display_data" @@ -444,7 +447,7 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| cable area: 0.0005874870798078997 |\n", + "| cable area: 0.0003597042619477489 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } @@ -466,8 +469,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:10.421743Z", - "start_time": "2024-10-16T09:49:10.326368Z" + "end_time": "2024-10-16T13:38:20.433954Z", + "start_time": "2024-10-16T13:38:20.351739Z" } }, "cell_type": "code", @@ -483,7 +486,7 @@ "text/plain": [ "
" ], - "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -493,7 +496,7 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| cable area: 0.0005874870798078997 |\n", + "| cable area: 0.0003597042619477489 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } @@ -503,27 +506,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:10.530934Z", - "start_time": "2024-10-16T09:49:10.528368Z" - } - }, - "cell_type": "code", - "source": [ - "###########################################################\n", - "# Create a conductor with the specified cable\n", - "conductor = SymmetricConductor(\n", - " cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3\n", - ")" - ], - "id": "6c93975bf7e99f07", - "outputs": [], - "execution_count": 12 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-16T09:49:10.936861Z", - "start_time": "2024-10-16T09:49:10.603105Z" + "end_time": "2024-10-16T13:38:20.770789Z", + "start_time": "2024-10-16T13:38:20.454313Z" } }, "cell_type": "code", @@ -531,15 +515,15 @@ "# optimize the number of stabilizer strands using the hot spot criteria.\n", "# Note: optimize_n_stab_ths changes adjust cable.dy while maintaining cable.dx. It could be possible to add a parameter\n", "# maintain constant the aspect ratio if necessary.\n", - "bluemira_print(f\"before optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", + "bluemira_print(f\"before optimization: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", "T_for_hts = T0\n", "result = cable.optimize_n_stab_ths(\n", " t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show\n", ")\n", - "bluemira_print(f\"after optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", + "bluemira_print(f\"after optimization: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", "\n", "cable.set_aspect_ratio(2)\n", - "bluemira_print(f\"Adjust aspect ratio: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")" + "bluemira_print(f\"Adjust aspect ratio: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")" ], "id": "21515b4b0918bd75", "outputs": [ @@ -548,14 +532,14 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| before optimization: conductor dx_cable = 0.03427789607919073, aspect |\n", - "| ratio = 2.0 |\n", + "| before optimization: dx_cable = 0.026821791959067497, aspect ratio = |\n", + "| 2.0 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Optimal n_stab: 651 |\n", + "| Optimal n_stab: 446 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Final temperature with optimal n_stab: 250.1764990506607 Kelvin |\n", + "| Final temperature with optimal n_stab: 249.37866723584887 Kelvin |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -564,7 +548,7 @@ "text/plain": [ "
" ], - "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -574,34 +558,52 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| after optimization: conductor dx_cable = 0.03427789607919073, aspect |\n", - "| ratio = 0.8772778305553156 |\n", + "| after optimization: dx_cable = 0.026821791959067497, aspect ratio = |\n", + "| 0.8227123912827806 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Adjust aspect ratio: conductor dx_cable = 0.05175598500172694, aspect |\n", - "| ratio = 1.9999999999999998 |\n", + "| Adjust aspect ratio: dx_cable = 0.04181949347006068, aspect ratio = |\n", + "| 2.0000000000000004 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], + "execution_count": 12 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T13:38:20.797577Z", + "start_time": "2024-10-16T13:38:20.794267Z" + } + }, + "cell_type": "code", + "source": [ + "###########################################################\n", + "# Create a conductor with the specified cable\n", + "conductor = SymmetricConductor(\n", + " cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3\n", + ")" + ], + "id": "fec23fcb3f35a042", + "outputs": [], "execution_count": 13 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:11.406604Z", - "start_time": "2024-10-16T09:49:11.012129Z" + "end_time": "2024-10-16T13:38:21.489291Z", + "start_time": "2024-10-16T13:38:20.846810Z" } }, "cell_type": "code", "source": [ "# case parameters\n", "layout = \"auto\" #\"layer\" or \"pancake\"\n", - "wp_reduction_factor = 0.9\n", - "min_gap_x = 0.05\n", + "wp_reduction_factor = 0.8\n", + "min_gap_x = 0.01\n", "n_layers_reduction = 4\n", "\n", - "\n", "# creation of the case\n", "wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case\n", "case = CaseTF(\n", @@ -617,7 +619,12 @@ "case.rearrange_conductors_in_wp(n_conductors=n_cond, cond=conductor, R_wp_i=case.R_wp_i, wp_reduction_factor=wp_reduction_factor, min_gap_x=min_gap_x,\n", " n_layers_reduction=n_layers_reduction, layout=layout)\n", "\n", - "case.plot(show=True, homogenized=False)" + "ax = case.plot(show=False, homogenized=False)\n", + "ax.set_title(\"Case design before optimization\")\n", + "plt.show()\n", + "\n", + "bluemira_print(f\"Previous number of conductors: {n_cond}\")\n", + "bluemira_print(f\"New number of conductors: {case.n_conductors}\")" ], "id": "b0c52e83717e11c7", "outputs": [ @@ -626,7 +633,8 @@ "output_type": "stream", "text": [ "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 11.0/91 have been added to complete the last layer. |\n", + "| WARNING: 2.0/198 have been added to complete the last winding pack |\n", + "| (nx=18, ny=11). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -635,20 +643,22 @@ "text/plain": [ "
" ], - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Previous number of conductors: 196.0 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| New number of conductors: 198 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] } ], "execution_count": 14 @@ -656,24 +666,25 @@ { "metadata": {}, "cell_type": "markdown", - "source": "## Optimize", + "source": "## Optimize cable jacket and case vault thickness", "id": "6a8bfbbae888a422" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T09:49:12.042018Z", - "start_time": "2024-10-16T09:49:11.421514Z" + "end_time": "2024-10-16T13:38:22.179029Z", + "start_time": "2024-10-16T13:38:21.537469Z" } }, "cell_type": "code", "source": [ "# Optimization parameters\n", - "bounds = [1e-5, 0.2]\n", + "bounds = [1e-5, 0.2] \n", "max_niter = 10\n", "err = 1e-3\n", "\n", - "case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y,bounds, layout, wp_reduction_factor, min_gap_x, n_layers_reduction, max_niter, err)\n", + "case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds, layout, wp_reduction_factor,\n", + " min_gap_x, n_layers_reduction, max_niter, err, n_cond)\n", "\n", "if show:\n", " scalex = np.array([2, 1])\n", @@ -717,10 +728,32 @@ "output_type": "stream", "text": [ "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 9/75 have been added to complete the last layer. |\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 11/105 have been added to complete the last layer. |\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", + "| (nx=20, ny=10). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -729,38 +762,82 @@ "output_type": "stream", "text": [ "Internal optimazion - iteration 1\n", - "before optimization: conductor jacket area = 0.0019526795500518083\n", - "Optimal dx_jacket: 0.00614102271010192\n", - "Averaged sigma in the x-direction: 511.53502071229445 MPa\n", - "279152.14189880114\n", - "after optimization: conductor jacket area = 0.0011043526775418486\n", + "before optimization: conductor jacket area = 0.00165458480410182\n", + "Optimal dx_jacket: 0.00564775836281768\n", + "Averaged sigma in the x-direction: 515.4370047801681 MPa\n", + "235961.49362376195\n", + "after optimization: conductor jacket area = 0.0008361478800221115\n", "before optimization: case dy_vault = 0.6\n", "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.4344424421751392\n", + "err_dy_jacket = 0.494647915326402\n", "err_dy_vault = 2.0008402940399646\n", - "tot_err = 2.435282736215104\n", + "tot_err = 2.4954882093663664\n", "Internal optimazion - iteration 2\n", - "before optimization: conductor jacket area = 0.0011043526775418486\n", - "Optimal dx_jacket: 0.006497507079772735\n", - "Averaged sigma in the x-direction: 513.8286085340883 MPa\n", - "313733.27858232206\n", - "after optimization: conductor jacket area = 0.001177725029914784\n", + "before optimization: conductor jacket area = 0.0008361478800221115\n", + "Optimal dx_jacket: 0.0056720954534214725\n", + "Averaged sigma in the x-direction: 514.8510609286991 MPa\n", + "237293.93259759306\n", + "after optimization: conductor jacket area = 0.0008403031436586584\n", "before optimization: case dy_vault = 0.19994399608392133\n", "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.06643923980539727\n", + "err_dy_jacket = 0.004969531988094018\n", "err_dy_vault = 0.0\n", - "tot_err = 0.06643923980539727\n", + "tot_err = 0.004969531988094018\n", "Internal optimazion - iteration 3\n", - "before optimization: conductor jacket area = 0.001177725029914784\n", - "Optimal dx_jacket: 0.006497734919877527\n", - "Averaged sigma in the x-direction: 513.8370469915408 MPa\n", - "313763.85513845686\n", - "after optimization: conductor jacket area = 0.0011777722495311196\n", + "before optimization: conductor jacket area = 0.0008403031436586584\n", + "Optimal dx_jacket: 0.005689655868704098\n", + "Averaged sigma in the x-direction: 514.423536407333 MPa\n", + "238249.7347572467\n", + "after optimization: conductor jacket area = 0.000843304314961606\n", + "before optimization: case dy_vault = 0.19994399608392133\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 0.0035715340655285274\n", + "err_dy_vault = 0.0\n", + "tot_err = 0.0035715340655285274\n", + "Internal optimazion - iteration 4\n", + "before optimization: conductor jacket area = 0.000843304314961606\n", + "Optimal dx_jacket: 0.0057023576573393625\n", + "Averaged sigma in the x-direction: 514.1130254096828 MPa\n", + "238939.13940692076\n", + "after optimization: conductor jacket area = 0.0008454766578540291\n", + "before optimization: case dy_vault = 0.19994399608392133\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 0.0025759893005196624\n", + "err_dy_vault = 0.0\n", + "tot_err = 0.0025759893005196624\n", + "Internal optimazion - iteration 5\n", + "before optimization: conductor jacket area = 0.0008454766578540291\n", + "Optimal dx_jacket: 0.0057115548570165365\n", + "Averaged sigma in the x-direction: 513.8879497171972 MPa\n", + "239437.66688431037\n", + "after optimization: conductor jacket area = 0.0008470504286795256\n", + "before optimization: case dy_vault = 0.19994399608392133\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 0.0018614006795775791\n", + "err_dy_vault = 0.0\n", + "tot_err = 0.0018614006795775791\n", + "Internal optimazion - iteration 6\n", + "before optimization: conductor jacket area = 0.0008470504286795256\n", + "Optimal dx_jacket: 0.00571821712495265\n", + "Averaged sigma in the x-direction: 513.724940553026 MPa\n", + "239798.57497994124\n", + "after optimization: conductor jacket area = 0.0008481908595044464\n", "before optimization: case dy_vault = 0.19994399608392133\n", "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 4.009392272059603e-05\n", + "err_dy_jacket = 0.0013463552892578744\n", "err_dy_vault = 0.0\n", - "tot_err = 4.009392272059603e-05\n" + "tot_err = 0.0013463552892578744\n", + "Internal optimazion - iteration 7\n", + "before optimization: conductor jacket area = 0.0008481908595044464\n", + "Optimal dx_jacket: 0.005723043651035138\n", + "Averaged sigma in the x-direction: 513.6069215920327 MPa\n", + "240059.97337964593\n", + "after optimization: conductor jacket area = 0.0008490172743066225\n", + "before optimization: case dy_vault = 0.19994399608392133\n", + "after optimization: case dy_vault = 0.19994399608392133\n", + "err_dy_jacket = 0.0009743264654596025\n", + "err_dy_vault = 0.0\n", + "tot_err = 0.0009743264654596025\n" ] }, { @@ -768,13 +845,39 @@ "text/plain": [ "
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" 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 15 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-16T13:38:22.198343Z", + "start_time": "2024-10-16T13:38:22.195265Z" + } + }, + "cell_type": "code", + "source": [ + "# new operational current\n", + "bluemira_print(f\"Operational current after optimization: {I_TF/case.n_conductors}\")" + ], + "id": "583be483879db7ad", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| Operational current after optimization: 58990.625 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + } + ], + "execution_count": 16 } ], "metadata": { diff --git a/examples/magnets/example_tf_wp_optimization.py b/examples/magnets/example_tf_wp_optimization.py index 2c391968da..cecdd00f0e 100644 --- a/examples/magnets/example_tf_wp_optimization.py +++ b/examples/magnets/example_tf_wp_optimization.py @@ -1,17 +1,15 @@ -# %% md +#%% md # # This is an example that shows the application of the WP module for the TF coils -# %% md +#%% md # ## Some import -# %% +#%% import matplotlib.pyplot as plt import numpy as np from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI from bluemira.base.look_and_feel import bluemira_print -from bluemira.magnets.cable import ( - DummyRectangularCableHTS, - DummyRectangularCableLTS, -) +from bluemira.magnets.cable import DummyRoundCableHTS, DummyRoundCableLTS, DummyRectangularCableLTS, \ + DummyRectangularCableHTS from bluemira.magnets.case_tf import CaseTF from bluemira.magnets.conductor import SymmetricConductor from bluemira.magnets.materials import AISI_316LN, Copper300, DummyInsulator @@ -20,60 +18,59 @@ delayed_exp_func, ) from bluemira.magnets.winding_pack import WindingPack - -# %% md +#%% md # ## Plot options -# %% +#%% +# Enable interactive mode +# %matplotlib notebook + show = True homogenized = False -# %% md -# +#%% md +# # ## Input (and derived) values # *Note: these values shall be provided internally by bluemira code (as reactor settings or as information coming from the TF coils builder)

-# +# # **Machine (generic)** -# %% +#%% R0 = 8.6 # [m] major machine radius B0 = 4.39 # [T] magnetic field @R0 -A = 2.8 # machine aspect ratio +A = 2.8 # machine aspect ratio n_TF = 16 # number of TF coils -ripple = 6e-3 # requirement on the maximum plasma ripple +ripple = 6e-3 # requirement on the maximum plasma ripple -a = R0 / A # minor radius -# %% md +a = R0 / A # minor radius +#%% md # **Inputs for the TF coils** -# %% -d = 1.9 # additional distance to calculate the max external radius of the inner TF leg -Iop = 90.0e3 # operational current in each conductor -dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP +#%% +d = 1.9 # additional distance to calculate the max external radius of the inner TF leg +Iop = 60.0e3 # operational current in each conductor +dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP T_sc = 4.2 # operational temperature of superconducting cable T_margin = 1.5 # temperature margin -t_delay = 3 # [s] -t0 = 0 # [s] -hotspot_target_temperature = 250.0 # [K] - +T0 = T_sc + T_margin # temperature considered for the superconducting cable +t_delay = 3 #[s] +t0 = 0 #[s] +hotspot_target_temperature = 250.0 #[K] Ri = R0 - a - d # [m] max external radius of the internal TF leg -Re = (R0 + a) * (1 / ripple) ** ( - 1 / n_TF -) # [m] max internal radius of the external TF leg +Re = (R0 + a) * (1 / ripple) ** (1 / n_TF) # [m] max internal radius of the external TF leg I_TF = B0 * R0 / MU_0_2PI / n_TF # total current in each TF coil # magnetic field generated by the TF coils with a correction factor that takes into account the ripple B_TF_r = lambda I_TF, n_TF, R: 1.08 * (MU_0_2PI * n_TF * I_TF / R) B_TF_i = B_TF_r(I_TF, n_TF, Ri) # max magnetic field on the inner TF leg -pm = B_TF_i**2 / (2 * MU_0) # magnetic pressure on the inner TF leg +pm = B_TF_i ** 2 / (2 * MU_0) # magnetic pressure on the inner TF leg # vertical tension acting on the equatorial section of inner TF leg # i.e. half of the whole F_Z -t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF**2 +t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2 -T0 = T_sc + T_margin -n_cond = np.floor(I_TF / Iop) # minimum number of conductors +n_cond = np.floor(I_TF / Iop) # minimum number of conductors bluemira_print(f"Total number of conductor: {n_cond}") -# %% md +#%% md # ***Additional data*** -# %% +#%% R_VV = Ri * 1.05 # Vacuum vessel radius S_VV = 90e6 # Vacuum vessel steel limit @@ -81,13 +78,13 @@ safety_factor = 1.5 * 1.3 S_Y = 1e9 / safety_factor # [Pa] steel allowable limit -# %% md +#%% md # ## Calculation of the maximum discharge time for the TF coils -# %% +#%% # inductance (here approximated... better estimation in bluemira) L = MU_0 * R0 * (n_TF * n_cond) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1 # Magnetic energy -E = 1 / 2 * L * n_TF * Iop**2 * 1e-9 +E = 1 / 2 * L * n_TF * Iop ** 2 * 1e-9 # Maximum tension... (empirical formula from Lorenzo... find a generic equation) V_MAX = (7 * R0 - 3) / 6 * 1.1e3 # Discharge characteristic times @@ -97,10 +94,10 @@ Tau_discharge = max([Tau_discharge1, Tau_discharge2]) tf = Tau_discharge bluemira_print(f"Maximum TF discharge time: {tf}") -# %% md +#%% md # ## Current and magnetic field behaviour during discharge -# -# %% +# +#%% I = delayed_exp_func(Iop, Tau_discharge, t_delay) B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay) @@ -113,26 +110,26 @@ fig, ax1 = plt.subplots() # Plot I on the left y-axis -ax1.plot(t, I_data, "b-o", label="Current (I)", markevery=30) -ax1.set_xlabel("Time [s]") -ax1.set_ylabel("Current (I) [A]", color="b") -ax1.tick_params("y", colors="b") +ax1.plot(t, I_data, 'b-o', label='Current (I)', markevery=30) +ax1.set_xlabel('Time [s]') +ax1.set_ylabel('Current (I) [A]', color='b') +ax1.tick_params('y', colors='b') # Create a twin y-axis for the magnetic field B ax2 = ax1.twinx() -ax2.plot(t, B_data, "r:s", label="Magnetic Field (B)", markevery=30) -ax2.set_ylabel("Magnetic Field (B) [T]", color="r") -ax2.tick_params("y", colors="r") +ax2.plot(t, B_data, 'r:s', label='Magnetic Field (B)', markevery=30) +ax2.set_ylabel('Magnetic Field (B) [T]', color='r') +ax2.tick_params('y', colors='r') # Add grid and title ax1.grid(True) -plt.title("Current (I) and Magnetic Field (B) vs Time") +plt.title('Current (I) and Magnetic Field (B) vs Time') # Show the plot plt.show() -# %% md +#%% md # ### Define materials (at the beginning the conductor is defined with a dummy number of stabilizer strands) -# %% +#%% ss316 = AISI_316LN() sc_strand = WireNb3Sn(d_strand=1e-3) copper300 = Copper300() @@ -143,17 +140,15 @@ Bt_arr = np.linspace(10, 16, 100) sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin) -# %% md +#%% md # #### Plot number of conductor vs Iop -# %% +#%% # Define the range of operating current (Iop) values -Iop_range = ( - np.linspace(30, 100, 100) * 1e3 -) # 5 equally spaced values between 30 and 100 A +Iop_range = np.linspace(30, 100, 100) * 1e3 # 5 equally spaced values between 30 and 100 A Ic_sc_arr = sc_strand.Ic(B=Bt_arr, T=T_sc, T_margin=T_margin) # Create a colormap to assign colors to different Iop values -from matplotlib import cm # Import colormap +import matplotlib.cm as cm # Import colormap colors = cm.viridis(np.linspace(0, 1, len(Iop_range))) # Use the 'viridis' colormap @@ -161,91 +156,81 @@ fig, ax = plt.subplots() # Plot the number of superconducting strands as a function of B for different Iop values -for Iop_ref, color in zip(Iop_range, colors, strict=False): +for Iop_ref, color in zip(Iop_range, colors): n_sc_strand = Iop_ref / Ic_sc_arr # Calculate number of strands - ax.plot(Bt_arr, n_sc_strand, color=color, label=f"Iop = {Iop} A") + ax.plot(Bt_arr, n_sc_strand, color=color, label=f'Iop = {Iop} A') # Add plot title, axis labels, and grid -ax.set_title("Number of strands as function of B and Iop") # Title -ax.set_xlabel("B [T]") # X-axis label -ax.set_ylabel("Nc strands") # Y-axis label +ax.set_title('Number of strands as function of B and Iop') # Title +ax.set_xlabel('B [T]') # X-axis label +ax.set_ylabel('Nc strands') # Y-axis label ax.grid(True) # Create a ScalarMappable to map colors to the colorbar -sm = plt.cm.ScalarMappable( - cmap="viridis", norm=plt.Normalize(vmin=Iop_range.min(), vmax=Iop_range.max()) -) +sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=Iop_range.min(), vmax=Iop_range.max())) sm.set_array([]) # Dummy array for the ScalarMappable # Add the colorbar to the figure cbar = fig.colorbar(sm, ax=ax) -cbar.set_label("Iop [A]") # Label the colorbar +cbar.set_label('Iop [A]') # Label the colorbar # Show the plot plt.show() -# %% md +#%% md # **Calculate number of superconducting strands considering the strand critical current at B_TF_i and T_sc + T_margin** -# %% +#%% Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) n_sc_strand = int(np.ceil(Iop / Ic_sc)) ########################################################### -dx = 0.05 # cable length... just a dummy value -B_ref = 15 # [T] Reference B field value (limit for LTS) +dx = 0.05 # cable length... just a dummy value +B_ref = 15 #[T] Reference B field value (limit for LTS) if B_TF_i < B_ref: cable = DummyRectangularCableLTS( - dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 + dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1, d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97 ) else: cable = DummyRectangularCableHTS( - dx, sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 + dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1, + d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97 ) cable.plot(0, 0, show=True) bluemira_print(f"cable area: {cable.area}") -# %% md -# -# %% md +#%% md +# +#%% md # ***Change cable aspect ratio*** -# %% -cable.set_aspect_ratio( - 2 -) # This adjusts the cable dimensions while maintaining the total cross-sectional area. +#%% +cable.set_aspect_ratio(2) # This adjusts the cable dimensions while maintaining the total cross-sectional area. cable.plot(0, 0, show=True) bluemira_print(f"cable area: {cable.area}") -# %% -########################################################### -# Create a conductor with the specified cable -conductor = SymmetricConductor( - cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3 -) -# %% +#%% # optimize the number of stabilizer strands using the hot spot criteria. # Note: optimize_n_stab_ths changes adjust cable.dy while maintaining cable.dx. It could be possible to add a parameter # maintain constant the aspect ratio if necessary. -bluemira_print( - f"before optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}" -) +bluemira_print(f"before optimization: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}") T_for_hts = T0 result = cable.optimize_n_stab_ths( t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show ) -bluemira_print( - f"after optimization: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}" -) +bluemira_print(f"after optimization: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}") cable.set_aspect_ratio(2) -bluemira_print( - f"Adjust aspect ratio: conductor dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}" +bluemira_print(f"Adjust aspect ratio: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}") +#%% +########################################################### +# Create a conductor with the specified cable +conductor = SymmetricConductor( + cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3 ) -# %% +#%% # case parameters -layout = "auto" # "layer" or "pancake" -wp_reduction_factor = 0.9 -min_gap_x = 0.05 +layout = "auto" #"layer" or "pancake" +wp_reduction_factor = 0.8 +min_gap_x = 0.01 n_layers_reduction = 4 - # creation of the case wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case case = CaseTF( @@ -258,39 +243,25 @@ ) # arrangement of conductors into the winding pack and case -case.rearrange_conductors_in_wp( - n_conductors=n_cond, - cond=conductor, - R_wp_i=case.R_wp_i, - wp_reduction_factor=wp_reduction_factor, - min_gap_x=min_gap_x, - n_layers_reduction=n_layers_reduction, - layout=layout, -) +case.rearrange_conductors_in_wp(n_conductors=n_cond, cond=conductor, R_wp_i=case.R_wp_i, wp_reduction_factor=wp_reduction_factor, min_gap_x=min_gap_x, + n_layers_reduction=n_layers_reduction, layout=layout) -case.plot(show=True, homogenized=False) -# %% md -# ## Optimize -# %% +ax = case.plot(show=False, homogenized=False) +ax.set_title("Case design before optimization") +plt.show() + +bluemira_print(f"Previous number of conductors: {n_cond}") +bluemira_print(f"New number of conductors: {case.n_conductors}") +#%% md +# ## Optimize cable jacket and case vault thickness +#%% # Optimization parameters -bounds = [1e-5, 0.2] +bounds = [1e-5, 0.2] max_niter = 10 err = 1e-3 -case.optimize_jacket_and_vault( - pm, - t_z, - T0, - B_TF_i, - S_Y, - bounds, - layout, - wp_reduction_factor, - min_gap_x, - n_layers_reduction, - max_niter, - err, -) +case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds, layout, wp_reduction_factor, + min_gap_x, n_layers_reduction, max_niter, err, n_cond) if show: scalex = np.array([2, 1]) @@ -326,3 +297,6 @@ ax.set_ylabel("Radial direction [m]") plt.show() +#%% +# new operational current +bluemira_print(f"Operational current after optimization: {I_TF/case.n_conductors}") \ No newline at end of file diff --git a/examples/magnets/tf_example.py b/examples/magnets/tf_example.py deleted file mode 100644 index 054a8befda..0000000000 --- a/examples/magnets/tf_example.py +++ /dev/null @@ -1,253 +0,0 @@ -# SPDX-FileCopyrightText: 2021-present M. Coleman, J. Cook, F. Franza -# SPDX-FileCopyrightText: 2021-present I.A. Maione, S. McIntosh -# SPDX-FileCopyrightText: 2021-present J. Morris, D. Short -# -# SPDX-License-Identifier: LGPL-2.1-or-later - -""" -Example for TF coils internal structure optimization. - -Note: in this example the conductor operational current is given as input. -""" - -import matplotlib.pyplot as plt -import numpy as np - -from bluemira.base.constants import MU_0, MU_0_2PI, MU_0_4PI -from bluemira.base.look_and_feel import bluemira_print -from bluemira.magnets.cable import DummyRoundCableHTS, DummyRoundCableLTS -from bluemira.magnets.case_tf import CaseTF -from bluemira.magnets.conductor import SquareConductor -from bluemira.magnets.materials import AISI_316LN, Copper300, DummyInsulator -from bluemira.magnets.strand import Strand, WireNb3Sn -from bluemira.magnets.utils import ( - delayed_exp_func, -) -from bluemira.magnets.winding_pack import WindingPack - -# plot options -show = True -homogenized = True # if True plot the WP as a homogenized block - -# input values -B0 = 4.39 # [T] magnetic field @R0 -R0 = 8.6 # [m] major machine radius -A = 2.8 -n_TF = 16 # number of TF coils -d = 1.9 -ripple = 6e-3 -a = R0 / A -Ri = R0 - a - d # [m] max external radius of the internal TF leg -Re = (R0 + a) * (1 / ripple) ** ( - 1 / n_TF -) # [m] max internal radius of the external TF part -dr_plasma_side = R0 * 2 / 3 * 1e-2 - -R_VV = Ri * 1.05 # Vacuum vessel radius -S_VV = 90e6 # Vacuum vessel steel limit - -I_TF = B0 * R0 / MU_0_2PI / n_TF # total current in each TF coil -B_TF_i = 1.08 * (MU_0_2PI * n_TF * I_TF / Ri) # max magnetic field on the inner TF leg -pm = B_TF_i ** 2 / (2 * MU_0) # magnetic pressure on the inner TF leg -# vertical tension acting on the equatorial section of inner TF leg -# i.e. half of the whole F_Z -t_z = 0.5 * np.log(Re / Ri) * MU_0_4PI * n_TF * I_TF ** 2 - -Iop = 90.0e3 # operational current in each conductor -# n_cond = int(np.ceil(I_TF / Iop)) # number of necessary conductors - -n_cond = np.floor(I_TF / Iop) -L = MU_0 * R0 * (n_TF * n_cond) ** 2 * (1 - np.sqrt(1 - (R0 - Ri) / R0)) / n_TF * 1.1 -E = 1 / 2 * L * n_TF * Iop ** 2 * 1e-9 -V_MAX = (7 * R0 - 3) / 6 * 1.1e3 -Tau_discharge1 = L * Iop / V_MAX -Tau_discharge2 = B0 * I_TF * n_TF * (R0 / A) ** 2 / (R_VV * S_VV) -Tau_discharge = max([Tau_discharge1, Tau_discharge2]) - -T_sc = 4.2 # operational temperature of superconducting cable -T_margin = 1.5 # temperature margin -T0 = T_sc + T_margin -t_delay = 3 -t0 = 0 -tf = Tau_discharge -hotspot_target_temperature = 250.0 - -# allowable stress values -safety_factor = 1.5 * 1.3 -S_Y = 1e9 / safety_factor # [Pa] steel allowable limit - -# Current and magnetic field behaviour -I = delayed_exp_func(Iop, Tau_discharge, t_delay) # noqa: E741 -B = delayed_exp_func(B_TF_i, Tau_discharge, t_delay) - -# define materials (the conductor is defined with a dummy number of stabilizer strands ) -ss316 = AISI_316LN() -sc_strand = WireNb3Sn(d_strand=1e-3) -copper300 = Copper300() -insulator = DummyInsulator() -stab_strand = Strand([copper300], [1], d_strand=1.0e-3) - -if True: - # plot the critical current in a range of B between [10,16] - Bt_arr = np.linspace(10,16,100) - sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin) - - ##### Plot number of conductor vs Iop##################### - # Define the range of operating current (Iop) values - Iop_range = np.linspace(30, 100, 100)*1e3 # 5 equally spaced values between 30 and 100 A - Ic_sc_arr = sc_strand.Ic(B=Bt_arr, T=T_sc, T_margin=T_margin) - - # Create a colormap to assign colors to different Iop values - import matplotlib.cm as cm # Import colormap - colors = cm.viridis(np.linspace(0, 1, len(Iop_range))) # Use the 'viridis' colormap - - # Create a figure and axis - fig, ax = plt.subplots() - - # Plot the number of superconducting strands as a function of B for different Iop values - for Iop_ref, color in zip(Iop_range, colors): - n_sc_strand = Iop_ref / Ic_sc_arr # Calculate number of strands - ax.plot(Bt_arr, n_sc_strand, color=color, label=f'Iop = {Iop} A') - # Add plot title, axis labels, and grid - ax.set_title('Number of strands as function of B and Iop') # Title - ax.set_xlabel('B [T]') # X-axis label - ax.set_ylabel('Nc strands') # Y-axis label - ax.grid(True) - - # Create a ScalarMappable to map colors to the colorbar - sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=Iop_range.min(), vmax=Iop_range.max())) - sm.set_array([]) # Dummy array for the ScalarMappable - - # Add the colorbar to the figure - cbar = fig.colorbar(sm, ax=ax) - cbar.set_label('Iop [A]') # Label the colorbar - - # Show the plot - plt.show() - -####################################################################### -# Calculate number of superconducting strands considering the strand critical current at B_TF_i and T_sc + T_margin -Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin) -n_sc_strand = int(np.ceil(Iop / Ic_sc)) - -########################################################### - -B_ref = 15 -if B_TF_i < B_ref: - cable = DummyRoundCableLTS( - sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 - ) -else: - cable = DummyRoundCableHTS( - sc_strand, stab_strand, n_sc_strand, 1, 1e-2, void_fraction=0.7 - ) -cable.plot(0,0, show=True) - - - -########################################################### -conductor = SquareConductor( - cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3 -) - -# optimize the number of stabilizer strands using the hot spot criteria -bluemira_print(f"before optimization: conductor dx_cable = {cable.dx}") -T_for_hts = T0 -result = cable.optimize_n_stab_ths( - t0, tf, T_for_hts, hotspot_target_temperature, B, I, bounds=[1, 10000], show=show -) -bluemira_print(f"after optimization: conductor dx_cable = {cable.dx}") - -# creation of case -wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case -case = CaseTF( - Ri=Ri, - dy_ps=dr_plasma_side, - dy_vault=0.6, - theta_TF=360 / n_TF, - mat_case=ss316, - WPs=[wp1], -) - -show = False - -# Some iterations to optimize case vault and cable jacket consistently. This should go -# in a kind of "optimization" or "convergence" loop. -max_niter = 10 -eps = 1e-3 -i = 0 -tot_err = 100 * eps -layout = "layer" -while i < max_niter and tot_err > eps: - i += 1 - print(f"Internal optimazion - iteration {i}") - print(f"before optimization: conductor dx_jacket = {conductor.dx_jacket}") - cond_dx_jacket0 = conductor.dx_jacket - t_z_cable_jacket = ( - t_z - * case.area_wps_jacket - / (case.area_jacket + case.area_wps_jacket) - / (np.sum([w.nx * w.ny for w in case.WPs])) - ) - conductor.optimize_jacket_conductor( - pm, t_z_cable_jacket, T0, B_TF_i, S_Y, bounds=[1e-5, 0.2] - ) - print(f"after optimization: conductor dx_jacket = {conductor.dx_jacket}") - delta_conductor_dx_jacket = abs(conductor.dx_jacket - cond_dx_jacket0) - err_dy_jacket = delta_conductor_dx_jacket / conductor.dy_jacket - - case.rearrange_conductors_in_wp( - n_cond, conductor, case.R_wp_i[0], case.dx_i * 0.8, 0.05, 4, layout = layout - ) - - case_dy_vault0 = case.dy_vault - print(f"before optimization: case dy_vault = {case.dy_vault}") - case.optimize_vault_radial_thickness( - pm, t_z, T=T0, B=B_TF_i, allowable_sigma=S_Y, bounds=[1e-2, 1] - ) - print(f"after optimization: case dy_vault = {case.dy_vault}") - delta_case_dy_vault = abs(case.dy_vault - case_dy_vault0) - err_dy_vault = delta_case_dy_vault / case.dy_vault - tot_err = err_dy_vault + err_dy_jacket - - print(f"err_dy_jacket = {err_dy_jacket}") - print(f"err_dy_vault = {err_dy_vault}") - print(f"tot_err = {tot_err}") - - -show = True -homogenized = False -if show: - scalex = np.array([2, 1]) - scaley = np.array([1, 1.2]) - - ax = case.plot(homogenized=homogenized) - ax.set_aspect("equal") - - # Fix the x and y limits - ax.set_xlim(-scalex[0] * case.dx_i, scalex[1] * case.dx_i) - ax.set_ylim(scaley[0] * 0, scaley[1] * case.Ri) - - deltax = [-case.dx_i / 2, case.dx_i / 2] - - ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Ri, case.Ri], "k:") - - for i in range(len(case.WPs)): - ax.plot( - [-scalex[0] * case.dx_i, -case.dx_i / 2], - [case.R_wp_i[i], case.R_wp_i[i]], - "k:", - ) - - ax.plot( - [-scalex[0] * case.dx_i, -case.dx_i / 2], - [case.R_wp_k[-1], case.R_wp_k[-1]], - "k:", - ) - ax.plot([-scalex[0] * case.dx_i, -case.dx_i / 2], [case.Rk, case.Rk], "k:") - - ax.set_title("Equatorial cross section of the TF WP") - ax.set_xlabel("Toroidal direction [m]") - ax.set_ylabel("Radial direction [m]") - - plt.show() From 9e7956f02de602d9381284174150531caf2ebc07 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 18 Oct 2024 14:42:16 +0200 Subject: [PATCH 15/17] modification on structural 0D model --- bluemira/magnets/case_tf.py | 28 +- bluemira/magnets/conductor.py | 78 +- bluemira/magnets/winding_pack.py | 8 +- .../magnets/example_tf_wp_optimization.ipynb | 1205 ++++++++++++++--- .../magnets/example_tf_wp_optimization.py | 26 +- 5 files changed, 1095 insertions(+), 250 deletions(-) diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index 8d06d7eee1..168b01bbd3 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -14,7 +14,7 @@ import numpy as np from scipy.optimize import minimize_scalar -from bluemira.base.look_and_feel import bluemira_warn +from bluemira.base.look_and_feel import bluemira_warn, bluemira_print from bluemira.magnets.conductor import Conductor from bluemira.magnets.materials import Material from bluemira.magnets.utils import parall_k, serie_k @@ -116,11 +116,16 @@ def area(self): return (self.dx_i + self.dx_k) * (self.Ri - self.Rk) / 2 @property - def area_jacket(self): - """Total jacket area (total case area - winding packs area)""" + def area_case_jacket(self): + """Total case jacket area (total case area - winding packs area)""" total_wp_area = np.sum([w.conductor.area * w.nx * w.ny for w in self.WPs]) return self.area - total_wp_area + @property + def area_wps(self): + """Tatal winding pack area""" + return np.sum([w.area for w in self.WPs]) + @property def area_wps_jacket(self): """Tatal jacket area in the winding packs""" @@ -199,10 +204,6 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): radial magnetic pressure fz: vertical tension acting on the case - Re: - external radius of the TF coil - I: - total current flowing in the case kwargs: additional arguments necessary to calculate the structural properties of the case @@ -224,12 +225,7 @@ def _tresca_stress(self, pm: float, fz: float, **kwargs): # As conservative approximation, the vertical force is considered to act only # on jackets and vault - total_case_area = (self.dx_i + self.dx_k) * (self.Ri - self.Rk) / 2 - total_wp_area = np.sum([w.conductor.area * w.nx * w.ny for w in self.WPs]) - total_wp_jacket_area = np.sum([ - w.conductor.area_jacket * w.nx * w.ny for w in self.WPs - ]) - sigma_z = fz / (total_case_area - total_wp_area + total_wp_jacket_area) + sigma_z = fz / (self.area_case_jacket + self.area_wps_jacket) return sigma_theta + sigma_z def optimize_vault_radial_thickness( @@ -270,6 +266,9 @@ def optimize_vault_radial_thickness( ValueError If the optimization process did not converge. """ + bluemira_print(f"pm: {pm}, fz: {fz}, T: {T}, B: {B}, allowable_sigma: {allowable_sigma}") + bluemira_print(f"bounds: {bounds}") + method = None if bounds is not None: method = "bounded" @@ -333,6 +332,7 @@ def _sigma_difference( """ self.dy_vault = dy_vault sigma = self._tresca_stress(pm, fz, T=T, B=B) + # bluemira_print(f"sigma: {sigma}, allowable_sigma: {allowable_sigma}, diff: {sigma - allowable_sigma}") return abs(sigma - allowable_sigma) def optimize_jacket_and_vault( @@ -366,7 +366,7 @@ def optimize_jacket_and_vault( t_z_cable_jacket = ( fz * self.area_wps_jacket - / (self.area_jacket + self.area_wps_jacket) + / (self.area_case_jacket + self.area_wps_jacket) / self.n_conductors ) conductor.optimize_jacket_conductor( diff --git a/bluemira/magnets/conductor.py b/bluemira/magnets/conductor.py index 73e7b13da5..bcc90bb3ce 100644 --- a/bluemira/magnets/conductor.py +++ b/bluemira/magnets/conductor.py @@ -180,54 +180,64 @@ def cp_v(self, **kwargs): # TODO: add a description of the 0D structural model, otherwise the following # function cannot be undestood - def Kx_topbot_ins(self, **kwargs): - return self.mat_ins.E(**kwargs) * self.dy / self.dx_ins + def _Kx_topbot_ins(self, **kwargs): + return self.mat_ins.E(**kwargs) * self.cable.dy / self.dx_ins - def Kx_lat_ins(self, **kwargs): - return self.mat_ins.E(**kwargs) * self.dy_ins / (self.dx - 2 * self.dx_ins) + def _Kx_lat_ins(self, **kwargs): + return self.mat_ins.E(**kwargs) * self.dy_ins / self.dx - def Kx_lat_jacket(self, **kwargs): + def _Kx_lat_jacket(self, **kwargs): return self.mat_jacket.E(**kwargs) * self.dy_jacket / (self.dx - 2 * self.dx_ins) - def Kx_topbot_jacket(self, **kwargs): + def _Kx_topbot_jacket(self, **kwargs): return self.mat_jacket.E(**kwargs) * self.cable.dy / self.dx_jacket - def Kx_cable(self, **kwargs): + def _Kx_cable(self, **kwargs): return self.cable.Kx(**kwargs) def Kx(self, **kwargs): - return serie_k([ - self.Kx_topbot_ins(**kwargs) / 2, - parall_k([ - 2 * self.Kx_lat_ins(**kwargs), - 2 * self.Kx_lat_jacket(**kwargs), - serie_k([self.Kx_cable(**kwargs), self.Kx_topbot_jacket(**kwargs) / 2]), + return parall_k([ + self._Kx_lat_ins(**kwargs), + self._Kx_lat_jacket(**kwargs), + serie_k([ + self._Kx_topbot_ins(**kwargs), + self._Kx_topbot_jacket(**kwargs), + self._Kx_cable(**kwargs), + self._Kx_topbot_jacket(**kwargs), + self._Kx_topbot_ins(**kwargs), ]), + self._Kx_lat_jacket(**kwargs), + self._Kx_lat_ins(**kwargs), ]) - def Ky_topbot_ins(self, **kwargs): - return self.mat_ins.E(**kwargs) * self.dx / self.dy_ins + def _Ky_topbot_ins(self, **kwargs): + return self.mat_ins.E(**kwargs) * self.cable.dx / self.dy_ins - def Ky_lat_ins(self, **kwargs): - return self.mat_ins.E(**kwargs) * self.dx_ins / (self.dy - 2 * self.dy_ins) + def _Ky_lat_ins(self, **kwargs): + return self.mat_ins.E(**kwargs) * self.dx_ins / self.dy - def Ky_lat_jacket(self, **kwargs): + def _Ky_lat_jacket(self, **kwargs): return self.mat_jacket.E(**kwargs) * self.dx_jacket / (self.dy - 2 * self.dy_ins) - def Ky_topbot_jacket(self, **kwargs): + def _Ky_topbot_jacket(self, **kwargs): return self.mat_jacket.E(**kwargs) * self.cable.dx / self.dy_jacket - def Ky_cable(self, **kwargs): + def _Ky_cable(self, **kwargs): return self.cable.Ky(**kwargs) def Ky(self, **kwargs): - return serie_k([ - self.Ky_topbot_ins(**kwargs) / 2, - parall_k([ - 2 * self.Ky_lat_ins(**kwargs), - 2 * self.Ky_lat_jacket(**kwargs), - serie_k([self.Ky_cable(**kwargs), self.Ky_topbot_jacket(**kwargs) / 2]), + return parall_k([ + self._Ky_lat_ins(**kwargs), + self._Ky_lat_jacket(**kwargs), + serie_k([ + self._Ky_topbot_ins(**kwargs), + self._Ky_topbot_jacket(**kwargs), + self._Ky_cable(**kwargs), + self._Ky_topbot_jacket(**kwargs), + self._Ky_topbot_ins(**kwargs), ]), + self._Ky_lat_jacket(**kwargs), + self._Ky_lat_ins(**kwargs), ]) def _tresca_sigma_jacket( @@ -267,23 +277,23 @@ def _tresca_sigma_jacket( saf_jacket = (self.cable.dx + 2 * self.dx_jacket) / (2 * self.dx_jacket) K = parall_k([ - 2 * self.Ky_lat_ins(T=T, B=B), - 2 * self.Ky_lat_jacket(T=T, B=B), - serie_k([self.Ky_cable(T=T, B=B), self.Ky_topbot_jacket(T=T, B=B) / 2]), + 2 * self._Ky_lat_ins(T=T, B=B), + 2 * self._Ky_lat_jacket(T=T, B=B), + serie_k([self._Ky_cable(T=T, B=B), self._Ky_topbot_jacket(T=T, B=B) / 2]), ]) - X_jacket = 2 * self.Ky_lat_jacket(T=T, B=B) / K + X_jacket = 2 * self._Ky_lat_jacket(T=T, B=B) / K else: saf_jacket = (self.cable.dy + 2 * self.dy_jacket) / (2 * self.dy_jacket) K = parall_k([ - 2 * self.Kx_lat_ins(T=T, B=B), - 2 * self.Kx_lat_jacket(T=T, B=B), - serie_k([self.Kx_cable(T=T, B=B), self.Kx_topbot_jacket(T=T, B=B) / 2]), + 2 * self._Kx_lat_ins(T=T, B=B), + 2 * self._Kx_lat_jacket(T=T, B=B), + serie_k([self._Kx_cable(T=T, B=B), self._Kx_topbot_jacket(T=T, B=B) / 2]), ]) - X_jacket = 2 * self.Kx_lat_jacket(T=T, B=B) / K + X_jacket = 2 * self._Kx_lat_jacket(T=T, B=B) / K tresca_stress = pressure * X_jacket * saf_jacket + f_z / self.area_jacket diff --git a/bluemira/magnets/winding_pack.py b/bluemira/magnets/winding_pack.py index 7a77cd300f..c53e90f3cb 100644 --- a/bluemira/magnets/winding_pack.py +++ b/bluemira/magnets/winding_pack.py @@ -37,6 +37,10 @@ def dy(self): """Total height of the winding pack along the y-axis.""" return self.conductor.dy * self.ny + @property + def area(self): + return self.dx * self.dy + @property def n_conductors(self): """Total number of conductors in the winding pack.""" @@ -60,7 +64,7 @@ def Kx(self, **kwargs) -> float: ------- The total equivalent stiffness along the x-axis. """ - return parall_k([serie_k([self.conductor.Kx(**kwargs)] * self.nx)] * self.ny) + return self.conductor.Kx(**kwargs) * self.ny / self.nx def Ky(self, **kwargs) -> float: """ @@ -75,7 +79,7 @@ def Ky(self, **kwargs) -> float: ------- The total equivalent stiffness along the y-axis. """ - return serie_k([parall_k([self.conductor.Ky(**kwargs)] * self.ny)] * self.nx) + return self.conductor.Ky(**kwargs) * self.nx / self.ny def plot(self, xc: float = 0, yc: float = 0, show: bool = False, ax=None, homogenized: bool = True): """ diff --git a/examples/magnets/example_tf_wp_optimization.ipynb b/examples/magnets/example_tf_wp_optimization.ipynb index 132a21d36b..f6f95a92c3 100644 --- a/examples/magnets/example_tf_wp_optimization.ipynb +++ b/examples/magnets/example_tf_wp_optimization.ipynb @@ -15,8 +15,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.043849Z", - "start_time": "2024-10-16T13:38:17.621479Z" + "end_time": "2024-10-18T12:33:48.213955Z", + "start_time": "2024-10-18T12:33:48.210187Z" } }, "cell_type": "code", @@ -39,7 +39,7 @@ ], "id": "54d702a3b1f86263", "outputs": [], - "execution_count": 1 + "execution_count": 16 }, { "metadata": {}, @@ -50,8 +50,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.049963Z", - "start_time": "2024-10-16T13:38:19.047456Z" + "end_time": "2024-10-18T12:33:48.260117Z", + "start_time": "2024-10-18T12:33:48.257042Z" } }, "cell_type": "code", @@ -64,7 +64,7 @@ ], "id": "78e8b10c23faa689", "outputs": [], - "execution_count": 2 + "execution_count": 17 }, { "metadata": {}, @@ -81,8 +81,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.323619Z", - "start_time": "2024-10-16T13:38:19.321327Z" + "end_time": "2024-10-18T12:33:48.306526Z", + "start_time": "2024-10-18T12:33:48.303271Z" } }, "cell_type": "code", @@ -97,7 +97,7 @@ ], "id": "60966584009a130", "outputs": [], - "execution_count": 3 + "execution_count": 18 }, { "metadata": {}, @@ -108,14 +108,14 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.375915Z", - "start_time": "2024-10-16T13:38:19.371526Z" + "end_time": "2024-10-18T12:33:48.356929Z", + "start_time": "2024-10-18T12:33:48.349760Z" } }, "cell_type": "code", "source": [ - "d = 1.9 # additional distance to calculate the max external radius of the inner TF leg\n", - "Iop = 60.0e3 # operational current in each conductor\n", + "d = 1.82 # additional distance to calculate the max external radius of the inner TF leg\n", + "Iop = 70.0e3 # operational current in each conductor\n", "dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP\n", "T_sc = 4.2 # operational temperature of superconducting cable\n", "T_margin = 1.5 # temperature margin\n", @@ -140,31 +140,31 @@ "n_cond = np.floor(I_TF / Iop) # minimum number of conductors\n", "bluemira_print(f\"Total number of conductor: {n_cond}\")" ], - "id": "f505e9d0370d72c3", + "id": "750ff0284348d66c", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Total number of conductor: 196.0 |\n", + "| Total number of conductor: 168.0 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 4 + "execution_count": 19 }, { "metadata": {}, "cell_type": "markdown", "source": "***Additional data***", - "id": "9cd6a2fe5da403c8" + "id": "a606090382de38f8" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.429256Z", - "start_time": "2024-10-16T13:38:19.425691Z" + "end_time": "2024-10-18T12:33:48.415506Z", + "start_time": "2024-10-18T12:33:48.412167Z" } }, "cell_type": "code", @@ -176,21 +176,21 @@ "safety_factor = 1.5 * 1.3\n", "S_Y = 1e9 / safety_factor # [Pa] steel allowable limit\n" ], - "id": "ab9069aa29c550df", + "id": "791a5e51845b952d", "outputs": [], - "execution_count": 5 + "execution_count": 20 }, { "metadata": {}, "cell_type": "markdown", "source": "## Calculation of the maximum discharge time for the TF coils", - "id": "af95628875cc373" + "id": "53f891d1919d697d" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.485718Z", - "start_time": "2024-10-16T13:38:19.479705Z" + "end_time": "2024-10-18T12:33:48.465435Z", + "start_time": "2024-10-18T12:33:48.460179Z" } }, "cell_type": "code", @@ -209,31 +209,31 @@ "tf = Tau_discharge\n", "bluemira_print(f\"Maximum TF discharge time: {tf}\")" ], - "id": "def20d4679fa5b1a", + "id": "d725927fd13d8fd6", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Maximum TF discharge time: 22.798740257170234 |\n", + "| Maximum TF discharge time: 22.30693384176132 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 6 + "execution_count": 21 }, { "metadata": {}, "cell_type": "markdown", "source": "## Current and magnetic field behaviour during discharge\n", - "id": "4efb3a18c4418ba1" + "id": "875d8f0847274e1f" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.806913Z", - "start_time": "2024-10-16T13:38:19.543626Z" + "end_time": "2024-10-18T12:33:48.708875Z", + "start_time": "2024-10-18T12:33:48.515887Z" } }, "cell_type": "code", @@ -268,32 +268,32 @@ "# Show the plot\n", "plt.show()" ], - "id": "af2bd8eb6ddb0b38", + "id": "984b72f4ccaadb06", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 7 + "execution_count": 22 }, { "metadata": {}, "cell_type": "markdown", "source": "### Define materials (at the beginning the conductor is defined with a dummy number of stabilizer strands)", - "id": "bf707d2cee6aa9a9" + "id": "485d63040c082608" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:19.960778Z", - "start_time": "2024-10-16T13:38:19.822775Z" + "end_time": "2024-10-18T12:33:48.841066Z", + "start_time": "2024-10-18T12:33:48.723451Z" } }, "cell_type": "code", @@ -308,7 +308,7 @@ "Bt_arr = np.linspace(10, 16, 100)\n", "sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin)\n" ], - "id": "fdb040f8a7113646", + "id": "99e72df13124370d", "outputs": [ { "data": { @@ -326,24 +326,24 @@ "" ] }, - "execution_count": 8, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 8 + "execution_count": 23 }, { "metadata": {}, "cell_type": "markdown", "source": "#### Plot number of conductor vs Iop", - "id": "9dec0e6bd1443511" + "id": "868f3e5894a341c7" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:20.248597Z", - "start_time": "2024-10-16T13:38:20.000628Z" + "end_time": "2024-10-18T12:33:49.110673Z", + "start_time": "2024-10-18T12:33:48.882743Z" } }, "cell_type": "code", @@ -381,7 +381,7 @@ "# Show the plot\n", "plt.show()\n" ], - "id": "151dbed8d8d5586a", + "id": "b81d77fe0194a673", "outputs": [ { "data": { @@ -394,19 +394,19 @@ "output_type": "display_data" } ], - "execution_count": 9 + "execution_count": 24 }, { "metadata": {}, "cell_type": "markdown", "source": "**Calculate number of superconducting strands considering the strand critical current at B_TF_i and T_sc + T_margin**", - "id": "2eb939b1d3c9a1f5" + "id": "bd0790ca6b1bf080" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:20.334096Z", - "start_time": "2024-10-16T13:38:20.268564Z" + "end_time": "2024-10-18T12:33:49.201611Z", + "start_time": "2024-10-18T12:33:49.127368Z" } }, "cell_type": "code", @@ -420,24 +420,25 @@ "\n", "if B_TF_i < B_ref:\n", " cable = DummyRectangularCableLTS(\n", - " dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1, d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97\n", + " dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=505,\n", + " d_cooling_channel=1e-2, void_fraction=0.7, cos_theta=0.97\n", " )\n", "else:\n", " cable = DummyRectangularCableHTS(\n", - " dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1,\n", - " d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97\n", + " dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=505,\n", + " d_cooling_channel=1e-2, void_fraction=0.7, cos_theta=0.97\n", " )\n", "cable.plot(0, 0, show=True)\n", "bluemira_print(f\"cable area: {cable.area}\")" ], - "id": "b6f5325528f9737f", + "id": "24b8d416381cb290", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -447,46 +448,47 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| cable area: 0.0003597042619477489 |\n", + "| cable area: 0.0010339726336685993 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 10 + "execution_count": 25 }, { "metadata": {}, "cell_type": "markdown", "source": "", - "id": "b2cee1da26df8bfe" + "id": "56fe8da8157d764d" }, { "metadata": {}, "cell_type": "markdown", "source": "***Change cable aspect ratio***", - "id": "1c58312b42bad5bd" + "id": "98f43735d63a31df" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:20.433954Z", - "start_time": "2024-10-16T13:38:20.351739Z" + "end_time": "2024-10-18T12:33:49.393519Z", + "start_time": "2024-10-18T12:33:49.294312Z" } }, "cell_type": "code", "source": [ - "cable.set_aspect_ratio(2) # This adjusts the cable dimensions while maintaining the total cross-sectional area.\n", + "aspect_ratio = 1\n", + "cable.set_aspect_ratio(aspect_ratio) # This adjusts the cable dimensions while maintaining the total cross-sectional area.\n", "cable.plot(0, 0, show=True)\n", "bluemira_print(f\"cable area: {cable.area}\")" ], - "id": "739bf1efd26b1cc7", + "id": "90fb56d6ed67d713", "outputs": [ { "data": { "text/plain": [ "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -496,18 +498,18 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| cable area: 0.0003597042619477489 |\n", + "| cable area: 0.0010339726336685993 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 11 + "execution_count": 26 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:20.770789Z", - "start_time": "2024-10-16T13:38:20.454313Z" + "end_time": "2024-10-18T12:33:49.756877Z", + "start_time": "2024-10-18T12:33:49.480568Z" } }, "cell_type": "code", @@ -522,24 +524,24 @@ ")\n", "bluemira_print(f\"after optimization: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")\n", "\n", - "cable.set_aspect_ratio(2)\n", + "cable.set_aspect_ratio(aspect_ratio)\n", "bluemira_print(f\"Adjust aspect ratio: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")" ], - "id": "21515b4b0918bd75", + "id": "1e0815dd39475200", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| before optimization: dx_cable = 0.026821791959067497, aspect ratio = |\n", - "| 2.0 |\n", + "| before optimization: dx_cable = 0.03215544485259999, aspect ratio = |\n", + "| 0.9999999999999998 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Optimal n_stab: 446 |\n", + "| Optimal n_stab: 514 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Final temperature with optimal n_stab: 249.37866723584887 Kelvin |\n", + "| Final temperature with optimal n_stab: 237.7118981259334 Kelvin |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -548,7 +550,7 @@ "text/plain": [ "
" ], - "image/png": 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" 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}, "metadata": {}, "output_type": "display_data" @@ -558,23 +560,23 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| after optimization: dx_cable = 0.026821791959067497, aspect ratio = |\n", - "| 0.8227123912827806 |\n", + "| after optimization: dx_cable = 0.03215544485259999, aspect ratio = |\n", + "| 0.990032118728541 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Adjust aspect ratio: dx_cable = 0.04181949347006068, aspect ratio = |\n", - "| 2.0000000000000004 |\n", + "| Adjust aspect ratio: dx_cable = 0.03231691381619191, aspect ratio = |\n", + "| 1.0000000000000002 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 12 + "execution_count": 27 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:20.797577Z", - "start_time": "2024-10-16T13:38:20.794267Z" + "end_time": "2024-10-18T12:33:49.806993Z", + "start_time": "2024-10-18T12:33:49.804415Z" } }, "cell_type": "code", @@ -585,24 +587,24 @@ " cable=cable, mat_jacket=ss316, mat_ins=insulator, dx_jacket=0.01, dx_ins=1e-3\n", ")" ], - "id": "fec23fcb3f35a042", + "id": "256e6bf3855c8dff", "outputs": [], - "execution_count": 13 + "execution_count": 28 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:21.489291Z", - "start_time": "2024-10-16T13:38:20.846810Z" + "end_time": "2024-10-18T12:33:50.447866Z", + "start_time": "2024-10-18T12:33:49.850664Z" } }, "cell_type": "code", "source": [ "# case parameters\n", "layout = \"auto\" #\"layer\" or \"pancake\"\n", - "wp_reduction_factor = 0.8\n", - "min_gap_x = 0.01\n", - "n_layers_reduction = 4\n", + "wp_reduction_factor = 0.7\n", + "min_gap_x = 2*dr_plasma_side\n", + "n_layers_reduction = 2\n", "\n", "# creation of the case\n", "wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case\n", @@ -626,15 +628,15 @@ "bluemira_print(f\"Previous number of conductors: {n_cond}\")\n", "bluemira_print(f\"New number of conductors: {case.n_conductors}\")" ], - "id": "b0c52e83717e11c7", + "id": "45b647fb60e42429", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 2.0/198 have been added to complete the last winding pack |\n", - "| (nx=18, ny=11). |\n", + "| WARNING: 1.0/17 have been added to complete the last winding pack |\n", + "| (nx=17, ny=1). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -643,7 +645,7 @@ "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" @@ -653,35 +655,35 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Previous number of conductors: 196.0 |\n", + "| Previous number of conductors: 168.0 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| New number of conductors: 198 |\n", + "| New number of conductors: 169 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 14 + "execution_count": 29 }, { "metadata": {}, "cell_type": "markdown", "source": "## Optimize cable jacket and case vault thickness", - "id": "6a8bfbbae888a422" + "id": "dfe3620b106d67ed" }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:22.179029Z", - "start_time": "2024-10-16T13:38:21.537469Z" + "end_time": "2024-10-18T12:33:51.216770Z", + "start_time": "2024-10-18T12:33:50.497271Z" } }, "cell_type": "code", "source": [ "# Optimization parameters\n", - "bounds = [1e-5, 0.2] \n", + "bounds = [1e-5, 2]\n", "max_niter = 10\n", - "err = 1e-3\n", + "err = 1e-5\n", "\n", "case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds, layout, wp_reduction_factor,\n", " min_gap_x, n_layers_reduction, max_niter, err, n_cond)\n", @@ -721,39 +723,737 @@ "\n", " plt.show()" ], - "id": "877e890c0426d693", + "id": "5cd70786e1c5e361", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 16.0/184 have been added to complete the last winding pack |\n", + "| (nx=23, ny=8). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 332047113.91974866, allowable_sigma: 512820512.8205128, diff: |\n", + "| -180773398.9007641 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 241604875.47285625, allowable_sigma: 512820512.8205128, diff: |\n", + "| -271215637.3476565 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 455612813.8214364, allowable_sigma: 512820512.8205128, diff: |\n", + "| -57207698.99907637 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 608751089.304492, allowable_sigma: 512820512.8205128, diff: |\n", + "| 95930576.48397923 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 462323385.903952, allowable_sigma: 512820512.8205128, diff: |\n", + "| -50497126.91656077 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 502619908.323683, allowable_sigma: 512820512.8205128, diff: |\n", + "| -10200604.496829748 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 537982296.6945287, allowable_sigma: 512820512.8205128, diff: |\n", + "| 25161783.874015927 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 506163528.14589435, allowable_sigma: 512820512.8205128, diff: |\n", + "| -6656984.674618423 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 514767768.23611915, allowable_sigma: 512820512.8205128, diff: |\n", + "| 1947255.4156063795 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 523366814.9164499, allowable_sigma: 512820512.8205128, diff: |\n", + "| 10546302.095937133 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 518424587.26568866, allowable_sigma: 512820512.8205128, diff: |\n", + "| 5604074.445175886 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512549117.6755499, allowable_sigma: 512820512.8205128, diff: |\n", + "| -271395.14496284723 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 510088828.6527809, allowable_sigma: 512820512.8205128, diff: |\n", + "| -2731684.167731881 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512639977.122945, allowable_sigma: 512820512.8205128, diff: |\n", + "| -180535.69756776094 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513197295.63484687, allowable_sigma: 512820512.8205128, diff: |\n", + "| 376782.8143340945 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512852693.840595, allowable_sigma: 512820512.8205128, diff: |\n", + "| 32181.020082235336 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513097289.0087082, allowable_sigma: 512820512.8205128, diff: |\n", + "| 276776.1881954074 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512946090.0622024, allowable_sigma: 512820512.8205128, diff: |\n", + "| 125577.2416896224 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512809168.6854433, allowable_sigma: 512820512.8205128, diff: |\n", + "| -11344.135069489479 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512768113.4574714, allowable_sigma: 512820512.8205128, diff: |\n", + "| -52399.36304140091 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 345616797.9811439, allowable_sigma: 512820512.8205128, diff: |\n", + "| -167203714.83936888 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 247900521.62276733, allowable_sigma: 512820512.8205128, diff: |\n", + "| -264919991.19774544 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 482853091.0125967, allowable_sigma: 512820512.8205128, diff: |\n", + "| -29967421.807916045 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 659370287.7291387, allowable_sigma: 512820512.8205128, diff: |\n", + "| 146549774.90862596 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 452874484.3230504, allowable_sigma: 512820512.8205128, diff: |\n", + "| -59946028.49746239 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 507340004.46259856, allowable_sigma: 512820512.8205128, diff: |\n", + "| -5480508.357914209 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 555453904.2854455, allowable_sigma: 512820512.8205128, diff: |\n", + "| 42633391.46493268 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512691422.74688816, allowable_sigma: 512820512.8205128, diff: |\n", + "| -129090.0736246109 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 528136137.25477016, allowable_sigma: 512820512.8205128, diff: |\n", + "| 15315624.434257388 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515013746.55549395, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2193233.7349811792 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512013954.3485621, allowable_sigma: 512820512.8205128, diff: |\n", + "| -806558.4719506502 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513141456.2201133, allowable_sigma: 512820512.8205128, diff: |\n", + "| 320943.3996005058 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512747351.99869025, allowable_sigma: 512820512.8205128, diff: |\n", + "| -73160.82182252407 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512857411.6498914, allowable_sigma: 512820512.8205128, diff: |\n", + "| 36898.82937860489 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512830940.88401806, allowable_sigma: 512820512.8205128, diff: |\n", + "| 10428.063505291939 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512804473.1708508, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16039.649661958218 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 349879111.39415807, allowable_sigma: 512820512.8205128, diff: |\n", + "| -162941401.4263547 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 249893848.5349875, allowable_sigma: 512820512.8205128, diff: |\n", + "| -262926664.28552526 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 491609709.1404632, allowable_sigma: 512820512.8205128, diff: |\n", + "| -21210803.6800496 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 676223007.6252224, allowable_sigma: 512820512.8205128, diff: |\n", + "| 163402494.80470967 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 454742051.48801386, allowable_sigma: 512820512.8205128, diff: |\n", + "| -58078461.33249891 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 547560870.8728577, allowable_sigma: 512820512.8205128, diff: |\n", + "| 34740358.05234492 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 506597853.4967215, allowable_sigma: 512820512.8205128, diff: |\n", + "| -6222659.3237912655 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513823797.3415109, allowable_sigma: 512820512.8205128, diff: |\n", + "| 1003284.5209981203 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 526151677.2605779, allowable_sigma: 512820512.8205128, diff: |\n", + "| 13331164.440065145 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 514111099.66574746, allowable_sigma: 512820512.8205128, diff: |\n", + "| 1290586.845234692 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 511746926.64839435, allowable_sigma: 512820512.8205128, diff: |\n", + "| -1073586.1721184254 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513028283.2969787, allowable_sigma: 512820512.8205128, diff: |\n", + "| 207770.47646594048 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512803969.43313956, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16543.387373209 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512399639.4397969, allowable_sigma: 512820512.8205128, diff: |\n", + "| -420873.380715847 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512771262.7761115, allowable_sigma: 512820512.8205128, diff: |\n", + "| -49250.04440128803 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512856945.5779121, allowable_sigma: 512820512.8205128, diff: |\n", + "| 36432.7573993206 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512830364.5420937, allowable_sigma: 512820512.8205128, diff: |\n", + "| 9851.721580922604 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 351210807.5630299, allowable_sigma: 512820512.8205128, diff: |\n", + "| -161609705.2574829 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250493238.99422365, allowable_sigma: 512820512.8205128, diff: |\n", + "| -262327273.82628912 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 494418454.1338878, allowable_sigma: 512820512.8205128, diff: |\n", + "| -18402058.686624944 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 681762681.8560188, allowable_sigma: 512820512.8205128, diff: |\n", + "| 168942169.035506 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 455543596.5522773, allowable_sigma: 512820512.8205128, diff: |\n", + "| -57276916.268235445 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 551099404.3194903, allowable_sigma: 512820512.8205128, diff: |\n", + "| 38278891.49897754 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505755870.51132715, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7064642.309185624 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 514993962.7063273, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2173449.8858145475 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 528148779.9507562, allowable_sigma: 512820512.8205128, diff: |\n", + "| 15328267.13024342 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 514007382.6471122, allowable_sigma: 512820512.8205128, diff: |\n", + "| 1186869.8265994191 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 511746826.63596225, allowable_sigma: 512820512.8205128, diff: |\n", + "| -1073686.1845505238 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509439824.8471668, allowable_sigma: 512820512.8205128, diff: |\n", + "| -3380687.973345995 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512789578.2397908, allowable_sigma: 512820512.8205128, diff: |\n", + "| -30934.580721974373 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512845282.5186647, allowable_sigma: 512820512.8205128, diff: |\n", + "| 24769.698151946068 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512886127.653309, allowable_sigma: 512820512.8205128, diff: |\n", + "| 65614.83279621601 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512818900.9344379, allowable_sigma: 512820512.8205128, diff: |\n", + "| -1611.8860749006271 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 351888555.6992467, allowable_sigma: 512820512.8205128, diff: |\n", + "| -160931957.12126607 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250797503.51447517, allowable_sigma: 512820512.8205128, diff: |\n", + "| -262023009.3060376 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 495852968.76336634, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16967544.05714643 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 684604663.0683082, allowable_sigma: 512820512.8205128, diff: |\n", + "| 171784150.24779546 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 455986865.5215896, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56833647.298923194 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 552909108.3432002, allowable_sigma: 512820512.8205128, diff: |\n", + "| 40088595.522687435 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505537741.34316295, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7282771.477349818 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515616931.99505866, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2796419.174545884 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 519470797.7135672, allowable_sigma: 512820512.8205128, diff: |\n", + "| 6650284.893054426 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512614536.6841078, allowable_sigma: 512820512.8205128, diff: |\n", + "| -205976.13640499115 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509885414.53158057, allowable_sigma: 512820512.8205128, diff: |\n", + "| -2935098.2889322042 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512770525.59479666, allowable_sigma: 512820512.8205128, diff: |\n", + "| -49987.225716114044 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513450641.74774045, allowable_sigma: 512820512.8205128, diff: |\n", + "| 630128.9272276759 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513030069.08701336, allowable_sigma: 512820512.8205128, diff: |\n", + "| 209556.26650059223 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512821100.0436568, allowable_sigma: 512820512.8205128, diff: |\n", + "| 587.2231440544128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512900896.65765613, allowable_sigma: 512820512.8205128, diff: |\n", + "| 80383.83714336157 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512847474.3338895, allowable_sigma: 512820512.8205128, diff: |\n", + "| 26961.5133767128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 352135118.80901194, allowable_sigma: 512820512.8205128, diff: |\n", + "| -160685394.01150084 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250908061.8303818, allowable_sigma: 512820512.8205128, diff: |\n", + "| -261912450.99013096 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 496375694.5478651, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16444818.272647679 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 685642416.0271809, allowable_sigma: 512820512.8205128, diff: |\n", + "| 172821903.20666814 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 456153683.5585109, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56666829.26200187 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 553568967.4033751, allowable_sigma: 512820512.8205128, diff: |\n", + "| 40748454.58286238 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505488317.69940114, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7332195.121111631 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515849322.6215456, allowable_sigma: 512820512.8205128, diff: |\n", + "| 3028809.801032841 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 518346380.2946321, allowable_sigma: 512820512.8205128, diff: |\n", + "| 5525867.474119306 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512456756.1878085, allowable_sigma: 512820512.8205128, diff: |\n", + "| -363756.63270425797 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509769808.00714135, allowable_sigma: 512820512.8205128, diff: |\n", + "| -3050704.81337142 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512795568.8269507, allowable_sigma: 512820512.8205128, diff: |\n", + "| -24943.993562042713 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513474032.52643734, allowable_sigma: 512820512.8205128, diff: |\n", + "| 653519.7059245706 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512889606.50582695, allowable_sigma: 512820512.8205128, diff: |\n", + "| 69093.68531417847 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512769203.21149117, allowable_sigma: 512820512.8205128, diff: |\n", + "| -51309.60902160406 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512823345.20053285, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2832.380020081997 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512849717.0384426, allowable_sigma: 512820512.8205128, diff: |\n", + "| 29204.217929840088 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 352102602.0704287, allowable_sigma: 512820512.8205128, diff: |\n", + "| -160717910.75008404 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250893485.4526702, allowable_sigma: 512820512.8205128, diff: |\n", + "| -261927027.36784258 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 496306731.32396007, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16513781.496552706 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 685505438.9756098, allowable_sigma: 512820512.8205128, diff: |\n", + "| 172684926.155097 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 456131517.8779267, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56688994.942586064 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 553481899.3829948, allowable_sigma: 512820512.8205128, diff: |\n", + "| 40661386.562482 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505493975.2698797, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7326537.550633073 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515818490.1082344, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2997977.287721634 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 518484138.54203933, allowable_sigma: 512820512.8205128, diff: |\n", + "| 5663625.721526563 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512475084.1125715, allowable_sigma: 512820512.8205128, diff: |\n", + "| -345428.7079412937 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509783205.0685272, allowable_sigma: 512820512.8205128, diff: |\n", + "| -3037307.75198555 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512791983.3643613, allowable_sigma: 512820512.8205128, diff: |\n", + "| -28529.456151485443 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513470263.3005805, allowable_sigma: 512820512.8205128, diff: |\n", + "| 649750.48006773 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512892692.19722164, allowable_sigma: 512820512.8205128, diff: |\n", + "| 72179.37670886517 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512765617.80462617, allowable_sigma: 512820512.8205128, diff: |\n", + "| -54895.015886604786 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512823116.4302219, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2603.6097091436386 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512849689.5183638, allowable_sigma: 512820512.8205128, diff: |\n", + "| 29176.697851002216 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 352093806.0225389, allowable_sigma: 512820512.8205128, diff: |\n", + "| -160726706.79797387 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250889542.20981035, allowable_sigma: 512820512.8205128, diff: |\n", + "| -261930970.61070243 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 496288077.5660194, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16532435.254493356 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 685468391.7258055, allowable_sigma: 512820512.8205128, diff: |\n", + "| 172647878.90529275 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 456125530.48249936, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56694982.33801341 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 553458349.1551106, allowable_sigma: 512820512.8205128, diff: |\n", + "| 40637836.334597826 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505495550.00762266, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7324962.812890112 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515810159.3982991, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2989646.5777863264 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 518521921.6068778, allowable_sigma: 512820512.8205128, diff: |\n", + "| 5701408.786365032 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512480164.70958877, allowable_sigma: 512820512.8205128, diff: |\n", + "| -340348.1109240055 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509786921.2779607, allowable_sigma: 512820512.8205128, diff: |\n", + "| -3033591.542552054 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512791028.07505834, allowable_sigma: 512820512.8205128, diff: |\n", + "| -29484.74545443058 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513469278.01824206, allowable_sigma: 512820512.8205128, diff: |\n", + "| 648765.1977292895 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512893558.2065623, allowable_sigma: 512820512.8205128, diff: |\n", + "| 73045.38604950905 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512764662.52866316, allowable_sigma: 512820512.8205128, diff: |\n", + "| -55850.29184961319 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512823066.58102036, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2553.760507583618 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512849989.4300138, allowable_sigma: 512820512.8205128, diff: |\n", + "| 29476.60950100422 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 352091439.29307556, allowable_sigma: 512820512.8205128, diff: |\n", + "| -160729073.5274372 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250888481.19644576, allowable_sigma: 512820512.8205128, diff: |\n", + "| -261932031.624067 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 496283058.5478101, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16537454.272702694 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 685458423.9689713, allowable_sigma: 512820512.8205128, diff: |\n", + "| 172637911.14845848 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 456123920.0961138, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56696592.72439897 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 553452012.7313267, allowable_sigma: 512820512.8205128, diff: |\n", + "| 40631499.91081393 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505495976.94940954, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7324535.871103227 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515807918.5829519, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2987405.7624391317 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 518532125.9358626, allowable_sigma: 512820512.8205128, diff: |\n", + "| 5711613.115349829 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512481540.75442064, allowable_sigma: 512820512.8205128, diff: |\n", + "| -338972.0660921335 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509787927.9548962, allowable_sigma: 512820512.8205128, diff: |\n", + "| -3032584.86561656 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512790772.1005679, allowable_sigma: 512820512.8205128, diff: |\n", + "| -29740.719944894314 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513469015.42652154, allowable_sigma: 512820512.8205128, diff: |\n", + "| 648502.6060087681 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512893793.51142794, allowable_sigma: 512820512.8205128, diff: |\n", + "| 73280.69091516733 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512764406.55763614, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56106.26287662983 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512823053.9913491, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2541.17083632946 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512850071.50962293, allowable_sigma: 512820512.8205128, diff: |\n", + "| 29558.689110159874 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 4.0/200 have been added to complete the last winding pack |\n", - "| (nx=20, ny=10). |\n", + "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", + "| (nx=25, ny=7). |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| bounds: [1e-05, 2] |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 352075785.77890205, allowable_sigma: 512820512.8205128, diff: |\n", + "| -160744727.04161072 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -762,82 +1462,211 @@ "output_type": "stream", "text": [ "Internal optimazion - iteration 1\n", - "before optimization: conductor jacket area = 0.00165458480410182\n", - "Optimal dx_jacket: 0.00564775836281768\n", - "Averaged sigma in the x-direction: 515.4370047801681 MPa\n", - "235961.49362376195\n", - "after optimization: conductor jacket area = 0.0008361478800221115\n", + "before optimization: conductor jacket area = 0.001692676552647676\n", + "Optimal dx_jacket: 0.004948261325397751\n", + "Averaged sigma in the x-direction: 510.33714914524325 MPa\n", + "227061.65185719344\n", + "after optimization: conductor jacket area = 0.0007375912997492067\n", "before optimization: case dy_vault = 0.6\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.494647915326402\n", - "err_dy_vault = 2.0008402940399646\n", - "tot_err = 2.4954882093663664\n", + "Optimal dy_vault: 0.39067938773912514\n", + "Tresca sigma: 512.8091686854433 MPa\n", + "after optimization: case dy_vault = 0.39067938773912514\n", + "err_dy_jacket = 0.5642455739134153\n", + "err_dy_vault = 0.5357861684800427\n", + "tot_err = 1.100031742393458\n", "Internal optimazion - iteration 2\n", - "before optimization: conductor jacket area = 0.0008361478800221115\n", - "Optimal dx_jacket: 0.0056720954534214725\n", - "Averaged sigma in the x-direction: 514.8510609286991 MPa\n", - "237293.93259759306\n", - "after optimization: conductor jacket area = 0.0008403031436586584\n", - "before optimization: case dy_vault = 0.19994399608392133\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.004969531988094018\n", - "err_dy_vault = 0.0\n", - "tot_err = 0.004969531988094018\n", + "before optimization: conductor jacket area = 0.0007375912997492067\n", + "Optimal dx_jacket: 0.003787822529545763\n", + "Averaged sigma in the x-direction: 513.9673425923443 MPa\n", + "144922.54838935137\n", + "after optimization: conductor jacket area = 0.0005470333350147793\n", + "before optimization: case dy_vault = 0.39067938773912514\n", + "Optimal dy_vault: 0.4317446661911663\n", + "Tresca sigma: 512.8309408840181 MPa\n", + "after optimization: case dy_vault = 0.4317446661911663\n", + "err_dy_jacket = 0.2583516980192422\n", + "err_dy_vault = 0.0951147325439301\n", + "tot_err = 0.3534664305631723\n", "Internal optimazion - iteration 3\n", - "before optimization: conductor jacket area = 0.0008403031436586584\n", - "Optimal dx_jacket: 0.005689655868704098\n", - "Averaged sigma in the x-direction: 514.423536407333 MPa\n", - "238249.7347572467\n", - "after optimization: conductor jacket area = 0.000843304314961606\n", - "before optimization: case dy_vault = 0.19994399608392133\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.0035715340655285274\n", - "err_dy_vault = 0.0\n", - "tot_err = 0.0035715340655285274\n", + "before optimization: conductor jacket area = 0.0005470333350147793\n", + "Optimal dx_jacket: 0.003252207472610154\n", + "Averaged sigma in the x-direction: 514.219316138129 MPa\n", + "107671.67845696783\n", + "after optimization: conductor jacket area = 0.0004627126481984757\n", + "before optimization: case dy_vault = 0.4317446661911663\n", + "Optimal dy_vault: 0.4440413655912122\n", + "Tresca sigma: 512.8303645420937 MPa\n", + "after optimization: case dy_vault = 0.4440413655912122\n", + "err_dy_jacket = 0.15414177056326103\n", + "err_dy_vault = 0.027692688909002096\n", + "tot_err = 0.18183445947226312\n", "Internal optimazion - iteration 4\n", - "before optimization: conductor jacket area = 0.000843304314961606\n", - "Optimal dx_jacket: 0.0057023576573393625\n", - "Averaged sigma in the x-direction: 514.1130254096828 MPa\n", - "238939.13940692076\n", - "after optimization: conductor jacket area = 0.0008454766578540291\n", - "before optimization: case dy_vault = 0.19994399608392133\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.0025759893005196624\n", - "err_dy_vault = 0.0\n", - "tot_err = 0.0025759893005196624\n", + "before optimization: conductor jacket area = 0.0004627126481984757\n", + "Optimal dx_jacket: 0.0030088586267110196\n", + "Averaged sigma in the x-direction: 514.7857786881332 MPa\n", + "91308.85419252724\n", + "after optimization: conductor jacket area = 0.00042516102064023547\n", + "before optimization: case dy_vault = 0.4440413655912122\n", + "Optimal dy_vault: 0.44791686397338104\n", + "Tresca sigma: 512.8189009344379 MPa\n", + "after optimization: case dy_vault = 0.44791686397338104\n", + "err_dy_jacket = 0.08115539461573751\n", + "err_dy_vault = 0.008652271646550785\n", + "tot_err = 0.0898076662622883\n", "Internal optimazion - iteration 5\n", - "before optimization: conductor jacket area = 0.0008454766578540291\n", - "Optimal dx_jacket: 0.0057115548570165365\n", - "Averaged sigma in the x-direction: 513.8879497171972 MPa\n", - "239437.66688431037\n", - "after optimization: conductor jacket area = 0.0008470504286795256\n", - "before optimization: case dy_vault = 0.19994399608392133\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.0018614006795775791\n", - "err_dy_vault = 0.0\n", - "tot_err = 0.0018614006795775791\n", + "before optimization: conductor jacket area = 0.00042516102064023547\n", + "Optimal dx_jacket: 0.002887030608161824\n", + "Averaged sigma in the x-direction: 516.5835600551138 MPa\n", + "83808.97760720368\n", + "after optimization: conductor jacket area = 0.0004065394603245476\n", + "before optimization: case dy_vault = 0.44791686397338104\n", + "Optimal dy_vault: 0.4498689380829232\n", + "Tresca sigma: 512.8211000436568 MPa\n", + "after optimization: case dy_vault = 0.4498689380829232\n", + "err_dy_jacket = 0.043798841877946124\n", + "err_dy_vault = 0.004339206253849778\n", + "tot_err = 0.0481380481317959\n", "Internal optimazion - iteration 6\n", - "before optimization: conductor jacket area = 0.0008470504286795256\n", - "Optimal dx_jacket: 0.00571821712495265\n", - "Averaged sigma in the x-direction: 513.724940553026 MPa\n", - "239798.57497994124\n", - "after optimization: conductor jacket area = 0.0008481908595044464\n", - "before optimization: case dy_vault = 0.19994399608392133\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.0013463552892578744\n", - "err_dy_vault = 0.0\n", - "tot_err = 0.0013463552892578744\n", + "before optimization: conductor jacket area = 0.0004065394603245476\n", + "Optimal dx_jacket: 0.002843048981604718\n", + "Averaged sigma in the x-direction: 514.715834009809 MPa\n", + "80094.05384952854\n", + "after optimization: conductor jacket area = 0.0003998459857021419\n", + "before optimization: case dy_vault = 0.4498689380829232\n", + "Optimal dy_vault: 0.45057558196352715\n", + "Tresca sigma: 512.8233452005328 MPa\n", + "after optimization: case dy_vault = 0.45057558196352715\n", + "err_dy_jacket = 0.01646451396639874\n", + "err_dy_vault = 0.0015683137499917402\n", + "tot_err = 0.01803282771639048\n", "Internal optimazion - iteration 7\n", - "before optimization: conductor jacket area = 0.0008481908595044464\n", - "Optimal dx_jacket: 0.005723043651035138\n", - "Averaged sigma in the x-direction: 513.6069215920327 MPa\n", - "240059.97337964593\n", - "after optimization: conductor jacket area = 0.0008490172743066225\n", - "before optimization: case dy_vault = 0.19994399608392133\n", - "after optimization: case dy_vault = 0.19994399608392133\n", - "err_dy_jacket = 0.0009743264654596025\n", - "err_dy_vault = 0.0\n", - "tot_err = 0.0009743264654596025\n" + "before optimization: conductor jacket area = 0.0003998459857021419\n", + "Optimal dx_jacket: 0.002848838910865934\n", + "Averaged sigma in the x-direction: 510.4155878418728 MPa\n", + "78759.4551109264\n", + "after optimization: conductor jacket area = 0.00040072625879492875\n", + "before optimization: case dy_vault = 0.45057558196352715\n", + "Optimal dy_vault: 0.45048235612893633\n", + "Tresca sigma: 512.823116430222 MPa\n", + "after optimization: case dy_vault = 0.45048235612893633\n", + "err_dy_jacket = 0.0022015303998640227\n", + "err_dy_vault = 0.00020694669463177046\n", + "tot_err = 0.002408477094495793\n", + "Internal optimazion - iteration 8\n", + "before optimization: conductor jacket area = 0.00040072625879492875\n", + "Optimal dx_jacket: 0.0028504056762085716\n", + "Averaged sigma in the x-direction: 510.59352752862225 MPa\n", + "78934.96452377425\n", + "after optimization: conductor jacket area = 0.0004009645083927146\n", + "before optimization: case dy_vault = 0.45048235612893633\n", + "Optimal dy_vault: 0.45045711986578807\n", + "Tresca sigma: 512.8230665810204 MPa\n", + "after optimization: case dy_vault = 0.45045711986578807\n", + "err_dy_jacket = 0.0005945445115134922\n", + "err_dy_vault = 5.602367469690353e-05\n", + "tot_err = 0.0006505681862103957\n", + "Internal optimazion - iteration 9\n", + "before optimization: conductor jacket area = 0.0004009645083927146\n", + "Optimal dx_jacket: 0.002850827280995097\n", + "Averaged sigma in the x-direction: 510.6420590429833 MPa\n", + "78982.46940751096\n", + "after optimization: conductor jacket area = 0.0004010286229233324\n", + "before optimization: case dy_vault = 0.45045711986578807\n", + "Optimal dy_vault: 0.4504503283750036\n", + "Tresca sigma: 512.8230539913491 MPa\n", + "after optimization: case dy_vault = 0.4504503283750036\n", + "err_dy_jacket = 0.00015990076247589884\n", + "err_dy_vault = 1.507711362754438e-05\n", + "tot_err = 0.00017497787610344323\n", + "Internal optimazion - iteration 10\n", + "before optimization: conductor jacket area = 0.0004010286229233324\n", + "Optimal dx_jacket: 0.002853616189122447\n", + "Averaged sigma in the x-direction: 510.2117343871516 MPa\n", + "78995.25346019538\n", + "after optimization: conductor jacket area = 0.0004014527752127272\n", + "before optimization: case dy_vault = 0.4504503283750036\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 250881463.5052841, allowable_sigma: 512820512.8205128, diff: |\n", + "| -261939049.31522867 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 496249863.8952868, allowable_sigma: 512820512.8205128, diff: |\n", + "| -16570648.925225973 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 685392502.1658069, allowable_sigma: 512820512.8205128, diff: |\n", + "| 172571989.34529412 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 456113275.7163553, allowable_sigma: 512820512.8205128, diff: |\n", + "| -56707237.10415745 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 553410105.575084, allowable_sigma: 512820512.8205128, diff: |\n", + "| 40589592.7545712 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 505498835.2970914, allowable_sigma: 512820512.8205128, diff: |\n", + "| -7321677.523421347 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 515793105.4011781, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2972592.58066535 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 518600027.4791579, allowable_sigma: 512820512.8205128, diff: |\n", + "| 5779514.658645153 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512490738.87867963, allowable_sigma: 512820512.8205128, diff: |\n", + "| -329773.94183313847 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 509794658.76854545, allowable_sigma: 512820512.8205128, diff: |\n", + "| -3025854.051967323 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512789090.44250596, allowable_sigma: 512820512.8205128, diff: |\n", + "| -31422.37800681591 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 513467305.6428993, allowable_sigma: 512820512.8205128, diff: |\n", + "| 646792.8223865032 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512895374.36296976, allowable_sigma: 512820512.8205128, diff: |\n", + "| 74861.54245698452 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512762724.9211324, allowable_sigma: 512820512.8205128, diff: |\n", + "| -57787.899380385876 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512822979.2076429, allowable_sigma: 512820512.8205128, diff: |\n", + "| 2466.3871301412582 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n", + "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", + "| sigma: 512850629.000417, allowable_sigma: 512820512.8205128, diff: |\n", + "| 30116.17990422249 |\n", + "+-------------------------------------------------------------------------+\u001B[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimal dy_vault: 0.45040539674335706\n", + "Tresca sigma: 512.8229792076429 MPa\n", + "after optimization: case dy_vault = 0.45040539674335706\n", + "err_dy_jacket = 0.0010576608879010005\n", + "err_dy_vault = 9.975819999364022e-05\n", + "tot_err = 0.0011574190878946408\n" ] }, { @@ -845,19 +1674,19 @@ "text/plain": [ "
" ], - "image/png": 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" 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}, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 15 + "execution_count": 30 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-16T13:38:22.198343Z", - "start_time": "2024-10-16T13:38:22.195265Z" + "end_time": "2024-10-18T12:33:51.235013Z", + "start_time": "2024-10-18T12:33:51.232064Z" } }, "cell_type": "code", @@ -872,12 +1701,12 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Operational current after optimization: 58990.625 |\n", + "| Operational current after optimization: 67417.85714285714 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 16 + "execution_count": 31 } ], "metadata": { diff --git a/examples/magnets/example_tf_wp_optimization.py b/examples/magnets/example_tf_wp_optimization.py index cecdd00f0e..fba5c1d2ed 100644 --- a/examples/magnets/example_tf_wp_optimization.py +++ b/examples/magnets/example_tf_wp_optimization.py @@ -43,8 +43,8 @@ #%% md # **Inputs for the TF coils** #%% -d = 1.9 # additional distance to calculate the max external radius of the inner TF leg -Iop = 60.0e3 # operational current in each conductor +d = 1.82 # additional distance to calculate the max external radius of the inner TF leg +Iop = 70.0e3 # operational current in each conductor dr_plasma_side = R0 * 2 / 3 * 1e-2 # thickness of the plate before the WP T_sc = 4.2 # operational temperature of superconducting cable T_margin = 1.5 # temperature margin @@ -188,12 +188,13 @@ if B_TF_i < B_ref: cable = DummyRectangularCableLTS( - dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1, d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97 + dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=505, + d_cooling_channel=1e-2, void_fraction=0.7, cos_theta=0.97 ) else: cable = DummyRectangularCableHTS( - dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=1, - d_cooling_channel=5e-3, void_fraction=0.7, cos_theta=0.97 + dx=dx, sc_strand=sc_strand, stab_strand=stab_strand, n_sc_strand=n_sc_strand, n_stab_strand=505, + d_cooling_channel=1e-2, void_fraction=0.7, cos_theta=0.97 ) cable.plot(0, 0, show=True) bluemira_print(f"cable area: {cable.area}") @@ -202,7 +203,8 @@ #%% md # ***Change cable aspect ratio*** #%% -cable.set_aspect_ratio(2) # This adjusts the cable dimensions while maintaining the total cross-sectional area. +aspect_ratio = 1 +cable.set_aspect_ratio(aspect_ratio) # This adjusts the cable dimensions while maintaining the total cross-sectional area. cable.plot(0, 0, show=True) bluemira_print(f"cable area: {cable.area}") #%% @@ -216,7 +218,7 @@ ) bluemira_print(f"after optimization: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}") -cable.set_aspect_ratio(2) +cable.set_aspect_ratio(aspect_ratio) bluemira_print(f"Adjust aspect ratio: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}") #%% ########################################################### @@ -227,9 +229,9 @@ #%% # case parameters layout = "auto" #"layer" or "pancake" -wp_reduction_factor = 0.8 -min_gap_x = 0.01 -n_layers_reduction = 4 +wp_reduction_factor = 0.7 +min_gap_x = 2*dr_plasma_side +n_layers_reduction = 2 # creation of the case wp1 = WindingPack(conductor, 1, 1) # just a dummy WP to create the case @@ -256,9 +258,9 @@ # ## Optimize cable jacket and case vault thickness #%% # Optimization parameters -bounds = [1e-5, 0.2] +bounds = [1e-5, 2] max_niter = 10 -err = 1e-3 +err = 1e-5 case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds, layout, wp_reduction_factor, min_gap_x, n_layers_reduction, max_niter, err, n_cond) From 9a8fa8793316a243118d9870eba9a8d042077704 Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 18 Oct 2024 19:30:59 +0200 Subject: [PATCH 16/17] correnction on optimization bounds --- bluemira/magnets/case_tf.py | 7 +- .../magnets/example_tf_wp_optimization.ipynb | 1165 ++++------------- 2 files changed, 260 insertions(+), 912 deletions(-) diff --git a/bluemira/magnets/case_tf.py b/bluemira/magnets/case_tf.py index 168b01bbd3..8741d9b29c 100644 --- a/bluemira/magnets/case_tf.py +++ b/bluemira/magnets/case_tf.py @@ -342,7 +342,8 @@ def optimize_jacket_and_vault( T: float, # noqa: N803 B: float, allowable_sigma: float, - bounds: np.array = None, + bounds_cond_jacket: np.array = None, + bounds_dy_vault: np.array = None, layout: str = "auto", wp_reduction_factor: float = 0.8, min_gap_x: float = 0.05, @@ -370,7 +371,7 @@ def optimize_jacket_and_vault( / self.n_conductors ) conductor.optimize_jacket_conductor( - pm, t_z_cable_jacket, T, B, allowable_sigma, bounds + pm, t_z_cable_jacket, T, B, allowable_sigma, bounds_cond_jacket ) print(t_z_cable_jacket) print(f"after optimization: conductor jacket area = {conductor.area_jacket}") @@ -390,7 +391,7 @@ def optimize_jacket_and_vault( case_dy_vault0 = self.dy_vault print(f"before optimization: case dy_vault = {self.dy_vault}") self.optimize_vault_radial_thickness( - pm=pm, fz=fz, T=T, B=B, allowable_sigma=allowable_sigma, bounds=bounds + pm=pm, fz=fz, T=T, B=B, allowable_sigma=allowable_sigma, bounds=bounds_dy_vault ) print(f"after optimization: case dy_vault = {self.dy_vault}") diff --git a/examples/magnets/example_tf_wp_optimization.ipynb b/examples/magnets/example_tf_wp_optimization.ipynb index f6f95a92c3..dfdf67622e 100644 --- a/examples/magnets/example_tf_wp_optimization.ipynb +++ b/examples/magnets/example_tf_wp_optimization.ipynb @@ -15,8 +15,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.213955Z", - "start_time": "2024-10-18T12:33:48.210187Z" + "end_time": "2024-10-18T17:30:26.333055Z", + "start_time": "2024-10-18T17:30:24.853335Z" } }, "cell_type": "code", @@ -39,7 +39,7 @@ ], "id": "54d702a3b1f86263", "outputs": [], - "execution_count": 16 + "execution_count": 1 }, { "metadata": {}, @@ -50,8 +50,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.260117Z", - "start_time": "2024-10-18T12:33:48.257042Z" + "end_time": "2024-10-18T17:30:26.339938Z", + "start_time": "2024-10-18T17:30:26.337233Z" } }, "cell_type": "code", @@ -64,7 +64,7 @@ ], "id": "78e8b10c23faa689", "outputs": [], - "execution_count": 17 + "execution_count": 2 }, { "metadata": {}, @@ -81,8 +81,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.306526Z", - "start_time": "2024-10-18T12:33:48.303271Z" + "end_time": "2024-10-18T17:30:26.616820Z", + "start_time": "2024-10-18T17:30:26.614265Z" } }, "cell_type": "code", @@ -97,7 +97,7 @@ ], "id": "60966584009a130", "outputs": [], - "execution_count": 18 + "execution_count": 3 }, { "metadata": {}, @@ -108,8 +108,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.356929Z", - "start_time": "2024-10-18T12:33:48.349760Z" + "end_time": "2024-10-18T17:30:26.660977Z", + "start_time": "2024-10-18T17:30:26.656208Z" } }, "cell_type": "code", @@ -152,7 +152,7 @@ ] } ], - "execution_count": 19 + "execution_count": 4 }, { "metadata": {}, @@ -163,8 +163,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.415506Z", - "start_time": "2024-10-18T12:33:48.412167Z" + "end_time": "2024-10-18T17:30:26.716025Z", + "start_time": "2024-10-18T17:30:26.713366Z" } }, "cell_type": "code", @@ -178,7 +178,7 @@ ], "id": "791a5e51845b952d", "outputs": [], - "execution_count": 20 + "execution_count": 5 }, { "metadata": {}, @@ -189,8 +189,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.465435Z", - "start_time": "2024-10-18T12:33:48.460179Z" + "end_time": "2024-10-18T17:30:26.765030Z", + "start_time": "2024-10-18T17:30:26.758897Z" } }, "cell_type": "code", @@ -221,7 +221,7 @@ ] } ], - "execution_count": 21 + "execution_count": 6 }, { "metadata": {}, @@ -232,8 +232,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.708875Z", - "start_time": "2024-10-18T12:33:48.515887Z" + "end_time": "2024-10-18T17:30:27.031251Z", + "start_time": "2024-10-18T17:30:26.814359Z" } }, "cell_type": "code", @@ -273,15 +273,15 @@ { "data": { "text/plain": [ - "
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uZEJeHx+zqrdzZ+nxx+NbSwAAIEl+/r6a+al0zz3xvxyUTLufWbOkHj2sqw0AAAAAgHSzaZMUHp50/MiR+O0//nD8x7Gr425TisNee7/h3LlNm4bOnaUSJdKtDgCAF2rQQOrTR/rkE8fxV16RWrc2v0gEAAAAAMCjde3qep/t/z/t+sADmVJKqj9Ee+WKNHWqNGWKY19eAACceffdpL8cvHhReuklS8oBAAAAACD92Hvd3u4jnaR4Ze/LL0uLF0tnzkihodLYseZRq5b0/PPS009LgYHpVhcAwEvkzStNnmx6u9vZFKN8X36qTVXLqe5rD1pXHAAAAAAAafXmm1ZXkESKw97x46UPPpBWrpTmzpV++MHcaGfLFmnrVmnwYKllS9PeoXVrKXv2jCwbAOBJ2rUz/21YsUKqrD2aqR5qpP8p9M0yutp9jwKKBFhdIgAAAAAAqeOGYW+q2jj4+kqPPSYtXSqdPClNmCBVr25WGt+8aQLgp54yPRh79cqokgEAnsZmM+1/3sz+nrapphrpf5KkUtH/alOrUdYWBwAAAACAl0h1z167QoWkgQOlbdvMo39/MxYbK124IH36aXz/4e+/l/bsSaeKAQAeqXRpqUmrXMqumw7j926eoH1fbLemKAAAAAAAvEiaw96EqleXJk0yq32XLJEefdSsArb3Fl60SKpWTapc2axu3r07PZ4VAOBp7v2in/bmquMwdlrFNPHN/xQdbVFRAAAAAAB4iXQJe+2yZTN9GVesMDdx++ADE/Dabyq3f7/09tsmHK5UKT2fGQDgCXyz+8p3zqeKkq9iZNPH6q/K2qsZB5poyhSrqwMAAAAAIIX++itj5796NU2tEtI17E2oSBFpyBBp1y4pJMT08M2fPz74PXAgo54ZAODOgp+qoe+aTlQD/aWB+liXlVeS9Prr0vHjFhcHAAAAAEBKNGokPfKItHlz+s577ZpZQVuunGmhkEoZFvYmVKeONHWqdOqU9MUXUsuWkk+mPDMAwB09uKKfwsrUcxi7ckXq1y++BRAAAAAAAG4rTx5p1Sqpfn2pSRNp7lzp8uW0z7d5s7lBWpky0quvSuHhZuVsKmVq5Jo9u/Tkk9KPP0rHjmXmMwMA3ElAgDRtWtLxFSukb7/N/HoAAAAAAEiVAwek55+XbDZp3TqpWzepWDGpbVuzMnfdOtfhb2ysdPCgtHChNGCA6Xdbv740ZYp07pwJfL/6yuxLpWy396rSrnhxq54ZAOAOHn5Yevpp84mPhPr3l5o1k/Lls6YuAAAAAABuqUgRs5p36FBp9Ghp6VLp+nXpu+/Mwy5HDqlgQalAASkiQjp/Xrp40fFjrfbt8uVNX9wXX5T8/NJUFs0UAACWmTgx6adSTp6U3hweaUU5AAAAAACkTuXK0pdfSocOSW+8Id1xR/xNy2JjTcB78qS52dqhQ9J//0kxMfH7/f2l9u2l5cvNat9evdIc9EopXNnbtKlZkTxnjllFnJ6OHpW6djXzr1mTvnMDANxb0aLm0y09ethHYvWkvtLQ6YO0q8bXqtrzHivLAwAAAAAgZYKCzArf0aNNi4fff5e2bDEB7+nT5sZr2bKZFb5lykhVqkgNGkj3329W/6aTFIW9a9eaMPbq1XR73jhXr8bPDwDIel58UVqwQDq2/l9NVR+10kpJUsSAFxXx1Db55/e3uEIAAAAAAFKhQgXz6NYt05+aNg4AAEv5+EizJlzWNtWMC3ol6Y4b+/TXY+9YWBkAAAAAAJ4lVTdoW75c2rw5fQs4cSJ95wMAeJ7gOnm0tslLeuC3Nx3GG61/X39/97wqPXanRZUBAAAAAOA5UhX2vv56RpUBAMjq7lkxXAcKf6MKEbskSeEqpIGapP2j7tBfD5vWRgAAAAAAwLUUt3FIeBO59H4AAJA9d3ZFTZ+taPnocz2tytqrxeqoLVttmjDB6uoAAAAAAHB/KVondeRIRpcBAIBU+fm6Grdut16ZW8lhfORIqW1b6a67rKkLAAAAAABPkKKwt0yZjC4DAACj75RKmvG7dOhQ/FhEhNS9u/Trr+aGbgAAAAAAICn+yQwAcCu5ckmffpp0fN06aebMzK8HAAAAAABPQdgLAHA7TZpIPXokHX/lFen48cyv5//Yu+/oqKq1AePPJJQA0oOAYgRUUBHpAgqCHVGxi4pdrlxA7F2vgJ8Fr13sDVHEroiKWGkqUgQVC0UBEUWKCtIl5ftj35BMGoEJmSQ8v7XOmjNn77P3Gz0LyDt73i1JkiRJUmlgsleSVCL997+wyy7R19JXr+Gt7o+Tke7unpIkSZIk5WSyV5JUIlWvDo89lvX+cD5kFs25+Nt/8/mAF+MXmCRJkiRJJZTJXklSiXXccXDBSSt5mgv4kCNpxEIA9n70Elb8sDy+wUmSJEmSVMKUuWTvjz9CpUoQiYTj2WcL7r96NdxyC+y/P1StCjVqwAEHwP33w6ZNhZtz5kw45xxISYGkpPC145NPhvHjC3d/RgYMGwZdukBycticaO+9Q23KpUsLN8bSpaH/3nuH+5OTw3jDhoXxJam0GvJ/mzg+MjrqWu2MP5hz9KVxikiSJEmSpJKpzCV7+/aFDRsK13fBAmjZEgYOhFmzYM0aWLUKpk2Dyy+Hjh3hr78KHuPpp6F9e3j++bBp0MaNsGQJvPEGHHooDB5c8P0bN0L37nDBBTBxIvzxB6xfD3PmwF13hST09OkFjzF9euh3113hvvXrwzgTJ4Zxu3cP80hSaVRn3zrM7vtgruuJP89nzKtr4xCRJEmSJEklU5lK9o4YAR99BI0abbnvP/+ErwfPnx9WAg8dCosXhwTwoEGQkABffgk9e+Y/xsSJ0KdPWAHcsiV8/DEsWwaTJ4dEb0ZGGGvkyPzHGDAAxo4N5/37h2TtkiXhZ6lVK4zXowesWJH3/StWhPZly6B27XDfkiUwe3YYD8L4AwZs+b+JJJVUBw49g2l1ugOwlspcxn0cxGdcdHkVVq2Kc3CSJEmSpB1PYmLRH+XKxRxWmUn2/vUXXHll+O9y331b7v/EE/Ddd+F8+HC4+GLYdVdo2DCs9L3jjtD24Yfw7rt5j3HllZCWFso2jBsXErx16kCHDvDee9CiReh37bV5rzaeNQueeiqc9+sHDz0ETZpAvXrQqxe8805IOi9ZAnfemXcMQ4aE9oSE0L9Xr3B/06ZhvL59Q7+nngrzSVJpFEmIsOvbj/FOueNpziwe4DLSSeTXX0MJG0mSJEmSilVGxvY5YhR7uriEuPrqsLr1ssuykqwFefTR8NqqFZx6au72yy+Hu++G5ctD32OOiW6fNi2rvMK114Zav9lVqBBKOJxwQlgx/M47cMop0X0eeyz8P6xQIawAzqljx7Bqd9SokKy9/XYoXz6rfdOmrGTx8ceHJHNOgweHPps2weOPhwSwJJVGu7TfjdFDR7Ggb/T1J56A006Dww6LT1ySJEmSpB3QwIEFt48ZExKIAPvsE+rA1qsXkoHLlsGUKfD992HTsXbt4OijiySsMpHs/fRTeOYZqF8/JDf//LPg/vPnh/+WkDsBm6l8+ZBAfeqpUJ5h3bqw8Vmmt9/OOs9vjO7dwz3r1oX+OftljtGlS1gRnJdTTgnJ3pUrw895yCFZbZMmsfnry/nFUKdOGP+jj2D0aJO9kkq3iy6CF18MZXSyu/DC8O2FqlXjE5ckSZIkaQdTULJ3yJCQ6N1337BC6cAD8+43eXL4RXfatLBi9LrrYg6r1Jdx2LQp1M3NyIB774Vq1bZ8z5dfZp23b59/v8y2DRvghx/yHqNBg1DGIS/ly4eVwwAzZkS3rVgRNnQrbAx5jbG1P8cvv+Rf+1eSSoOEhPAhXFJS9PWffw7f8JAkSZIkKa4mTYIbbwybin32Wf6JXghf6580CXbfHW66KffKpm0Qc7L3q6/g/vvhrLNCfI0awc47h/q3zZqFFcjXXANvvMF22UTnv/8Nq3QPOwxOP71w98ydm3XeuHH+/bJv9DZ7dt5jFHR/9jHmzYP09Kzrc+YULobddw/JjYJiSEgI/bYUQ855Jak02muvUNYmp8cfh0/G5FEgXZIkSZKk4vLgg+H12muhevUt969RI6zoTU/PujcG21TGYe7csLJq5MiwOVh22esIL1kSEpQffBDeJyaGMgTnnhvq5GavP7stfvoJbr011Lx9+OHC35d9dWt+5RMgJK0z/fFH3mMUdH/2MTZuhDVrslYeFzaG8uXD//M//8w/hpo1C96sr6CfQ5JKo0sugddfDx+SApTnH27kNhof/wJ/L5hBtQaF+JqHJEmSJElF7Ysvwmvr1oW/J7PvlCkxT79Vyd5p00JN3LFjc28QV758SDrWqhWO9etDgvKvv+Dvv0Of1NRQO/ajj+DKK8NmagMGRNfC3Rp9+4YSCzfcAE2bFv6+tWuzznN+FTi7SpWyztesyXuMgu7Pa4zMZG9hY8g+RlHFsCXz52cwbVpavu3164ejtEhLS4t6lYqbz+D28dRT0Lp1Ak3Wf82znEdLvoZUmHj4lRz03WPxDq/E8TlUSeBzqHjzGVRJ4HOoksDnUCVBzucwI3uisTRbvjy8btiKb55m9i2C+quFSvYuXBiSs6NGZSV4q1eHHj1C6Yb27WH//cPK3bwsXRoS01OnhkTv1Knh2g03wH33wW23hc11tsbIkfDhh9CwYShpsTUK++xk7xeJ5N0nv+tbGqMwY+fsm7NfftcLG0N+br45wurV+fzPBP71r9/o02dJvu0l1axZs+IdgnZwPoNF744TfqXfiydTntTN1w6e8xSjL+3Irue3iGNkJZfPoUoCn0PFm8+gSgKfQ5UEPocqCTKfw4ULF8Y3kKJSpw789ltYLdupU+HuGTs2vCYnxzx9oZK9++4byhBEInDccXD22eG1QoXCTVK3bkgM9+gRyi78+CM891w4Fi0Km8798Ueo7VsYK1fCFVeE8wcfjF65Whg77ZR1vmFD9Pvssifgq1SJbqtSJcSxfn3Bc+U3RvY5CztGzhgyx9jWGPJzyy0ZHHRQQSt761K/ft0tD1RCpKWlMWvWLJo3b05ifp9ISNuRz+D2s/+w/fnu3Y60/HtS1PWWj/0fO/3nB6onx1gvqAzxOVRJ4HOoePMZVEngc6iSwOdQJUHO53Dx4sXxDqloHHIIjBgRVrged1xYJVuQqVND30gEunaNefpCJXs3boSePeHmm2HvvWOekz33hFtuCeM980zYaGdrVjYPHhxWBvfoEf6bba3sSfLly/NP9mauugaoXTv3GCtXRvcpaIwKFaLnyRlDflJTwzz5xQChPTU1/7q9Bf0ceWncOEK7dmXvD/vExET/ElNc+QwWvcTERGq+8QzrDt+fyoRPvr5nH85PG8b+NyXx5JNxDrAE8jlUSeBzqHjzGVRJ4HOoksDnUCVB5nMYKczX0UuDK6+El14Kyc5DDgkrVi+4ABo3ju43f35IjN53X1jJWa4cXH11zNMnFKbTt9+GsglFkejNrly5sKp33jzo1avw9y1YEF5Hjw5J75xHo0ZZfc8/P+t65mrwJk1yj1XQPJD7Z88co6D7s7c3aQIJ2f5rZ68xXNAYP/8cNuMrKIa0tLBCeksx5JxXksqC3Q/bk2knDSGNBIZwLa2ZwVTa89RT8P778Y5OkiRJkrRDadECHnggnG/cCHfcAXvtFco7NGsG++0XzvfaK7StXx8Slw8+GOrkxqhQyd599ol5ngKVLw977LF958iuTZus84I2uctsS0rK/d8gc4xffoEl+ZSvTU2FGTPCec4N+JKTYbfdCh9DXmNs7c+RklIkpT8kqcTp/PLF9G49k+sZwkaydq3s3RtWrYpjYJIkSZKkHU/fvvDWW9CgQdhMKyMj1LD94Qf4/vtwnnm9QYOwovXf/y6SqQuV7C1p7rsPZs7M/3j33ay+gwdnXd9ll3CtceNQhxjgtdfyniM1Nfx3BjjsMKhcObo9e/mI/MYYMwbWrcvdP+cY48fnv9le5tg1auSu6dy5c9gor6AYVqyACRPyj0GSyoKEcgnc9Mr+uf6sXrw4fINGkiRJkqRideyx8NNPIWnXpw8ceGD4yn3TpuG8T5/QNn8+HHNMkU1bqJq9JU32Mg15qVEj6zwlBVq2zN2nb18YMCCsvH39dTj55Oj2++8PdYEz++bUrh20bQvTp8N//wvnnJOVeAXYtAkGDQrnDRqE/7859ekDjz4K//wTktJDh0a3T50aPgSAsDqtfI59hsqXD9fvuQdGjQr9Dzggus+gQWH8SCSUzJCksmqPPWDIELjrkkUkk/UJ2oyn4bMWcNBBhRwoOTn85SFJkiRJUizKlYOTTgpHcU1ZbDOVMBddBI89Bt99B2efHRK7J5wQkrTDh4cN5ACOOCL/5Po998Chh4aVY4ccAvfeG8puzJ8PN9wQVhMD3HlnKAWR0/77h2Ttk0/CQw+FhOyAAVCtGnz8MVxySajXW78+XHtt3jFcd12op7xkSYjzgQfCSuS//w7J44cfDv169y6Ssh+SVKL1P24RF13WlIrpOXb9vGQrBklKgjlzTPhKkiRJkkqdQid7M5OfRenmm4t+zMKqUAHefhsOPzwkZ/v3D0d2bdrAyy/nP8bBB4eEcb9+IbF7yCHR7ZEIDBwIZ56Z/xhDh4a6v2PHhvOcq3t33jmUk8iv1m5ycmg/5hhYtizvje66dcs9riSVRQl/rsid6N1aGzaEGjgmeyVJkiRJpUyhk72DBoXkZVGKZ7IXQjmIr74KNYAzS2QkJkKTJiFBe/HFuUsn5NS7d0gK33tvqL27bBnUqgUdO4aVuV27Fnx/xYqhtu+zz8KwYWGl8fr1YfO2Hj3gqqugbt2Cx2jbFr75Bu6+OyR+f/kFKlUKG/ydfz6cd17R/7+TJEmSJEmSVLJsVRmHjIyim3h7Jh8bNix8rFWrhqRzLInnVq3g+ee3/f5IJCRlzz9/28eoWxfuuisckiRJkiRJkrajxo2LfsxIJGzqFoNCJ3vHjYtpHkmSJEmSJEkqGxYuLPoxi2B1bKGTvV26xDyXJEmSJEmSJJV+554b7wjytFVlHCRJkiRJkiRphzdsWLwjyFNCvAOQJEmSJEmSJMXOZK8kSZIkSZKk0ufdd+H006FhQ0hKguRkaNEC+vSBsWPjHV1cFKqMw5AhMGAAVKmyfYL44gv44w845pjtM74kSZIkSZKkMmLVKujVKyR7s9u4MSQZv/kG5syBbt3iE19Ov/4Kv/8O69ZB27ZQqdJ2m6pQK3tvuAEaN4a77w4xFZUpU6B7dzjoIPjyy6IbV5IkSZIkSVIZtG5dSOK++y6UKweXXBJWki5fDkuWwMcfQ79+ULt2/OO89daw6jglBQ44ALp2hQULovu98gqcc074OYpAoVb2tm8fErPXXgu33AInnghnnQWHHw6RyNZNuGQJvPACPP88fPstZGSEZHaLFtsSviRJkiRJkqQdxsCBIblboQK88w4ccUR0e716cOih8Ykt06JFcPTRMHt2SH5myiuR2ro1nHFGOD/rrJAUjkGhkr2TJ8Pw4XDzzfDLLzBiRDhq1AiJ4PbtQ1x160KtWlCzJmzYAH/+GY65c0OyeOpU+OEHSE8PP2dCQvhZ7rgDdtstpp9DkiRJkiRJUln2yy9w333h/Oqrcyd6S4LUVDjuuJAETUiAU06Bgw8ONXLzsuee0KkTfPopvP128SR7Ac49NyRmn34aHnggJHD/+gvefz8chZGZyK5QIdROvuYa2HffbQlbkiRJkiRJ0g5l2DBISwvlGy69NN7R5O3ZZ2HWrBDjqFGhhi3kn+yF0GfSJPj885inL1TN3kwVKkDfvmEF8iefwIUXQv36IYm7paNcuVCWYuhQWLw4/NwmeiVJkiRJkiQVynvvhdcOHaBOnazrqanR5RLi6ZVXQrmGCy/MSvRuScuW4XXu3JinL/TK3py6dg0HwI8/hg3Wfvopa2O5cuVCOYfdd4dmzaBNG6hcOeZ4JUmSJEmSJO1oUlPhq6/Cedu2sHp1qA374ovw88+QmAh77QUnnQRXXBFqzcbDN9+E1xNOKPw9mYnrP/6IefptTvZmt+ee4ZAkKa6SkyEpKRSO30YbIkmkJiWzUxGGJUmSJEk7vCVLwpEpLY1Kc+eGzb0SE6m4YEHB9y9fnvW7XvnyYQOxH3/Mak9NDXVyb7stbD42Zgw0b170P8eW/PVXeK1fv/D3pKcX2fRFkuyVJKlESEmBOXNgxYpCdf/5o3nUuPZfPEI/XuVUIMKKjGSOui+FJ5/cvqFKkiRJ0g7l8cdh8ODNbxOB7BVeG1SsGEoE5GfVqqzzBx6Af/4Jm4LddFNY0fv77/DUUyHZu3gxHH98WGW7UzEv5alePazQzR7vlvz0U3itXTvm6U32SpLKlpSUcBTC7q1bc//G7txwc9Wo6089BT16hA1UJUmSJElFoE+f8IvW/6SlpTFn7lyaNmlCYmIiiydNgkceyf/+7Ktf//kHTj01lHDIlJICt9wSkq1XXQULFsATT4SSDsVpr71CsnfaNOjcuXD3vPFGeG3RIubpt2qDNkmSypoBN1SlU6fc1y+8EJYuLf54JEmSJKlMql8/lF7Idqzfe+/N5xsbNSr4/pwrdG+5Je9+l1wSSvwBjBoVc9hbrVu3sFnco4+GpPSWfPQRvP562NStsBu6FcBkryRph5aYGMo5VakSfX35crjggpKzoaskSZIk7dBq1w4J0czzvffOu1/58nDAAeH8+++LJ7bs+vWDqlVh/vxQZmLNmrz7paWF0hYnnBBWLdepA+efH/P0JnslSTu8xo3h/vtzXx8zBh59KK3Y45EkSZIk5VClCjRoEM5r1iy4b2b7339v35jyUrs2mzeBeeutUF7izDOz2h98MLzfddeQGF63DhIS4NlnoVKlmKc32StJEqFswwknRF/rwniOuHQffnzru7jEJEmSJEnKpl278PrHHwX3y2yvXn37xpOf006DF14ICeqVK+Hll7NWJT/5ZHi/bFn4KmnlyuF9t25FMrXJXkmSCH/vPvlkKCNVnn+4nev5hEPZK2Meaaefyca/N8Y7REmSJEnasR1/fHj96y+YNSvvPhs3wpQp4bxVq+KJKy+nnw5z58I114RN2zIyoo/ddgv1hefMgZNPLrJpTfZKkvQ/ycnw3JMb+YyDuJ4hJBAK9jbd8A2TD70hztFJkiRJ0g7ulFPCCh2Am27Ke5OVu+4KyWAICdd4qlcPhgyB2bNh1SqYNw9++CFsEvPzz6Ge4K67FumUJnslScrm8GMqsrZ151zXu355L1OHTolDRJIkSZIkIJQ8uPvucD56dKjFN2UK/PlnSKJecQXcfHNob9cOzj47bqHmUrUq7LEHNG0a6vpuJyZ7JUnKocPHtzMnaf/N7zdSgcu5lxNvb7fF0lCSJEmSpO3ozDPhjjvCpmajR0OHDiF5uu++cN99YbVv69Zhc7Ty5eMdbbGLKdk7cWI41q8v/D0bNmTdJ0lSSZRUI4nEl0ayniS+Y18OYCr3czm//Z7Av/6V9zeFJEmSJEnF5Lrr4PPPoVevUPu2QgWoWRMOPhgefRQmT84q97CDKRfLzV27hiT6N9+E5Hlh/Ppr1n2pqbHMLknS9rPn8c14fcBYzhp6ABuotPn6m2/C009D795xDE6SJEmSdnTt24cjXi64ILxGIuGXxJzXt0XOsbZBTMle2PbVTa6KkiSVdCc90IWu82Ds2Ojrl14aPjBu0iQ+cUmSJEmS4uzZZ0NyFqITtNmvb42MjJKR7N1a6enhNcFqwZKkEi4SgWHDYP/9w2apmdatC98W+vzzHbIElCRJkiQpJSXvpG5+14tJsSd7f/stvFatWtwzS5K09erVg2eegeOOi74+fToMGgS33RaXsCRJkiRJ8bRw4dZdLyZFsr62MMnqTZvghx/g1lvDe7/6KkkqLY49Fvr2jb5Wjk1Uuv0/fP3ghPgEJUmSJElSDlu1sjcxMfe1jAzYb7+tmzQSgR49tu4eSZLi6e67Ydw4mD0b9uBHXqAX7ZnKr1cMZ9Xx31B99xrxDlGSJEmSVJxq1gy1aj//HJo2jXc0wFau7M3IiD7yu76lo3NnuOKKov5RJEnafipXhpEj4fzE5/iKlrRnKgC7pv3C950uIiPdnUclSZIkaYeyahWsXAlpaXm3f/ddSAaXK75Kuls107nnRr8fPjxrlW6NGvnfF4lAUhLssgt06gRdu259oJIkxVurVnDOUUvZaczaqOsdF7/KxPOO5ODnescpMkmSJElSiZVRfIuDtirZO2xY9Pvhw8PrbbfBvvsWVUiSJJVcB791JTN2Hkvrvz7ZfO179uHKV9oz/Dr/PpQkSZIkxU9MG7QNHAg33ww771xU4UiSVLIllEug/ofPsyKSDMAT/Iu2TGf6xub07Anr18c5QEmSJEnSDiumghEDBxZVGJIklR712+zClIHP02/Q37zKaZuvf/ttqEn/6KNxDE6SJEmStMOKaWWvJEk7qvYDu7HrZafluv7YY/D663EISJIkSZK0wyuyreDS0+H772H+fFi9Ov9N6LI755yiml2SpOI3ZAhMmAAzZ0Zf790b2raF3XePT1ySJEmSpB1TzMnef/4JG7Q99hisWFH4+yIRk72SpNKtYkV4+WVo3RrWrMm6vnIlnHlmSASXK7KPVSVJkiRJKlhMv4Ju3AhHHgmffgoZGUUVkiRJpcdee8Ejj+T+AHP553N5p8d4ThhzUXwCkyRJkiQVj/PPhypVcl9fuzbr/NBDtzxOJAIffxxTKDElex98ECZNCuf77gsDBoSvrdaqBQlWA5Yk7SDOPhs+/BCef/5/73mOR+hH5ffWMfOePWl1ZSH+UpckSZIklU7Tp+ffFomE1wkTCh4jIyOrbwxiSva++GJ4bd8exo2DpKSY45EkqVR6+GGY9flqrvipH2czYvP1+tecxYruX5O8T504RidJkiRJ2i5KWLmDmNbfzpsXEs7XXGOiV5K0Y6taFUb+30/05OWo6/XSl7Cgy3mkp6bHKTJJkiRJ0naRnl60R1pazCHFlOxNTAyvjRvHHIckSaXePme0ZPIJ/811feryhgy9LzUOEUmSJEmSdiQxJXv32iu8Ll9eFKFIklT6Hfz6pUzd+VgA/qIGJ/IGF/MwV99YgS+/jHNwkiRJkqQyLaZk7xlnhLIUo0cXVTiSJJVukYQIe0wcxjtJp9CCrxnFiQBs2gSnnw6rV8c5QEmSJElSmRVTsvfii6FlS3jiibBBmyRJgtpNk9npvVdZHEmJuv7jj3DRRSWufr8kSZIkqYyIKdlboQKMHQtt20K3bnDVVTBzJmzYUFThSZJUOnXtCjfdlPv6Sy/B448XeziSJEmSpB1AzBu01a8PkyeHr6fed19I/FapEtoKOsqVK6ofQZKkkunmm6FTp9zXL7ssfDgqSZIkSVJRiinZm5GRdeR8X5hDkqSyrFw5ePFFSE6Ovr5xYwYvH/UMqxatik9gkiRJkqQyKab1teeeW1RhSJJUNjVoAM8/D0cfHd5XYxVP0ZtTl7/G5APfo8OiV4gkROIbpCRJkiSpTIgp2TtsWFGFIUlS2dWtG9xwA4y5fSavcip78hMAHX99jQk9H6bLqxfHOUJJkiRJUlkQUxkHSZJUOIMHw+M1r9+c6M3U4bUr+f656XGKSpIkSZJUlpjslSSpGJQrBykfPcPySJ2o66uozi3XrmXlyvjEJUmSJEkqO4o82fvrr/DllzBpEqxfX9SjS5JUetVrvQu/DBlJOqFG73i60JKvePn3LlxwgZuXSpIkSZJiUyTJ3nXr4NZboWFDSEmBAw6Arl1hwYLofq+8AuecA5dcUhSzSpJU+rS+5nAmdBnIbdzA4XzEEnYB4M034YEH4hycJEmSJCk269bBgw+GY+nSLff//fes/v/8E/P0MW3QBrBoUdhhfPbs6BVJkTw2Fm/dGs44I5yfdVZICkuStKM5+OOB3HokpH0Sff3qq6FjR2jfPj5xSZIkSZJi9O67cNllYUXsxYXYjLtOHbj3XvjlF9h9dzj++Jimj2llb2oqHHcc/PBDSO6eeioMHZp//z33hE6dwvnbb8cysyRJpVdiIrzwAtStG309NRVOOw3+/DM+cUmSJEmSYvTGG+H11FMhoRCp18RE6NkzrKJ99dWYp48p2fvsszBrVth0ZvRoePll6N+/4Hu6dw+xf/55LDNLklS61asHL76Y++/+RYvgvPOs3ytJkiRJpdK334ZVsZkrXgsjs++sWTFPH1Oy95VXQuwXXhiSuIXRsmV4nTs3lpklSSr9DjkEBg/Off3zt1cw+rw3ij8gSZIkSVJsFi8Or7vtVvh7dt01vP76a8zTx5Ts/eab8HrCCYW/p06d8PrHH7HMLElS2XDDDXDkkVnvD+QzvqIlxzx3Gt888mn8ApMkSZIkbb3MTdZSUwt/T2bftWtjnj6mZO9ff4XX+vULf096eiwzSpJUtiQkwIgRsGv9dK7mv0ygCw34lXKksfOA01j2ze/xDlGSJEmSVFg77xxeZ88u/D2ZfWvXjnn6mJK91auH11WrCn/PTz+F1yKIXZKkMqFOHXjr9m+5nRsoR9rm6/XSl/DbwT3ZtG5THKOTJEmSJBVa27ZhE5bnny/8PcOHh1q5rVvHPH1Myd699gqv06YV/p7MDelatIhlZkmSypY25+3Pp0ffnuv6glW1GHjdxjhEJEmSJEnaaiedFF4/+QSGDt1y/6FDYdy4cH7yyTFPH1Oyt1u3kKh+9NGschQF+egjeP31kKgu7IZukiTtKLq8czVf1D8RgDQSuIY7OYk3uGPoTrz2WpyDkyRJkiRtWc+esO++IWl62WVwzjnw9de5+339NZx9dugTiUCTJuF9jGJK9vbrB1Wrwvz5cPrpsGZN3v3S0uDxx8NGbunp4euq558fy8ySJJU9kYQI+3wxjOlJnTiCD7mLa4AIEP7e3JqST5IkSZKkOEhICKUNatYMCd8XXgjlGWrWDEngZs3CeevWMHJk6FOjBrz5Zrg31uljubl2bXjyyXD+1luQkgJnnpnV/uCD4f2uu4bE8Lp1IeZnn4VKlWKZWZKksql6SnWSpk5kSuVDo66vWRO+0ZPfB6uSJEmSpBKiSROYPh06dAjJ3IyMsOnZ7Nnwww/hPPN6x46hRu7eexfJ1OViHeC008Jq3YsugpUr4eWXw8pjyEoEZ2SE18qVQ73hbt1inVWSpLJrv+YRnnwSevWKvv799/Cvf4UPfzP/rpUkSZIklUCNGsHnn8P48TB6NHz5JSxfHtrq1IE2baBHD+jatUinjTnZC6GEQ9eucP/9MGoUzJ0b3b7bbnDiiXD11WGVryRJKtiZZ8LkyfDQQ9HXX3opfPB7ySXxiUuSJEmStBW6di3yhG5BiiTZC1CvHgwZEo7Vq2HZMkhNheTkUO5BkiRtnXvuCd/8+eKL6OufXP42XcvXYv++B8UnMEmSJElSiRRTzd7WrcPx+OPR16tWhT32gKZNTfRKkrStKlSAV18N3/ABSCCNW7mRUek9qHPxaSz/dml8A5QkSZIklSgxJXtnzYKvvy6y+sGSJCmHBg1C6YadI8t5n6O4kdsBqJ/+G4s7n07qhtQ4RyhJkiRJKiliKuNQty4sWRJW8kqSpO3j0ENhxDEvcvg7H0ddb7VyPOO63MghU+6MU2SSJEmStINq3Di8RiLw00+5r2+LnGNtg5iSva1ahWTvnDmhnIMkSdo+Dn9rAFN2/YT2v7+1+dp6knhu6t789QacdFIcg5MkSZKkHc3CheE1Esn7+rbIOdY2iCnZe+GF8O678OijcMYZMcciSZLyEUmIsPeU4Szcsy0NN/3IfBpxMq/zFa14/TzYZ59wSJIkSZKKwbnnbt31YhJTsveEE0L8w4fDOefAI4/ATjsVUWSSJClK9ZTqLH3xDd447RYuTH+CldQEYPXq8Hfy1KlQvXp8Y5QkSZKkHcKwYVt3vZjElOx97jno2hVmzoQXXoB33oHjjoMWLaBmTUhMLPj+c86JZXZJknY8TU5uzpRnX2Vljr9D586Fs86Ct96ChJi2X5UkSZIklVYxJXvPOy+6lMTKlTBiRDi2JBIx2StJ0rY4++ywivehh6Kvv/MODB4cDkmSJElSHCxaFF533XXLK2EzpaXBr7+G85SUmKaPee1PRkbWkfP9lg5JkrRt7r0XDj449/VbboFRo4o9HEmSJEkSQMOG0LgxzJlT+HsWLsy6L0YxrewdNy7m+SVJ0jYoXx5efRXatIHFi7OuV2Idf516GT+9eTl7HOuObZIkSZJU7LZ1lWsRrI6NKdnbpUvM80uSpG20887w5pvQqRNs3Agp/MybnEjr1JksOGkCq36cSvUUd2yTJEmSpBItM8mbvV7uNoqpjMMVV4Rj9OiY45AkSdugbVt4/HHoyjim05bWzASg0aa5zGl3Fump6XGOUJIkSZJUoOXLw2uVKjEPFVOy94EHwlHYWsOSJKnonXsuDGzxFnVYEXX9gGXv8MmRQ+IUlSRJkiTtwAq7SnfNGnjwwXAe75q9tWvDH3+EzeUkSVL8HPT5XXy1y1e0XDVh87Xv2Zt7x7Ug4e4ZdOmSRqW5cyE9fcuf0iYnx7wDrCRJkiTtEPJL0B55ZNhspSAbN8KyZeH3tEgEunWLOZyYkr177RWSvb/9Bi1bxhyLJEnaRuUrl2fXz17h9/1bUi99CQD7MpsxHAtX87/3hZSUFHaONeErSZIkSQVbuDD3tYwM+PXXrRunaVO45pqYw4kp2duzJ0yeDC+/DN27xxyLJEmKQZ1mOzN/0F1w81mxDbRhA6xYYbJXkiRJkrbk4IOjSzZMmBDet2lTcA3eSCQstNlll7Drds+eUKlSzOHElOzt2xeGD4cXXoDOnaF375jjkSRJMWh8zD5wc7yjkCRJkqQdxPjx0e8T/rdF2rPPwr6F/n5lkYkp2btkCTz5JFxwAfTpAy+9BL16QYsWULPmlksCumBIkiRJkiRJUplxzjlh1W7NmnGZPqZkb8OGWauUMzJg3LhwFEYkAqmpscwuSZIkSZIkSSXIs8/GdfqYkr0Qkrx5nUuSJEmSJEnSDi8jA/78E9atCzV6t1QOIQYxJXsHDiyqMCRJkiRJkiSpjMjICBudPfUUTJkC//wTSh188010Ld/33oPJk6FaNbjqqpinNdkrSZIkSZIkSUXlr7/g5JNhwoTwvqByCPXqwa23hkTw0UdDs2YxTZ0Q092SJEmSJEmSpCynnALjx4ckb9u2cOWV+fdt1Qpatw7no0bFPLXJXkmSJEmSJEkqCq+8AuPGhZW6jzwSSjjcdVfB9/ToERLDkybFPL3JXkmSJEmSJEkqCi+8EF579oR//7tw92Su7J09O+bpY6rZO3FibJMffHBs90uSJEmSJElSiTF9eljVe8YZhb+nXr3wunx5zNPHlOzt2jXEvi0iEUhNjWV2SZIkSZIkSSpB/vgjvKakFP6ehP8VX0hLi3n6mMs4ZGRs+yFJkiRJkiRJZcZOO4XXdesKf8+iReG1Vq2Yp49pZe+4cVvus3Yt/PADjBgBX38NnTrB4MFZCWtJkiRJkiRJKhMaNoS//oKZM6Fjx8Ld8+674XXffWOePqZkb5cuhevXvTtceSXcdBPcfjs8/zw880wsM0uSpO1pxnWv0PqD1vEOQ5IkSZJKl8MPhxkz4OmnoW/fLdfA/eqrkCyNRODII2OevljX1956a4h5+HAYNao4Z5YkaQeRnAxJSTEPs/+HdzHznk+KICBJkiRJ2oH07w8VK4Yk7iWXQHp6/n3ffx+OPhr++SeUf7joopinj2ll77a48EL44AN47DE44YTinl2SpDIuJQXmzIEVK6Iup6WlMWfuXJo2aUJiYmJU28IFGSzv2Y92aVM3XytHOg2vPoX5+06m8dFNiyV0SZIkSSr1dtsNhgyByy+HRx6Bjz6CE0/Man/99VDvduzYUPM2IyOs6n3wQahRI+bpiz3Zu+ee4XXmzOKeWZKkHURKSu6dX9PSWJ+QAC1bQo5kb8PWsGLwLaTd1J1Esj51TspYzx0XzOOOWU1JTi6GuCVJkiSpLLj0UtiwIdS0nTMH7rwzq5zDoEFZ/TIywsZmd90F555bJFMX+zZpq1eH17//Lu6ZJUlSftreeBSfnnDP5ve/U5eujOep34/lpJNg48Y4BidJkiRJpc2118L06XDaaVC5ckjsZj8qVIAePWDKlLAKuIgU+8reV14Jr/XqFffMkiSpIAe/fikT9/uBGj9M5jjeZhG7AzBpUigd9eyzW95bQJIkSZL0Py1awEsvQVoafP89LFsGqalhr5VmzYpkv5Wcii3Zu3o13HsvPPpokW0uJ0mSilAkIULH6Q9xyrEbWDSualTbc89BkyZw441xCk6SJEmSSqvERGjevFimiinZe+ihW+6Tng5//glz58KmTWGVcvXqcP31scwsSZK2h/KVyzP8jfIceCD88EN02003wV57hW8hSZIkSZJKnpiSvePHh1W6GRmFv2ePPeCFF6Bhw1hmliRJ20uNGvDuu9C+PSxfHt12zjlh77cOHeISmiRJkiSpADElew8+eMu1+xIToWpVaNwYunaFo4+GcsVeKViSJG2NRo1g1KjwLZ7sm7NFNq5n9qFX0OCDa2nQqWG8wpMkSZKkki0tDcaMgQkTYP78UOM2La3geyIR+PjjmKaNeWWvJEkqmw48EIYNgzPPDO93ZilvcTwd1k9h3uGTWDX3M6qnVI9vkJIkSZJU0kyZAmedFZK8mQoqjZBZOqEIdsR2ja0kScrXGWfAvHnwysBveYdjacjPAOy18Tumt+5Jy8XvUC7Jf05IkiRJEgA//QRHHglr1oQEbvnyYbfrWrUgIWG7T+9vZ5IkqUD/+Q90en4oDX/8Oep62z/e5/2DbubI6bcXxQfQkiRJklT63XZbKNmQkACDBsFll4Uat8Vkq9LJq1bB6NHhmDlz6yaaMSPr3tWrt+5eSZIUP5EIHDjtAb6pelDU9a/Zn94z+jJ0aJwCkyRJkqSS5qOPwi9R/fuHlTPFmOiFrUz23nQTnHgi9OoFFSps3UQVK4b7TjwRBg/eunslSVJ8JdVIYpcpb/JzucYAvMMxdOJTFrMbl18Ob78d5wAlSZIkqSRYtiy8nnJKXKYvdLJ3xQp44olwPngwNGu2dRM1awb/93+hVMXDD8Nff23d/ZIkKb6S96lD6qh3ubfidRzPW6whfEKdng6nnw7TpsU5QEmSJEmKt9q1w2uVKnGZvtDJ3pdegk2boF49uPjibZusXz/YdVf4558wniRJKl32OGZvWrx7BwnlEqOur1sHxx4LCxbEKTBJkiRJKgnatQuvc+bEZfpCJ3vHjQvlJk48cetLOGSqUAFOOims7v34420bI9Mvv8DQoXDWWbD//lC/fhi/alVo3hwuuQRmzy7cWKtXwy23hHGqVoUaNeCAA+D++0OCuzBmzoRzzoGUFEhKgl12gZNPhvHjC3d/RgYMGwZdukByMlSuDHvvDddcA0uXFm6MpUtD/733DvcnJ4fxhg0L40uSVBQOOwwefTT39WXL4Oij4c8/iz8mSZIkSSoRBgwIibjHHovL9IVO9n71VXg99NDYJuzSJXq8bfXWWyGh+8ILMGsW/P57SMyuWQPffhsSwfvvDw89VPA4CxZAy5YwcGAYZ82asBHdtGlw+eXQseOWS048/TS0bw/PPx+S0Bs3wpIl8MYb4b/XlmoUb9wI3bvDBRfAxInwxx+wfn34AOCuu8LPMX16wWNMnx763XVXuG/9+jDOxIlh3O7dwzySJBWF3r3hhhtyX58zBy7s9isbVm4o/qAkSZIkKd4OOwyuvx4mTYJ//SuUOChGW1WzF8KK1Vhk3r98eWzjVKoExxwD99wDn3wSVvGuWAHffQdPPQV77hmSvwMGwLvv5j3GP//AccfB/PlhvKFDYfHikAAeNAgSEuDLL6Fnz/zjmDgR+vQJc7VsGVYsL1sGkyeHRG9GRhhr5Mj8xxgwAMaODef9+4dflJcsgREjoFatMF6PHln/D3JasSK0L1sWyoKMGBHunz07jAdh/AEDtvAfVZKkrXDrreEbNtm14CsemnYAM/Y/l/TU9PgEJkmSJEnx8txz0LQpHHggPPMMNGoEl14aVvo+99yWjxiVK2zHzFWh5cvHNmHm/bGuMr3wwnDkVLs27LtvKKHQrBn89ltY7XrMMbn7PvFESA4DDB8Op56a1TZwYEgAX3stfPhhSBjnNcaVV0JaWkhijxsXSkAA1KkD770XykF8/XUY56STQomH7GbNCslpCDWNs69E7tULGjeGTp1C8vbOO8PPktOQIaE9IQHeeQc6dAjX69UL46Wnh6/bPvVUSPg2b57nf1JJkrZKJBK+3fLrr+HvwCN5n9c4haqsYddfXmH8gbvTdep/4x2mJEmSJBWf884Lvyxl+v33LZceyBSJhDqxMSj0yt7MjeSWLYtpvs33Z463vdSoERK+EFbn5iWz3mCrVtGJ3kyXXx6Sttn7ZjdtWlZ5hWuvzUr0ZqpQIauEw+LFIRGb02OPhdW/FSqEFcA5dewYVu1CSNbmrCG8aVNWsvj447MSvdkNHhyS7BkZ8PjjudslSdpWFSqEskWX7vY673IMVVmzua3rtLuYcNrDcYxOkiRJkuIgI2PbjxgVemXvrruGRPTkyWHzlW31xRdZ421vmauIc66mhVC64fvvw/kpp+R///HHh2Tqxx+HncYrV85qf/vtrPP8xujePdyzbl3on7Nf5hhdumQllnM65RQYNQpWroRPP4VDDslqmzQp1BguKIY6dcL4H30Eo0cX/sMESZIKo0YNuPrldqzotDP10pdEtS159VPeGtWP40+I5H2zJEmSJJUl48bFdfpCJ3sPPjisYn355ax6tlsrPR1eeimsSO7ceevv3xobNmQlUtu1y92efbVv+/b5j9O+fUj2btgAP/wAbdrkHqNBg/xrGZcvH1YOf/YZzJgR3bZiRdjQrTAxZJoxIzrZuzU/x0cfhflWrIDk5Pz7SpK0tXbtmMKckWOocnrnzat77+IqruVOks6MMH58KG0kSZIkSWValy5xnb7QKdvMUgI//gj33bdtkz34IMybFz1eUUpPD7VrR48Oyel586BiRbjlltx9587NOm/cOP8xGzXKOp89O+8xCro/+xjz5oUYM82ZU7gYdt89K7meXwwJCaHflmLIOa8kSUWlac+WzLn1NTZSgYsZyjXcRQYJrF8Pxx4LP/0U7wglSZIkqWwrdLL34IPhoINC6YjrrsuqE1tYzzwDV10VVvUeeGDRJrm7dQvjJiaGFbbHHx/q6e6zT1jN2rZt7ntWrMg6z698AsDOO2ed//FH3mMUdH/2MTZuhDVZpQwLHUP58ln1gPOLoWZNKFfAOu2Cfg5JkopK2xuP4o3//sTDXBx1ffnyUAYq+999kiRJkqSiVegyDgBDh0KnTrB+PfTpEzZkufJKOPTQ6E3mMmVkwCefwL33wtix4X3lymGc7W3nneGSS6Bly7zb167NOs+rpm+mSpWyzrMnarOPUdD9eY1RrdrWxZB9jKKKoSDz52cwbVpavu3164ejtEhLS4t6lYqbz6BKguJ8Dk+7oj7frUrnttuiP1OeNw969Mjggw/So/5e0o7DPw8Vbz6DKgl8DlUS+ByqJMj5HGYUweZk2spkb8uWMGIEnH46bNoE778fjsqVoUULqFsXdtopJBOXLoWvvw4bk0FI9JYvD889l38CdluNGgWpqaFEwh9/hE3M7rgD+vYNieV33okuY5AZT2Fk75dXQrug61saozBj5+ybs19+1wsbQ15uvjnC6tWJ+bb/61+/0afPknzbS6pZs2bFOwTt4HwGVRIU13N4wgnw1VcNeffd2lHXJ0+OcNxxf3PnnfNJzP+vGpVx/nmoePMZVEngc6iSwOdQJUHmc7hw4cL4BlJU8qonuyWRSFjJWb06NGkSNg2rWnWbpt+qZC+EX94mTAgJ359/DtfWroXJk3P3zZ5gTEkJm7N16LBNcRYo+6rWatVCYveUU8JGZlOmhPrAX38dvancTjtlnW/YEP0+uw0bss6rVIluq1IFVq4MK50Lkt8Y2ecs7Bg5Y8gcY1tjyMstt2Rw0EEFreytS/36dQsepARJS0tj1qxZNG/enEQzC4oDn0GVBPF4Dl99FY49NoNPPon+lLH++PdYc/7nHPzV/UQStvAJpMoU/zxUvPkMqiTwOVRJ4HOokiDnc7h48eJ4h1Q0Bg3a8krLLalQAU46CW67DRo23KpbtzrZC9C+fdgYbPhwePpp+PLLsLI2p8TEkIju3RvOOSfEWVwqVYIhQ0LC99tvQzmJww/Pak9Ozjpfvjz/ZO/y5VnntaMXJ5GcHJK92fsUNEaFCtHz5IwhP6mpYZ78YoDQnpqaf93egn6OnBo3jtCuXdn7wz4xMdG/xBRXPoMqCYrzOaxUKZR86twZMheNXM693MuV8D2MPyKZruMHFUssKln881Dx5jOoksDnUCWBz6FKgsznMBJrgrQkyf5V/C2VF8irz8aNYdXsO+/Au++GurqFVOgN2nIqXz4kcSdPhr/+gs8/h9GjQ5mHt96Czz4L17/4IvQrzkRvpvbts85nzoxua9Ik63zBgvzHyN629955j1HQ/dnbmzSJXl3ctGnhYvj551CioqAY0tJg0aItx5BzXkmStqfq1WHMGEhpkM6dXBMSvf/TdcJgJp75WByjkyRJkqQilp4eknQdOoQkbo8eYRXMokXhq/cbNoTzN96A444LfTp0CMm7P/+EiRPhX/8KSeDVq8MK31WrCj39Nid7s6tSJcR07LFw5pkhzo4d818tW1yyrzbO+eFAmzZZ51Om5D9GZltSEuyzT95j/PILLMmnhG1qKsyYEc5bt45uS06G3XYrfAx5jbG1P0dKSvSKYkmStrcGDeCj536jd+TpXG0dXxzA+4/Oj0NUkiRJkrQdrF0LRx0F06bBiy+GzcZOOCH8YlShQjgaNAjX3noLRo6EqVPDPYmJYRXv44+HZHBCQtig7NFHCz19kSR7S6qJE7POGzeObmvcGPbdN5y/9lre96emhtXKAIcdFjaiy+6447LO8xtjzJisTeqy9885xvjxsGJF3mNkjl2jRu5V2507h1VTBcWwYkWos5xfDJIkbW97HdKAXx97h3VU2nztH8pzNs9z/OWNo/7OliRJkqRS68EH4YcfQqmDnj233P/000PfuXPh/vuzrvfoAaeeGlb+jhlT6OlLbbJ39uyC2//6C669NpxXrRpdrzdT377hdcYMeP313O333w9Ll0b3za5dO2jbNpz/97+5V1Rv2hRqMkNI2B97bO4x+vQJq47/+QcGD87dPnVqSPJD+P9evnx0e2Y5DQgfFEydmnuMQYPC+JEIXHRR7nZJkopD84s68u1/XiGVRFazE90Zw8uczsaN4d8xbgYtSZIkqdR75ZWQhDvllMLfc+qp4TVngvLkk8PrlhKh2ZTaZO9++4XVzsOHhw3YVqwIZS2+/TYk0Fu0gO++C33vuAOqVcs9xkUXQbNm4fzss+GRR+C330KN3FtugeuuC21HHAHHHJN3HPfcE1ZYL14cNoPLXKE7dSocfXRWreA77wylIHLaf/+sZO1DD8Ell8C8eSHJPHIkdO8eSn3Ur5+VvM7puutCe3p6iHPkyHD/vHlhvIcfDv169w7zSZIULwfcciyf/Ws4XZjAx2R9ErtqFXTrFv4OliRJkqRSa/7/ytTVqlX4ezL75tzUq1Gj8LoVNXvLFX7WkiUtLax4zVz1mpekJLj9dujfP+/2ChXg7bfDqt/580O/nH3btIGXX85/joMPhsceg379QmL3kEOi2yMRGDgw1DLOz9Choe7v2LHhfOjQ6Paddw7lJPKrtZucHNqPOQaWLYNevXL36dYt97iSJMVDlyd6cWojmHlD9PXffgtlqj791PrykiRJkkq5efOgVavC981L5iZkVasWetpSu7J34sRQnuDQQ2GPPcJmcBUqQJ06IQE7cCDMmQOXX17wOI0awVdfhRIKzZuHzeaqVQvlGe69FyZPhpo1Cx6jd++wAdpZZ2XVWq5XD048ET75JMRSkIoVQ+mNZ54JNXhr1YJKlaBJE7jqKvjmm6xyEflp2zb0u+qqcF+lSmGczp3DuGPGhHkkSSoJrrsOBgzIfX3OnFD2aO3a4o9JkiRJkmLWpEl4zfyqfWE89FD0vZkWLQqvW7EaptSu7O3cORxFoWpVuPnmcGyrVq3g+ee3/f5IBM4/Pxzbqm5duOuucEiSVJJFIlm18V95Jbpt6pR0nuz4LP2/OJvylcvneb8kSZIklUinnw5ffhm+snjWWaFubF71ZQH+/juUC/j00/BL0umnR7dPmhRemzYt9PSlNtkrSZJKt4QEeO65UOv+k0/CtXJs4hku4OxZI/h0/4kcNHcYkYRIfAOVJEmSpMK65JKsTcZefDF83f6EE8LX8nfeOfRZtgymTQv1aTPr8TZrFu7NtGkTvPRSSAIfdVShpzfZK0mS4qZiRXjzTejSBeZ9tYbXOIVuvA9Ap5+GM/6A2nSZercJX0mSJEmlQ/ny8NFHYXOtL7+ElStD8nf48Nx9MzLCa6tW8O674d5Mv/8OV1wRzk8+udDTl9qavZIkqWyoVg3eew9eqnLh5kRvpq5f3svHx9wbp8gkSZIkaRvsvHPY4Ouhh8KK3YyMvI999oEHH4SpU8MGYNntthtceWU46tYt9NSu7JUkSXFXrx40f+s2lh8xjjoZyzdf/436XDb2KPo9EkpZSZIkSVKpkJAQfonp1y+s0p01C/78M7TVqgX77Qf16xf5tCZ7JUlSibD7YXvyw/NjqXhWV6qxmp9ozOF8xEIacfHFUKMGnHlmvKOUJEmSpK1Ur17ulbvbiWUcJElSibFPr9bMf+AdpkUOoBOfspBGQPiG07nnhjJWkiRJkqS8meyVJEklSstLDmbpW1+wPDH6K02pqXDKKTBpUpwCkyRJkqQSzjIOkiSpxDn2uAjDh8PZZ2dtUAuwYQMceyyMGwetW8cvPkmSJEkqlJUr4auvYMUKWL8++hecvJxzTkzTmeyVJEklUq9e4d9FF18cff3vv+G0I1fywcgVND5yz7jEJkmSJEkF+vRTuPlmmDCh8PdEIjEney3jIEmSSqz+/eH//i/62s4s5bU/ulKx+6H8NuWX+AQmSZIkSfl55hk45JCQ6M3I2LojRiZ7JUlSiXbjjXD55eF8dxbyKZ1oydfsmvYLGw4+ghU/LI9vgJIkSZKU6aefoG9fSE+HZs3gzTezdpqOREL79Onw6KPQokW43rkzfP89zJ8f8/QmeyVJUokWicDdd8MVpyziUzqxFz9ubmv8zxyWt+nG34v/jmOEkiRJkvQ/Q4fCpk1QqxZMnAjHHw8pKVntjRqFDUj69IEvv4QBA8Iu1JddBrvvHvP0JnslSVKJl5AAdz6/C4vrH5Cr7c/1SZxxWhrr1sUhMEmSJEnKbvz4sGKlf3+oUaPgvgkJ8MAD0LEjfPghPP98zNOb7JUkSaVCuaRytPx+JDNqHrb52liO4kg+YMzkmpx4ImzcGMcAJUmSJOnnn8Nru3ZZ1yKRrPPU1Nz39O0b6vWOGBHz9CZ7JUlSqZFUI4m9vn2Tb6scwCucSg9Gs44qAHzwAfTsGb4xJUmSJElxsXZteN1116xrlStnna9alfueffYJr99+G/P0JnslSVKpUnWXquwy6wNu3fdFNlEhqu2tt+DssyEtLU7BSZIkSdqxVasWXrN/7bB27azzBQty37N6dXj944+YpzfZK0mSSp1ajarz/keJ7Lln7raXX4bevcPmt5IkSZJUrDJ/Scme1K1aFXbbLZyPG5f7nk8/Da/Vq8c8vcleSZJUKtWvDx9/HL2xbaZnn4WB5/1MRnpGscclSZIkaQfWvn14nTYt+nr37qEu7913w+zZWdc//xzuuSfU9e3QIebpTfZKkqRSKyUFPvkkJH6z68Bkrny+BRPaX2PCV5IkSVLxOeqokNQdNSr6+hVXQPnysGIFNG8ObdvCvvvCwQdn1fG97LKYpy8X8wiSJElxtMceYYVvly6wfDl0YTzv0p0qrKfr9LuZ3u5v2j7ZZ+sGTU7Oe8mwJEmSJBXkyCPhnHPCRiLz50PjxuH6XnvBM8/AhRfCP//AjBlZ90QicPvtcMghMU9vsleSJJV6++wDH34Igw/6gJFre5BE1mYIbWc8AW2e2LoBk5JgzhwTvpIkSZK2Trlyoa5cXnr1CmUehg2DWbMgNRWaNAnJ4dati2b6IhlFkiQpzlq0gP97qCap5yfGPtiGDeHrVSZ7JUmSJBWlPfeE227bbsNbs1eSJJUZzc5rx2/XPhjvMCRJkiQpLkz2SpKkMqXJaa3iHYIkSZIkxYXJXkmSJEmSJEkqA6zZK0mSJEmSJElb66STina8SARefz2mIUz2SpIkSZIkSdLWGjUqJGhLEJO9kiRJkiRJkrStMjLiHcFm1uyVJEmSJEmSpG1VuTKceSa8/z6kp8d2xMhkryRJkiRJkiRtrTPPhEqVYN06ePFF6NYNUlLgxhth7ty4hGSyV5IkSZIkSZK21ogR8Pvv8OSTcNBB4drixTBkCOyzD3ToAI8/DitXFltIJnslSZIkSZIkaVvstBNceCFMnAg//gj/+Q80bBjq+E6dCv36Qf360LMnvPtukZRqKIjJXkmSJEmSJEmKVaNGMHgw/PQTjBsH554LVarAxo3w2mvQowfsuitcfTXMmrVdQjDZK0mSlIdp0+IdgSRJkqRSq0sXGDYslHkYPhy6dg3Xly6Fe++Fli1DmYciZrJXkiQpD5ddBhMmxDsKSZIkSaVa5cpw9tnw8cewcCEMHAgVK4YyD998U+TTmeyVJEnKw/oN0OPoTUx9fGa8Q5EkSZJU2n33HQwdCk88Eco6bCflttvIkiRJ8ZCcDElJsGHDNg+xniT+ogZPru9F83+/zZd/vkWb648swiAlSZIklXl//AEjR4YyDjP/t4gkIwMiETj0ULjggiKf0mSvJEkqW1JSYM4cWLFiq299/HF4/An4k5rczo2cxqsANLuhB9NT36Ttf44u6mglSZIklSWpqfDuu/Dss/Dee7BpU0jwAuy1F5xzTjh22227TG+yV5IklT0pKeHYShc9BkvqZdD4lnM5kxc3X09iI81vPoEp6W/RfmC3ooxUkiRJUlkwY0ZYwfvii2FFL4Qkb/XqcNppcO65cOCB2z0Mk72SJEn/E4nAoMERxn/WBD6ObltOHc69dS+GtIATTohLeJIkSZJKkt9/hxEjQpL3++/DtYwMSEiAww+H884LvzwkJRVbSCZ7JUmScuj60U2MP7o8XcdeB8Bv1OdQPmFe6h6cemr4sP6UU+IcpCRJkqT42m03SE/PKtOw994hwXvWWbDLLnEJyWSvJElSHrq+dy0TepSj6dt3hUQvTYBQguv00+GFF6BnzzgHKUmSJO3ofvwRmjfP2qB52LCQcC0OaWnh64GVKoUVvO3bh+uvvbbtY15ySUwhmeyVJEnKR5fRV/L4kAuYc33NqOtpaXDmmWGvhbPOilNwkiRJkqBv36xEb7xs2AAvvRSOWEQiMSd7E2KLQJIkqWzrc11NHnkk9/X09LCJ7vDhxR+TJEmSJEK93I8+gkaN4hdDRkbRHjFyZa8kSdIW9O0L5crBRRdFX8/IgGvOW8Zuk8dz6GOnxSc4SZIkaUf0119w5ZWQmAj33RefXZTHjSv+ObfAZK8kSVIh/OtfIeF74YVZH7gns5yPOIzmj3/LhJUr6PJSv/gGKUmSJO0orr4ali2Dyy6DFi3iE0OXLvGZtwCWcZAkSSqk888PZRsSEqAWf/ARh9OcbwHo8nJ/Jhzz3zhHKEmSJO0APv0UnnkG6teHwYPjHU2JYrJXkiRpK5x9NrzwXBrvcTQt+CaqrcuYa3ntpBeKotSWJEmSpLxs2gR9+oSv2917L1SrFu+IShSTvZIkSVvp9F6JpF1+NZtyVMT6mEM5+82TuOqqItlbQZIkSVJO//0vfP89HHYYnH56vKMpcazZK0mStA063nsqUypVpOXtp1KRf/icjhzPW2ygEvfeC2vXwiOPhJIPkiRJ0g5vyZJwZEpLo9LcuZCeDomJVFywYMtj/PQT3HorVKgADz+8/WItxUz2SpIkbaP2t/VgRvV3WX/9YI5Jf5u17LS57fHHQ8J32LCwsZskSZK0Q3v88aj6uonAvtmaG1SsCLvvXvAYffvChg1www3QtOl2CbO081cPSZKkGLS+5nA+7XgYGcdG4O/othEjQsL3xRehYsX4xCdJkiSVCH36QI8em9+mpaUxZ+5cmjZpQmJiIosnTQpfjcvPyJHw4YfQsCHcdNP2j7eUMtkrSZIUo06dI3zyCRx5JPz5Z3Tbm2/CCSfA669D5cpxCU+SJEmKv/r1w5EpLY31CQnQsiUkJrJx8eL87125Eq64Ipw/+CBUqrQ9Iy3VrCInSZJUBNq0gQkToF693G2rx37K17sdw+rfVhd/YJIkSVJpN3gwLF0aVgYfd1y8oynRTPZKkiQVkf32g4kTYbfdsq61YTpj6E7HP8fwc5MjWLngr/gFKEmSJJVGmZu3jR4NkUjuo1GjrL7nn591feHCuIQbTyZ7JUmSitBee8GkSbDnnrAfs3ifo6hGWNG739opLN33EJZ/uzTOUUqSJEkqi6zZK0mSVMR23z2s8F241+XUXhtdxLfphq/5z6GjuHBqHxo2jE98kiRJUqly330waFD+7b/9BsccE84HD87aCG6XXbZ7aCWNyV5JkqTtoH59qPDlS/zQ6ij2WT9j8/U7uYZbl/fhmYPggw+gWbM4BilJkiSVBtnLNOSlRo2s85SUsOnbDsoyDpIkSdtJ7abJ7DL7E76peiAAT3Eh1zEECIsPDj4YpkyJZ4SSJEmSyhKTvZIkSdtR9ZTq7PHjBzyz5+38m8eAyOa2P/+Eww6DDz+MX3ySJEmSyg6TvZIkSdtZlZ2rcNZ313PyabkraK1dG8qLvfpqHAKTJEmSVKZYs1eSJKkYVKgAI0dCzZrw+OPRbZs2Qc+ekDhzOifd3jY+AUqSJEmlVcOGkJER7yhKBFf2SpIkFZPERHj0Ubjxxtxtl2bcx0l3tGP8UXeQke4/VCVJkiRtPZO9kiRJxSgSgVtvhXvvzbp2DsO5jysA6PrBDUw44GoTvpIkSZK2msleSZKkOLj8cnj2WTg+YTRPc2FUW9cv7+GTZgNITY1PbJIkSZJKJ5O9kiRJcXLuuXD9dZCaYxuFfyjPkNnHc8opsH59nIKTJEmSVOqY7JUkSYqj9rf1YPZ9Y/mbqgCkE6EXL/ARR/DWW3DkkfDXX3EOUpIkSVKpYLJXkiQpzlpe1pVfR4xjeaQO/+YxXuPUzW2ffgqdOsEvv8QxQEmSJEmlgsleSZKkEmCfXm1Y+cUc3k+5KFfb999Dx47w3XdxCEySJElSqWGyV5IkqYTY64CafPYZ7Ldf7rZffw0rfCdNzCj+wCRJkiSVCiZ7JUmSSpAGDWDiROjcOXfbupUbWX3IcXxx7ZvFH5gkSZKkEs9kryRJUglTsyZ88AGcdFLWtQjpPMt5dE9/l3b/PYWJZzwavwAlSZIklUgmeyVJkkqgpCR45RXo1w8gg3u4kjN4CYBE0jn4pX6M7/wfMtIt6yBJkiQpMNkrSZJUQiUmwkMPwbB/T+Vy7s/VXu/TV7n43NWkphZ/bJIkSZJKHpO9kiRJJVgkAuc92p5J5z9DKombry+hHt0YyyMjqnHCCbB2bfxilCRJklQymOyVJEkqBTo/cz4zB77FWiqzimp0Yyw/0xCAd9+Fww6DZcviG6MkSZKk+DLZK0mSVEq0G3QMC54ex7nVRvENLaLapkyBjh1h7tw4BSdJkiQp7kz2SpIklSL7XXAA/512CA0b5m6bPz8kfD/9tNjDkiRJklQCmOyVJEkqZZo0gc8/h5Ytc7f9+ScMOPQ7Prv81WKPS5IkSVJ8lYt3AJIkSdp69evDxIlw2mkwdmzW9XosYdSm7ux+/yKmTb2Etg+cQyQhsvUTJCdDSkrRBSxJkiRpuzPZK0mSVEpVrQqjR0P//vDkk1CFNbzDsezOIgDaff4gtHtw2wZPSoI5c0z4SpIkSaWIyV5JkqRSrHx5ePxxaNQwg2Y3nkkbZhTNwBs2wIoVJnslSZKkUsSavZIkSaVcJALX3xChdr/T2UiFeIcjSZIkKU5M9kqSJJURBz18JrMf/JC/qRrvUCRJkiTFgcleSZKkMqTFgIP5+4Fh8Q5DkiRJUhyY7JUkSSpjGnRqFO8QJEmSJMWByV5JkiRJkiRJKgNM9kqSJEmSJElSGWCyV5IkSZIkSZLKAJO9kiRJkiRJklQGmOyVJEmSJEmSpDLAZK8kSZIkSZIklQEmeyVJkpSnf/6JdwSSJEmStobJXkmSJOXp3/+G5cvjHYUkSZKkwjLZK0mSpDx99TV0aruBOW9+H+9QJEmSJBWCyV5JkiTlI4ObFv2LXU86gC+ueSPewUiSJEnaApO9kiRJZU1yMiQlxTTEepI4iTc4mxHsxFo63HUy4w+9hfS0jCIKUpIkSVJRKxfvACRJklTEUlJgzhxYsWKrb12zBq6/Hv7+/BuGcUFUW9dxA3mj2QaO+vJ2qlQpqmAlSZIkFRWTvZIkSWVRSko4ttJOwP0T4eOD3ybh8+hVvMuow+Vz+lDzIHjrLdh99yKKVZIkSVKRsIyDJEmSoiQmwpGfDeTTPs+xgYoAbKIcJ/M6i9idr7+Gdu1g0qQ4BypJkiQpisleSZIk5anTY2fz09MT+D2hPv15mE/pvLlt+XI47DB48sk4BihJkiQpisleSZIk5avZBe1J//YHvmp3Ua62TZvgootgwIBwLkmSJCm+TPZKkiSpQLvsU50JE6BXr7zbH3oIeneazZ/z/ijewCRJkiRFMdkrSZKkLapUCZ5/Hu68EyKR6LZkljN4ajfW7tuWOS9/FZf4JEmSJJnslSRJUiFFInDNNfD221C1arhWnn94jVNoyM/slrqQ3U4/kM/6vRDfQCVJkqQdlMleSZIkbZVjjoEpU2DPPeEBLqULEze3VWY9Bz16Fo+fONY6vpIkSVIxM9krSZKkrbbPPjBl4kba1Z6fq200x9F31JEccQQsWxaH4CRJkqQdlMleSZIkbZNa9SvS6rcxjO9w3eZrc2jC2TxPBglMmABt2sD06XEMUpIkSdqBmOyVJEnSNkuskEjXyXcw+YpXWRKpz4m8yd9U39y+eDF06gTPPhu/GCVJkqQdhcleSZIkxazjPafw59Sf2LTnvrnaNm6E88+HAQMibNoUiUN0kiRJ0o7BZK8kSZKKRLO2lZg2Dbp3z7v90UcTePu0L1n2ze/FG5gkSZK0gzDZK0mSpCJTowa8/TbcfHPutu68y//90odI23Z88+hnxR6bJEmSVNaZ7JUkSVKRSkiAwYNh1CioWjVc25N5vEAvEsigXvoS9unXlfEnPkBGekZcY5UkSZLKEpO9kiRJ2i6OPx6mToXWTdbwJidSg1Wb28qTSvtR1zHg2AWsXh3HICVJkqQyxGSvJEmStpu994Zxn2Swpv5eudr68QgPv9eYdu3gu+/iEJwkSZJUxpTaZO/69fDmm/Dvf0ObNqE+XPnyUKcOHHYYPPJI6LMlq1fDLbfA/vuHrxnWqAEHHAD33w+bNhUulpkz4ZxzICUFkpJgl13g5JNh/PjC3Z+RAcOGQZcukJwMlSuHX4yuuQaWLi3cGEuXhv577x3uT04O4w0bFsaXJEmKl2q7VuWARa8z7sjbSfvfPz+fpDfPcj4Ac+aEf3+9+GI8o5QkSZJKv3LxDmBb1a1Lnl/5W7ECPvkkHEOHwujRsFfuhSQALFgAhx8O8+dHX582LRwjRsCHH0LNmvnH8fTT0LdvdGJ4yRJ4442QjB44MBz52bgRTjgBxo6Nvj5nDtx1FwwfDu++C23b5j/G9OlwzDGwbFnWtfXrYeLEcLzySqiZV7Fi/mNIkiRtTwnlEjh4zDW8e+UuVHnkJQZsGhrVvm4dnHkmfP453HMPVKgQp0AlSZKkUqzUruxdvTokL884A156CX76Cf78E776KiRfIxGYPRuOPBLWrMl9/z//wHHHhURvpUohMbx4cUgADxoUNhb58kvo2TP/GCZOhD59QqK3ZUv4+OOQcJ08GQ49NKyoHTQIRo7Mf4wBA7ISvf37hyTvkiUh0VyrVhivR4+QxM7LihWhfdkyqF073LdkSfjZ+/cPfcaODfNIkiTF265n78deP75DmwOT8mx/6CE4+GD45ZdiDkySJEkqA0ptsrd/f/j555BI7dkTGjcOK3BbtAglHIYMCf0WLgzvc3riiazacMOHw8UXw667QsOGYSXuHXeEtg8/DCtr83LllZCWFso2jBsXErx16kCHDvDeeyEWgGuvhQ0bct8/axY89VQ479cv/HLTpAnUqwe9esE774Sk85IlcOedeccwZEhoT0gI/Xv1Cvc3bRrG69s39HvqqTCfJElSvO26ayh3ddllebd/NWUD8/boxpdDPizOsCRJkqRSr9Qmex96KJRyyM8VV4SVrhASrzk9+mh4bdUKTj01d/vll4fEbfa+2U2bFsonQEjm1qgR3V6hAgweHM4XLw6J2Jweeyys/q1QIawAzqljx7BqF0KyNmcN4U2bspLFxx8fksw5DR4cahlnZMDjj+dulyRJiofy5eG+++Dll2GnnaLbHuQSDt30Pq2uP4rxh/0f6anp8QlSkiRJKmVKbbJ3S8qVC6tkAX77Lbpt/nz4/vtwfsoped9fvnxIoEIoz7BuXXT7229nnec3RvfuYbO0nP1zjtGlS1ZiOafMsVeuhE8/jW6bNAlWrSo4hjp1wvgQ6hdLkiSVJKedFj5E33ff8P48hnERTwKQQAZdP7mZmXW7sXzuX3GMUpIkSSodymyyF+D338NrtWrR17/8Muu8ffv8789s27ABfvgh7zEaNAhlHPJSvnxYOQwwY0Z024oVWbXoChNDXmNs7c/xyy/51/6VJEmKl733hilT4JLjFvAI/XK1p/25krZdqjBhQhyCkyRJkkqRMpvsnTkzbLYGucsbzJ2bdd64cf5jNGqUdT57dt5jFHR/9jHmzYP0bN9AnDOncDHsvnuox1tQDAkJod+WYsg5ryRJUkmx005w/6iGTD31bv6h/ObrK6lOT15m0e8VOPRQuPXWsGeCJEmSpNzKxTuA7eXqq8NrQgL06RPdln11a37lEwB23jnr/I8/8h6joPuzj7FxI6xZk7XKuLAxlC8f6gH/+Wf+MdSsGcpWbCkGyD1GXubPz2DatPx/i6pfPxylRdr/fiNM8zdDxYnPoEoCn0OVBIV5Dju9+G++O6IVtfqeQYO0RZzPMBYSPrlOT4f//AfGj8/guefSC9y/QcqLfxaqJPA5VEngc6iSIOdzmJGREc9wyowymey9665QZxegb1/Yb7/o9rVrs86TkvIfp1KlrPM1a/Ieo6D78xojM9lb2Biyj1FUMWzJzTdHWL06Md/2f/3rN/r0WbLlgUqYWbNmxTsE7eB8BlUS+ByqJNjic9i6IvNfe44R18xi1LwTczV//HGE/fdP57bb5tO2bSH+cSPl4J+FKgl8DlUS+ByqJMh8DhcuXBjfQMqIMpfsHTsWrr8+nDdvHhK/ORX2g4Ls/SKRvPvkd31LYxRm7Jx9c/bL73phY8jPLbdkcNBBBa3srUv9+qVnKU1aWhqzZs2iefPmJCbmn8SWthefQZUEPocqCbbqOWwJBx7biXL3p3P99RFSU6P/EfPHH+Xp168Jj/aeznn3tyCxgs+1tsw/C1US+ByqJPA5VEmQ8zlcvHhxvEMqE8pUsnfaNDj11FDHbbfdYMyY6FWtmXbaKet8w4bo99lt2JB1XqVKdFuVKrByJaxfX3BM+Y2Rfc7CjpEzhswxtjWG/DRuHKFdu7L3h31iYqJ/iSmufAZVEvgcqiTYmufwqqugUyfo2RMWLYpu2z/9K855ojOzXutMyoQR1Nmv9HwYrfjyz0KVBD6HKgl8DlUSZD6HkcKsUNQWlZkN2mbPhu7dQ5mCOnXgww+hQYO8+yYnZ50vX57/mNnbatfOe4yC7s/eXqFCdIK3sDGkpoakckExrFwZ+m0phrzGkCRJKuk6dAib7/bokXWtKn/zCqeRxEba/PkR6S1aMvOeT+IXpCRJklQClIlk788/wxFHhA3LqleH99+Hpk3z79+kSdb5ggX598vetvfeeY9R0P3Z25s0CZvFZcoeX0Fj/Pxz2IykoBjS0nKvdMkrhpzzSpIklRa1asGoUXDvvVAuMYMnuIi9+HFze93039nvqqO4+5JFBX4ILkmSJJVlpT7Zu2xZSPQuXhxKNrzzDrRqVfA9bdpknU+Zkn+/zLakJNhnn7zH+OUXWJLPXmWpqTBjRjhv3Tq6LTk5lJoobAx5jbG1P0dKSvSKYkmSpNIkEoHLL4cvRi+jU+IXudpv40auHprCwQeD+3tIkiRpR1Sqk72rVsFRR8G8eVC+PLzxRqjptiWNG8O++4bz117Lu09qKoweHc4POwwqV45uP+64rPP8xhgzBtaty90/5xjjx4dVyXnJHLtGjdw/W+fOYSVzQTGsWAETJuQfgyRJUmnTpntddpo3kyn1jt98bRKduJWbAJg8GVq2hHceWRQ+ed/Wo6CvTkmSJEklUKlN9m7YEOq2ffVVKI/wwgvQrVvh7+/bN7zOmAGvv567/f77YenS6L7ZtWsHbduG8//+NySes9u0CQYNCucNGsCxx+Yeo0+fsELln39g8ODc7VOnwltvhfPevUNCO7vy5cN1CF9rnDo19xiDBoXxIxG46KLc7ZIkSaVRjUY1OeDXN5lwwn38Tl168QJp2fYerrZqEYf1bxq+CrWtR9OmJnwlSZJUqpTKZG9aWtiReeLE8P6ee+Doo8PmbPkdOV10ETRrFs7PPhseeQR++y3UyL3lFrjuutB2xBFwzDF5x3HPPZCYGEpIHHJI1grdqVNDPDNnhn533hlKQeS0//5ZydqHHoJLLgmrlJcuhZEjw4Zz6elQvz5ce23eMVx3XWhPTw9xjhwZ7p83L4z38MOhX+/eYT5JkqSyIpIQocubl/H7pz+RtFdKVFsyK6jEhtgm2LAh/69fSZIkSSVQuS13KXl++SWrxAKE2m2XX17wPRkZ0e8rVIC334bDD4f586F//3Bk16YNvPxy/mMefDA89hj06xcSu4ccEt0eicDAgXDmmfmPMXRo+HnGjg3nQ4dGt++8c/hZ86u1m5wc2o85JtQv7tUrd59u3XKPK0mSVFa0PKgKM2bAxRfD8OHxjkaSJEmKn1K5sreoNGoUykAMHgzNm0OVKlCtWijPcO+9od5bzZoFj9G7d9gA7ayzQrmGChWgXj048UT45JOQ7C1IxYqhtu8zz4QavLVqhY3mmjSBq66Cb77JKheRn7ZtQ7+rrgr3VaoUxuncOYw7ZkyYR5IkqazaaSd49lkYMQKqVo13NJIkSVJ8lMqVvQ0b5l6pu62qVoWbbw7HtmrVCp5/ftvvj0Tg/PPDsa3q1oW77gqHJEnSjqpXL+jQAQYfD3wX72gkSZKk4rVDr+yVJElS2bPHHvD0M/GOQpIkSSp+JnslSZJU5pQvld9fkyRJkmJjsleSJEmSJEmSygCTvZIkSZIkSZJUBpjslSRJkiRJkqQywGSvJEmSJEmSJJUBJnslSZKkfPw0+tt4hyBJkiQVmvsUS5IkSfl4dvAiyifA9ddD+fLxjkaSJEkqmCt7JUmSpHy8y9EMHAgHHgg//BDvaCRJkqSCmeyVJEmS8hUBYPp0aNUK7r0X0tPjHJIkSZKUD5O9kiRJUiFs3AhXXgmHHAILFsQ7GkmSJCk3k72SJEkqe5KTISkppiE2RpJYQXKu6xMnQqfmqxh//rNkpGfENIckSZJUlNygTZIkSWVPSgrMmQMrVmzzEOlVkjnxkRQefDB3261rL6Prs88y/Z2XaTD2Keq12TWGYCVJkqSiYbJXkiRJZVNKSji2USXggQfg+OPh/PNh0aJw/ThGcz7PAtB2xVhWttuPz/o+zIFDzyCSEIk9bkmSJGkbWcZBkiRJKsChh8I334SEb21W8CT/imqvkbGSTY88Qc/TMmJZSCxJkiTFzGSvJEmStAXVq8Mzz8Cbd8+HhOh/Qq9mJ85nGK++nsB++8Hbb8cpSEmSJO3wTPZKkiRJhdT5ygMo98O3fN7gtM3XLuc+FtIIgKVLoUcPOPdc+PPPeEUpSZKkHZXJXkmSJGkr1G5SmwN/eZnPB7zIKxV68TQX5urz3HPQrBmMGlX88UmSJGnHZbJXkiRJ2gYHPng6nRaM4Oij896U7fff4cQT4aGOL7Dih+XFHJ0kSZJ2RCZ7JUmSpG20yy7w7rvw5JOw00652zswmX5fnE1Gs2Z8fvkrZKRnFH+QkiRJ2mGY7JUkSZJiEIlA794waxYcfnjW9Yps4BkuIIEM6mQs58D7ezKlwcks++b3+AUrSZKkMs1kryRJklQEGjaEDz4Iq3yrVYOBDGYfZkf1ab3kHU7svJznn4cMF/lKkiSpiJnslSRJkopI5irf72alc8jO3+dqv5Wb+Pzv5pxzDhx3HCxeHIcgJUmSVGaZ7JUkSZKKWIOUBNovGcWnfZ7jr0hNAL5mf4Zw3eY+774LzZrB00+7yleSJElFw2SvJEmStB1EEiJ0euxs/pnxHZ/vcgrnM4xNVIjq8/ffYSXwkUfCwoXxiVOSJEllh8leSZIkaTuq27I+HRe/yrUvtSY5Oe8+H30EPff5hgk97iF1Q2rxBihJkqQyw2SvJEmStJ1FItCzJ3z/fXjNKYE0HtzwL7q8fRVza3dg9osziz9ISZIklXomeyVJkqRiUqcOvPQSvPEG1K2bdb0/D9OeqQDsu+5L9jyzHZ90uJ51ay3mK0mSpMIz2StJkiQVsxNPDKt8zzkHdmMRt3NDVHs50vhqykb2ax7hgw/iFKQkSZJKHZO9kiRJUhzUqgXDh8OrDy3jz3I7R7UtZHdu5hYWLICjjoKzz4bly+MUqCRJkkoNk72SJElSHLXv35bkJd8y7oBrSCURgL48ylp22txnxAjYZx947jnIsLKDJEmS8mGyV5IkSYqzysmVOWTKnfz44nQerz+IsRydq88ff8C554aVvvN/MuMrSZKk3Ez2SpIkSSXE3qe35MJFA7nnHqhcOe8+H36YwbdNTmJ89/+yad2m4g1QkiRJJZrJXkmSJKkEKVcOrrgCvvsOunXL3X4mI+mRPoqu713Lgtpt+Oaxz4s/SEmSJJVIJnslSZKkEqhhQxgzBkaOhDp1wrXarOB+Ltvcp8mGWezf9yCeOGQkK1bEJUxJkiSVICZ7JUmSpBIqEoEzzoDZs+H88+FurqIO0Vnd36nLNeO7s/fe8PTTkJ4ep2AlSZIUdyZ7JUmSpBKuVi145hlocfc5LCy/Z1TbldzDKmrwxx/Quzd07gzffBOnQCVJkhRX5eIdgCRJkqTCaXXloaw//xvGnziEjhOH8CmdGMmZUX0+/xxat4ZLL4XBFy5ipw0x1HdIToaUlBijliRJUnEx2StJkiSVIpVqVaLrhMEs/PAsnhucAJ9FcvVJS4NX713Ebfc2BTZs+2RJSTBnjglfSZKkUsIyDpIkSVIp1PCIvXh20h689BLUr5+7PZkVJMWS6AXYsAF3fpMkSSo9TPZKkiRJpVQkAj17hg3cLr0UErL9674S6+IXmCRJkuLCZK8kSZJUylWrBvffD9OnQ/v24dpFPBnXmCRJklT8TPZKkiRJZUSrVmGDtqfuX0MPRsc7HEmSJBUzk72SJElSGZKQABdeuhO88mq8Q5EkSVIxM9krSZIklUE196gV7xAkSZJUzEz2SpIkSZIkSVIZYLJXkiRJkiRJksoAk72SJEmS8pWeHu8IJEmSVFgmeyVJkiTl69xzYcqUeEchSZKkwjDZK0mSJClf330PHTrAWWfBL7/EOxpJkiQVxGSvJEmSpC164QVo2hQGDYK1a+MdjSRJkvJisleSJElSoaxfD4MHw/kNx/FZ3xGkp1rQV5IkqSQx2StJkiSp0MqxiUEr+nPQY2fzfY2OzHpicrxDkiRJ0v+Y7JUkSZKUr0MPiX7fh8fZlx8A2G/tVJr3OZCP9+zDokVxCE6SJElRTPZKkiRJZVFyMiQlxTZGUhJ3P5vMuHHQogXU4C8GMzBXtw9/akTTpvCf/8CaNbFNKUmSpG1XLt4BSJIkSdoOUlJgzhxYsWLbx0hOhpQUuqbAl1/CmP/MJn1IOcjI6rKAhtzPZWzcALfeCk8/DXfcAWefDQkuLZEkSSpWJnslSZKksiolJRxFIDERjru9I3/3m8f4U++g4xf3UpF/uJq72EjWCuIlS+C88+CBB+Cuu+Cww4pkekmSJBWCn7VLkiRJKrRqDarRdfIdLB0/m1f3uZnXOTnPfjNnwuGHw2UHTmXem98Wc5SSJEk7JpO9kiRJkrZaSpdGnPr9YCZMiNC6dd59EkjjwskX0vikFkxq2psl038t3iAlSZJ2MCZ7JUmSJG2zgw+GadNg2DCoVy+67Ryeoznfkkg6nec+TfV2e/HBoUNYvTo+sUqSJJV1JnslSZIkxSQhIdTpnTcPBg6EypWhEuu4lZui+lVmPRPHpbLHHvDII7BpU3zilSRJKqtM9kqSJEkqEjvtBIMGwY8/wpCjxlOP36Paf2UX7uUKli+H/v1hv/1g1CjIyIhLuJIkSWWOyV5JkiRJRap+fbhkbHcWjPqGqTsfu/n6f/g/1lN58/u5c+HEE0MpiC++iEekkiRJZYvJXkmSJEnbxZ7HN+OApW8z855PGFPrLIZzbp79Pv0UOnaEaw+bzvwxs4s5SkmSpLLDZK8kSZKk7arVFYfQbfnzPP9CIrvvnnefCOmc/sm/2P2YZkxs2pvfpvxSvEFKkiSVASZ7JUmSJG13CQlw5pkwezbcfTfUqBHdfhqv0IqvSCSdg+c+Ta0Oe/FBx4GsWBGXcCVJkkolk72SJEmSik1SElx5Jfz0E1xxBVSoAOXYxK3cFN2PjXz6RSKNG8Mtt8Dq1XEKWJIkqRQx2StJkiSp2NWqBffcE1b6/t/hE2nM/Kj2ZdThPi5n9WoYOBD22AMefBA2boxTwJIkSaWAyV5JkiRJcdOoEVz34WH8+NrXTKl73Obrt3Eja6i6+f3y5XDppdC0KQwfDmlp8YhWkiSpZDPZK0mSJCnumpzcnPa/j2bWY5/xcXJPHuPfefb7+Wc47zzotecUplw/ioz0jOINVJIkqQQz2StJkiSpxGje50AOXfYSo8ZUpEWL/HplcOnCy2g/5ES+q9aB6be9b9JXkiQJk72SJEmSSphIBI4+GmbMgJEjQ73e7I7jbTryBQD7rZ1K25u6MaV2d8Z9YsJXkiTt2MrFOwBJkiRJyktCApxxBpxyCjz9NAweDMt+T+N2bsjV98OVbbn5sAiHHAK33AKdOv2vYdEiWLEinKelUWnuXEhPh8TEwgWRnAwpKUXzA0mSJG1nJnslSZIklWjly8O//w3nnAMv3jSbBvf/CtkW8a6kOvdyBQDjxkHnznDkkXBn/0W07NkUNmwAIBHYd2snT0qCOXNM+EqSpFLBMg6SJEmSSoXKleHCe5vBT/MZf9CNrGYnAO7hSlZSM6rvBx/ABcev2Jzo3WYbNmStDJYkSSrhTPZKkiRJKlVqNKpJ109v5Z/ZC/i4w408kXRpvEOSJEkqEUz2SpIkSSqVajdN5rDJt/L1gmpcdhlUrBjviCRJkuLLZK8kSZKkUq1ePbjvPvjpJ+jXL9T4lSRJZdD69fDmm6GYf5s2UKNG+Iu/Th047DB45JHQZwdmsleSJElSmbDrrvDwwzBvHvTuDYmRjC3fJEmSSo+6deGkk+Dxx2HGDFi1ClJTQ339Tz6B/v2hdevwj4EdlMleSZIkSWXK7rvDk0/Cmy/FuDmbJEkqWVavDnWbzjgDXnopfK3nzz/hq6+gb1+IRGD2bDjySFizJt7RxoXJXkmSJEllUoM9K8U7BEmSVJT694eff4aRI6FnT2jcGGrWhBYtQgmHIUNCv4ULw/sdkMleSZIkSZIkSSXfQw+FUg75ueIKqF07nL/3XvHEVMKY7JUkSZKkAvwwdmG8Q5AkSYVRrhw0aRLOf/stvrHEicleSZIkSSrA5TdW4pBD4MMPIcM93yRJKtl+/z28VqsW3zjixGSvJEmSJBVgGXUZPz7s9XLAAfDmm5CeHu+oJElSLjNnwoIF4bxDh/jGEicmeyVJkiSpkKZPh5NOgv32g+eeg02b4h2RJEna7Oqrw2tCAvTpE99Y4qRcvAOQJEmSpNLmhx/g3HOhXP8+7HpMCw545Hwq1aoU77AkSSq5liwJR6a0NCrNnRu+LpOYSMXMFbnb6q674OOPw3nfvuGT2R2QyV5JkiRJKkClJGBD7ustmcmZa56Al2HZq7cw5cjLaf1kX6o12DFrBEqSVKDHH4fBgze/TQT2zdbcoGJF2H33bRt77Fi4/vpw3rx5SPzuoEz2SpIkSVIB3nkH7p8IDz4IK1dmXb+eOzaf75y+lJ3HXsdje/zGr9c8wCWXQJ06xR+rJEklVp8+0KPH5rdpaWnMmTuXpk2akJiYyOJJk+CRR7Z+3GnT4NRTIS0NdtsNxoyBSjvut22s2StJkiSpbEpOhqSk2MZISqLmXskMHgw//wz//S/UrQt7MZdTeC2qayqJ3PnPZdx6a1iYdMklWXvESJK0w6tfH1q3jjrW77335vONjRpt/ZizZ0P37rBmTfiU9cMPoUGDoo+9FHFlryRJkqSyKSUF5syBFSuA3CuICiU5OYwDVKsW9n0ZMADev+5Xfn14d3ZLXbi564ucwULCL6rr18PQofDww3DaaeG+1q2L9KeTJGnH9vPPcMQR4e/56tXh/fehadN4RxV3JnslSZIklV0pKZuTtaSlsT4hAVq2hMIme/OQlATH338Im+6Yx2dXvETdYXew58bvGcJ1ufqmp8NLL4Xjwvbf8u/jfqXN9UcSSYhs8/ySJO3wli0Lid7Fi0PJhnfegVat4h1ViWAZB0mSJEnaBuUrleOgR8+i8ZpZTBgymSrtmhXYv9uUQbS9qRtzqrTis74j2LRuUzFFKklSGbJqFRx1FMybB+XLwxtvQKdO8Y6qxDDZK0mSJEkxSCiXQJdrOzBlSigVePjhufvswY+cxBsA7L3haw567Gx+rb4PD961kdWrizlgSZJKqw0bwiZvX30FCQnwwgvQrVu8oypRTPZKkiRJUhGIREKi98MPYcYMOOOMrGoRV3IPCWRE9Z+QehCXXlORlBS44Qb4/fc4BC1JUmmRlgY9e8LEieH9PffA0UeHzdnyO3ZAJnslSZIkqYi1agUjR8KPP8IV/TZwCq/l6nM3VwGwciXccQfsvjtccAHMmlXMwUqSVBr88guMHp31/vLLoWrVgo8dkMleSZIkSdpOGjaEex5OInHubMYf9n8sj+wMwBiO5luaR/X95x8YNgz23z+sEP7sni9IT02PQ9SSJKm0MtkrSZIkSdtZrb1q0/Wjm9hpxUIm9nqcZxv8p8D+v3/8LQdd1ZEFVZox4YzHWLt8XTFFKklSCdWwIWRkbN2xAzLZK0mSJEnFpFKtShw84iJeXNiR11+H9u3z7ncZ9wOwxz+z6fJSXzbW3Y3hp7zNr78WX6ySJKn0MdkrSZIkScUsMRFOOgkmTw77zBx/fNjgDaAOyziLEVH9a2T8xa2v703DhtCrF0yfXvwxS5Kkks9kryRJkiTFSSQCnTvDqFEwdy4MGAAXl3+CJDZG9XuHY/mRvUhNDRu/tWsX7nvjjbA5uSRJEkC5eAcgSZIkSYI994QHH4RVV17MhP4V2XPsUHZN+wWAe7kiV/9PPw1Ho0Zw/bm/0fPCnajWoFrugRctghUrtj2w5GRISdn2+yVJUrEx2StJkiRJJUj13WvQ5Z2rSd1wOZ9f/wZ/vjCWCcu75Nt/wQKoPOhqIoNGM6H5uTS442L2OGbv0LhoETRtChs2bHtASUkwZ44JX0mSSgHLOEiSJElSCVQuqRwH3ncaxy57hsmTI5x2Wqj1m9Mu/MppvEJV1tBl1sPscew+TEk+hrfeTCdt6YrYEr0Q7o9lZbAkSSo2JnslSZIkqYTr0AFefhnmz4erroLq1bPaLuYhypMa1X/uH7U44aQEevQo5kAlSVJcmeyVJEmSpFIiJQXuugt++SXU992v0Vr68Hiufg9xMQBLfi/uCCVJUjyZ7JUkSZKkUqZqVRgwAL6ancS8G57ly1pHbG6bRlumckAco5MkSfFisleSJEmSSqnECom0v60Hbf74gPnv/sCE/S/moaSrgUi8Q5MkSXFQLt4BSJIkSZJi17j73jTuPpRWf8MBz8NDDwGz4x2VJEkqTq7slSRJkqQypFo16N8fvv8eHn0k3tFIkqTiZLJXkiRJksqgSATat493FJIkqTiZ7JUkSZIkFWjGda/w9+K/4x2GJEnaApO9kiRJkqQCtf7wThJ224WJ+/Rh9osz4x2OJEnKh8leSZIkSdIW7cRaDp79BHue2Y6jWi/nmWdg3bp4RyVJkrIz2StJkiRJKrRRnMAHM+tw4YWwyy4wYAB8/XW8o5IkSVCKk70ZGfDDDzB8eNhptl07qFgxbEKQlFT4cVavhltugf33h6pVoUYNOOAAuP9+2LSpcGPMnAnnnAMpKWHuXXaBk0+G8eML/7MMGwZdukByMlSuDHvvDddcA0uXFm6MpUtD/733DvcnJ4fxhg0L40uSJElSUXiMf28+X7UKHnoIWrYMv5M9/jisWukvIJIkxUu5eAewrX7+GfbdN7YxFiyAww+H+fOjr0+bFo4RI+DDD6FmzfzHePpp6Ns3OjG8ZAm88Qa8+SYMHBiO/GzcCCecAGPHRl+fMwfuuisks999F9q2zX+M6dPhmGNg2bKsa+vXw8SJ4XjlFRg1KiTDJUmSJGlrfVfjIPZZ+Tk/sQefcGiefaZPD8cu/U6kZqPqVL2sN/v360QkIVLM0UqStOMqtSt7s9t1VzjxROjcufD3/PMPHHdcSPRWqgRDh8LixSEBPGgQJCTAl19Cz575jzFxIvTpExK9LVvCxx+HhOvkyXDooWFF7aBBMHJk/mMMGJCV6O3fPyR5lywJieZatcJ4PXrAihV5379iRWhftgxq1w73LVkCs2eH8SCMP2BA4f/bSJIkSVJ2zT5+kN8mzWfcWc+wc938f43cnYUckz6aTj89R4sBB7MwaW8+PPGRQn9jUZIkxabUJntr1w6rVZcsCUnaN94ICdbCeuIJ+O67cD58OFx8cUgaN2wYVuLecUdo+/DDsLI2L1deCWlpoWzDuHFh/jp1oEMHeO89aNEi9Lv2WtiwIff9s2bBU0+F8379wtefmjSBevWgVy94552QdF6yBO68M+8YhgwJ7QkJoX+vXuH+pk3DeH37hn5PPRXmkyRJkrQDSU7eujp3eUlKguRkGnRqyEXPd2bRInj11bx//zqfYSSQVcah0aa5zBv1LQ0awEknhd+tUlNjC0eSJOWv1CZ7q1aF448Pic1t8eij4bVVKzj11Nztl18eErfZ+2Y3bVr4ihKEZG6NGtHtFSrA4MHhfPHikIjN6bHHwurfChXCCuCcOnYMq3YhJGtz1hDetCkrWXz88SHJnNPgwVC+fJjn8cdzt0uSJEkqw1JSwtcHv/xy2485c8I4/1OhApxySvhm448/wo03hgUwCaRxAc/kCuEpepOaGsrcHXtsWGDzn/+Eb1VKkqSiVWqTvbGYPx++/z6cn3JK3n3Klw8JVAj/iFm3Lrr97bezzvMbo3v3sFlazv45x+jSJSuxnFPm2CtXwqefRrdNmhQ2RCgohjp1wvgAo0fn3UeSJElSGZaSAq1bb/uRLdGb0x57wK23hj1Vxoz4i1/rt2NTtq1hZtCKmbSOuufXX8M9jRvDYYdmMOGS11m7bO12+/ElSdqR7JDJ3i+/zDpv3z7/fpltGzbADz/kPUaDBuFT7LyULx9WDgPMmBHdtmIF/PJL4WPIa4yt/Tl++SX/2r+SJEmStK3KlYOjeiXT4bc3+OvrXxh/9J3ML9+Ep7mwwPtWj5tGl6GnkF63HpOaXMBXD0wgPTW9mKKWJKns2SGTvXPnZp03bpx/v0aNss5nz857jILuzz7GvHmQnu3fLHPmFC6G3XcP9XgLiiEhIfTbUgw555UkSZKkorbz/vXoOuYaGm2YzekfX8Q554RNsfNyHs8CUJU1dJ43jJaXdeXWukMZNCh8I1OSJG2dclvuUvZkX92aX/kEgJ13zjr/44+8xyjo/uxjbNwIa9ZAtWpbF0P58qEe8J9/5h9DzZrhk/QtxQC5x8jL/PkZTJuWlm97/frhKC3S0tKiXqXi5jOoksDnUCWBz6HizWew+B3YJYEDu6Rx333w0ksRnnkmwpdfRgCoyAZO56Wo/ulEePLPk1g8OOw/0rlzBueem8HJJ2dQtWo8foKi53OoksDnUCVBzucwIyOjoO4qpB0y2bs2Wzmogjamzf7p85o1eY+xpY1tc46RmewtbAzZxyiqGLbk5psjrF6dmG/7v/71G336LNnyQCXMrFmz4h2CdnA+gyoJfA5VEvgcKt58BuOjfftwzJlTibfeSqbi2+Ootf6vqD4fcxiL2W3z+0mTIkyaFOHii9M45JCV9DhmGW3ariGhXOn/kqrPoUoCn0OVBJnP4cKFC+MbSBmxQyZ7C/tBQfZ+kUjeffK7vqUxCjN2zr45++V3vbAx5OeWWzI46KCCVvbWpX79ulseqIRIS0tj1qxZNG/enMTE/JPY0vbiM6iSwOdQJYHPoeLNZ7BkaNkSevaEjX8fx+e3vkLiiOdps2wM5UhjOOfmec+GDYm8915tEt6bzLGJ/ZnX/ixS/nM2DY/Yq3iDLwI+hyoJfA5VEuR8DhcvXhzvkMqEHTLZu9NOWecbNkS/z27DhqzzKlWi26pUgZUrYf36gufKb4zscxZ2jJwxZI6xrTHkp3HjCO3alb0/7BMTE/1LTHHlM6iSwOdQJYHPoeLNZ7BkqFyzMgfecyrccyrLv13Ktze9yLwfT4Tv8r/nXIbTIG0RDT6/HY6+ne+qHMCMy57jqEuaRpWvy2XRoth2q05OhpSUbb8/Dz6HKgl8DlUSZD6HkcKsUNQW7ZDJ3uTkrPPly/NP9i5fnnVeu3buMVaujO5T0BgVKkTPkzOG/KSmhnnyiwFCe2pq/nV7C/o5JEmSJCne6uxXl0NGXcYXGfDVVzB8OLzwQnR+tiZ/0oPRUfc1XPst7W/blQ1D4KijoFcvOP74HItcFi2Cpk2jV8FsraSksNt1ESd8JUkqaqW/0NE2aNIk63zBgvz7ZW/be++8xyjo/uztTZpAQrb/2k2bFi6Gn3+G9PSCY0hLC/9+2VIMOeeVJEmSpJIkEoFWreD+++HXX2HUKDjhhLCw5XReoiL/RPUfxQmsZSfS0mDMmJDsrVsXzj4b3n8/LIphxYrYEr0Q7o9lZbAkScVkh0z2tmmTdT5lSv79MtuSkmCfffIe45dfYEk+e5WlpsKMGeG8devotuRk2G23wseQ1xhb+3OkpESvKJYkSZKkkqpChbBK9803w+9cx56/Mz9Uiv6laARn5bpv7VoYMQK6dYMBtUfy5Rl3F1fIkiTF3Q6Z7G3cGPbdN5y/9lrefVJTYfT/viF02GFQuXJ0+3HHZZ3nN8aYMbBuXe7+OccYPz7/D4kzx65RAzp1im7r3BmqVy84hhUrYMKE/GOQJEmSpJIuORm6P3MK+6z7krmvfcP4dlfzTfnWfMgRBd7X6+9HaDP3xWKKUpKk+Nshk70AffuG1xkz4PXXc7fffz8sXRrdN7t27aBt23D+3//CqlXR7Zs2waBB4bxBAzj22Nxj9OkTvqb0zz8weHDu9qlT4a23wnnv3lC+fHR7+fLhOoSvN02dmnuMQYPC+JEIXHRR7nZJkiRJKk2anNycrlP/S7P1X/LhJ+W44AKoVi13v4YsoBOfFX+AkiTFUalO9n7/PXzxRdaxeHG4npERff2LL+Dvv6PvvegiaNYsnJ99NjzyCPz2W6iRe8stcN11oe2II+CYY/Ke/557IDExzHvIIVkrdKdOhaOPhpkzQ7877wylIHLaf/+sZO1DD8Ell8C8eSHJPHIkdO8e6vXWrw/XXpt3DNddF9rT00OcI0eG++fNC+M9/HDo17t3mE+SJEmSyoLExPB72NNPh9+BXn011PfNXCRzJiPjGp8kSfFQLt4BxKJfv6wSBdn98w907Bh9bdw46No1632FCvD223D44TB/PvTvH47s2rSBl1/Of/6DD4bHHgtxzJwZ/qGRXSQCAwfCmWfmP8bQoaHu79ix4Xzo0Oj2nXcO5STyq7WbnBzajzkGli0LGxLk1K1b7nElSZIkqaxISoJTTgnHn3+GxG+DgT/B0nhHJklS8SrVK3tj1agRfPVVKKHQvDlUqRK+/tO2Ldx7L0yeDDVrFjxG795hA7SzzgrlGipUgHr14MQT4ZNPQrK3IBUrhtq+zzwTavDWqgWVKkGTJnDVVfDNN1nlIvLTtm3od9VV4b5KlcI4nTuHcceMCfNIkiRJUllXq1YomXfM78+weNICpnfoF++QJEkqNqV6Ze/48bGPUbUq3HxzOLZVq1bw/PPbfn8kAuefH45tVbcu3HVXOCRJkiRJ/H979x1edXn/f/yZSUIShiQge4gM2VscYIuouBAKBduK69s6sdrab9XaKl5aR38tttYOa8VZ3DgAF6hFlCFCZIQhArJHCCNAAiE5vz/ON0diBiPnJOHwfFzX5+rnfD73fZ/7eL17gFfu3B+andWKZo9fC73+VumxPv4Y+ndyIY0kqWY7oVf2SpIkSZJ0JH7xy+Aim6uuCv725IED1T0jSZJKM+yVJEmSJOkI7NoFzzwTfGZKw4bwzOl/Z+7db5G/M7+6pyZJEmDYK0mSJEnSUcvflc/wOf9L3weGcqB+Qz5t9WNm/WYyeXnVPTNJ0onMsFeSJEmSpKN0Hu+Txh4A6pDLmd/8h82//zcZGTB6NLz6KuzdW82TlCSdcAx7JUmSJEk6jAFnQ0LCt69H8GqpNq8ygr174aWXYORIyMiAESPgxRchN7cKJytJOmEZ9kqSJEmSdBiPPgpbtsDTT8OlFxVyAe+WuL+fRCZzcYlreXnw2mtw+eXB4Pel9r/jsxueZ++6PVU3cUnSCcWwV5IkSZKkI1C/Plx5Jbw5OY5aa1bw6fXPMefkoeRTi/c5j93ULbdv2v5tjFjxAGf/6yrOGDaILzPO4/0rn2f9+ir8AJKkqGfYK0mSJEmKXunpkJRUuTGSkoLjHKJuy3qc+fef0G/TGxRs2AZ//gs/+AEkJ5c9xDAmEUcRAPEU0nPHhyx/dg7Nm0OfPvDAA5CVBYFA5aYqSTqxxVf3BCRJkiRJipgWLWD5csjOPvYx0tOD45QjrUkal9ySxiW3BB/KNnVq8AFtkyfDvn3BNmXt8fsGlwEwb17wuPtuOPVUGDYMLrsM+vWDWJdoSZKOgmGvJEmSJCm6tWhRYVgbTikpwYezjRwZDHrfew/efDGPPi/PK9Euh/rMYECp/l99BY88Ao88EmBWwkAOtD2N2j8eRteff4/E1MQq+QySpOOXPyOUJEmSJCkCatcOrtJ9+qVkknds5PN7JjOj3bVsi8lgMhdzkIRy+3ZiCacXfMKApf+k990XkJeWwWud7+HllyE3two/hCTpuOLKXkmSJEmSIiypXhJ97r2Iwt9ewBdzf8Yp29vwi49g0iRYvbp0+8t4o8TruuxmwZIEHhgFCQlwzjlwySXBo1WrI5jA2rUR3cpCklQzGPZKkiRJklSF4hLj6DWkPmdeDP/v/8GiRfDGG8HgNzMz2GYYk0r1K97jt6AAPvggeNxyC3TuDBdfDCO7raDb8FOIS4wr2XHtWmjfHvLzj33SSUnBvY8NfCWpRnMbB0mSJEmSqklMDHTtCr/7HSxYEFzl+5dH8omrm0bhIf9k/4q2LKFTmWMsXgx/fOgAbS/vTU5SYz5pexWzbn+N3Rv+b7+H7OzKBb0Q7F+ZlcGSpCph2CtJkiRJUg3RqhWM/VUS3Xd+zI6szXxy9VPMaXQpr8RdDsSU228AM6hDLhmBbZz99TP0/+MIpje/isGDYeLEKpu+JKmauY2DJEmSJEk1UHrHDM5+6mrgajrtgQ7vB7d7mDoVtm8v2fYS3i7V/+3ARUybBtunweVVMmNJUnUz7JUkSZIkqYZLTYXhw4NHYSHMng2TJ8Pbb8OSJYEyw96pXFgNM5UkVSe3cZAkSZIk6TgSFwdnngkPPhjcr/fr5YWs+8FtzGtwHgdIAGAOfdnCydU8U0lSVXNlryRJkiRJx7E27eJp8+pYYCy7N+TyxaPvM2d+IukLfaaaJJ1oDHslSZIkSYoSdZqm0f8PP6A/MLYQ5s6FL/4FTKj82Ms+3sSpnQuJS4yr/GCSpIhwGwdJkiRJkqJQXBz07w833xye8VJ+eT07kxrxaasf8+kNz5O9dFt4BpYkhY1hryRJkiRJOqzmrKdBYDtnfvMfzvzHFXx92sX06QN33QUffwwHDlT3DCVJbuMgSZIkSZKO2rtcwLx5MG9e8GFxKSkwcCCcd17w6NABYmKqe5aSdGIx7JUkSZIkSUftHYaUeL13L0ydGjwAJqTczClN9hF7/nm0v3EQ6R0zqmGWknRicRsHSZIkSZJ0WEvq9SefWgBk04DP6VNu2zgOMnTvC5z91QTO/OvlpJ/WkHlp53DHrwN8+CHs319Vs5akE4thryRJkiRJOqxO0/9K0bYcPh83lXcG/ZG27eLKbdubedRnZ4lrG/fU4eFHYhg0COrXhyFDYPx4WLwYAoEIT16SThBu4yBJkiRJko5I7fTa9PndEPoAVwDffAMffADvvw/TpsGOHcF25/F+qb4fMDh0npcH774bPAAaNoQ7Tn2NXm1yaHXNIJoPaE1MrBv+StLRMuyVJEmSJEnHpGVL+J//CR6FhTB/fjD47fi3bRRsjCeBg6G273NeueNs3Qq9tz7K2Z/OhOdgfVxLVrcZxIY7/8qA85Np0qQqPo0kHf8MeyVJkiRJimbp6ZCUBPn5xz5GUlJwnArExUGfPsGD3zxG7sbfs+Af/yXvrfdJ/CqLFfvaldu3Nns5ndmh180Kv6Hoqw8YcE0SAB07wve/HzzOOQdOOukYP8fatZCdfYydCf43aNHi2PtLUoQZ9kqSJEmSFM1atIDly6s85Exrkkbf+y6G+y4GYO26kls+bN/+bduzmFliFTDAdAYBwa0cli4NHo8/DjEx0KMHXN51Ceecso4OPz2b1EYph5/Q2rXQvn3lQ+/lyw18JdVYhr2SJEmSJEW7Fi2qPaBs3hyuuSZ4FBXBggXB8PfDD2HwRx/xnayXD/l+meMEAsHtIn4y/0l68ygHfpvAl3X6kdPlHGJvvYXeQzJIKSv7zc6uXNALwf7Z2dX+31KSymPYK0mSJEmSqlRsLPTqFTzuuAP27x5H5oQh7HxtOidlfkjH3Dnlhr3Fvs+HACRSQLfdMyn69FPSP72N3PjgVhLnnAMDB8KZZ0JqahV8Vw0Z6wAAJ0JJREFUKEmqAQx7JUmSJElStapVpxbdfz4Qfj4QuI89W/by5PwUPvwQpk+HzMzgit5i6WyjGwtLjLGILuzgJDgIs2YFjwcfDO4l3KsXXNsqh59V6aeSpKpn2CtJkiRJkmqU1EYpDBkCQ4YEX2/fDv/9bzD4/fBD6LLso1J9/svAMscqLIS5c2HI3JmRnLIk1QiGvZIkSZIkqUZr0ACGDw8eAJsWfo9P//4cRdM/pPWq6TQrXMvHnFPhGD35IvITlaRqZtgrSZIkSZKOK427ZtD47z8BfgLA+plrGL44nbpzgiuAV68u3aeXYa+kE4BhryRJkiRJOq41O6sVPzkLfnJ98PXatcHQt/jYv3ItTdkUlvfKyoJ2XSHeREVSDRRb3ROQJEmSJEkKpxYt4Ior4Mkn4auvYPay+mSOvD8sY//kCqhXD849F8aNC+4jvGdPWIaWpErz51CSJEmSJCmqNWmfRpM7hsArd4dlvL17gyHv9OnB13Fx0KMH3JnyF5r0bsIpV55FRpeTw/JeknQ0DHslSZIkSZIqobAQFs7bz4X8L0n/3Q9/hDUJbVnUbgTrb3qQ/v2hc2e3fpAUeX7NSJIkSZIkVVIvviCJ/aHXrQpW8t8lG7nxxuDrlBTo2xf69w8ep58O6enVNFlJUcuwV5IkSZIk6QidOwg2LYHNm0teP4uZpdrOon/ofO9e+Oij4FHsjyc9QN96yynq259Gl/XnlKGdiU8yqpF07PwGkSRJkiRJOkKPPAIP94BVq2DmzG+Ps5aVDntnc3qFY52T8xo9cxbAqufgRdhNGqMGbKbnWbUjt/p37VrYsoXkFSugqCi44fDRSE8PPgFPUo1k2CtJkiRJknQUYmLglFOCx5VXBq9lL/03c579lLwPZpK+bCaN937FYjqXO0Zt9tKVhSWuracZ786ozbszvr3Wtu232z7065pHl+5xJKYmHtvE166F9u2Jy8/ntGMbAZKSYPlyA1+phjLslSRJkiRJqqT0jhmkP3gZPHgZAPl7DjJjYTyzZhE6Nm78tn0fPieewhJjHLrtQ7GVK4PHc8/BT3mOv3ALi1O6s/2UvsSf0ZdGV15Am77pxMYewSSzsyE//9g/JAT7Z2cb9ko1lGGvJEmSJEmKfunpwVWplQk7k5KOeF+FpNR4zjgDzjgj+DoQgHXrvg1+G7y1mgOrE0ikINTnM86ocMy+zCWJ/XTeOwcWzoGFcNY/PmFJvbPo0yf4ALji4+STj/lTSjqOGfZKkiRJkqTo16JFcPuB7OxjH6MS+9XGxAS7tmgBo0YBj15N/s7LWfjifHKmzKLW/FksLzgbtpU/Rl/mlnh9kDgW0IN9O+GDD4JHsebNoX/vAm7IH0+98/pyyg97kXZMM5d0PDHslSRJkiRJJ4bitLWGSKqXRNfrz4Drgyt6PwkEt9UtXv07ezYsWAAFBZDCHjqxpET/JXRiHylljr1uHTRYt5hz+DW8A0W3xbAuoRXNI/6pJFUnw15JkiRJkqQaICYGWrYMHqNHB6/l5QUD36xpu5n91GiabpxLq4KVAMylb4XjHboSOJYAzQtWR2zukmoGw15JkiRJkqQaKjmZ/9v7twn87gUAcr7azqqX58H6DC5eD3PmwLYytn/47rYPkqKfYa8kSZIkSdJx5KRTG3DSb86nN/BTgg9/W7sW5s799pg3D/ru++4ev7HEU1Tp91+0CNp2DAbRkmoWw15JkiRJkqTj2KHbP4wcGbx28CCsemsiM96eC3PmkrFmLoG8fE5jaaXf78qrYOG10KkTjG76CWekfEn973Wn9WXdSGviY+Ck6mTYK0mSJEmSFGXi46Hd8M60G94ZuAaAvE8+hwEV7/N7pAoLYeFC+NnCFxnI3+BV4CZYk9CW8d+fTMOz29OjB3TvDo0bBwNpSZFn2CtJkiRJknQCSE6JC/uYvZlX4nWLgq956r0m7Hnv22sZGYSC3x6dC+jb4GtannsqcYnhn490ojPslSRJkiRJ0lGLp4BufFni2krasoeSWzls2wbvvx88urGETHqwj2RWpXRle8se7D5vJA1Hf58uXaB27SqY+Nq1kJ197P3T06FFi/DNRwojw15JkiRJkiQdsUsuhoI1EL8ki6TA/hL3MuleYd8eLACgNnl03jsHsubwy6y2/OnR7xMbC+3bB1cAd+8OXbtCly7QpEkYt4FYuzb4Jvn5xz5GUhIsX27gqxrJsFeSJEmSJElHbNw4GNcT9u3owOLXZpHzYSZkZnLS2kzmHegPBeX37U5mqWvFAXFRESxdGjwmTvz2/sfx51I35SC7WnYlplsXTvped1qN7ENq6jFMPju7ckEvBPtnZxv2qkYy7JUkSZIkSdJRq12/Fp3/53T4n9ND135/EK7+CjIzg8eCBcGjeNeEisLessRxkH4HZ5K0az8s/C8shKznOpJ2TRZt2ny7+rf4f9u2hTi3AtYJzLBXkiRJkiRJYREfDx07Bo/LLw9eCwRg06Zg+Hvgb8P57MtGNNmygFYFK1lLc3JoUO54p/IVSZTcKmIRXQBYtSp4vPHGt/eSkuDGpm9yWcybFHbqQtoZXWlxURcyOjUM8yeVaibDXkmSJEmSpBNBenowDa3sfrXp6UfVJSYmuO9ukybAhT8Hfg5A7sZcsj9az193Blf/ZmbC4sWw/5BstwuLSo23kK7lvld+PrT6ehpnMwFWAm8Cv4ZL6n1CXq+zGNIIfnlUs5eOL4a9kiRJkiRJJ4IWLWD5cgq3bGH5ihW0b9eOuKPd8yA9PWx71aY1SaPnjzvS85BrBQWwbBksXAiLFkHG1Dw2ZjWjSeH6UJvilb3l6crCUtdm7uzEzumQg2GvopthryRJkiRJ0omiRQto2pS82Fjo3r3GbXCbkBDce7dLcZ770JXAlexclcM3Uxazc8ZC2iX14/SVwVXAe/Z8d4RAqdXA62jGTupXweyl6mfYK0mSJEmSpBqtXpuTqDd2AIwdwMD/u1ZUBGvWBFcAL1oUXA28acEW6q7cVaLv4VYCH4spP3+fOm0zqX9WJ1oOOY20Jmlhfw/pWBj2SpIkSZIk6bgTGwtt2gSPoUOLr55MXs4e1ryTxfaPFlG4YCFL8zrRKAe2bAnfezef+QJdZy6Gp4Ov18a15mff+4p2HePo2BE6dAg+pK5Ro+CexVJVMeyVJEmSJElS1Eg+KZmOP+4FP+4FwECC+/Ru3QrfTAKur/x7tGFVidf5hfG8Ny2O96aVbFe3bjD4/V6T5Zyf9wbJPTrQcGBHmg9sQ3ySsZzCz6qSJEmSJElS1GvYEBr2Cc9Yqewr8XopHctst2sXzJkDnfmEc7gD3gUehAMk8L9NJvD16T+mQ4dvVwK3bw9p7gihSjDslSRJkiRJkiphGR0qvN+BZSVeJ1LA5xub8PHrpds2bRoMf2/dcz9pzeuR2rMdjQa0p0m/5sTGx4Zz2opChr2SJEmSJEnSUVh6y9/Zuv4AMUuzqLthCV8FekFu+e07srT0GOWsBt6wATZvOMhU7iNxTgG8Grw+O7Y/13X+jPbt4dxz4aqrIDExDB9GUcWwV5IkSZIkSToKHa/sS8eePUOvnwQeyobly2HpUli2LHgsXQqrV0OHQMmVvTupyxYalTt+a1aTSEGJa6uKWrFwISxcCK+8Am+/HTykQxn2SpIkSZIkSZWUnh48zjyz5PX8fFg79U0++2QpB75cSsKqZeTsTiA5P4a8vLLHKmsl8AralXg9eXJwFXDTpuH6BIoGhr2SJEmSJElShCQlQbvhnWk3vHOJ63uKYN26b1cAH7oauMPWZaXGWU77Eq8TEiAjI6JT13HIsFeSJEmSJEknhvT0YPqan3/sYyQlBceppNhYaNkyeJx/fsl7O9Zcx6L3z2bn3BUUZi2n1jcr2JvcjcR1cOAApKTAM8+4Z69KM+yVJEmSJEnSiaFFi+DGutnZxz5GenpwnAiq36ou9X/WH37WP3TtLaCwMDj1jIxgWCx9l2GvJEmSJEmSThwtWkQ8rI2UuDhoVP5z3ST8GYAkSZIkSZIkRQHDXkmSJEmSJEmKAoa9kiRJkiRJkhQFDHslSZIkSZIkKQoY9kqSJEmSJElSFDDslSRJkiRJkqQoYNgrSZIkSZIkSVHAsFeSJEmSJEmSooBhryRJkiRJkiRFAcNeSZIkSZIkSYoChr2SJEmSJEmSFAUMeyVJkiRJkiQpChj2SpIkSZIkSVIUMOyVJEmSJEmSpChg2CtJkiRJkiRJUcCwV5IkSZIkSZKigGGvJEmSJEmSJEUBw15JkiRJkiRJigKGvZIkSZIkSZIUBQx7JUmSJEmSJCkKGPZKkiRJkiRJUhQw7JUkSZIkSZKkKGDYK0mSJEmSJElRwLBXkiRJkiRJkqKAYa8kSZIkSZIkRQHDXkmSJEmSJEmKAoa9kiRJkiRJkhQFDHslSZIkSZIkKQoY9kqSJEmSJElSFDDslSRJkiRJkqQoYNgrSZIkSZIkSVHAsFeSJEmSJEmSooBhr6Lepk2b+Oc//8mmTZuqeyo6QVmDqgmsQ9UE1qGqmzWomsA6VE1gHaomsA4jw7BXUW/Tpk3861//8stD1cYaVE1gHaomsA5V3axB1QTWoWoC61A1gXUYGYa9kiRJkiRJkhQFDHslSZIkSZIkKQoY9kqSJEmSJElSFDDslSRJkiRJkqQoYNgrSZIkSZIkSVHAsFeSJEmSJEmSokB8dU9ANcfevXsBmD9/fjXPJLxWrVpFWloan376KZs2baru6egEZA2qJrAOVRNYh6pu1qBqAutQNYF1qJrgu3VYnEcV51M6NjGBQCBQ3ZNQzXDvvfcyceLE6p6GJEmSJEmSTlCXX3459957b3VP47jlyl6FjBkzBoD27duTkpJSzbORJEmSJEnSiWLv3r0sX748lE/p2LiyV5IkSZIkSZKigA9okyRJkiRJkqQoYNgrSZIkSZIkSVHAsFdR64033uCCCy6gUaNGJCUl0aZNG2688Ua+/vrr6p6ajnOBQIClS5fyzDPPcNNNN9GnTx9q1apFTEwMSUlJRzxObm4u9913H127diUtLY169erRt29fHn30UQoKCiL4CXS8y8vLY9KkSVx//fX06tWLevXqkZCQQEZGBoMGDeJvf/sbeXl5hx3HGtSxWrduHY899hg/+clP6Nq1K40bNyYxMZG0tDS6dOnCLbfcwrJly45oLOtQ4bZy5UqSk5OJiYkhJiaGp59+usL21qAqY82aNaFaq+hITU2tcBzrUOE0ZcoURo8eTatWrUhKSiI9PZ1u3bpx3XXX8e6775bbzzrUsWrVqtURfRce7s9mazBMAlKUKSoqClxzzTUBoMwjNTU1MGXKlOqepo5jq1evLre+atWqdURjrFq1KtCmTZtyx+nVq1cgJycnwp9Ex6u0tLRya6f46NChQ2DFihXljmENqjIee+yxw9ZgQkJC4LHHHqtwHOtQkXDuueeWqKMJEyaU29YaVGVV9PfCQ4+UlJRyx7AOFS47d+4MXHTRRRXW4sCBA8vsax2qMlq2bHlE34XFx7x580qNYQ2Gjyt7FXUefPBBnnrqKQB++MMf8uWXX7J161befPNNWrVqxZ49exg1ahQrVqyo5pkqGjRt2pRhw4Zx9tlnH3GfAwcOcMkll7Bq1SqSk5N57LHHWL9+PatXr+bee+8lNjaWL774glGjRkVw5jqe5ebmUqtWLS6//HJefPFFvv76a3JycsjMzOSGG24gJiaGZcuWcd5557Fnz55S/a1BVVZycjIXXXQRf/zjH/nwww9ZtmwZ2dnZLFmyhCeffJK2bdtSUFDA2LFjmTJlSpljWIeKhOeff55p06bRunXrw7a1BhVuU6dOJTc3t8xjy5YtZfaxDhUu+/bt44ILLmDKlCnEx8dzyy23MHv2bLZt28amTZuYPn06N954Iw0aNCjV1zpUZWVlZZX7/Zebm8vu3btp2rQpAB07dqRXr14l+luDYVbdabMUTps3bw6kpKQEgMBFF10UKCoqKnF/1apVofsjRoyoplnqeLd79+7AG2+8Edi0aVPo2j333HPEK3sPXRH38ssvl7r/8MMPh+5Pnjw5rHNXdLjpppsCmzdvLvf+oTX08MMPl7pvDSrSduzYEWjSpEmFK4isQ4VbTk5OoGHDhoG4uLjAG2+8cdiVvdagwuHQlb0fffTRUfe3DhUut99+ewAIJCYmBt5///2j6msdKtI+/vjjUA39/ve/L3XfGgwvw15FlUceeST0BZCZmVlmm1tvvTUABGJjYysMS6SjcTRh72mnnRYAAj169Cjz/oEDBwIZGRmhH1pIR6ugoCDQoEGDABA455xzSt23BlUVxo4dG4Dg9kllsQ4Vbtdee20ACNx6660lArjywl5rUOFQ2bDXOlQ4rF27NhAXFxcAAr/5zW+Our91qEgr/jM6JiYmsHbt2lL3rcHwchsHRZW3334bgFNPPZVu3bqV2WbEiBEAFBUVMXXq1CqbmwSwatUqsrKygG9r8bsSEhIYOnQoANOnT2ffvn1VNj9Fh/j4eNq1awfAxo0bS9yzBlVVEhISAMp8cKV1qHCbOXMmTz31FI0bN2bcuHGHbW8NqiawDhUuEyZMoLCwkPj4eH7+858fVV/rUJGWn5/Pq6++CsA555xD8+bNS9y3BsPPsFdRZcGCBQD069ev3Da9e/cmLi4OgPnz51fJvKRiX3zxRei8ojotvpefn8/SpUsjPi9Fn82bNwNQp06dEtetQVWF/Pz80A9g+/TpU+q+dahwKigo4LrrriMQCPCnP/2p1PdeWaxBRdKBAweOqJ11qHB55513ADj99NPJyMgIXT948CCBQKDCvtahIu2tt95i165dAIwZM6bUfWsw/Ax7FTU2bNgQehBRmzZtym1Xq1YtGjduDMCyZcuqZG5SsUMfDFhRnR76YBnrVEdrwYIFrF69Ggj+pf9Q1qAipaioiE2bNvHWW28xYMAAvvrqK2rVqsV9991Xqq11qHB65JFHyMrKYtCgQYwePfqI+liDioSbb76Z1NRUatWqRVJSEj179uS3v/0tW7duLbO9dahwOHjwIJmZmUBwYVNubi533XUXrVu3JjExkcTERE477TTuvvtucnJySvW3DhVpzz33HBB8wO8PfvCDUvetwfAz7FXUyM7ODp0f+tPMsjRs2BCA7du3R3RO0ncdaZ0W1yhYpzp6v/rVrwCIjY3luuuuK3HPGlS4XXDBBcTExBAXF0eTJk0YOnQon3/+OR07dmTatGn07t27VB/rUOHy9ddfc//995OYmMjjjz9+xP2sQUXCkiVL2Lt3LwD79+9nwYIF3H///XTo0IH33nuvVHvrUOGwbds28vPzgeCvuvfs2ZMHH3yQNWvWEAgEOHjwIEuXLuWBBx6gW7duLFq0qER/61CRlJ2dHfr+u+yyy0hLSyuzTTFrMDwMexU1iv9iBWXvD3io5ORkgNBKYKmqHGmdFtcoWKc6On/4wx+YPn06ADfccAOdO3cucd8aVFVo2LAht9xyC927dy/zvnWocLnhhhvIz8/n9ttvp3379kfczxpUuMTGxnL++efzzDPPsGjRInbt2sXevXtZsGABt956K3FxcezYsYNhw4aV2kLOOlQ4FP96PMCf//xnVq5cyejRo1m8eDH79+/nm2++4be//S2xsbGsX7+eoUOHlqgj61CRNHHiRAoKCoCyt3AAazASDHsVNQ7diygmJuaI2h6unRRuh9szq6x21qmO1Lvvvsudd94JQJcuXfjDH/5Qqo01qHB74403yM3NZdeuXaxatYpnn32WBg0acMMNN9CvX7/QliKHsg4VDv/5z3/44IMPaNWqFXffffdR9bUGFS4tWrTg3XffZcyYMXTu3Jk6depQu3Ztunfvzvjx45k0aRKxsbHk5eXxi1/8okRf61DhUFRUFDo/cOAAI0eOZOLEiXTq1InExERatGjBfffdxyOPPALA6tWreeKJJ0J9rENF0vPPPw/AySefzODBg8tsYw2Gn2GvokZqamroPC8vr8K2xb/mkpKSEtE5Sd91aJ0W12FZDr1nnepIfP7554wcOZLCwkKaN2/O1KlTS/z0u5g1qHBLSkoiNTWVOnXq0Lp1a6644gq++OIL+vXrR1ZWFpdeemmJf4iCdajK27lzZyg4+8tf/lLm911FrEFVlUsuuYQf/vCHAPz3v/9l48aNoXvWocLh0DoCytwrH+CWW24hPT0dCP6gtqz+1qHCacWKFcydOxeAH/3oR8TFxZXZzhoMP8NeRY3iP7gguG9RRYrvN2jQIKJzkr7rSOv00HvWqQ5n2bJlXHjhhezZs4eMjAw++OADmjVrVmZba1BVITk5mYceegiAxYsX8+GHH5a4bx2qssaNG8eWLVu49NJLueSSS466vzWoqnTxxReHzosfpAXWocKjQYMGoVWODRo0oEOHDmW2S0hIoG/fvgBkZWWFrluHipTiB7MBXHHFFeW2swbDz7BXUaNp06ahnwiV9Sujxfbv3x/6iXp5fxBKkdKuXbvQeUV1eug961QV+eabbxg8eDDZ2dnUrVuX9957r8J9K61BVZV+/fqFzhcsWFDinnWoyiqujbfeeouYmJhSx6FP7L766qtD19esWQNYg6pahz5UaOfOnaFz61DhkJKSEvohf/369StsW3x/9+7doWvWoSIhEAiEtnDo3Llzuc9xAGswEgx7FVV69OgBwJw5c8pt88UXX1BYWAhAz549q2ReUrFevXqFziuq0+J7SUlJdOzYMeLz0vFp69atDB48mPXr15OcnMzkyZND34PlsQZVVQ4ePBg6/+6+atahqps1qKq0efPm0Hm9evVC59ahwqVPnz4AbN++vcJ2xffr1q0bumYdKhJmzpwZ+gFreQ9mK2YNhp9hr6JK8a/xrVixgkWLFpXZ5tVXXwWCT8698MILq2xuEkCbNm047bTTgG9r8bsOHjzIW2+9BcCgQYOoXbt2lc1Px49du3Zx/vnn89VXX5GQkMDrr7/OWWedddh+1qCqyowZM0Lnbdq0KXHPOlRljR8/ngULFpR7TJkyJdR23LhxoetNmjQBrEFVrTfffDN0fujqNutQ4TJ06FAAduzYUe6/g/fv3x8Kyw5dHGAdKhKKt3CIjY3lRz/6UYVtrcHwM+xVVBkzZkzo//S/+c1vSt1fs2ZN6Mmjw4cPp1GjRlU6PwnghhtuAGD+/Pm89tprpe4/+uijbNmypURb6VD5+flceumlZGZmEhsbywsvvMAFF1xwxP2tQVXWsmXLKry/Y8cOfv3rXwOQlpbGueeeW6qNdajKaN26Nd27dy/3KP5HI0CLFi1C1xMTE0PXrUGFw4YNGyq8//LLL/P6668D8L3vfS/0A4di1qHCYcSIETRu3BiAu+++m0AgUKrNH/7wB3bs2AHA6NGjS9yzDhVO+/fv55VXXgGCwWzTpk0P28caDLOAFGUeeOCBABAAAqNGjQosXLgwsHXr1sDbb78daN26dQAIpKamBpYvX17dU9VxbMmSJYFZs2aFjmuvvTYABBITE0tcnzVrVmDXrl0l+u7fvz/QqVOnABBITk4OPP7444ENGzYE1qxZExg3blwgLi4uAAQGDx5cTZ9ONdnBgwcDl156aeh7bvz48YHc3NwKj++yBlVZcXFxgaFDhwaefvrpwKJFiwLbtm0LbN++PbBo0aLAn//850Dz5s1DNfrXv/61zDGsQ0XS6tWrQzU4YcKEMttYgwqH9PT0wIgRIwLPP/98YMmSJYHs7OzA1q1bA5988kngpz/9aSAmJiYABFJSUgKZmZml+luHCpcXXngh9L136aWXBmbPnh3Yvn17ICsrK3DbbbeFarFPnz6BAwcOlOhrHSqcXnnllVAtPvvss0fUxxoML8NeRZ2ioqLANddcE/py+e6RmpoamDJlSnVPU8e5gQMHlltj3z0++uijUv1XrVoVaNOmTbl9evXqFcjJyan6D6Ya79AA40iPsliDqowjqbukpKTAn/70pwrHsQ4VKUcS9gYC1qAqr27duof9PmzSpEng448/LncM61Dh8uCDDwZiY2PLraWePXsGNm7cWGZf61DhUrwwJSUlJbBnz54j7mcNho/bOCjqxMTE8O9//5vXX3+d888/n4yMDGrVqkXr1q25/vrryczMdK9eVbvWrVuTmZnJuHHj6NKlCykpKdSpU4fevXvzpz/9iVmzZh32abpSZViDqowZM2Zw77338v3vf59TTjmF1NRUEhMTycjIYMCAAdxzzz0sX76c2267rcJxrENVN2tQlfXUU09x66230r9/f1q0aEHt2rVJTEzk5JNP5vzzz+exxx5j2bJlDBw4sNwxrEOFyx133MFnn33Gj3/8Y5o3b05iYiL169dnwIAB/P3vf2fWrFmh7R6+yzpUOGzfvp133nkHCG6dmZKScsR9rcHwiQkEytjMRZIkSZIkSZJ0XHFlryRJkiRJkiRFAcNeSZIkSZIkSYoChr2SJEmSJEmSFAUMeyVJkiRJkiQpChj2SpIkSZIkSVIUMOyVJEmSJEmSpChg2CtJkiRJkiRJUcCwV5IkSZIkSZKigGGvJEmSJEmSJEUBw15JkiRJkiRJigLx1T0BSZIk6Wi0b98egJtvvpmxY8dW82yqxpw5cxgzZkyp68OGDeOhhx4K63v17t2b3NzcUteXL18e1veRJElS+Bn2SpIkKeLWr1/PoEGDKj3Ogw8+GIbZSJIkSdHJsFeSJEk6jvz+97+nS5cuANStWzfs47/00ksUFhYC8OijjzJ9+vSwv4ckSZIiw7BXkiRJEdeoUSPefvvtcu/feeedLF68GIB///vfNGzYsMx2J598MsOHD4/IHI8XzZo1o127dhEb/5RTTgmd16lTJ2LvI0mSpPAz7JUkSVLEJSQkVBhQ1q5dO3TeqlUrmjVrVhXTkiRJkqJKbHVPQJIkSZIkSZJUea7slSRJ0nGlffv2ANx8882MHTu2xL05c+YwZswYIPgwt+HDhzN9+nQmTpzI0qVL2bNnD82aNeOSSy5hzJgxJVYUz5gxgxdeeIGsrCx27txJ48aNGTJkCNddd12JduWZMWMGb731FgsWLCA7O5vY2FgaNWpEv379GDNmTIntESItEAgwdepU3n77bbKyssjJySEhIYH69euTnp5Oz549OfPMMzn77LOrbE6SJEmKPMNeSZIkRa3777+f5557rsS1lStXMn78eGbMmMGTTz5JcnIyDz/8MBMmTCjR7ptvvuEf//gHn376Kc8++2y5ge/u3bu57bbbmDlzZql7q1evZvXq1bz88svcfvvtXHvtteH7cOXIy8vjxhtv5LPPPitxvaCggH379rFhwwa+/PJLXnjhBRYtWhTx+UiSJKnqGPZKkiQpKr300ktkZmYycOBARo4cSZMmTdi0aRNPPPEEX375JV988QVPPPEE9erVY8KECQwYMICRI0fStGlTNm/ezBNPPEFmZiaLFi3in//8J7fddlup99i/fz9XXnklWVlZxMbGctFFF3HuuefSrFkzioqKWLJkCc8++yyrVq3ikUceoU6dOowcOTKin/vxxx8PBb09evRgxIgRtGzZktTUVHbv3s2qVauYNWsWs2bNiug8JEmSVPUMeyVJkhSVMjMzufLKK7nrrrtC1zp16sSZZ57JRRddxIYNG3j22Wc5ePBgme3OOOOMULuXXnqJsWPHEh9f8q/P48ePJysri1q1avHPf/6T/v37l7jftWtXhg0bxk9/+lPmzp3Lww8/zJAhQ0hNTY3Y5548eXLovZ977jkSEhJK3O/Xrx+XX345O3fujNgcJEmSVD18QJskSZKiUuPGjfnVr35V6npycjLDhg0DYO/evZx00knltrvssssA2LFjBytXrixxf+fOnbz44osA/OxnPysV9BZLSkrinnvuASA3N5d33333mD/TkcjOzgagZ8+epYLeQ9WrVy+i85AkSVLVM+yVJElSVBo8eHC5YWfxQ94O165Dhw6h8/Xr15e4N3PmTPLy8gAYMmRIhXNp27ZtKFydP3/+YedeGQ0bNgTgo48+IicnJ6LvJUmSpJrFbRwkSZIUlVq1alXuvbS0tKNut2fPnhL3Dn242YUXXnjE8ypeeRspw4cP57HHHuObb75h8ODBDB48mP79+9OzZ0+aN28e0feWJElS9TLslSRJUlRKTk4u915sbOxRtysqKipxb/v27cc0r+LVwJFy/fXXs337dl588UX27NnDpEmTmDRpEhDc2mLAgAGMGjWKTp06RXQekiRJqnqGvZIkSdIxKCwsDJ1PmjSp1MPbylNRuBwO8fHx3HPPPVx11VVMmTKFOXPmsHDhQvbt28emTZt46aWXeOmll7jqqqu48847IzoXSZIkVS3DXkmSJOkY1K9fP3Req1YtTjnllGqcTWktW7bkxhtv5MYbb+TgwYMsWbKEadOmMXHiRHJzc3n66afp2LFj6CF0kiRJOv75gDZJkiTpGJx22mmh85kzZ1bjTA4vPj6ebt268ctf/pIJEyaErr/zzjvVOCtJkiSFm2GvJEmSdAwGDhxIQkICAM8880zE9+INly5dulC3bl0AduzYUc2zkSRJUjgZ9kqSJEnHICMjg9GjRwOwYcMGfvnLX1YY+BYUFPD666+TnZ0dsTnt3LmTadOmlXqY3KEWLlzIrl27AGjevHnE5iJJkqSq5569kiRJ0jG6/fbb+fLLL1m4cCHTp0/nwgsvZNSoUXTr1o169eqxb98+1q1bx/z58/nggw/Iycnh/fffJz09PSLz2bNnDzfddBMnn3wygwcPpmvXrjRr1oykpCRycnL4/PPPmThxIgAxMTGhsFqSJEnRwbBXkiRJOkZJSUk8/fTT3H333UydOpWNGzcyfvz4ctsnJiaSmJgY8Xlt3ryZ5557rsJ53H333fTp0yfic5EkSVLVMeyVJEmSKiElJYXx48dz9dVXM2nSJObNm8fmzZvZu3cvycnJNG7cmPbt29O/f38GDx4c2i83Epo2bcqbb77J7NmzmT17NuvWrSM7O5vc3Fxq165Ny5YtOf300xk9erRbOEiSJEWhmEAgEKjuSUiSJEkq35w5cxgzZgwAzz77LP369auS973jjjuYNGkSAMuXL6+S95QkSdKxc2WvJEmSdBxZv3499evXB6Bu3bo0atQorON//fXXFBYWArB79+6wji1JkqTIMuyVJEmSjiN33XVX6HzYsGE89NBDYR1/1KhR5ObmhnVMSZIkVY3Y6p6AJEmSJEmSJKny3LNXkiRJkiRJkqKAK3slSZIkSZIkKQoY9kqSJEmSJElSFDDslSRJkiRJkqQoYNgrSZIkSZIkSVHAsFeSJEmSJEmSooBhryRJkiRJkiRFAcNeSZIkSZIkSYoChr2SJEmSJEmSFAUMeyVJkiRJkiQpChj2SpIkSZIkSVIUMOyVJEmSJEmSpCjw/wFD4J6GAUwMVgAAAABJRU5ErkJggg==" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 22 + "execution_count": 7 }, { "metadata": {}, @@ -292,8 +292,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:48.841066Z", - "start_time": "2024-10-18T12:33:48.723451Z" + "end_time": "2024-10-18T17:30:27.154303Z", + "start_time": "2024-10-18T17:30:27.044433Z" } }, "cell_type": "code", @@ -313,9 +313,9 @@ { "data": { "text/plain": [ - "
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HH35YCQkJslqtOnDgQKHVKV0NVYr7kca8fC59+/bNchTdP//8o6ioKHl4eGQ7WrROnTpq0aKF0tPT7Ua5Xmvo0KFZtrdt21bS1Tkri1vmQhuBgYFZBnGurq4aMWKE3bbXa9iwoe09FETmea8NEk+dOqUTJ06oWbNmtmslM3C8Nkjct2+fLUC79dZb833uXr16Zbny8NmzZ/Xnn39Kkh5++OEs9/X09NRtt90mSfr9999zPI+Hh4eeeuopSVdHQV64cCFfdT7yyCNZtg8fPlwuLi66dOmSbcGZ69WpU0dt27ZVq1atVL16dVksFp04cUJff/11to+gT5w4UcuXL1ePHj1swWpMTIx+/vlnvf/++7r77rv1zDPP2B6PzspDDz1kF7hnMvN6BwCgoHi0GQAASJItpMhcrOFGVKlSRT4+Pjd8nPzKnOMxv2HOzz//rEmTJuUYBkjKd+iRlStXrtgW/biR0Kkg8vq5XD9CMtPBgwclXV25OzNUy0pmMHLq1Kks+/39/bNsr1q1qqTCuQbzK3Nk2fWrlF8rs+/MmTOG+QklqVGjRjdUQ2aQeP78eR05ckSNGze2mx8xU/Xq1VW/fn0dPHhQcXFx8vLy0h9//CHp6ryOBZnLM7fP3MnJSc8991y2+588eVJS9p/5tfr166elS5fq8OHDmjdvnqZMmXLDdVasWFE1atRQdHS0jh49qo4dOxq2GTRokAYNGmT7OTw8XDNnztRPP/2kgQMH6j//+U+Wc262b99e7du3V0pKig4dOqTQ0FDt2bNH27dv18WLF/Xtt9/a5jzMKoDP7nrPvBfNuN4BACgogkQAACBJqlmzpiQpLi5OFy5cuKHHmwt7br+8yhxVmdXIquycPn1aTz/9tBITE9WzZ08NGzZMjRo1UsWKFW2jiIYMGaI///zTsKJwQcTHx9teV6xY8YaPlx95/VyufXT1WpmrDycmJtoWP8lJdnNiZldH5iPAVqs1L2UWqsxrJ6egtVq1arbX8fHxhiDxRq/7Jk2ayNvbW7Gxsdq5c6ddkHj9KMl27dopIiJCu3fvVrdu3WyjGAs6v2l2n/mlS5ckXZ1X8EY+82s5OTlp4sSJGjdunFauXKlhw4bled7Laz+D6/n4+Cg6OtruHstJgwYN9PHHH+v+++9XWFiY5s6dq+nTp2e7vZubm1q2bKmWLVtq4MCBSkhI0OTJk7V161YFBwfru+++0z333GPYL7vfbeb0AmZc7wAAFBRBIgAAkPS/xyWlq49M3nXXXSZWUzCZwU5cXFye99m8ebMSExPVokULffTRR1nOZ1cYIxEzZa4EK/0vmMuLzNAhIyMj221SU1MLXlgeZNbeqlUrffHFF0V6ruKWee2cO3cu221iYmJsr6/9HAuLxWJRu3bttHXrVu3cuVNDhw7Vzp077eZHzNSuXTutXbtWO3fu1O23326bH/GWW24p1Joy36ePj4/++9//Ftpxu3Tpovbt22vXrl364IMP9N577+Vpv3Pnzql27drZ9kn5+2xcXFzUuXNnhYWFaf/+/XneT7oaHL/xxhv6/vvvbUFrVkEiAACOhDkSAQCAJKl58+a2xwaXLVtWKkfJZD56mpeRU5lOnDgh6eoiDlmFiLGxsYU6h5mnp6fq1KkjKX91Zo5qyinoym6et8KS+fs9dOiQbQSfo2jQoIEkKTQ0NNttMh+dr1GjRpHNbZkZBO7atUtRUVGKjo62mx8x07XzKf79999KTk6WZP8IdGHI/MzPnTtX6HP5vfDCC7JYLPr666/zPP/okSNHsmyPi4uzLe6T30fMMwP4nEL67Hh5edkWRSqMEcsAAJR0BIkAAEDS1dFQzzzzjCTpr7/+0ieffJLrPn/88Yf++uuvQq0jc0GDvDwieb1evXrJYrHo4MGD+vnnn/N1vuxWGF6wYIHS09PzXUtOMkd7Ll++PM+PYdavX1+SFB0drbNnzxr6Dxw4oODg4EKrMStt27ZVzZo1lZycrAULFhTpua6X+TllBmaFrUuXLpKu/h6vXcQkU2pqqpYsWWK3bVHIDAgvXryo5cuXS8o6HKxZs6b8/Px06NAhfffdd5Kuzh9Y2HOT+vr62lahnjNnTqEeu2XLlrrrrrtktVr17rvv5mmfzM/gesuXL1daWpoqVqyoNm3a5LmG5ORku4V2rpVTaJ/p2LFjOn/+vKT/hdEAADgygkQAAGDTo0cPjR07VpL00Ucf6dlnnzWM0LJarQoODtYLL7ygRx55pFAf+5WkevXqSbo60iq/oyIbN26s/v37S5Kee+45bd682W6UUVpamn744Qdt377d1pYZ0nz33Xf69ttvbe2pqamaP3++Fi9ebAuxCsuYMWNUrVo1RUZGasyYMQoLC7PrP3funBYuXKjY2FhbW0BAgHx9fZWamqpp06bZLdDwzz//6Mknn8xyoYfC5OrqqsmTJ0uSPv30U73//vuGx7OTk5P1888/21bmLSx169aVJNuiIoWtbdu2uv322yVJU6ZMsRshFxcXpxdffFHR0dEqX7687R4pCg0bNrTNA/j5559Lyn7ew3bt2ikjI0Nr167NcbsbNXnyZLm6umrTpk166aWXDEF2Wlqadu3apSlTpmQbyGdn4sSJcnV11W+//abTp0/nuv3u3bv14Ycf2j3Gv3XrVn366aeSrq7qfO2chOvXr9fKlSuzDAVDQ0M1ZswYRUVF2a3KnenTTz9V//79tWbNGsP+VqtVO3bs0OOPPy6r1SovLy8eawYAlAnMkQgAAOw8//zzqlmzpt577z198803+uabb+Tj46MaNWooIyNDp06dsq1uXKdOHVvAU1juu+8+rVixQt9++6327t2rOnXqyNnZWU2bNtXLL7+c6/5Tp07V5cuX9d1332nixImaPn26/Pz8FB8fr+joaKWkpGjChAnq1q2bJKlbt2667bbb9N///lfPPPOMZsyYIR8fHx0/flxxcXF66KGHFB4eblvMojB4e3tr/vz5euyxx2zzqvn5+aly5co6e/aszp49K6vVqrvuusv22KTFYtErr7yiJ554Qj/88INuu+021a9fX3FxcYqMjFSnTp3Utm1bbdq0qdDqzMrdd9+tixcv6o033tCnn36qzz77TP7+/qpQoYIuX76syMjIIpmr8f7779e7776r6dOna+XKlfL29pbFYtEDDzygfv36Fco5Zs6cqdGjR+vQoUPq16+f/P39Vb58eYWFhSkpKUkeHh5677335OfnVyjny06HDh20efNmJSUlZTk/Yqb27dtr/fr1ttG7hT0/YqabbrpJH374oSZNmqQvv/xSGzZsUL169VSpUiXFx8frxIkTtpGiTzzxRL6O7efnp4ceekjLly/P08jfyZMn69///rdWrlypevXqKSYmxhZAdu3aVY8++qjd9tHR0ZozZ46mT5+umjVrysfHR05OTjp9+rQtEPX09NTMmTMNK3ZbLBYFBwcrODhYU6dOte2fkpKi06dP20J0Ly8vzZo1y5SV6gEAKG4EiQAAwGDo0KG65557tG7dOu3YsUPh4eE6fPiwXFxcVK1aNd16663q2bOnevbsKTc3t0I9d+vWrTVnzhwtXbpUhw4d0t69e/M1d5m7u7tmz56tH374QevWrdP+/fsVGhoqLy8vNW7cWHfccYf69u1r297JyUnz5s3T/PnztWnTJkVHRysxMVEBAQEaOHCg+vbtq2HDhhXqe5Suzkn59ddfa8WKFfrhhx8UERGhM2fOqFq1arr99tt15513qnr16nb7dO3aVYsXL9a8efP0zz//KDw8XHXr1tWUKVM0bNiwPAWthWHw4MHq2LGjVqxYod9//13R0dFKT0+Xj4+PWrdurVtuuUXdu3cv1HOOGjVKFotFmzZt0vHjx22jOAtzTkAfHx+tWbNGq1at0jfffKPw8HClpKSoRo0auu222zR69GjbiNmilBkkSspyfsRM1y6QlLlQS1Hp0aOHtmzZopUrV+rXX3/V8ePHFRUVJR8fHzVv3lw333yzunfvbpv/Mz8ef/xxbdiwIU/zbg4dOlR+fn5atGiRQkJClJycrGbNmmnAgAEaPHiwnJ2d7ba/77775OLioj///FMREREKDw9XamqqPD09dfPNN+u2227ToEGDsgwBJ06cqC5duui3337T3r17debMGYWFhdkeoW7fvr1uv/12DRw40Bb4AwDg6CzW0jiTOgAAAAAAAIBixRyJAAAAAAAAAHJFkAgAAAAAAAAgVwSJAAAAAAAAAHJFkAgAAAAAAAAgVwSJAAAAAAAAAHJFkAgAAAAAAAAgVwSJAAAAAAAAAHJFkAgAAAAAAAAgVwSJAAAgz3bu3KmAgAC7/x5//PFCPUdUVJThHMOGDSvUcyB/1q9fz+dQAnXr1s1wr1y+fLlQzzFs2DDDOaKiogr1HKXR7NmzFRAQoMmTJ5tdCgAAxcrF7AIAAMiPgICAAu33wAMPaMaMGYVcTdFYvHixrdY6depo+/bt+T7GiRMntG3bNu3cuVOhoaGKiYmRi4uLateurVtvvVXDhw9XvXr1Clyjm5ubgoKCJEmNGjUy9E+ePFkbNmxQ+/bttXz58iyPER4erlGjRunUqVOqX7++Fi1apDp16sjd3V1t27aVJMXGxioiIqLAdV4rKipK3bt3L9C+EyZM0JNPPlkodaBkioqK0s6dO3XgwAEdOHBAhw4dUlJSUoHvwWt169ZN0dHROW4za9Ys3XXXXQU6fpMmTeTp6SlJcnZ2tuvbuXOnhg8fLknatm2bfH19DfunpaXpxRdf1ObNm+Xi4qI33nhDffv2tR07LS1NkrRnz54C1VeYLl++rKVLl0oS92Qx+f7777Vq1SqFhIQoMTFRNWvWVNeuXTVu3Dh5e3sX6Jh5+bN83bp1atGihaG9qO8nAEDOCBIBAKVKZsB0veDgYKWkpKhWrVqqVauWob9+/fpFXFnhOHbsmD788MMbOkZ6erp69uxp+7ly5cpq0qSJ4uLiFBERobCwMK1du1Zvvvmm7rvvvgKdo1q1alq9enWBawwODtbYsWMVGxur5s2ba+HChapatarh2OvXr9eUKVMKfJ5rXRtQXi8zIKlfv36WfzHO6poqS7y8vOTv7+/Qv4elS5dq2bJlRXqOawO/61WpUqXAx33llVfUoUOHAu2blJSkp59+Wj/99JM8PDz04YcfqmvXrrb+V1991fa6oP+QU5guX76sOXPmSDI3SKxSpYr8/f1VrVo102ooDtOmTdOqVaskSbVr11bt2rUVFhamJUuWaNOmTVq+fLkaNmxY4OMHBQXJzc0ty74KFSrkuG9R3U8AgJwRJAIASpXswqvMEQr9+/cvtaNUMjIyNGXKFKWlpalbt24FHgVltVpVvnx5DRo0SH379lXTpk1lsVgkSZGRkXrppZe0a9cuTZ48Wc2aNbuhvwQWxM6dOzV+/HjFx8erffv2mjt3brZ/GSxMOYWfmQHJuHHj1K9fvyKvpbTp2bOnXTjtiKpUqaJOnTqpefPmCgoKUnh4uN5///1CPceNBH5FIS4uTo899pj+/PNPeXl5ae7cuWrXrp3ZZZUKQ4cO1dChQ80uo0itXbtWq1atkouLi2bOnGn7h6fY2Fg9/fTT2rVrlx5//HFt3rxZrq6uBTrHrFmzshwlmxcl7X4CgLKCIBEAgBJiyZIl2rt3r8aNGydXV9cCB4nOzs7avn17liMy/Pz8NGfOHPXq1UsXLlzQF198UWgj/vJi27ZtevbZZ5WcnKxu3brpww8/lLu7e7GdH8jO9XN9rl+/3qRKisf58+c1evRoHTx4UFWrVtVnn32mZs2amV0WSoj09HTNnj1bkjRy5Ei70eve3t768MMP1aNHD0VERGjjxo0aOHCgWaUCAIoZi60AAFACHDt2TLNmzZK/v78mTJhwQ8eyWCw5PtZVqVIltWnTRpIUFhZ2Q+fKj40bN+qpp55ScnKyHnjgAc2ePbvUhYjXLjpitVq1evVq9evXT23atNEtt9yip556SuHh4bbtDxw4oKeeekq33367WrRood69e2vt2rVZHjslJUXff/+9XnnlFfXu3VsdOnRQUFCQbr/9dk2YMEG///57tnVlLrqxc+dORUREaPLkyercubMCAwMNAdnGjRv14IMPqk2bNrr55ps1ePBgff3114bjZPe+czr36dOn9corr6hTp04KCgpS586dNXXqVJ0/fz7Pv2MUvejoaD388MM6ePCg6tSpo1WrVpkWIlqtVn311VcaOXKkOnTooMDAQLVv3169evXSc889p61bt9q2HTZsmN08p9cvAnNt+JvXe+LPP//UO++8o4EDB+r2229XUFCQOnTooGHDhmnt2rVKT0/Psu6cFlu5dlGao0eP6rnnntNtt92moKAg9ejRQ++8847i4+ML49dXZP7880+dOXNGkvTwww8b+qtWrapevXpJkjZt2lSstQEAzMWIRAAATJb5SHNycrJef/31bOeLKkzJycmSpHLlyhX5uaSr88+99dZbslqteuSRR/Tiiy/aHrcurV588UV99dVX8vX1Vd26dRUeHq6tW7dq9+7d+uKLL3TkyBE988wzcnNzU926dZWenq7Dhw/rlVdeUXx8vEaOHGl3vIiICE2YMEEWi0Xe3t6qUaOGnJycdOrUKX3//ff6/vvvNXHiRD366KPZ1vT3339r7ty5SktLU6NGjeTl5WW3+MbLL7+sdevWSZJ8fHxUq1YtHT9+XM8995wOHjx4Q7+Pw4cP66mnnlJ8fLwaNWokd3d3RUZGas2aNdq5c6e+/PLLYnmEvSRbs2aNFi9erISEBFWpUkWBgYHq3bt3sc49efToUY0aNUpnzpxR48aN9dlnn6lGjRrFdv7rvfTSS7YAsFq1amratKkSExN15swZff3114qKirIFVk2aNFFCQoKCg4MlGefMzZxn9Vq53RNPPPGELl68qIoVK8rHx0c1atTQuXPntGvXLu3atUs//PCDPvnkE8MiNnnx+++/6/XXX5fFYlGDBg3k6uqqyMhILVy4UHv27NGKFSsKdNzisHfvXklXF/yqU6dOltu0a9dOGzZs0D///KOMjAw5OeV/jMq8efN09uxZJScny8fHR61atVLv3r3zNMdhSbifAKAsIkgEAOAaP//8s+bNm1egffv3768BAwbke7/Fixdr7969Gjp0qG6++eYCnTs/zpw5o127dkmS2rdvX+Tn++ijj/Txxx9Lkp555hmNHz++yM9Z1Pbu3auwsDCtXLnS9pmdO3dOY8aM0cGDBzVt2jT9888/Gj16tMaPHy83NzdZrVZ9+OGHmjdvnmbNmqUBAwbYBWve3t6aOXOmOnXqZLfgi9Vq1S+//KIXX3xR77//vm6//XY1b948y7pmzZqlu+++W1OnTlWlSpUkXV1MQ5K+/PJLrVu3Ts7OzvrXv/6lQYMGyWKxKCMjQ1988YWmT59eoCAg08yZM9WrVy+9+uqrqly5siRp//79GjdunCIiIrRkyZJ8j7YdPHhwgWqpVq2aPvroowLtW5QyR35m2rJliz766CM9/vjjhpGjRWH//v167bXXdPHiRbVu3Vqffvqp7bMyw6FDh7R+/XpVqFBBH3/8sW699Va7/v3799sF3K+++qrd6ut5WfApp3tCkiZOnKh27drJ39/fbr/Dhw/rpZde0k8//aQVK1ZoxIgR+X5/06ZN09ChQ/X000/b/tHm119/1YQJE7Rnzx5t2rTJtjp2XsTExOipp57Kdx2S1Lx5c7uFc3KTObK6Xr162W6T2ZeUlKTo6Gj5+fnlu67rR2hv3rxZH3zwgV5++eVc/zw1+34CgLKKIBEAgGucP3/etoJvfnXs2DHf+4SHh2vWrFmqU6eOnnvuuQKdNz+sVqv+9a9/KTU1VdWrV1f//v2L9Hx79uyxhZbTpk3TQw89VKTnKy6pqal6+eWX7YJfHx8fPfPMMxo3bpx+/fVX3XHHHXr66adt/RaLRRMmTNCXX36pmJgY7dy50+4xTR8fnyxDBYvFos6dO+vZZ5/V1KlT9eWXX2YbJPr7+2vmzJlycfnf/8Xz8PCQJH366aeSrs539uCDD9r6nZyc9NBDD+n48eNatGhRwX4hujpyacaMGXaLLrRo0UKjR4/W22+/re3bt+c7SCzovZjdCCqztGrVSmPGjFH79u1Vq1YtWSwWhYSEaOHChfrxxx81a9YsWSyWIg/Zn3/+eaWlpaljx476+OOPVb58+SI9X24yp1a49dZbDSGidPX6adGixQ2dI6d7QpIGDRqU5X5NmjTRO++8o7vuukvr1q0rUJDYpk0bw6PPd9xxh/r376+VK1dq27Zt+QoSk5OTC3xPXPv+8+LSpUuSlGPQfG3f5cuX83X82267Tffff79atmyp2rVrKzU1VXv37tXcuXO1Z88evfzyy3J1dVWfPn0M+5aU+wkAyiqCRAAArtGvX79iW7U3IyNDL730kpKTkzVt2jRVqFChyM/54Ycf6scff5STk5PefvvtYg0STp06VWznKmoVK1bUPffcY2gPCgqyvc4qNHV1dVXTpk0VExOjEydOZHnsvXv3avv27QoLC1NcXJzS0tIkSVeuXJEk22OdWenbt2+WgUF4eLiOHz8uKftRfoMHD76hIPGhhx7KcuXWzMdPIyIi8n3M0NDQAtdTknzwwQeGtptvvlk333yzXnnlFa1du1affPKJHnjgAdWsWbPI6zl//rySkpJMDxIzH0H9+++/FR4ergYNGhT6ObK7J64VGRmpb7/9VgcPHtSFCxdsUz9kOnLkiJKSkuwCyLzIak5R6eo9sXLlynzfE76+vsV2T2SO2sxpNeZr57hNTEzM1/Gv/67x8PBQp06ddNttt+nRRx/Vjh07NGPGDN15552GKThK2v0EAGUNQSIAACZZtGiR9u7dqwceeEB33HFHsZxv3rx5slgsmj59epYjgApb27ZtFRgYqMWLF2vevHlKT0/X888/X+TnLWp169bNco7Ha+doy+6RwMxtrl9sISkpSc8//7y+//77HM994cKFbPsaN26cZXvmY4qenp7ZPn5Yt25dVahQocCLQFz/aGgmHx8fSVJCQkKBjuvoXnjhBW3cuFEpKSnatm2bhgwZUmTneuutt/Taa68pNDRUI0aM0NKlS+0eoy9ubdq0Ufv27bVr1y7de++9atWqldq3b6+WLVuqffv2qlix4g2fI7t7ItPcuXM1Z84cW2CfFavVqkuXLuU7SMzunsj8DijJ90Tme01NTc12m2sD18Kab9fZ2VlTpkzRvffeq9jYWO3cuVNdunTJ8/7FeT8BQFnFqs0AAJjg+PHj+uijj+Tj45Plqp+FbcmSJZo5c6YsFov+9a9/aeDAgUV+zkyTJ0/W6NGjJUkLFizQzJkzi+3cRSW7kVzXhovZ/cU6cxur1WrX/u677+r7779XpUqVNG3aNH377bfau3evDh06pNDQUC1ZskSScgw8sjtnZjiY22InN7IYSn7fL66qVKmSLew6duxYkZ6rbdu2+uyzz+Tp6anDhw9r+PDhpq6obbFYNG/ePD3++OOqVauW9u7dq08//VRPPPGEOnbsqKefflrR0dE3dI6cAq7vvvtOH374odLT0/XYY4/pyy+/1O7duxUSEqLQ0FC7+RlzCtTye+7MuUhL8j2RGeJevHgx222u7SuM0DdTo0aNbPNZ5veeKM77CQDKKkYkAgBwjeJabOXIkSNKTk6WxWLRvffea+jPHKly6tQp3XbbbZKujibq1KlTvutasmSJ3nrrLUnS1KlTC7yAxY2YNGmSnJ2dNX/+fC1atMi2UjWuSk9P14YNGyRJM2bMULdu3Qzb5DQSMTeZj83nNtqwoKMRi4qjLbaSnczHRwsSVuVXmzZttGjRIo0ePVpHjhzRsGHDtHTpUlWrVq3Iz52VChUq6Omnn9bTTz+tqKgo7dmzR7///ru2bt2qLVu2KDg4WBs3bpSXl1ehnztzBfORI0fq2WefNfTfyD1XFIpzsZXMx8wzp0TISmafh4dHoc9LeiP3RHH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" }, "metadata": {}, "output_type": "display_data" @@ -326,12 +326,12 @@ "" ] }, - "execution_count": 23, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 23 + "execution_count": 8 }, { "metadata": {}, @@ -342,8 +342,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:49.110673Z", - "start_time": "2024-10-18T12:33:48.882743Z" + "end_time": "2024-10-18T17:30:27.378235Z", + "start_time": "2024-10-18T17:30:27.187548Z" } }, "cell_type": "code", @@ -386,15 +386,15 @@ { "data": { "text/plain": [ - "
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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 24 + "execution_count": 9 }, { "metadata": {}, @@ -405,8 +405,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:49.201611Z", - "start_time": "2024-10-18T12:33:49.127368Z" + "end_time": "2024-10-18T17:30:27.462088Z", + "start_time": "2024-10-18T17:30:27.395341Z" } }, "cell_type": "code", @@ -436,9 +436,9 @@ { "data": { "text/plain": [ - "
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" 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" }, "metadata": {}, "output_type": "display_data" @@ -453,7 +453,7 @@ ] } ], - "execution_count": 25 + "execution_count": 10 }, { "metadata": {}, @@ -470,13 +470,13 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:49.393519Z", - "start_time": "2024-10-18T12:33:49.294312Z" + "end_time": "2024-10-18T17:30:27.598151Z", + "start_time": "2024-10-18T17:30:27.517927Z" } }, "cell_type": "code", "source": [ - "aspect_ratio = 1\n", + "aspect_ratio = 1.3\n", "cable.set_aspect_ratio(aspect_ratio) # This adjusts the cable dimensions while maintaining the total cross-sectional area.\n", "cable.plot(0, 0, show=True)\n", "bluemira_print(f\"cable area: {cable.area}\")" @@ -486,9 +486,9 @@ { "data": { "text/plain": [ - "
" + "
" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -503,13 +503,13 @@ ] } ], - "execution_count": 26 + "execution_count": 11 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:49.756877Z", - "start_time": "2024-10-18T12:33:49.480568Z" + "end_time": "2024-10-18T17:30:27.866024Z", + "start_time": "2024-10-18T17:30:27.626561Z" } }, "cell_type": "code", @@ -534,8 +534,8 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| before optimization: dx_cable = 0.03215544485259999, aspect ratio = |\n", - "| 0.9999999999999998 |\n", + "| before optimization: dx_cable = 0.036662848004065086, aspect ratio = |\n", + "| 1.3 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| Optimal n_stab: 514 |\n", @@ -548,9 +548,9 @@ { "data": { "text/plain": [ - "
" + "
" ], - "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -560,23 +560,23 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| after optimization: dx_cable = 0.03215544485259999, aspect ratio = |\n", - "| 0.990032118728541 |\n", + "| after optimization: dx_cable = 0.036662848004065086, aspect ratio = |\n", + "| 1.2870417543471038 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Adjust aspect ratio: dx_cable = 0.03231691381619191, aspect ratio = |\n", - "| 1.0000000000000002 |\n", + "| Adjust aspect ratio: dx_cable = 0.036846950948268814, aspect ratio = |\n", + "| 1.3000000000000003 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 27 + "execution_count": 12 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:49.806993Z", - "start_time": "2024-10-18T12:33:49.804415Z" + "end_time": "2024-10-18T17:30:27.917456Z", + "start_time": "2024-10-18T17:30:27.914488Z" } }, "cell_type": "code", @@ -589,13 +589,13 @@ ], "id": "256e6bf3855c8dff", "outputs": [], - "execution_count": 28 + "execution_count": 13 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:50.447866Z", - "start_time": "2024-10-18T12:33:49.850664Z" + "end_time": "2024-10-18T17:30:28.517221Z", + "start_time": "2024-10-18T17:30:27.961063Z" } }, "cell_type": "code", @@ -635,17 +635,17 @@ "output_type": "stream", "text": [ "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 1.0/17 have been added to complete the last winding pack |\n", - "| (nx=17, ny=1). |\n", + "| WARNING: 2.0/170 have been added to complete the last winding pack |\n", + "| (nx=17, ny=10). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, { "data": { "text/plain": [ - "
" + "
" ], - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -658,12 +658,12 @@ "| Previous number of conductors: 168.0 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| New number of conductors: 169 |\n", + "| New number of conductors: 170 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 29 + "execution_count": 14 }, { "metadata": {}, @@ -674,18 +674,19 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:51.216770Z", - "start_time": "2024-10-18T12:33:50.497271Z" + "end_time": "2024-10-18T17:30:29.124236Z", + "start_time": "2024-10-18T17:30:28.540278Z" } }, "cell_type": "code", "source": [ "# Optimization parameters\n", - "bounds = [1e-5, 2]\n", - "max_niter = 10\n", + "bounds_cond_jacket = [1e-5, 0.1]\n", + "bounds_dy_vault = [0.1, 2]\n", + "max_niter = 100\n", "err = 1e-5\n", "\n", - "case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds, layout, wp_reduction_factor,\n", + "case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds_cond_jacket, bounds_dy_vault, layout, wp_reduction_factor,\n", " min_gap_x, n_layers_reduction, max_niter, err, n_cond)\n", "\n", "if show:\n", @@ -729,731 +730,140 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 16.0/184 have been added to complete the last winding pack |\n", - "| (nx=23, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 332047113.91974866, allowable_sigma: 512820512.8205128, diff: |\n", - "| -180773398.9007641 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 241604875.47285625, allowable_sigma: 512820512.8205128, diff: |\n", - "| -271215637.3476565 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 455612813.8214364, allowable_sigma: 512820512.8205128, diff: |\n", - "| -57207698.99907637 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 608751089.304492, allowable_sigma: 512820512.8205128, diff: |\n", - "| 95930576.48397923 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 462323385.903952, allowable_sigma: 512820512.8205128, diff: |\n", - "| -50497126.91656077 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 502619908.323683, allowable_sigma: 512820512.8205128, diff: |\n", - "| -10200604.496829748 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 537982296.6945287, allowable_sigma: 512820512.8205128, diff: |\n", - "| 25161783.874015927 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 506163528.14589435, allowable_sigma: 512820512.8205128, diff: |\n", - "| -6656984.674618423 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 514767768.23611915, allowable_sigma: 512820512.8205128, diff: |\n", - "| 1947255.4156063795 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 523366814.9164499, allowable_sigma: 512820512.8205128, diff: |\n", - "| 10546302.095937133 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 518424587.26568866, allowable_sigma: 512820512.8205128, diff: |\n", - "| 5604074.445175886 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512549117.6755499, allowable_sigma: 512820512.8205128, diff: |\n", - "| -271395.14496284723 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 510088828.6527809, allowable_sigma: 512820512.8205128, diff: |\n", - "| -2731684.167731881 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512639977.122945, allowable_sigma: 512820512.8205128, diff: |\n", - "| -180535.69756776094 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513197295.63484687, allowable_sigma: 512820512.8205128, diff: |\n", - "| 376782.8143340945 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512852693.840595, allowable_sigma: 512820512.8205128, diff: |\n", - "| 32181.020082235336 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513097289.0087082, allowable_sigma: 512820512.8205128, diff: |\n", - "| 276776.1881954074 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512946090.0622024, allowable_sigma: 512820512.8205128, diff: |\n", - "| 125577.2416896224 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512809168.6854433, allowable_sigma: 512820512.8205128, diff: |\n", - "| -11344.135069489479 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512768113.4574714, allowable_sigma: 512820512.8205128, diff: |\n", - "| -52399.36304140091 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 345616797.9811439, allowable_sigma: 512820512.8205128, diff: |\n", - "| -167203714.83936888 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 247900521.62276733, allowable_sigma: 512820512.8205128, diff: |\n", - "| -264919991.19774544 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 482853091.0125967, allowable_sigma: 512820512.8205128, diff: |\n", - "| -29967421.807916045 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 659370287.7291387, allowable_sigma: 512820512.8205128, diff: |\n", - "| 146549774.90862596 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 452874484.3230504, allowable_sigma: 512820512.8205128, diff: |\n", - "| -59946028.49746239 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 507340004.46259856, allowable_sigma: 512820512.8205128, diff: |\n", - "| -5480508.357914209 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 555453904.2854455, allowable_sigma: 512820512.8205128, diff: |\n", - "| 42633391.46493268 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512691422.74688816, allowable_sigma: 512820512.8205128, diff: |\n", - "| -129090.0736246109 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 528136137.25477016, allowable_sigma: 512820512.8205128, diff: |\n", - "| 15315624.434257388 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515013746.55549395, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2193233.7349811792 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512013954.3485621, allowable_sigma: 512820512.8205128, diff: |\n", - "| -806558.4719506502 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513141456.2201133, allowable_sigma: 512820512.8205128, diff: |\n", - "| 320943.3996005058 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512747351.99869025, allowable_sigma: 512820512.8205128, diff: |\n", - "| -73160.82182252407 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512857411.6498914, allowable_sigma: 512820512.8205128, diff: |\n", - "| 36898.82937860489 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512830940.88401806, allowable_sigma: 512820512.8205128, diff: |\n", - "| 10428.063505291939 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512804473.1708508, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16039.649661958218 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 349879111.39415807, allowable_sigma: 512820512.8205128, diff: |\n", - "| -162941401.4263547 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 249893848.5349875, allowable_sigma: 512820512.8205128, diff: |\n", - "| -262926664.28552526 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 491609709.1404632, allowable_sigma: 512820512.8205128, diff: |\n", - "| -21210803.6800496 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 676223007.6252224, allowable_sigma: 512820512.8205128, diff: |\n", - "| 163402494.80470967 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 454742051.48801386, allowable_sigma: 512820512.8205128, diff: |\n", - "| -58078461.33249891 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 547560870.8728577, allowable_sigma: 512820512.8205128, diff: |\n", - "| 34740358.05234492 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 506597853.4967215, allowable_sigma: 512820512.8205128, diff: |\n", - "| -6222659.3237912655 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513823797.3415109, allowable_sigma: 512820512.8205128, diff: |\n", - "| 1003284.5209981203 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 526151677.2605779, allowable_sigma: 512820512.8205128, diff: |\n", - "| 13331164.440065145 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 514111099.66574746, allowable_sigma: 512820512.8205128, diff: |\n", - "| 1290586.845234692 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 511746926.64839435, allowable_sigma: 512820512.8205128, diff: |\n", - "| -1073586.1721184254 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513028283.2969787, allowable_sigma: 512820512.8205128, diff: |\n", - "| 207770.47646594048 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512803969.43313956, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16543.387373209 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512399639.4397969, allowable_sigma: 512820512.8205128, diff: |\n", - "| -420873.380715847 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512771262.7761115, allowable_sigma: 512820512.8205128, diff: |\n", - "| -49250.04440128803 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512856945.5779121, allowable_sigma: 512820512.8205128, diff: |\n", - "| 36432.7573993206 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512830364.5420937, allowable_sigma: 512820512.8205128, diff: |\n", - "| 9851.721580922604 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 351210807.5630299, allowable_sigma: 512820512.8205128, diff: |\n", - "| -161609705.2574829 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250493238.99422365, allowable_sigma: 512820512.8205128, diff: |\n", - "| -262327273.82628912 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 494418454.1338878, allowable_sigma: 512820512.8205128, diff: |\n", - "| -18402058.686624944 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 681762681.8560188, allowable_sigma: 512820512.8205128, diff: |\n", - "| 168942169.035506 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 455543596.5522773, allowable_sigma: 512820512.8205128, diff: |\n", - "| -57276916.268235445 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 551099404.3194903, allowable_sigma: 512820512.8205128, diff: |\n", - "| 38278891.49897754 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505755870.51132715, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7064642.309185624 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 514993962.7063273, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2173449.8858145475 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 528148779.9507562, allowable_sigma: 512820512.8205128, diff: |\n", - "| 15328267.13024342 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 514007382.6471122, allowable_sigma: 512820512.8205128, diff: |\n", - "| 1186869.8265994191 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 511746826.63596225, allowable_sigma: 512820512.8205128, diff: |\n", - "| -1073686.1845505238 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509439824.8471668, allowable_sigma: 512820512.8205128, diff: |\n", - "| -3380687.973345995 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512789578.2397908, allowable_sigma: 512820512.8205128, diff: |\n", - "| -30934.580721974373 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512845282.5186647, allowable_sigma: 512820512.8205128, diff: |\n", - "| 24769.698151946068 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512886127.653309, allowable_sigma: 512820512.8205128, diff: |\n", - "| 65614.83279621601 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512818900.9344379, allowable_sigma: 512820512.8205128, diff: |\n", - "| -1611.8860749006271 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 351888555.6992467, allowable_sigma: 512820512.8205128, diff: |\n", - "| -160931957.12126607 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250797503.51447517, allowable_sigma: 512820512.8205128, diff: |\n", - "| -262023009.3060376 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 495852968.76336634, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16967544.05714643 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 684604663.0683082, allowable_sigma: 512820512.8205128, diff: |\n", - "| 171784150.24779546 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 455986865.5215896, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56833647.298923194 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 552909108.3432002, allowable_sigma: 512820512.8205128, diff: |\n", - "| 40088595.522687435 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505537741.34316295, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7282771.477349818 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515616931.99505866, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2796419.174545884 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 519470797.7135672, allowable_sigma: 512820512.8205128, diff: |\n", - "| 6650284.893054426 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512614536.6841078, allowable_sigma: 512820512.8205128, diff: |\n", - "| -205976.13640499115 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509885414.53158057, allowable_sigma: 512820512.8205128, diff: |\n", - "| -2935098.2889322042 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512770525.59479666, allowable_sigma: 512820512.8205128, diff: |\n", - "| -49987.225716114044 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513450641.74774045, allowable_sigma: 512820512.8205128, diff: |\n", - "| 630128.9272276759 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513030069.08701336, allowable_sigma: 512820512.8205128, diff: |\n", - "| 209556.26650059223 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512821100.0436568, allowable_sigma: 512820512.8205128, diff: |\n", - "| 587.2231440544128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512900896.65765613, allowable_sigma: 512820512.8205128, diff: |\n", - "| 80383.83714336157 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512847474.3338895, allowable_sigma: 512820512.8205128, diff: |\n", - "| 26961.5133767128 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 352135118.80901194, allowable_sigma: 512820512.8205128, diff: |\n", - "| -160685394.01150084 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250908061.8303818, allowable_sigma: 512820512.8205128, diff: |\n", - "| -261912450.99013096 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 496375694.5478651, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16444818.272647679 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 685642416.0271809, allowable_sigma: 512820512.8205128, diff: |\n", - "| 172821903.20666814 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 456153683.5585109, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56666829.26200187 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 553568967.4033751, allowable_sigma: 512820512.8205128, diff: |\n", - "| 40748454.58286238 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505488317.69940114, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7332195.121111631 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515849322.6215456, allowable_sigma: 512820512.8205128, diff: |\n", - "| 3028809.801032841 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 518346380.2946321, allowable_sigma: 512820512.8205128, diff: |\n", - "| 5525867.474119306 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512456756.1878085, allowable_sigma: 512820512.8205128, diff: |\n", - "| -363756.63270425797 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509769808.00714135, allowable_sigma: 512820512.8205128, diff: |\n", - "| -3050704.81337142 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512795568.8269507, allowable_sigma: 512820512.8205128, diff: |\n", - "| -24943.993562042713 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513474032.52643734, allowable_sigma: 512820512.8205128, diff: |\n", - "| 653519.7059245706 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512889606.50582695, allowable_sigma: 512820512.8205128, diff: |\n", - "| 69093.68531417847 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512769203.21149117, allowable_sigma: 512820512.8205128, diff: |\n", - "| -51309.60902160406 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512823345.20053285, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2832.380020081997 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512849717.0384426, allowable_sigma: 512820512.8205128, diff: |\n", - "| 29204.217929840088 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 352102602.0704287, allowable_sigma: 512820512.8205128, diff: |\n", - "| -160717910.75008404 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250893485.4526702, allowable_sigma: 512820512.8205128, diff: |\n", - "| -261927027.36784258 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 496306731.32396007, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16513781.496552706 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 685505438.9756098, allowable_sigma: 512820512.8205128, diff: |\n", - "| 172684926.155097 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 456131517.8779267, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56688994.942586064 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 553481899.3829948, allowable_sigma: 512820512.8205128, diff: |\n", - "| 40661386.562482 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505493975.2698797, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7326537.550633073 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515818490.1082344, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2997977.287721634 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 518484138.54203933, allowable_sigma: 512820512.8205128, diff: |\n", - "| 5663625.721526563 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512475084.1125715, allowable_sigma: 512820512.8205128, diff: |\n", - "| -345428.7079412937 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509783205.0685272, allowable_sigma: 512820512.8205128, diff: |\n", - "| -3037307.75198555 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512791983.3643613, allowable_sigma: 512820512.8205128, diff: |\n", - "| -28529.456151485443 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513470263.3005805, allowable_sigma: 512820512.8205128, diff: |\n", - "| 649750.48006773 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512892692.19722164, allowable_sigma: 512820512.8205128, diff: |\n", - "| 72179.37670886517 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512765617.80462617, allowable_sigma: 512820512.8205128, diff: |\n", - "| -54895.015886604786 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512823116.4302219, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2603.6097091436386 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512849689.5183638, allowable_sigma: 512820512.8205128, diff: |\n", - "| 29176.697851002216 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 352093806.0225389, allowable_sigma: 512820512.8205128, diff: |\n", - "| -160726706.79797387 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250889542.20981035, allowable_sigma: 512820512.8205128, diff: |\n", - "| -261930970.61070243 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 496288077.5660194, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16532435.254493356 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 685468391.7258055, allowable_sigma: 512820512.8205128, diff: |\n", - "| 172647878.90529275 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 456125530.48249936, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56694982.33801341 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 553458349.1551106, allowable_sigma: 512820512.8205128, diff: |\n", - "| 40637836.334597826 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505495550.00762266, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7324962.812890112 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515810159.3982991, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2989646.5777863264 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 518521921.6068778, allowable_sigma: 512820512.8205128, diff: |\n", - "| 5701408.786365032 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512480164.70958877, allowable_sigma: 512820512.8205128, diff: |\n", - "| -340348.1109240055 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509786921.2779607, allowable_sigma: 512820512.8205128, diff: |\n", - "| -3033591.542552054 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512791028.07505834, allowable_sigma: 512820512.8205128, diff: |\n", - "| -29484.74545443058 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513469278.01824206, allowable_sigma: 512820512.8205128, diff: |\n", - "| 648765.1977292895 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512893558.2065623, allowable_sigma: 512820512.8205128, diff: |\n", - "| 73045.38604950905 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512764662.52866316, allowable_sigma: 512820512.8205128, diff: |\n", - "| -55850.29184961319 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512823066.58102036, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2553.760507583618 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512849989.4300138, allowable_sigma: 512820512.8205128, diff: |\n", - "| 29476.60950100422 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 352091439.29307556, allowable_sigma: 512820512.8205128, diff: |\n", - "| -160729073.5274372 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250888481.19644576, allowable_sigma: 512820512.8205128, diff: |\n", - "| -261932031.624067 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 496283058.5478101, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16537454.272702694 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 685458423.9689713, allowable_sigma: 512820512.8205128, diff: |\n", - "| 172637911.14845848 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 456123920.0961138, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56696592.72439897 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 553452012.7313267, allowable_sigma: 512820512.8205128, diff: |\n", - "| 40631499.91081393 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505495976.94940954, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7324535.871103227 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515807918.5829519, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2987405.7624391317 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 518532125.9358626, allowable_sigma: 512820512.8205128, diff: |\n", - "| 5711613.115349829 |\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512481540.75442064, allowable_sigma: 512820512.8205128, diff: |\n", - "| -338972.0660921335 |\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509787927.9548962, allowable_sigma: 512820512.8205128, diff: |\n", - "| -3032584.86561656 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512790772.1005679, allowable_sigma: 512820512.8205128, diff: |\n", - "| -29740.719944894314 |\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513469015.42652154, allowable_sigma: 512820512.8205128, diff: |\n", - "| 648502.6060087681 |\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512893793.51142794, allowable_sigma: 512820512.8205128, diff: |\n", - "| 73280.69091516733 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512764406.55763614, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56106.26287662983 |\n", + "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512823053.9913491, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2541.17083632946 |\n", + "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", + "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512850071.50962293, allowable_sigma: 512820512.8205128, diff: |\n", - "| 29558.689110159874 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 7.0/175 have been added to complete the last winding pack |\n", - "| (nx=25, ny=7). |\n", + "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", + "| (nx=22, ny=8). |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n", "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [1e-05, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 352075785.77890205, allowable_sigma: 512820512.8205128, diff: |\n", - "| -160744727.04161072 |\n", + "| bounds: [0.1, 2] |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] }, @@ -1462,231 +872,168 @@ "output_type": "stream", "text": [ "Internal optimazion - iteration 1\n", - "before optimization: conductor jacket area = 0.001692676552647676\n", - "Optimal dx_jacket: 0.004948261325397751\n", - "Averaged sigma in the x-direction: 510.33714914524325 MPa\n", - "227061.65185719344\n", - "after optimization: conductor jacket area = 0.0007375912997492067\n", + "before optimization: conductor jacket area = 0.001703815187400281\n", + "Optimal dx_jacket: 0.005085979267589973\n", + "Averaged sigma in the x-direction: 514.3521612867105 MPa\n", + "226070.8532465582\n", + "after optimization: conductor jacket area = 0.0007665864416300967\n", "before optimization: case dy_vault = 0.6\n", - "Optimal dy_vault: 0.39067938773912514\n", - "Tresca sigma: 512.8091686854433 MPa\n", - "after optimization: case dy_vault = 0.39067938773912514\n", - "err_dy_jacket = 0.5642455739134153\n", - "err_dy_vault = 0.5357861684800427\n", - "tot_err = 1.100031742393458\n", + "after optimization: case dy_vault = 0.3972599551567517\n", + "err_dy_jacket = 0.5500765298378568\n", + "err_dy_vault = 0.5103460396939596\n", + "tot_err = 1.0604225695318164\n", "Internal optimazion - iteration 2\n", - "before optimization: conductor jacket area = 0.0007375912997492067\n", - "Optimal dx_jacket: 0.003787822529545763\n", - "Averaged sigma in the x-direction: 513.9673425923443 MPa\n", - "144922.54838935137\n", - "after optimization: conductor jacket area = 0.0005470333350147793\n", - "before optimization: case dy_vault = 0.39067938773912514\n", - "Optimal dy_vault: 0.4317446661911663\n", - "Tresca sigma: 512.8309408840181 MPa\n", - "after optimization: case dy_vault = 0.4317446661911663\n", - "err_dy_jacket = 0.2583516980192422\n", - "err_dy_vault = 0.0951147325439301\n", - "tot_err = 0.3534664305631723\n", + "before optimization: conductor jacket area = 0.0007665864416300967\n", + "Optimal dx_jacket: 0.0041096168242123286\n", + "Averaged sigma in the x-direction: 511.4847569151833 MPa\n", + "152009.78620165147\n", + "after optimization: conductor jacket area = 0.0006033738847477704\n", + "before optimization: case dy_vault = 0.3972599551567517\n", + "after optimization: case dy_vault = 0.4142671608339444\n", + "err_dy_jacket = 0.21290822276384816\n", + "err_dy_vault = 0.041053714330038\n", + "tot_err = 0.25396193709388615\n", "Internal optimazion - iteration 3\n", - "before optimization: conductor jacket area = 0.0005470333350147793\n", - "Optimal dx_jacket: 0.003252207472610154\n", - "Averaged sigma in the x-direction: 514.219316138129 MPa\n", - "107671.67845696783\n", - "after optimization: conductor jacket area = 0.0004627126481984757\n", - "before optimization: case dy_vault = 0.4317446661911663\n", - "Optimal dy_vault: 0.4440413655912122\n", - "Tresca sigma: 512.8303645420937 MPa\n", - "after optimization: case dy_vault = 0.4440413655912122\n", - "err_dy_jacket = 0.15414177056326103\n", - "err_dy_vault = 0.027692688909002096\n", - "tot_err = 0.18183445947226312\n", + "before optimization: conductor jacket area = 0.0006033738847477704\n", + "Optimal dx_jacket: 0.0036265972891163915\n", + "Averaged sigma in the x-direction: 514.1360542202177 MPa\n", + "119221.90068381246\n", + "after optimization: conductor jacket area = 0.0005254500940031694\n", + "before optimization: case dy_vault = 0.4142671608339444\n", + "after optimization: case dy_vault = 0.4276533619789009\n", + "err_dy_jacket = 0.12914677402250455\n", + "err_dy_vault = 0.03130152206219992\n", + "tot_err = 0.16044829608470446\n", "Internal optimazion - iteration 4\n", - "before optimization: conductor jacket area = 0.0004627126481984757\n", - "Optimal dx_jacket: 0.0030088586267110196\n", - "Averaged sigma in the x-direction: 514.7857786881332 MPa\n", - "91308.85419252724\n", - "after optimization: conductor jacket area = 0.00042516102064023547\n", - "before optimization: case dy_vault = 0.4440413655912122\n", - "Optimal dy_vault: 0.44791686397338104\n", - "Tresca sigma: 512.8189009344379 MPa\n", - "after optimization: case dy_vault = 0.44791686397338104\n", - "err_dy_jacket = 0.08115539461573751\n", - "err_dy_vault = 0.008652271646550785\n", - "tot_err = 0.0898076662622883\n", + "before optimization: conductor jacket area = 0.0005254500940031694\n", + "Optimal dx_jacket: 0.0034064935432520835\n", + "Averaged sigma in the x-direction: 514.6851043912599 MPa\n", + "104130.72752879365\n", + "after optimization: conductor jacket area = 0.0004905605947881787\n", + "before optimization: case dy_vault = 0.4276533619789009\n", + "after optimization: case dy_vault = 0.4316397811250321\n", + "err_dy_jacket = 0.06639926343752879\n", + "err_dy_vault = 0.009235523045954944\n", + "tot_err = 0.07563478648348373\n", "Internal optimazion - iteration 5\n", - "before optimization: conductor jacket area = 0.00042516102064023547\n", - "Optimal dx_jacket: 0.002887030608161824\n", - "Averaged sigma in the x-direction: 516.5835600551138 MPa\n", - "83808.97760720368\n", - "after optimization: conductor jacket area = 0.0004065394603245476\n", - "before optimization: case dy_vault = 0.44791686397338104\n", - "Optimal dy_vault: 0.4498689380829232\n", - "Tresca sigma: 512.8211000436568 MPa\n", - "after optimization: case dy_vault = 0.4498689380829232\n", - "err_dy_jacket = 0.043798841877946124\n", - "err_dy_vault = 0.004339206253849778\n", - "tot_err = 0.0481380481317959\n", + "before optimization: conductor jacket area = 0.0004905605947881787\n", + "Optimal dx_jacket: 0.003324123643457865\n", + "Averaged sigma in the x-direction: 511.9433091704933 MPa\n", + "97116.29964924548\n", + "after optimization: conductor jacket area = 0.00047760348110165445\n", + "before optimization: case dy_vault = 0.4316397811250321\n", + "after optimization: case dy_vault = 0.43310532352337405\n", + "err_dy_jacket = 0.026412870956581933\n", + "err_dy_vault = 0.003383801395973522\n", + "tot_err = 0.029796672352555453\n", "Internal optimazion - iteration 6\n", - "before optimization: conductor jacket area = 0.0004065394603245476\n", - "Optimal dx_jacket: 0.002843048981604718\n", - "Averaged sigma in the x-direction: 514.715834009809 MPa\n", - "80094.05384952854\n", - "after optimization: conductor jacket area = 0.0003998459857021419\n", - "before optimization: case dy_vault = 0.4498689380829232\n", - "Optimal dy_vault: 0.45057558196352715\n", - "Tresca sigma: 512.8233452005328 MPa\n", - "after optimization: case dy_vault = 0.45057558196352715\n", - "err_dy_jacket = 0.01646451396639874\n", - "err_dy_vault = 0.0015683137499917402\n", - "tot_err = 0.01803282771639048\n", + "before optimization: conductor jacket area = 0.00047760348110165445\n", + "Optimal dx_jacket: 0.0032663223970235632\n", + "Averaged sigma in the x-direction: 514.8539942746442 MPa\n", + "94518.22015277074\n", + "after optimization: conductor jacket area = 0.000468543522823692\n", + "before optimization: case dy_vault = 0.43310532352337405\n", + "after optimization: case dy_vault = 0.434163077739628\n", + "err_dy_jacket = 0.018969623623899267\n", + "err_dy_vault = 0.0024363062417948667\n", + "tot_err = 0.021405929865694135\n", "Internal optimazion - iteration 7\n", - "before optimization: conductor jacket area = 0.0003998459857021419\n", - "Optimal dx_jacket: 0.002848838910865934\n", - "Averaged sigma in the x-direction: 510.4155878418728 MPa\n", - "78759.4551109264\n", - "after optimization: conductor jacket area = 0.00040072625879492875\n", - "before optimization: case dy_vault = 0.45057558196352715\n", - "Optimal dy_vault: 0.45048235612893633\n", - "Tresca sigma: 512.823116430222 MPa\n", - "after optimization: case dy_vault = 0.45048235612893633\n", - "err_dy_jacket = 0.0022015303998640227\n", - "err_dy_vault = 0.00020694669463177046\n", - "tot_err = 0.002408477094495793\n", + "before optimization: conductor jacket area = 0.000468543522823692\n", + "Optimal dx_jacket: 0.0032487505774551266\n", + "Averaged sigma in the x-direction: 513.5448966863272 MPa\n", + "92698.73794725668\n", + "after optimization: conductor jacket area = 0.00046579455555420275\n", + "before optimization: case dy_vault = 0.434163077739628\n", + "after optimization: case dy_vault = 0.4344977584191138\n", + "err_dy_jacket = 0.005867047852721333\n", + "err_dy_vault = 0.0007702702096865635\n", + "tot_err = 0.006637318062407896\n", "Internal optimazion - iteration 8\n", - "before optimization: conductor jacket area = 0.00040072625879492875\n", - "Optimal dx_jacket: 0.0028504056762085716\n", - "Averaged sigma in the x-direction: 510.59352752862225 MPa\n", - "78934.96452377425\n", - "after optimization: conductor jacket area = 0.0004009645083927146\n", - "before optimization: case dy_vault = 0.45048235612893633\n", - "Optimal dy_vault: 0.45045711986578807\n", - "Tresca sigma: 512.8230665810204 MPa\n", - "after optimization: case dy_vault = 0.45045711986578807\n", - "err_dy_jacket = 0.0005945445115134922\n", - "err_dy_vault = 5.602367469690353e-05\n", - "tot_err = 0.0006505681862103957\n", + "before optimization: conductor jacket area = 0.00046579455555420275\n", + "Optimal dx_jacket: 0.0032432671784032707\n", + "Averaged sigma in the x-direction: 513.1632034826006 MPa\n", + "92145.15956182206\n", + "after optimization: conductor jacket area = 0.0004649372283619356\n", + "before optimization: case dy_vault = 0.4344977584191138\n", + "after optimization: case dy_vault = 0.4345971836353921\n", + "err_dy_jacket = 0.0018405693712909461\n", + "err_dy_vault = 0.00022877556510289833\n", + "tot_err = 0.0020693449363938443\n", "Internal optimazion - iteration 9\n", - "before optimization: conductor jacket area = 0.0004009645083927146\n", - "Optimal dx_jacket: 0.002850827280995097\n", - "Averaged sigma in the x-direction: 510.6420590429833 MPa\n", - "78982.46940751096\n", - "after optimization: conductor jacket area = 0.0004010286229233324\n", - "before optimization: case dy_vault = 0.45045711986578807\n", - "Optimal dy_vault: 0.4504503283750036\n", - "Tresca sigma: 512.8230539913491 MPa\n", - "after optimization: case dy_vault = 0.4504503283750036\n", - "err_dy_jacket = 0.00015990076247589884\n", - "err_dy_vault = 1.507711362754438e-05\n", - "tot_err = 0.00017497787610344323\n", + "before optimization: conductor jacket area = 0.0004649372283619356\n", + "Optimal dx_jacket: 0.0032415557150168935\n", + "Averaged sigma in the x-direction: 513.0454643960619 MPa\n", + "91973.1852881221\n", + "after optimization: conductor jacket area = 0.0004646696910185549\n", + "before optimization: case dy_vault = 0.4345971836353921\n", + "after optimization: case dy_vault = 0.4346282242978704\n", + "err_dy_jacket = 0.0005754268040081244\n", + "err_dy_vault = 7.141888341103928e-05\n", + "tot_err = 0.0006468456874191636\n", "Internal optimazion - iteration 10\n", - "before optimization: conductor jacket area = 0.0004010286229233324\n", - "Optimal dx_jacket: 0.002853616189122447\n", - "Averaged sigma in the x-direction: 510.2117343871516 MPa\n", - "78995.25346019538\n", - "after optimization: conductor jacket area = 0.0004014527752127272\n", - "before optimization: case dy_vault = 0.4504503283750036\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 250881463.5052841, allowable_sigma: 512820512.8205128, diff: |\n", - "| -261939049.31522867 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 496249863.8952868, allowable_sigma: 512820512.8205128, diff: |\n", - "| -16570648.925225973 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 685392502.1658069, allowable_sigma: 512820512.8205128, diff: |\n", - "| 172571989.34529412 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 456113275.7163553, allowable_sigma: 512820512.8205128, diff: |\n", - "| -56707237.10415745 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 553410105.575084, allowable_sigma: 512820512.8205128, diff: |\n", - "| 40589592.7545712 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 505498835.2970914, allowable_sigma: 512820512.8205128, diff: |\n", - "| -7321677.523421347 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 515793105.4011781, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2972592.58066535 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 518600027.4791579, allowable_sigma: 512820512.8205128, diff: |\n", - "| 5779514.658645153 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512490738.87867963, allowable_sigma: 512820512.8205128, diff: |\n", - "| -329773.94183313847 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 509794658.76854545, allowable_sigma: 512820512.8205128, diff: |\n", - "| -3025854.051967323 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512789090.44250596, allowable_sigma: 512820512.8205128, diff: |\n", - "| -31422.37800681591 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 513467305.6428993, allowable_sigma: 512820512.8205128, diff: |\n", - "| 646792.8223865032 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512895374.36296976, allowable_sigma: 512820512.8205128, diff: |\n", - "| 74861.54245698452 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512762724.9211324, allowable_sigma: 512820512.8205128, diff: |\n", - "| -57787.899380385876 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512822979.2076429, allowable_sigma: 512820512.8205128, diff: |\n", - "| 2466.3871301412582 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| sigma: 512850629.000417, allowable_sigma: 512820512.8205128, diff: |\n", - "| 30116.17990422249 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal dy_vault: 0.45040539674335706\n", - "Tresca sigma: 512.8229792076429 MPa\n", - "after optimization: case dy_vault = 0.45040539674335706\n", - "err_dy_jacket = 0.0010576608879010005\n", - "err_dy_vault = 9.975819999364022e-05\n", - "tot_err = 0.0011574190878946408\n" + "before optimization: conductor jacket area = 0.0004646696910185549\n", + "Optimal dx_jacket: 0.0032410210653212226\n", + "Averaged sigma in the x-direction: 513.0087751285605 MPa\n", + "91919.5196358774\n", + "after optimization: conductor jacket area = 0.0004645861189484285\n", + "before optimization: case dy_vault = 0.4346282242978704\n", + "after optimization: case dy_vault = 0.4346379219849359\n", + "err_dy_jacket = 0.0001798526388566911\n", + "err_dy_vault = 2.231210526040317e-05\n", + "tot_err = 0.0002021647441170943\n", + "Internal optimazion - iteration 11\n", + "before optimization: conductor jacket area = 0.0004645861189484285\n", + "Optimal dx_jacket: 0.0032408540049125483\n", + "Averaged sigma in the x-direction: 512.9973184411222 MPa\n", + "91902.75587103948\n", + "after optimization: conductor jacket area = 0.00046456000589983133\n", + "before optimization: case dy_vault = 0.4346379219849359\n", + "after optimization: case dy_vault = 0.4346409522692004\n", + "err_dy_jacket = 5.620712184913668e-05\n", + "err_dy_vault = 6.971925329850067e-06\n", + "tot_err = 6.317904717898675e-05\n", + "Internal optimazion - iteration 12\n", + "before optimization: conductor jacket area = 0.00046456000589983133\n", + "Optimal dx_jacket: 0.00324080180040045\n", + "Averaged sigma in the x-direction: 512.9937390413941 MPa\n", + "91897.51784722139\n", + "after optimization: conductor jacket area = 0.00046455184590954305\n", + "before optimization: case dy_vault = 0.4346409522692004\n", + "after optimization: case dy_vault = 0.4346418992066607\n", + "err_dy_jacket = 1.756498662101196e-05\n", + "err_dy_vault = 2.1786612427521574e-06\n", + "tot_err = 1.974364786376412e-05\n", + "Internal optimazion - iteration 13\n", + "before optimization: conductor jacket area = 0.00046455184590954305\n", + "Optimal dx_jacket: 0.00324078548672434\n", + "Averaged sigma in the x-direction: 512.992620560733 MPa\n", + "91895.881033058\n", + "after optimization: conductor jacket area = 0.0004645492959536128\n", + "before optimization: case dy_vault = 0.4346418992066607\n", + "after optimization: case dy_vault = 0.43464219512110225\n", + "err_dy_jacket = 5.489066403830805e-06\n", + "err_dy_vault = 6.808230882394393e-07\n", + "tot_err = 6.169889492070245e-06\n" ] }, { "data": { "text/plain": [ - "
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" ], - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 30 + "execution_count": 15 }, { "metadata": { "ExecuteTime": { - "end_time": "2024-10-18T12:33:51.235013Z", - "start_time": "2024-10-18T12:33:51.232064Z" + "end_time": "2024-10-18T17:30:29.143743Z", + "start_time": "2024-10-18T17:30:29.140121Z" } }, "cell_type": "code", @@ -1701,12 +1048,12 @@ "output_type": "stream", "text": [ "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Operational current after optimization: 67417.85714285714 |\n", + "| Operational current after optimization: 67034.80113636363 |\n", "+-------------------------------------------------------------------------+\u001B[0m\n" ] } ], - "execution_count": 31 + "execution_count": 16 } ], "metadata": { From 9dc4d5a446f4dd0ba19f79c58a51bfdeb7ee4e2f Mon Sep 17 00:00:00 2001 From: ivanmaione Date: Fri, 18 Oct 2024 19:32:36 +0200 Subject: [PATCH 17/17] correnction on optimization bounds --- .../magnets/example_tf_wp_optimization.ipynb | 654 ++---------------- .../magnets/example_tf_wp_optimization.py | 9 +- 2 files changed, 48 insertions(+), 615 deletions(-) diff --git a/examples/magnets/example_tf_wp_optimization.ipynb b/examples/magnets/example_tf_wp_optimization.ipynb index dfdf67622e..042758d02b 100644 --- a/examples/magnets/example_tf_wp_optimization.ipynb +++ b/examples/magnets/example_tf_wp_optimization.ipynb @@ -13,12 +13,7 @@ "id": "8dd4426f35141a5a" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:26.333055Z", - "start_time": "2024-10-18T17:30:24.853335Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", @@ -39,7 +34,7 @@ ], "id": "54d702a3b1f86263", "outputs": [], - "execution_count": 1 + "execution_count": null }, { "metadata": {}, @@ -48,12 +43,7 @@ "id": "a709656b9dc95948" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:26.339938Z", - "start_time": "2024-10-18T17:30:26.337233Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# Enable interactive mode\n", @@ -64,7 +54,7 @@ ], "id": "78e8b10c23faa689", "outputs": [], - "execution_count": 2 + "execution_count": null }, { "metadata": {}, @@ -79,12 +69,7 @@ "id": "b13898853001e74c" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:26.616820Z", - "start_time": "2024-10-18T17:30:26.614265Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "R0 = 8.6 # [m] major machine radius\n", @@ -97,7 +82,7 @@ ], "id": "60966584009a130", "outputs": [], - "execution_count": 3 + "execution_count": null }, { "metadata": {}, @@ -106,12 +91,7 @@ "id": "25080dfa3f05156c" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:26.660977Z", - "start_time": "2024-10-18T17:30:26.656208Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "d = 1.82 # additional distance to calculate the max external radius of the inner TF leg\n", @@ -141,18 +121,8 @@ "bluemira_print(f\"Total number of conductor: {n_cond}\")" ], "id": "750ff0284348d66c", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Total number of conductor: 168.0 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 4 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -161,12 +131,7 @@ "id": "a606090382de38f8" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:26.716025Z", - "start_time": "2024-10-18T17:30:26.713366Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "R_VV = Ri * 1.05 # Vacuum vessel radius\n", @@ -178,7 +143,7 @@ ], "id": "791a5e51845b952d", "outputs": [], - "execution_count": 5 + "execution_count": null }, { "metadata": {}, @@ -187,12 +152,7 @@ "id": "53f891d1919d697d" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:26.765030Z", - "start_time": "2024-10-18T17:30:26.758897Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# inductance (here approximated... better estimation in bluemira)\n", @@ -210,18 +170,8 @@ "bluemira_print(f\"Maximum TF discharge time: {tf}\")" ], "id": "d725927fd13d8fd6", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Maximum TF discharge time: 22.30693384176132 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 6 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -230,12 +180,7 @@ "id": "875d8f0847274e1f" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.031251Z", - "start_time": "2024-10-18T17:30:26.814359Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "I = delayed_exp_func(Iop, Tau_discharge, t_delay)\n", @@ -269,19 +214,8 @@ "plt.show()" ], "id": "984b72f4ccaadb06", - "outputs": [ - { - "data": { - "text/plain": [ - "
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alLlz5263bf4tGZakvDIhlFQsXXrppTRs2JBTTjmFp556ilmzZm0ePnbdddcxfPhwAPbbbz922WUX7r77bhYuXMjMmTPp37//Drdt+Dd77LEHp5xyCoMHD2bMmDEsXLiQ7777jsmTJ3PjjTfmqq7q1atTtmxZ3n//fZYtW/avQzwPOOAAUlJS+Oyzz3ZY77fffstvv/1G+/btsy1Tq1YtypYty+jRo1m8eDFfffVVnjdQP/7446lcuTJXXnklc+bMYfbs2Vx11VV5+nMeOXIkn376Kfvuu+92r++5556sX7+eUaNG8cMPPzBlypQs8/Eg9ER16NCBYcOG8eGHH7Jw4UKGDBmSp+0rctreqampfPfdd8ybN4+VK1duk/Bs0rdvX7788kuuu+46Zs+ezZIlS5g8eXKOVhnNifLly9O3b19GjBjBI488wrfffsvixYt57733uPbaa7MdktmjRw/S0tI272s5ffp0BgwYkKM2bN++fbZ/L1evXs1vv/3Gb7/9xty5c7nhhhtYt27dNvNLP/nkEzp06LDDZzVt2nRzr/aSJUuyzIP966+/GDhwIB9//DE//PAD8+fPZ/LkyVSsWDFhkq3LLruMmTNncu211/LVV1/xww8/MG3aNO68805mzpwZ7/AkFUMOGZVULO200048++yzPP3004wfP567774bCCsRHnrooZsXI6lQoQL33HMPQ4cOpUuXLtSpU4f+/ftv3pYirwYOHMhee+3Fs88+y+DBgylTpgypqakcccQRuaqnTJkyDBw4kAcffJCnnnqKjRs3ZtvTsvPOO9OlSxfGjRvH0Ucf/a/1vvTSS7Rt2/ZfF96pVKkSd999N3feeSedO3dm99135/zzz892lcd/k5KSwqOPPsqgQYM45ZRT2G233bjgggv+dT5YdsqVK5dlntzWDjvsMC699FKeeOIJ7rrrLvbdd19uuukmTjnllCzlhgwZwqBBg7jooosoW7YsXbt2pXPnzjlKqLeWk/bu0aMHX375JT179mTNmjVcdNFFXHzxxdvUdeCBB/LEE09w//33c/rppxOJRNhzzz3z/XdyS+eff/7mVUwfeOABkpKSNm/Lkt3w3912240RI0YwePBgunXrRu3atbnqqqu2WTl3e3r06MHjjz/OrFmzaNq0aZZrW35dFStWpEGDBjz00ENZ5vUtWLCAb775hv/+9785+vpOOukkhgwZQsuWLbOsNFyqVCn+/PNPbrzxRpYtW0b58uVp1qwZo0aN2mYoZ7y0bt2a0aNH88ADD3Duueeybt06qlWrRqtWrdzrUFJMRKKxWn1BklTofv31Vzp16sTo0aOzrAK6pVWrVnHUUUcxYsSI7W7ZUJKdeuqpVKpUKdt96ZR3Q4YM4aeffsrTXL2rr76a8uXLM2jQoBhEJkklmz2EklSMVK9endtvv51ff/0124RwyZIlXHPNNSU+GZw5cyY///wzTZo0IS0tjRdeeIHPP/+cRx55JN6hFUsXX3wxo0aNYu3atdku0LQ969evp27duttdZEWSlH/2EEqSSqTp06czePDgzQsP1a9fnwsvvDBXeyNKklTUmRBKkiRJUgnlKqOSJEmSVEKZEEqSJElSCWVCKEmSJEkllKuMFpKFCxfy1FNP0bBhQ3baaad4hyNJkiRpO/766y/mzZvHmWeemWUv0+LKhLCQPPXUU4wZMybeYUiSJEnKoZtuuineIcScCWEhadiwIQCnnXYaLVu2jFsc0WiUxYsXU7duXSKRSNziUFa2S2KyXRKT7ZJ4bJPEZLskJtslMW3ZLp9//jljxozZ/PN7cWdCWEg2DRNt2bIlJ5xwQtziSE9P54svvqB58+YkJyfHLQ5lZbskJtslMdkuicc2SUy2S2KyXRLTlu0SiUQYM2ZMiZnm5aIykiRJklRCmRBKkiRJUgllQihJkiRJJZQJoSRJkiSVUCaEkiRJklRCmRBKkiRJUgllQihJkiRJJZQJoSRJkiSVUCaEkiRJklRCmRBKkiRJUgllQihJkiRJJZQJoSRJkiSVUCaEkiRJklRCmRBKkiRJUgllQihJkiRJJZQJoSRJkiSVUCaEkiRJklRCmRBKkiRJUgllQihJkiRJJVSpeAegGFuyBJYv3/wxujGd5NnziaZnQKnkzHJVq0JqahwClCRJkhQvCdVDePbZEIlkf/zyS2bZ11+HNm2gXDmoUQP69oXVq7etc/XqcK1GjVC2TZtw7/bEos64WrIEGjaEVq02H6XatqH5eadTqm2bLOdp2DCUlyRJklRiJFRCeOON8MknWY/33w9JV9u2IQEDePtt6NwZ6tWDiRNh8GAYOxa6dIFoNLO+aDScGzs2lJk4MdzTuTO8807WZ8eizrhbvhzS0nJWNi0tS0+iJEmSpOIvoYaM7rlnOLY0bhz8/Tecd17muX79oGlTGDMGkv5JaStVgm7dYNIkOO64cO7VV+Hdd0MdXbuGcx06hA6xfv1g2rTY1ilJkiRJiSyhEsLteeIJKF8eTjklfF66FGbMgDvvzEzcAE48MSRw48dnJm/jx8Ouu4ZrmyQlwZlnwhVXhLpq145NnUXRkCGwbNc1dP20HwB/l6nAywcMzVJm179+4tgZtwKwaqfdeb3VDVmu7/b7PA6fdQ8Av1ZsyLvNLt18rV49uOwyKF06hl+EJEmSpBxL6ITw55/D3LyePaFChXBuzpzw2qRJ1rJJSdC4McyenXluzhxo1ChrkgfQrFnm9dq1Y1NndhYujDJtWnqWczVrhqOgRTem56qBn38BfiCNu3kIgF+ozjGfZ00IG/E7Q/65/hVNeWhq1oSwPT9x0z/X36IjD310aZbrS5dmcNddUZRVenp6llclBtslMdkuicc2SUy2S2KyXRLTlu0SjZasn1UTOiF88klIT886XHTFivBaufK25StXhrlzs5Zt0GD75QBWroxdndkZMCDCmjXJWc717v0Tffr8/O835kHy7Pk0L/Ba82fS/Ys4qs5cahxWI96hJKRZs2bFOwRth+2SmGyXxGObJCbbJTHZLolp1qxZLF68ON5hFKqETghHjYK99oJDD932WiSSszpyWi5WdW7t5pujHHzw1j2E1alZs3reK81GND0jV+Wv7Z/Bqprlee+z0MO3sUw5HmqbtY6yf1XnvZnh+rqdKvNQy6zXy6/am/dmhevrK9bioabh+v+eWke7T27n2owhfHPLgex36Zt5+pqKq/T0dGbNmkXTpk1JTk7e8Q0qFLZLYrJdEo9tkphsl8RkuySmLdtl6dKl8Q6nUCVsQvjhhzBvHtx2W9bzVaqE1029eltauTJrL1+VKtmXg8yysagzO/XrR9h//0L65i+Vu+d0754ELXcCLth8ruM2papmub6t2tu9fnT9pdQ6+lbKsIGWq97lu9fms2fnfXMVX0mQnJzsfw4JyHZJTLZL4rFNEpPtkphsl8SUnJxMJD+9P0VQQm07saUnnoDkZDjrrKznGzcOr1vO6wPIyAjz97acB9i4MXzzTbi2pU099JvKxqJOZVXvqL2ZUbsLAF+zL5OfWRbfgCRJkiQlZkL411/w/PNw9NFQq1bWa7VrQ+vWMHp01qRswgT4/fewR+AmXbvCqlXh2iYZGfD006GOTXXHok5tK3r9DRzIxzRmDje9exgbNsQ7IkmSJKlkS8gho2PHwp9/wrnnbv/60KEhWezZE3r3hiVL4Oqrw36AnTplluvUKZzr1Sskdqmp8Oij8OWXMHly7OuMu6pVISUlZ5vTp6SE8jHUtnczfhwM/ADLlsHEiZl7OUqSJEkqfAmZEI4cGXKTE07Y/vWOHUMP3YABIUGrUAG6dw9J3ZZDfiORsG9g//5wzTWwenXYfH7CBDj88NjXGXepqWEi5vLlm09t3JjOnDnzady4AaW2nGNYtWooH0PJyXDOOXDzzeHz44+bEEqSJEnxlJAJ4Qcf7LjMsceGY0cqVIAHHwxHPOqMu9TULIleJD2djclJRJo3DxlaITvnHLjlFohG4bXX4McfHWYrSZIkxUtCziFU8VW3LhxxeAZH8CajM07lm953xjskSZIkqcRKyB5CFW8Dmr5Eu7f/D4Dv35xOxsbLSSrl7yYkSZKkwuZP4Sp0rQcex++RSgDU2fgdnz82I84RSZIkSSWTCaEKXUrFFD7Z/1Lu5AoaMYf/frR/vEOSJEmSSiSHjCouaj0ykOOah/eLXoD77oOKFeMZkSRJklTy2EOouNhvP2jVKrxPS4MxY+IbjyRJklQSmRAqbs47L/P944/HLw5JkiSppDIhVNycdhqkpEAZ1lF3xgt88/L8eIckSZIklSgmhIqbihXhzlbP8iO1eIHu/Drg/niHJEmSJJUoJoSKqwP/rxZVWQHAfrOeIW1VWpwjkiRJkkoOE0LFVfNLDmVx6b1YRF3uil7OhHEb4h2SJEmSVGKYECquIkkRJlz2DnvyHbdyIw+P3iXeIUmSJEklhgmh4u6ky/YgKTn8VXznHViwIM4BSZIkSSWEG9Mr7nbfuIRL2i1nynvh86u3QJ8+ULYsRCJbFKxaFVJT4xKjJEmSVByZECq+liyBhg25K22LxWSe+ufYWkoKzJtnUihJkiQVEIeMKr6WL4e0HK4smpYWykuSJEkqECaEkiRJklRCmRBKkiRJUgllQqi4ikZjW16SJElS9kwIFVfr1uWu/Pr1sYlDkiRJKolMCBVXZcvmrnyZMrGJQ5IkSSqJTAgVV1n2GYxBeUmSJEnZMyGUJEmSpBLKhFCSJEmSSigTQsVX1aqQkpKzsikpobwkSZKkAlEq3gGohEtNhXnzYPnyzaeiUfj7z40sPeJsPth4AC9zIpffsQcdulcN5SVJkiQVCBNCxV9qapZELwKUB0ZfO4ebbwmryGx8GzpcGZ/wJEmSpOLKIaNKWOf1imxeVfT11+H77+MbjyRJklTcmBAqYaWmwrHHhvfRKDz6aHzjkSRJkoobE0IltPPPD6/lWMvq+59i/Z/r4xuQJEmSVIyYECqhHXccDK4wlB+pxb1/nMX0G8fHOyRJkiSp2DAhVEIrVQoOaL2RSqwCoNyoh+IbkCRJklSMmBAq4TW6qxcbKMV89ubJVSfyzdfReIckSZIkFQsmhEp41ferQf+jZrIPc7mHy3h4RCTeIUmSJEnFggmhioTO/ZsS/eev65NPwl9/xTkgSZIkqRgwIVSR0L497LNPeP/HH/Dcc3ENR5IkSSoWTAhVJEQicMEFmZ9H37uCaIZzCSVJkqT8MCFUkXHWWXBYmU94hp689tXufP3ktHiHJEmSJBVpJoQqMipWhFvqPk5PnqUs61k52C0oJEmSpPwwIVSRUvXGCze/X79wKStXOGxUkiRJyisTQhUp+57eigdqDaYpX3FExps8+ZRbUEiSJEl5ZUKoIqf8Ldcym6YAPPwwRO0klCRJUmFYuhQuvRQOOQR23jmsfDhlyvbLvv46tGkD5cpBjRrQty+sXl2o4eaECaGKnFNOgUqVwvv58+Gdd+IbjyRJkkqIBQtgzBgoXx6OPDL7cm+/DZ07Q716MHEiDB4MY8dCly4J15thQqgip3x5OPvszM8PPhi3UCRJklSSHHooLFsGb7wB552Xfbl+/aBp05A8duwI554Ljz4K774LkyYVXrw5YEKoIumCCyCZjXRlHH3GHc3P03+Md0iSJEkq7pJykD4tXQozZsAZZ2Qtf+KJYZjb+PExCy8vSsU7ACkvGjSAN3c7nQ7LxgIw5cpHqPneoDhHJUmSpCLl55/DkZ5OufnzISODsosW5a/OOXPCa5MmWc8nJUHjxjB7dv7qL2AmhCqyyp19CgwPCeHeH45kfdpAyqTY6S1JkqQcGjECBg0iGWj0z6naZctCnTp5r3PFivBaufK21ypXhrlz8153DPjTs4qs1oOO54sy+zOIAeyf8SkvvuRfZ0mSJOVCnz4wYwbpU6fy9TPPkD51KkuHDSuYuiNFY3s0ewhVZJVKKcVrAz/jpuvDN9t998Fpp8U5KEmSJBUdNWuGIz2dv5OSoHlz1v38c/7qrFIlvG7qKdzSypXb7zmMI7tUVKT16h2hbNnw/pNPwvxdSZIkKW4aNw6vW88VzMgI8wu3nlsYZyaEKtKqVYNTT838fP/98YtFkiRJonZtaN0aRo8OSeAmEybA77+HvQgTiAmhiryLLw6vO7OGCs88wIp5y+MbkCRJkoqvF14Ix0cfhc/vvRc+v/ZaZpmhQ+HLL6FnT3jnHRg1Cnr1gg4doFOnuISdHecQqshr1QrurHMvvb6/gQob1zDlsjW0f61/vMOSJElScdS9e9bPN90UXuvUgcWLw/uOHUOP4IABIQGsUCHcN3Rowi02Y0KoYqFtpypUeGgNAHu9+RAb066iVIp/vSVJklTAotGclTv22HAkOIeMqljYf3h3liVVZxZNuCX9Ol6dkLHjmyRJkqQSzoRQxUKZncvw9EVTacZXPEIf7n24TLxDkiRJkhKeCaGKjVOvSaVUqTAm+5134Ouv4xyQJEmSlOBMCFVs1KoF3bplfnYLCkmSJOnfmRCqWLnoosz3b4/6gT+WrolfMJIkSVKCMyFUsdKuHfTYexrP83/M+bseX1zyRLxDkiRJkhKWCaGKlUgELjjwS/6PFylFOqkTHyBjoyuOSpIkSdtjQqhip9WdPfg9UgmAeRvq886Lv8c5IkmSJCkxmRCq2ClftTyvHP8YjZjDsbzOHSOrxDskSZIkKSGZEKpYan9vN+YlNQLgjTfcgkKSJEnaHhNCFUt16mTdguLee+MXiyRJkpSoTAhVbF12Web7p56Clb+lxy0WSZIkKRGZEKrYOuggaNNyA6fwHO/8fQBfnnVnvEOSJEmSEkqpeAcgxUokAncc8CKHzDwNgJ8m/8iGtZdTunzpOEcmSZIkJQZ7CFWstR3WjV+TagBQOf03pvz3i/gGJEmSJCUQE0IVa2V2LsPMY2/gem5lD35g4MT94x2SJEmSlDAcMqpir9UTfemaCuvWwfJPYOpUaNMm3lFJkiRJ8WcPoYq93XaDnj0zP99zT/xikSRJkhJJQiaE77wDRx4Ju+4K5ctDo0bwyCNZy7z+eujlKVcOatSAvn1h9ept61q9OlyrUSOUbdMm3Ls9sahTieHSSzPf/+9/8OOP8YtFkiRJShQJlxCOHAlHHQV77QXPPQcTJoTka/36zDJvvw2dO0O9ejBxIgweDGPHQpcuEI1mlotGw7mxY0OZiRPDPZ07h6RzS7GoU4mjWTPo0AF2ZRWXbryDz8+2m1CSJElKqDmES5aE5G/wYLjmmszzHTtmLdevHzRtCmPGQNI/KW2lStCtG0yaBMcdF869+iq8+y6MGwddu4ZzHTpAq1ahjmnTYlunEsuNXWax/7sHsjN/seLtKvy98nzKVS4X77AkSZKkuEmoHsLHHw+vF1+cfZmlS2HGDDjjjMzEDeDEE0MCN3585rnx48Ow0xNPzDyXlARnngnTp4e6YlWnEs+hFzRiZanqAFSJrmDqlc/FOSJJkiQpvhKqh/CDD2DffeHFF+GWW2DBAqhZE04/HW6+GcqUgTlzQtkmTbLem5QEjRvD7NmZ5+bMCfMPk7ZKe5s1y7xeu3Zs6szOwoVRpk1Lz3KuZs1wFIb09PQsryVKMizsfDErxj/Jf7mMrz47hWkb04lE4h1YCW+XBGa7JCbbJfHYJonJdklMtkti2rJdolvOFysBEioh/OmncFxySUgIGzcOwzMHD4YffoDRo2HFilC2cuVt769cGebOzfy8YgU0aLD9cgArV2aWK+g6szNgQIQ1a5KznOvd+yf69Pn5328sYLNmzSrU5yWKpIsP5pDJF/HX2lLwDYwYMZ8DDlgT77A2K6ntkuhsl8RkuyQe2yQx2S6JyXZJTLNmzWLx4sXxDqNQJVRCmJEBa9aEeXynnhrOtW8fFpQZOhQGDcosm9Nendz0/sSizq3dfHOUgw/euoewOjVrVs97pbmQnp7OrFmzaNq0KcnJyTu+oRjq1TuyeeuJl1/emwsuyIhvQNguicp2SUy2S+KxTRKT7ZKYbJfEtGW7LC1hc8ASKiGsUgW+/RaOOSbr+U6dQkI4c2YoA5m9eltauTJrL1+VKtmXg8yysagzO/XrR9h///h/8ycnJ5fYf4Quvxzuvx/S0+HNNyN8/XUyTZvGO6qgJLdLIrNdEpPtknhsk8RkuyQm2yUxJScnE0mE+USFKKEWldn0Q/nWw3Yz/unA2TSnD7LO69tUZs6crPMAGzeGb77JvH+TTT30m8rGok4lrjp14P/+L7zfkwV80veZ+AYkSZIkxUlCJYTduoXXSZOynp80KQzT3H//sGBL69ZhPuGWSdmECfD772GPwE26doVVq8K1TTIy4OmnQx21aoVzsahTie2qi9IYR1fm04CzPziXXz4v3DmckiRJUiJIqCGjxxwDxx4b9iJcvjxzUZm77oILLgg9OxCGjx59NPTsCb17h/0Lr7467AfYqVNmfZ06hXO9eoXELjUVHn0UvvwSJk/O+uxY1KnE1bpdCqUrrCBpdZQybOCbi+6nxke3xTssSZIkqVAlVA8hwPPPh2Rr+PCQfI0dG1Ycvf/+zDIdO4YeugULQplrroHu3cMegVsO+Y1Ewrnu3UOZTp3CPRMmwOGHZ31uLOpUYku76CoAXuF4hs/qxF9/xTkgSZIkqZAlVA8hwE47wR13hOPfHHtsOHakQgV48MFw7Egs6lTi2n9QZ456dj5vLt4b1sDIkXDRRfGOSpIkSSo8CddDKBWWpFJJdOu39+bPd98dVh6VJEmSSgoTQpVoZ56Zue3IwoXw8svxjUeSJEkqTCaEKtHKl4f//GfTpyiv3jQtnuFIkiRJhcqEUCVe375wWqnnmU5rHp/VhlmPfhrvkCRJkqRCYUKoEq96dbiw/hu0YiYAfw66M84RSZIkSYXDhFACat5+BQB/UZ6pP9Zi4XfROEckSZIkxZ4JoQTsdUIjhu43hj34gcv4L/+9J7LjmyRJkqQizoRQ+sf+d57K71QG4PHHYcWKOAckSZIkxZgJofSPww+HFi3C+7Vr4YEH4huPJEmSFGsmhNI/IhG45prMz4/cm8ba39fFLyBJkiQpxkwIpS383/9Bs9RV9GcI01fUZVqfx+IdkiRJkhQzJoTSFkqVgnsPHMMQrqMGv1L/pTvYmLYx3mFJkiRJMWFCKG2lzYNn81ukGgAZGzN47cFFcY5IkiRJig0TQmkr5SqX49Muwzidp9mLBdz41N5E3ZZQkiRJxZAJobQdBz92DuN3Op2NlObLL+GNN+IdkSRJklTwTAil7ahcGc4/P/PzsGHxi0WSJEmKFRNCKRuXXx4WmQGYMgWmfua4UUmSJBUvJoRSNvbYA3r2hL2ZzyP0Jq376fEOSZIkSSpQpeIdgJTIrjvrR/Z8cl+SySDjhwiL3hhIvaMbxDssSZIkqUDYQyj9iwYdajG9+nEAJBHl8xvHxTkiSZIkqeCYEEo7UG5gP17heA7mQ077sj8//xzviCRJkqSCYUIo7UCzCw9m+MGv8DEHs3493HNPvCOSJEmSCoZzCKUcuOncJVzz0XIAPr4f1hwLpUtD2bIQiWxRsGpVSE2NT5CSJElSLpkQSjuyZAkd+zZkJmnh819A+2zKpqTAvHkmhZIkSSoSHDIq7cjy5UTS0nJWNi0Nli+PbTySJElSATEhlCRJkqQSyoRQkiRJkkooE0JpB6LR2JaXJEmS4sWEUNqBdetyV379+tjEIUmSJBU0E0JpB8qWzV35MmViE4ckSZJU0EwIpR3Iss9gDMpLkiRJ8WJCKEmSJEkllAmhJEmSJJVQJoTSjlStCikpOSubkhLKS5IkSUVAqXgHICW81FSYNw+WL998KhoNq4nOPnMYuy94jyc5i6UtTuT+8bVDeUmSJKkIMCGUciI1NUuiFwHKAru99Ah7Ni/H3+ll4HPosRQOMh+UJElSEeGQUSkf9miyK6edlbnPxG23xTEYSZIkKZdMCKV86t8fkv75Tpo0CT7/PL7xSJIkSTllQijl0957wymnhPc1+YmPzx8V13gkSZKknDIhlArAdf0zeJALWUQ9Lpx+Lt9N/CbeIUmSJEk7ZEIoFYAmzZJoWeNnyrKeJKL8fOmQeIckSZIk7ZAJoVRAdhl6PQAf0I7bFp/OwoVxDkiSJEnaARNCqYA0Omt/LjzwCw7lA17POIrBg+MdkSRJkvTvTAilAnT67fttfv/kk9hLKEmSpIRmQigVoIMPhiOPDO83bsReQkmSJCU0E0KpgA0cmPn+21EfseSD7+MXjCRJkvQvTAilAnbwwdCn7RdM5kjeS2/H971uiXdIkiRJ0naZEEox8J8z/+RI3gLggPlPsmSKkwklSZKUeEwIpRho9p92zKh8BBtJ5knO4t4RZeMdkiRJkrQNE0IpVu65lwbMpzePcc8LtVi0KN4BSZIkSVmZEEox0ur0fdnziPqAK45KkiQpMZkQSjG05Yqjo0ZhL6EkSZISigmhFEPt2sERR/zzYeMG/nfJh3GNR5IkSdqSCaEUYwMHRDmbkcxlH66Y2IEf3rebUJIkSYnBhFCKsXaHRLi48mj2ZCGl2cii851MKEmSpMRgQigVglI3h8mEv1GVV75txOLF8Y1HkiRJAhNCqVA063sIgxuPph6LuDPjclcclSRJUkIwIZQKySEP9eAvdgZg5EjsJZQkSVLcmRBKheSQQ6Bjx/B+40a45Zb4xiNJkiSZEEqFaNCgzPevjfqVhe8vjV8wkiRJKvFMCKVCdPDB8H+Hr2Qw1/JtRn1+OePqeIckSZKkEsyEUCpkN5+ziGsZyk6s5aAlz/Hti1/FOyRJkiSVUCaEUiHb9/RWfFqzKwAzaMkj962Lc0SSJEkqqUwIpTio8vBtnMSLtGY6d7y3P9OmxTsiSZIklUQmhFIc7H3CvqT06AZEABg40G9FSZIkFT5/CpXi5KabIDk5vJ88OcLnn+8c13gkSZKUA59/DiecADVrws47Q5MmcPfdYV+xIsiEUIqTvfeGs8/O/Pz6nX8QzYjGLR5JkiTtwMKF0L49LF0K990HL78MnTrBFVfAZZfFO7o8MSGU4mjAAGhRahYv0YUX5rbl8+FvxjskSZIkZWf8eFi9Gl54Af7v/6BjRxg+HE48EZ5+Ot7R5YkJoRRHqalw976P0IWXAdhp8AB7CSVJkhLVhg3hdddds56vWBHKlSv0cAqCCaEUZ/s+fR1rKUcGET5f24BX//dXvEOSJEkqGX7+GWbOhJkzKTd3LsycSdlFi7Iv37MnVKoEF18M338fegtffBH+9z+4+urCi7sAlYp3AFJJt9t+NRnT6WFum9SSOTSh6WDodDIk+esaSZKk2BoxAgYNIhlo9M+p2mXLQp062y9fuzZ8/DF06wZ164ZzSUlw661w5ZWFEHDB80dOKQF0HNmTxTvtC8CsWeGXTJIkSYqxPn1gxgzSp07l62eeIX3qVJYOG5Z9+cWL4fjjoXr1MJ/w3Xfhxhth0CC4+ebCirpA2UMoJYAqVaBHj1959NHdARg4MMxTLuV3qCRJUuzUrBmO9HT+TkqC5s1Z9/PP2Zfv3x/WroVJkzLnDLZvD5FISArPOiv73sUElVA9hFOmhD/L7R3Ll2ct+/rr0KZNaIcaNaBv3zCEd2urV4drNWqEsm3ahHu3JxZ1SjnVs+evVKoUFpRZMD+dl+74Ls4RSZIkKYsvvoBGjbZdQKZ1a8jIgG++iUtY+ZFQCeEmw4bBJ59kPSpWzLz+9tvQuTPUqwcTJ8LgwTB2LHTpAtEtFmiMRsO5sWNDmYkTwz2dO8M772R9ZizqlHJj550zuPrKdE7iBWbRlINu6EDaqrR4hyVJkqRNdt8dZs8OvYRb+uST8Fq7duHHlE8JOSCtQQM44IDsr/frB02bwpgxmQtvVKoU5nZOmgTHHRfOvfpqGNY7bhx07RrOdegArVqFOqZNi22dUm71Pe9v/h5wMdUzfoF0mHLGA7SfUDQnKEuSJBU7l1wSkoBjjoFLLw3bT7z7Ltx+Oxx5JDRpEu8Icy0hewj/zdKlMGMGnHFG1lUYTzwxJHDjx2eeGz8+tNGJJ2aeS0qCM8+E6dNDXbGqU8qLnaqVZ94pAwFYxa68+nYKq1bFNyZJkiT9o0sXePNNKFMG/vOfkBSMHx8WlnnppXhHlycJ2UPYp09YUGPnncMczVtvzUy258wJr1sn30lJ0Lhx6MHdZM6cMMR36+X7mzXLvF67dmzqzM7ChVGmTUvPcm7TXNbCkJ6enuVViWHLdmkz4mzumryCW1dcyO9/VyZ5aAa33eZm9fHg90tisl0Sj22SmGyXxGS7JKYt2yUa3cHPXUccEY5iIqESwl13hcsvh8MOCz1zX38NQ4bAgQeGYblNmsCKFaFs5crb3l+5Msydm/l5xYow/HR75QBWrswsV9B1ZmfAgAhr1iRnOde790/06fMvqxnFwKxZswr1ecqZTe2y4epT+b1/+Ev13/9C+/ZzqFZtQxwjK9n8fklMtkvisU0Sk+2SmGyXxDRr1iwWL14c7zAKVUIlhC1ahGOTQw+FTp1CInjTTfDCC5nXIpGc1ZnTcrGqc2s33xzl4IO37iGsTs2a1fNeaS6kp6cza9YsmjZtSnJy8o5vUKHYul322w9eeCHK9OkR1q1LYty4Jjz0kL2Ehc3vl8RkuyQe2yQx2S6JyXZJTFu2y9ISNgcsoRLC7UlNhXbtMhfuqVIlvG7q1dvSypVZe/mqVMm+HGSWjUWd2alfP8L++8f/mz85Odl/hBLQlu0ybBh07BjOv/z4Svr1TGPPw4reylXFgd8vicl2STy2SWKyXRKT7ZKYkpOTieSn96cIKhKLykSjmXP2GjcOr1vO64Ow7cecOVnnATZuHLYCycjIWnZTD/2msrGoU8qvww+H4w//i+u4jfkZe7Ks52XxDkmSJEnFTMInhIsXw4cfZm5DUbt22Pdx9OisSdmECfD772Hhn026doVVq8K1TTIy4OmnQx21asWuTqkgDO81n9u4gV1ZzYE/vsjsxz+Ld0iSJEkqRhJqyGjPnlC/PrRsGTainzMnDJtLSoKbb84sN3QoHH10KN+7NyxZAldfHfYD7NQps1ynTuFcr14hsUtNhUcfhS+/hMmTsz47FnVK+bXPaS346NrTOPj7McymMSPu38C95+ZvHqskSZK0SUL1EDZtGnrezjkHjjoKBg8OK45Onw777ptZrmPHUG7BgpCgXXMNdO8etgDZ8gflSCSc6949lOnUKdwzYUIYjrelWNQpFYQ9Rt3Keckj2Y8vuf+LdrzxRrwjkiRJUnGRUD2E/fuHIyeOPTYcO1KhAjz4YDjiUaeUX6nt61P+wvpk3B8+9+8ffmGy9V6YkiRJUm75I6VUBNxwA+y0U3j/5ZcwZkx845EkSVLxYEIoFQHVq8NVV2V+Hn3Nl6xbvS5+AUmSJKlYMCGUiogrr4TWlRfyDD2Z9FNzPj1nRLxDkiRJUhFnQigVEbvsAnd0eJWePAtA45du4Y8lf8Q5KkmSJBVlJoRSEXLgqD58X6o+AO9EO3Df8L/jHJEkSZKKMhNCqQgps3MZFl33KAfxEafwP259rAaLF8c7KkmSJBVVJoRSEXPYTYfDgQcBsG4dXH99nAOSJElSkWVCKBUxkQjceWfm52efhalT4xePJEmSii4TQqkIOvBA6N49vI+QwYvnTCSaEY1vUJIkSSpyTAilImroUDgi+V1m0IphXx/PZ9e9HO+QJEmSVMSUincAkvKmfn24vvlEWsz4AoDqd13D+hs6UWbnMvENTJIkSXnz/vv5u3+//WDXXXN1iwmhVIQ1f/4Gft9zJKWj6xm54XSqj8ig75XxjkqSJEl50r59WDAir958Ew4/PFe3mBBKRVjFepV4sc//uOjhxvxCTSoPhp7nQcWK8Y5MkiRJeXL++XDAAbm7Z80auPTSPD3OhFAq4o6/5wj6vQl8BytXwm23we23xzsqSZIk5ckhh0CPHrm7Z8UKuOSSPD3ORWWkIq5MGRg2LPPzvffCokXxi0eSJEl5NGQItGiR+/t22incu/feub7VHkKpGOjWDbq1XsKi6cvZef0aPjvhA+qO6sS6dVC27FZD0atWhdTUuMUqSZKkbPTrl7f7UlLyfK8JoVQMRH5YwvNfNSSJtHBiNtD6RlK2VzglBebNMymUJElKRIcfDtdfDx07FsrjHDIqFQfLl5O0Pi1nZdPSYPny2MYjSZKkvJkyBX79tdAeZ0IoSZIkSSWUCaEkSZIklVDOIZSKgWgUcrOFaW7LS5IkqRCtWAFLluS8fD7WhjAhlIqBdevY/gIy2Vi/HsrGLBpJkiTly2WXhSMnIhHYuDHPjzIhlIqBsrnM7sqUiU0ckiRJKgDt2kH9+oXyKBNCqRiI5HL8Z27LS5IkqRD16QM9ehTKo1xURpIkSZJKKBNCSZIkSSqhTAil4qBqVUjJ2bIy0bIpobwkSZJKPOcQSsVBairMmwfLl28+FY2G1UQzMuCkk+CXX8P57udW5dp8LE0sSZKkGHr3Xdh330J7nAmhVFykpmbZgyZC5tYSZ90Dp54a3s95HLpfAXvtVegRSpIkaUcOO6xQH+eQUakEOPnksHoxROm8/kXmHnVJvEOSJEnS1sqXh7Fjc3/fypXh3vfey/WtJoRSCRCJwD13bOAtjuBF/o/Oi+5j5vC34h2WJEmStpSWFub75FY0Gu5NT8/1rQ4ZlUqIlm1L80GDejA/fN5w82A2XnEEpfxXQJIkKXFcdhlcf33u7snIyPNG0/4oKJUg+7x4GyuajWdk9Gxu+etGbnkQLnH0qCRJUmI49NA8J3bUqweVKuX6NhNCqQSp1qQ69966mKuv3xmAG2+EU06B6tXjHJgkSZJgypRCf6RzCKUS5sKrd968kvHq1dCvX3zjkSRJUvyYEEolTOnScP/9mZ+ffBI+/jAPk5clSZJU5JkQSiXQ4YeHrSjKsZZBDGDnow9m47rcr0olSZKkos2EUCqh7hyezrRIWwZwC83WfspHZzwc75AkSZJUyEwIpRKqdp1kfjv69M2f1780kWW/RuMYkSRJkgqbCaFUgh30/OVMK3cI5/AER298lf7X5nGZY0mSJBVJJoRSCVZm5zKsGv8eoziHKEmMHAmffBLvqCRJklRY3IdQKuGOPCrCSSfBiy+GzxddBFOnQnJyfOOSJEkSMH8+zJkDy5aFTeurVYMmTWDvvQukehNCSdx1F7z2GqxdCzNnwjO3/8xZ/WvGOyxJkqSSad48ePhh+N//4JdfwrnoP2s9RP6Z4lO9OpxyClxwATRsmOdHOWRUEqmpcP31sAdLeIGTOPG6RqyY+1u8w5IkSSpZFi4Me4M1agSPPQbNmsHAgfDUUzBpErz6ang/YADstx88+mgoe/LJsGhRnh6Zox7C/Awdi0Rg8uSw75mkxHXlFVG63HwijdZ9AVH44MRrOWTeY/EOS5IkqeTYd9+QBD75JHTtCjvt9O/l//oLxo2De+4J96al5fqROUoIo1E45BCoXz93laelhV5OSYmvbEqEtTcOhRuOAWDe/AhlPsmg7YEOJJAkSSoUzz8PJ5yQ8/I77QRnnBGOV17J0yNzPIewTx/o0SN3lS9fDmPH5jYkSfHS+vqjeeGp67hj/vF8xgE0/w9MmwalnG0sSZIUe7lJBgvo3hz96v/oo2H33XNfedmy4d6qVXN/r6T4aPPmbcwqfwAAX3wB990X33gkSZIUOzlKCF97Ddq3z33lu+wS7m3WLPf3SoqP1FQYNCjz8403wg8/xC8eSZKkEunLL+GbbzI/b9wIjzwCp54Kxx0XFptZvjzfj4nZ5KBvv41VzZJi7dJLoWnT8H79X+t57qTn4xuQJElSSfH779CqFbRsGfYbbN8+7A12/PFhi4n//S/0ut1yC7RuDb/+mq/HFWhCuHYtjBwZFqDZZ5+CrFlSYSpdGkaMgHZ8yExacvW0k/ns+rxNVJYkSVIu3HUXfP55WGX0wgth+nQ4/XR4910YMiRsGj1tGlx7bRjGdcst+XpcgSwV8dFH8MQTYVGcP/+EChXgtNMKomZJ8XLggXBro9E0+XoOALWHXcSfFx/OzjV2jnNkkiRJxdi4cWFfweeeC59btoReveDyy6Ffv8xyrVqFvQcnTcrX4/LcQ/jzzzB0aOgJPPRQGDUq9Ga++ir89hs880y+4pKUAPZ7dQi/RXZjNbswNP1qBg0vF++QJEmSirelS0OCtcmmxVzatdu27CGHwI8/5utxuUoIN24MCWvnzlCnDtxwQ1iAYtiwsFdhr15w7LFhuJmkoq9i3Yp8cf3z7Ms33M/F3H1vMl9+Ge+oJEmSirE1a6BixczPu+6a9XVLFSqEJC0fcpwQXnEF1KoF//d/sHAh3HwzfP89TJ4M3bvnKwZJCeyImw+l0RG1AEhPD3uSZmTEOShJkiQViBzPIfzvf2GvveCFF0LPpKSSIRKBBx8Mq46uWweffRZWPL7ggnhHJkmSVEwtXhwWjwH444/w+u23WXsOIcwhzKccJ4RNm8KsWWGxm1NPhbPOgv33z/fzJRUBe+8N110XtrupxjJSLrmBZQfdzG7NasQ7NEmSpOLnxhvDsaX//GfbctFo+O19PuQ4IfzyS5gxAx5/HJ59Fh56CBo0gLPP3v78RknFS79+sGLEC9z0U28qbVjFRyf8xW6LR8c7LEmSpOJl4MBCfVyutp1o1Socd98NL74Ytpq47rpwLRKBTz8NC+Js3ZMpqegrWxbOvrIKla5cBcDB3z/LxyP6c1CfpvENTJIkqTgp5IQwT9tOlC0LPXrAW2+FBWY2rTY6dChUrw7HHBPmGEkqXlpc0YGP6p/BfPbmCN7k9GFNWbs23lFJkiQpr/K8D+EmderAoEFhPuMbb0C3bvD++3DhhQURnqRE0/CNe+lQ+Sve5ggWLYIBA+IdkSRJUjGRn0Vi8nhvvhPCLR15JIwZEzatv+++gqxZUqKouldFhv43ZfPnu++GadPiGJAkSVJx0bBh2OPr669zfs/s2XDeebDPPnl6ZIEmhJvsuuv2F8GRVDycfjocdVR4n5EBfc7byIa1G+IblCRJUlE3bhx8+GHY4qFlyzA37+WXQ9L300/w44/h/fjxYTGXFi1gv/3gk0/CvXmQo4Rw+HD45pvcV56WFu794Yfc3yspcUUiMGIElC8PLZjJY7Pa8FGX2+MdliRJUtHWuTN89RWMHg277AKDB4c5efvtB3vsERZu2W+/cG7o0FDmmWfC/oDHHZenR+ZoldH+/aF2bdh339xV/tdfcO210Lp1iF9S8VG3Ljxx3kecdN9hlCKdtDe/ZuFrJ1H/2IbxDk2SJKnoSk4OG7+feiosWwbvvQdz5sBvv4XfylerBo0ahe0dqlfP9+NyvO3EBx/Axo25q/zPP3MbjqSi5P/uOIBvnmhNk78+I4MkRl4xi0FHNyQpJoPRJUmSSpjddoPu3cMRIzlOCEeMCEduRSK5v0dS0ZBcJpmyTz/GpJP6cVH0PhbNrU/tR8NcaEmSJCW+HCWE776bv4fst1/+7peUuPbu2oSnb3iVRbeEz9dcE4a/16oV37gkSZK0YzlKCA87LNZhSCrKrr8enn8e5s6F1avDKsPjxztCQJIkKdE500dSvpUtC489lpkAfvjKCt4Z/Gl8g5IkSdIOmRBKKhAHHwz/uTDKKTzHN+xLsxtP5PfvVsY7LEmSJP0LE0JJBWbwjX9zV/LV7MZvVIsuY/YxV8Y7JEmSJP2LhE4IzzknDEHr0mXba6+/Dm3aQLlyUKMG9O0b5i5tbfXqcK1GjVC2TZtw7/bEok6pJKlQozw/Xv8QAEvYg5sX9GDSpDgHJUmSpGzleNuJwvb222GRigoVtn+tc2c46SQYMgS+/z6sbPjNN+HapnlM0WhIJr/6CoYPhzp14JFHwr2TJ8Phh8e2Tqkk2n9QZx6Z/DhXffp/rKEC35wPs2dDxYrxjkySJCnB3Xxz7u+JRODGG/P8yIRMCP/+O+xjNnAgPPDAttf79YOmTWHMGDZvgF2pEnTrBpMmwXHHhXOvvhq2zBg3Drp2Dec6dIBWrUId06bFtk6ppDpp4rkMaAxrfoUff4QrroAnnoh3VJIkSQnuppu2Pbdlz9TW56PRfCeECTlkdMAA2GknuPzyba8tXQozZsAZZ2QmbgAnnhgSuPHjM8+NHw+77hqubZKUBGeeCdOnh7piVadUklWpAiNGZH4eORKHjkqSJO3IokVZj1mzoHlzaN0ann0WvvgCPv8cRo8OPVItW4Yy+ZDrHsLly+Gll0Iv2Zw5sGxZSEqrVYMmTaB9+5As7bZb3gKaMQPuuQc++ABKbSe6OXPCa5MmWc8nJUHjxmFo2pZlGzXKmuQBNGuWeb127djUmZ2FC6NMm5ae5VzNmuEoDOnp6VlelRiKY7t07gw9ekR49tkkmjCL0iddx8o5o9i1TsV4h5ZjxbFdigPbJfHYJonJdklMtkti2rJdolv3xBWmOnWyfr7kkrC/1/vvZ02O9tsP/u//4NBD4eGH4d578/zIHCeEs2fDbbeFZHD9+hBX7doh5mgUVq6EF18MQy4vvjgMp7z++m2TrH+zcSP06gW9e0Pbttsvs2JFeK1cedtrlSuHjbG3LNugwfbLQYg5VnVmZ8CACGvWJGc517v3T/Tp8/O/31jAZuXzNwmKjeLWLueem0zt8e9xy9qrKZO2gbfaX0yll4reyqPFrV2KC9sl8dgmicl2SUy2S2KaNWsWixcvjncYmf73P7juuu33lJUuDaeeGhZAiXVCeN558OSTUK8eXHstHHNM6J0sXTprufXrQw/mq6+GHs0WLeCss8KG1Tlxxx3w668wePCOy24aSltQ5WJV59ZuvjnKwQdv3UNYnZo1q+e90lxIT09n1qxZNG3alOTk5B3foEJRnNtlp0sWUWboBgAO+mE8H395Gx3OSo1zVDlTnNulKLNdEo9tkphsl8RkuySmLdtlaSLNAVu9Gv74I/vrf/yx/W0RciFHCeEXX4S5c507/3u5MmVCz17btmGBnFdegUGDchbIkiWh7H33hR7HVavC+YwM2LAhfC5fPsxNgsxevS2tXJm1l69KlezLQWbZWNSZnfr1I+y/f/y/+ZOTk/1HKAEVx3Y5YEhXPnquJ9HFizmHkfw9oB6zuxWtVUeLY7sUB7ZL4rFNEpPtkphsl8SUnJxMJD+9PwWtRYuwymaPHrDnnlmvLVgQrrVsma9H5GhRmRkzdpwMbs8JJ4R7c2LhQkhLC8NFK1XKPH74ISxGUakSPPVUmNMHWef1QUgc58zJOkS1ceOwbURGRtaym3roN5WNRZ2SMu3z3ghO3u09FrD35lVHJUmStAPDhoVewMaNw/DQgQPDSqSnnBISj9WrYejQfD0iYbadaN48LFSztVNPhb32gltvhYYNw+IrrVuHhXUuuyxzcZcJE+D337NuYt+1Kzz+eLi2aVXQjAx4+ulQR61a4Vzt2gVfp6RMVVJ34qFH4OIuS6jKcr4YCR82h4MPhnXrwpzkLL+Mq1oVUovGsFJJkqSYadcOpkwJ2y/8739Zrx1wANx1V3jNh4RJCCtWDCuUbi0lJfxsuOW1oUPh6KOhZ8/Qo7hkCVx9ddgPsFOnzHKdOoVzvXqFxC41FR59FL78Mmwiv6VY1Ckp04ktlnBsUkPKZKSFE5eGl5TtFU5JgXnzTAolSZLatoWPP4bffgvDKqPRMHy0WrUCqT7HCeEJJ+Su4kgEXn45t+HkTMeOoYduwICQoFWoAN27h6Ruy16GSCTMfezfH665JvSoNm0a7j388NjXKWkLy5dnJoM7kpYW9rgxIZQkSQqqVSuwJHBLOU4IJ07MXcUFNRczu1Vfjz02HDtSoQI8+GA4diQWdUqSJElSjixZkrf78vFL9BwnhFsvoiJJkiRJKkB16+atZy09fcdlspEwcwglFW/RKOTmn7fclpckSSryBgwouKGWOWRCKKlQrFuXzQIy2Vi/HsrGLBpJkqR8eOcdGDIEpk4Nm6bXrRu2Kzj//PzVe9NNBRBc7uRoH8JevfI2nHXxYjjvvNzfJ6n4KZvL7K5MmdjEIUmSlC8jR8JRR4W98Z57Lqwu2bdv+G12EZSjHsKvvoIGDcK+e6efDkccAeXKbb/s33+H7Reefjr82bRoUZDhSiqqcjv6oZBHS0iSJO3YkiUh+Rs8OGw5sEnHjrF75po1cPfdIcn69Vd46ik48MCwIvuDD8LJJ8M+++S5+hwlhFOnhk3bb7stJIWlSkHjxmH7i8qVw1yf33+HBQvg66/DnMYGDeCJJ8K+fpIkSZJU5D3+eHi9+OLCed5vv4XN6RcuDD2SCxeGHjgIm7U/+SSsWhU2qM+jHA0ZhZDYff01vP12GAa6fn3Yj++xx8Kfy/jxYfjseefBm2/CN9+YDEqSJEkqRj74APbdF158ERo2hORkqF07bFIeiyGjN9wAv/wCn34anh2NZr1+4okhQcuHXC8q06FDOCBsRbFiRRjaVaWKQ7wk/YuqVSElJWw6vwPrIilEKlTFaYSSJCmmfv45HOnplJs/HzIyKLtoUfblf/opHJdcArfcEoZNvvtuGEL6ww9hWGVBmjgR/vMfaNUqJF5bq18fRo3K1yPytcpoUhJUq5av50sqKVJTYd68MN79H9Fo+GVamTKw9Ivl9PhPRf5aX4rl0aqc/kQqgwfHMV5JklT8jRgBgwaRDDT651TtsmWhTp3tl8/ICHP6xoyBU08N59q3Dz/QDB0KgwaFoZ0FZfnyf68vKSlHv2z/N247IanwpKaG4x8RMreW2KMVnPJ35pD8YcOgc2c46KBCj1KSJJUUffrACSeQnp7OvPnzadigAUs//jgs1rI9VarAt9/CMcdkPd+pU0gIZ84s2ISwRg347rvsr3/+eZafrfIix3MIJSnW/vOfsIoxhF/AXdbzN/785c/4BiVJkoqvmjWhZUto2ZK/99kHWrZkXb162Zdv2jS8bj2XLyMjvCYVcHrVqVNYsOXnn7e99tlnYcXRE0/M1yNMCCUljKSksLVPxYpwJJN5eXEzPj/00niHJUmSFHTrFl4nTcp6ftKksKDK/vsX7PMGDgxbPLRoAddeG57x5JNw2mlw6KGw++7Qr1++HuGQUUkJpXZtGNPvC4659mgAan77BJ9ccQwH3tU9zpFJkqQS75hj4Nhjw16Ey5dnLipz111wwQXZzz3Mqxo1wgqjF10U9vSLRsOG75FI6D186KGwD2A+2EMoKeEc0785H9Y/E4CfqMnQRyqzZEmcg5IkSQJ4/nno1QuGDw9J2dixYcXR+++PzfP22ANefhlWrgzDRD/9NOxPOGFC+E16PuWph/D998P2G9mtMLp8ediz8NBD8xOapJJsvw/u55l9d+Ky1Tez4q+q/H56+AVccnK8I5MkSSXaTjvBHXeEozBVqFDwQ1LJYw9hhw5h8/nsvP125l6FkpQXu+y+C3tPfpBVyVWBsBfr0KFxDkqSJCmWliwhy7CoTZ93dORDnnoIt15UZ2sbNxb8AjuSSp62bcN2PjfcED4PHBhWIW3bNr5xSZIkxUTdumF+4N9/h42aN33ekfT0PD8yz4vKZBfXxo2hh7Bq1bzWLEmZ+veHyZPDUPW90ufy++HXs3reSCrUrhDv0CRJkgrWgAEh0SpVKuvnGMpxQjhkSDggxHTeeWEfx62tXRt6EM89t6BClFSSJSfDM8/Abfs8xV1rL6D82r/58LCdaffdk/EOTZIkqWDddBOsXp05JPOmm2L+yBwP7Nx9d2jVKhzRaOi93PR509G6dVho55Zb4J57Yhi1pBJljz3grAvKUZ6/AWi18H9M+O93cY5KkiQpBipVCiuXbvL333DzzbB4cUwel+MewrPOCgdAvXphcYcTTohJTJK0jQPv7M4HE86lwrfT6cGzLB24J192Cb+ckiRJKja2XrBl7dqwqEK7djH5wSdPcwgXLSroMCRpx1p8cC8HHJzM19+lwGo4/XSYMiVzmL0kSVKxtKNVPfMh32uBrl0LP/xQ4KufStI2dq6+E6OeS9mcAH70URhBIUmSpLzJU0KYkRGGjNaqBbvsEnou69Xb9pCkgta6Ndx2W+bnh2/5jWkPTY9fQJIkSUVYngZa9e8Pd9wBjRrBSSdBlSoFHZYkZe+qq8L2NmmT3+NZelD6onR+O+RLqjWpHu/QJEmS8m/6dEhJCe/XrAmvH34Iq1Ztv3y3bnl+VJ4SwmeegaOOgtdei/m2GJK0jaQkePqxdWyoezq1Mn6CDJje/kyq/Po6Scn+oyRJkoq4e+7ZdtuGm27aNvmKRsO5wt6Y/vffoWtXk0FJ8bPbHmX5/PanqHllR36jGtetuJL2wyJcd128I5MkScqHkSML9XF5SgibNoWffy7oUCQpd1pc0YFxHz5F35c68gs1eftGOOSQcEiSJBVJm/b6KyR5WlRm4EB4+OGwuqgkxdOJz5/OPu1rAmHBq9NOg+XL4xyUJElSEZGnHsIZM6BOnbCoTNeuYUXR5OSsZSIRuPHGgghRkrKXnAyjR8N++4VE8McfodcZ63hpYmkiyfneWUeSJKlYy1NCeNNNme+feWb7ZUwIJRWW3XeHp5+GY4+FPVnAja+fwntdetB+wpXxDk2SJCmh5SkhXLSooMOQpPw55hi45+zPOXvUYVRgDRsmfsXsx9vR5Ly28Q5NkiQpYeUpIaxTp6DDkKT8u/CBJsx9sQlN13xCOsk82m8BN3VrS6VK8Y5MkiQpMeV7gs2CBfDRR/DHHwURjiTlXenypan8xnNMSz6AA/iUe1f05JxzwhY9kiRJ2laeE8KJE2HPPaFhQzj00LDQDMCyZbDXXvDCCwUVoiTlXK0DU/n5xY/5kuYAvPwy3HFHfGOSJElKVHlKCKdMCauLVqoUtqDY8rfvu+0WEsXnniugCCUpl044McJVV2V+vvZaeO+9+MUjSZKUJ1ddBfXrZ3+9fn3o3z9fj8hTQnjzzWGJ96lToW/fba8feCDMnJmvuCQpX4YMydygvkL6Sn455mx+/eLn+AYlSZKUG5Mmwf/9X/bXTz4ZXn01X4/IU0I4fTr07AlJ2dxduzb88kt+wpKk/ClVCsaOhSMrz2AGrTgl7Ul+bn8qG9M2xjs0SZKknPn+e9h77+yv77VXKJMPeUoI09OhbNnsry9fDmXK5DUkSSoYNWvC8BvXkMoSAJr/8T6jzngrzlFJkiTlUOnS8Ouv2V//t2s5lKeEcN994YMPsr8+cWIYUipJ8db8svZ8cMxgfqcinZlA7xeOYfz4eEclSZKUA/vtB88/Dxs2bHttwwb43/+gWbN8PSJPCeF554VVRB9/HDIywrlIBNauhUsugU8+gfPPz1dcklRgDnv1Gq44ag6v0hmAs84KW+ZIkiQltIsuglmzoHPnsK3D+vUhEZw5E044AWbPhv/8J1+PyNPG9BdeGPYe7N0brrwyJIOnnQYrVoThpOecE+YYSlIiiCRFuHvs7rzfChYuhNWrw/zsTz6BcuXiHZ0kSVI2uncPC7jcfju89VZIvCKR0CsXjYZkrEePfD0iTwkhwDPPwEknhde5c0M8bdvCmWeG85KUSCpWhBdfDKsgp6XB11+u5+ljnuf8KT3CP6ySJEmJaNiwkGCNHh2GOEWj0KBB6JFr2zbf1ec6IUxLC0NVGzYMexF27ZrvGCSpUDRvDg88AAPP+4Hn6c4B73/GB2f/zSFP9op3aJIkSdlr0yYcMZDrOYRly0KvXvD557EIR5Ji69xz4YlGd3IAnwHQ5qm+fPmiEwolSVLJlOsewkgEUlPDHBxJKorafTCEubXfY8+/Z3MFd/HyJXsyox1Urx7vyCRJUol27rkh4XrkEUhODp93JBIJq33mUZ7mEJ51Vpg7eNll7jcoqegpV7kcO09+ia7H/cCrqw+Bn8Kc7bfe8t80SZIUR6NGhQTvoYdCQjhq1I7viUdCeNBBMG5cmI/Tty/suSeUL79tuUMPzXNckhRTtdvV5fJxdXntqLBQ1wcfhIW67rsv3pFJkqQSa9Oeftl9joE8JYRHHpn5/uKLt12gLxoN59LT8xOaJMVWx44wfDhcdVX4fP/9UY7YbRYn3pi/DV4lSZKKijwlhCNHFnQYkhQfV1wRtveZ8NyfjOQcjh4wga9TP6DRWfvHOzRJklTSJSfD009nv9fg2LHhWj564nKdEK5fD/XqQc2asPfeeX6uJCWESAQeewzef603x/7xAgCVzuvKstafU2WfynGOTpIklWjRaP6u50CuE8Lk5DDM6s47TQglFQ877QRNXhnMyvaTqRxdydvph/HBGYu494HFlPtuPuv+zqBcueSsw+OrVg1LLkuSJMXS1vPztvTdd7DLLvmqPk8JYY0aBZKMSlLC2OPQenzV7z52Hno2p/Msp3/+LBwEjbK7ISUF5s0zKZQkSQVrzJhwbHLHHVk/b/L77zB1KnTokK/H5WkOYffu8MILcMkl/56wSlJR0qz7PjB0Q84Kp6XB8uUmhJIkqWDNnw8TJ4b3kQh8/nk4trbzznDYYXD//fl6XFJeburVC/78E446Cl59FebOhSVLtj0kSZIkSbkwcGDYbiIjIwzLfOaZzM9bHqtXw+TJsNde+XpcnnoImzQJyWo0Cu+8k305t52QJEmSpDxatAiqVYvpI/KUEA4Y4FBRScVPNAq5+actt+UlSZJypU6d8PrXX/DJJ/Drr3DEEVC9eoE9Ik8J4U03FdjzJSlhrFsHKbkov349lI1ZNJIkScBDD8G114YhopEIvPlmSAh/+w322APuvRfOPz/P1edpDqEkFUdlc5ndlSkTmzgkSZIAePFF6NsX2rcPGydvudVDtWpwzDHw8sv5ekSeegjffz9n5Q49NC+1S1J85HYovEPnJUlSTN1+e9hWYvx4WLEirO65pdat4dFH8/WIPCWE7dvn7AchF5WRJEmSpDyaNQuGDcv+es2asGxZvh6Rp4Rw5Mhtz23cCN99B6NGQd260KdPvuKSpIS38tvlVG4Z7ygkSVKxlZwctpjIzk8/wU475esReUoIzzor+2tXXw0t/QFJUlFUtSqkpIRN53Ng2bnXUv6Yw0jZ1aVlJElSDOy3H7zxBlxyybbXMjLg+edh//3z9YgCX1SmUqUwtHX48IKuWZJiLDUV5s2DGTNgxgzSp07l66ef4e8PpxKdHs59ffGDbPznn84/1pbiqvNXZ5nfLUmSVGAuugheew1uvBFWrgznMjLCzyvdu8OcOdtPFnMhTz2EO1KpEixcGIuaJSnGUlPDAZCezt9JSZRp3pxIcjIAjVq25L2f01nywmf05lHW/S+F3feD666LY8ySJKl4OuWUMI/wtttgyJBw7phjwmqj0SgMGgTHHpuvRxR4QpiWBk8/DTVqFHTNkpQYDh3blwsv7Mu6R8LqWtdfD3vuGf7NliRJKlC33grdusHo0TB3bkgE994bzjgjrDKaT3lKCM89d/vnf/8dPv447JF4++35CUuSElckKcJ998PCRWFvWAhzq1NrbuDAQ0vHNzhJklT8tGwZs4Va8pQQjhq1/fOVK0ODBnD33dCjRz6ikqQEV7p0mMd90EHw9dfQcd2r1OxwMd+/+SZ1Dt8z3uFJkiTlSJ4Swn9b+VSSSopdd4VXX4V79nuCO1b3Jjkjg4XHHsequZ9QsV6leIcnSZKKgyVLYMQI+PbbsDn91qvZRSLw9tt5rj4mi8pIUklRty70+m8T1p1blvL8TXT9BvqetpKR71eiTJl4RydJkoq0116Drl1h/XrYZZcwJLOA5TghjEZh2LCwWMzZZ2dfbtQoWLYMrrkm/8FJUlHQ+Jw2fPL106TfcTddeYnln1Wj9PkwcmT4pZ0kSVKeXHttSALHj4c2bWLyiBzvQ/jcc2Elvb33/vdye+0V4n7xxfyGJklFx4G3n8Snw95nOdUAePLJsEK0JElSns2dC5dfHrNkEHKREI4ZA4cdBgcf/O/l2rWDww8PW09IUkly5dVJnH9+5ucbb4Tnnt4Qv4AkSVLRVq0alC0b00fkOCGcNg2OPjpnZY88EqZOzX0wH38cnlGrFqSkhK//8MPD0Nmtvf56SJTLlQvDWPv2hdWrty23enW4VqNGKNumTbh3e2JRp6SSIxKB+++Ho44Kn5vzOW3O3Icv7/8gvoFJkqSi6YwzYNy4mD4ixwnhihU532x+t91C+dz6/Xdo2BDuvDMkWI88EhLiTp3CkNVN3n4bOneGevVg4kQYPBjGjoUuXbIuuhONhnNjx4YyEyeGezp3hnfeyfrsWNQpqeQpXRr+9z84r+7bfMAh1GchdS45gQWvfB3v0CRJUlFz9tmwbh2ceGJINhYtCquObn3kQ44Xldlll5wneStXwk475T6Y444Lx5aOPz6s4vfII3DqqeFcv37QtGkYxpr0T0pbqRJ06waTJmXW8eqr8O67Ianu2jWc69ABWrUKdUyblvmcWNQpqWTadVcY9Oze/NmuAjtn/EVGNMJVvf/ggVZhBIQkSVKO7LNPGIIUjYaeqOykp+f5ETlOCBs1gsmT4Yordlx28uRQviCUKgUVK4bfugMsXQozZoRexKQt+jdPPDEkcOPHZyZv48eHH8xOPDGzXFISnHlm+DqWLoXatWNTp6SSrdaBqcwb8xq/nXYmJ2c8x9xl+7KoE7z/fvg3RJIkaYcGDIj5kuU5Tgi7dYOrroJXXoETTsi+3Msvw5tvwh135D2ojIxw/PYbPPYYzJ+fWd+cOeG1SZOs9yQlQePGMHt25rk5c0JimrTVwNhmzTKv164dmzqzs3BhlGnTsmbwNWuGozCk//Pbg/R8/BZBBc92SUz5bZe9TmrC269O57sTSsEG+Oor6NIlyquvZsR6fnix5vdL4rFNEpPtkphsl8S0ZbtEt974PZ5uuinmj8hxQnjBBTBiBHTvHhLD3r3DUM5NFi2CRx8NvWx77x3K59XJJ2duW7HLLmEY5zHHhM+bhq1ub0/GypXDyqybrFgBDRpsvxyEoa2xqjM7AwZEWLMmOcu53r1/ok+fn//9xgI2a9asQn2ecsZ2SUz5aZcq1WDAgMrceGM9AKZMidDzhKVcd9sykkrleBq3tsPvl8RjmyQm2yUx2S6JadasWSxevDjeYRSqHCeE5cqFuXSdO8OQITB0aEjWKlQIq26uWROGtjZsGIa3liuX96CGDw/z8X75BZ59NswdHDUKevbMLJPTntPc9LDGos6t3XxzlIMP3rqHsDo1a1bPe6W5kJ6ezqxZs2jatCnJyck7vkGFwnZJTAXVLs2bQ+nSGfTvn0RtfuCWt4/jtz+O5dBPhxZcsCWI3y+JxzZJTLZLYrJdEtOW7bJ06dJ4h5Pp/ff//XokEhKvPfaA6nnLJ3KcEALUrw8zZ4ZhnC+8EIZS/vJLSAoPOQROOgl69cpfMrjpOfXrh/fHHw9r18JFF8Fpp0GVKuH89ha4Wbkyay9flSrZl4PMsrGoMzv160fYf//4f/MnJyf7j1ACsl0SU0G0yzXXwF/zlnL+yAOpzY8wfQ7vda/NYeMuLaAoSx6/XxKPbZKYbJfEZLskpuTkZCIxnrOXK+3b57w3qmnT0Gu3aWhlDuV6vFJKSkjOpkyB5cth/frwOmUKXHxx/pPB7TngAFi1KswpbNw4nNtyXh+EOYdz5mSdB9i4MXzzTbi2pU099JvKxqJOSdpSJAIDH67JD7UOAGAdZXj4peo8/3ycA5MkSYnriSegZcswNPOCC+Duu8PRp08417o13HMP/Oc/sHhx6E2bMiVXjygSE1imTAkrjVapEhZsad0aRo/OmpRNmBD2MezSJfNc164hkZwwIfNcRgY8/XSoY9Py77GoU5K2llwmmRazn+GjysdzNG/wHKdy+unw3nvxjkySJCWkv/4KvW/z58MDD8All4TjwQfDQie//grJyXDffaHXqnLlML8vF3I1ZDTWevaEOnXCnn5Vq8LPP4dEa/Lk8DWW+ifaoUPh6KND+d69w16MV18d9gPs1Cmzvk6dwrlevUJil5oaFr758stQ55ZiUackbS2lYgqNFrzCb+2Ar8MoixNOCL/4atEi3tFJkqSEcu+9ITnZbbdtr9WoEa79979w4YVh24JevUKymAsJ1UN44IFh0/fzz4eOHaFvX9i4MWx1cdFFmeU6dgw9dAsWhATtmmvC6qfjx2cdYhuJhHPdu4cynTqFeyZMgMMPz/rsWNQpSdtTqRK89lrmiILVq+GEo9ex6L0l8Q1MkiQlliVLoHz57K/vtFMos0m9epCWlqtHJFQP4UUXZU38/s2xx4ZjRypUCElyThLlWNQpSduTmhpGFRxyCKxb+SdP/NaV0h2/5edPP6Jma8eeS5IkwvDJZ58NcwTLlMl6bf16eOaZUGaTpUu335v4LxIqIZSkkqRRI5j0apS/Dz6R9hnvQDp82+5oVi6YSuXa//LbQEmSVDJcemkYNtm2bRgWumlD9Hnz4KGHwsqW99+fWf6ll8LCJrlgQihJcdT2gAjTb+3P+us+oAwbeHTdmbz/f+V56y3Yeed4RydJkuLqwgvD3JJBg8Iqo5vmskWjULYs3HZbKANhqOjgwZlJYw6ZEEpSnLW+9kg+XvYMI/+7msfoBZ+FfV0nTNh2dIgkSSph+vULi6y8+SYsWhSSwXr14Mgjs26CnpICxx2X6+pNCCUpARx098l81RAe++eXfJMnw5lnhu1w3LdYkqQSrlIlOPnkmFSdUKuMSlJJdsEFcOutmZ/Hjo3yyHEvE82Ixi8oSZJUrJkQSlICue46uOwySCKdh7mAC9/ownuH3hjvsCRJUrx8913YimH//WGvvaB+/azHnnvmq3oTQklKIJEI3HknPN7mEfrwCADtP7qNF88YH9/AJElS4fvyS2jRAh57LGwzsXBh2Htw3TpYvDjMK0lNzdcjTAglKcEkJUHPd3vxWfUTAHiCczj5meO59944ByZJkgrXgAFQrlzYXuLtt8O5e+6BH3+EkSNh1Sq47758PcKEUJISUOnypdlv7lju2+cBevEYGSRz6aXw+OPxjkySJBWaDz8MK4zuvXfmlhMZGeH1rLPghBOgf/98PcKEUJISVErFFM6d/h8Obpf5T3Xv3jBmTByDkiRJhWft2rDFBIR9BwH+/DPzetu28NFH+XqE205IUgLbaSd49VXo2BGmTw9bD33ZYyiNJ5eh2cXtgXBu3brw/8SmXx4CULVqvucVSJKkOKpZE376KbzfeWeoUAG+/hq6dAnnNl3LBxNCSUpwFSrAG29A+/bQbdZN3MQgGEU4gAiQsr0bU1Jg3jyTQkmSiqo2bbL2AB55JNx1F+yxB6Snh/mDbdrk6xEOGZWkIqByZXjz5bX0KjUq5zelpcHy5TGLSZIkxdh554UfAv7+O3weOhTKlAnzB889N7y//fZ8PcIeQkkqIqrXK89vTzwEZ3aKdyiSJKkwHHlkODbZc0+YPx/eeQdKlYKDD4Zdd83XI0wIJakIqda4erxDkCRJ8bTzzmF10QLikFFJKkKi0diWlyRJJYs9hJJUhKxbl80CMtlYvx7KxiwaSZJU4HLb+xeJwMsv5/lxJoSSVISUzWV2V6ZMbOKQJEkxMnFiSPJyOswny55TueeQUUkqQnL7b34+/4+QJEmFLRIJW0f17AlvvQUZGf9+pKfn63EmhJIkSZKUKH74Aa67Dj77LKwwWq8eDBoE338fk8eZEEpSMfbdd/GOQJIk5cruu8P114ftJd59Fw47DO64I2w50bEjjB4d9houICaEklSUVK0ahpHkwN+kcHnvP1nwytcxDkqSJMXEoYfCqFHw888wYkRYXe7MM6FmTXjuuQJ5hIvKSFJRkpoK8+bB8uWbT0WjYTXRMmXCtIM5c+DCC4G/1vDsHz0o22UD3774Nnt3bRK/uCVJUt7tvDOcdx7st18YTvrWW6EHsQCYEEpSUZOaGo5/RMi6tUTjlnD7nunsekhTamf8CFH48/9O5MvP5rJf69KFHq4kScqH336DZ56BkSPDb32rVYMrroAzziiQ6h0yKknFUNuDktk44gn+oAJ/UZ4zMp6kw1GlmT493pFJkqQdSk8Pewt26QK1a0P//rDXXvDSS/Djj2FOYb16BfIoE0JJKqaa9DqAH598m9N2nshHtOP338Nc9E8/jXdkkiQVE+ecE+ZrdOlScHVeeWVYWKZbN1i0CIYNC0nguHFh0/rk5IJ7Fg4ZlaRirdGZrbm5GXxyZJh2uHp1WMF60qtRDjnUTQolScqzt9+G55+HChUKtt6774Zy5eDUU6FVq7DX4FNPZV8+EoHLL8/z40wIJamYa94cpkwJvYO//grJf66CDify+fCBtLjy8DhHJ0lSEfT339CnDwwcCA88EJv6x4wJx46YEEqSdqRxY3jvPTi+/Rqe/OVYDsz4lL+vOo7paeNpff3R8Q5PkqSiZcAA2GmnkIgVdEL47rsFW98OmBBKUgnRsCFMHrOC0kf8COlQjjQeGvgLXfeDzp3jHZ0kSUXEjBlwzz3wwQdQKgbp1GGHFXyd/8JFZSSpBKnbvi7RKe/zfan6nM8Inkg/i27dwhQISZJKnJ9/hpkzYeZMys2dCzNnUnbRouzLb9wIvXpB797Qtm3hxRlD9hBKUglTu11dfvx6FlOOKw/fwoYNYd76mjVw7rnxjk6SpEI0YgQMGkQy0OifU7XLloU6dbZf/o47woT8wYMLK8KYs4dQkkqgWnuX5733wtxCCAuY9TlvA6+c81J8A5MkqTD16QMzZpA+dSpfP/MM6VOnsnTYsO2XXbIEBg2Cm2+GaBRWrQpHRkb47eqqVbB+fSEGXzBMCCWphKpZMyw0s//+kEQ6T3IWJ4zqxpTDBhLNiMY7PEmSYq9mTWjZElq25O999oGWLVmX3YbvCxdCWloYLlqpUubxww8waVJ4/2/bQyQoh4xKUglWpUrYRmlUi/vp8V1Y2rr9+zczomtber/UiSR/bShJUtC8+fZXAD31VNhrL7j11rCCWxFjQihJJdwuu0Cvqeczbd832X/Zq9zLxVz6yrF8eh48+mhsFlCTJKnIqVgR2rff9nxKClStuv1rRYC/+5UkUa5yOZoveonH9h/BZfwXiDBqVPil57p1cQ5OkiTFjAmhJAmA0uVLc84n53N+n8z/Gl58EU44PspfK80KJUnarsWLYfz4eEeRZyaEkqTNkpPhoYfgmmsyz7V581YW1mnPym9XxC8wSZIUE84MkSRlEYnAsGFhqsQv193DLQyAP+G7Ju1Y+/EUareqHu8QJUlSAbGHUJK0XddeCyf1SCH9n/8qvl6/FwcdX4XZs+McmCRJKjAmhJKkbB06ug/Trn6edyOHcwpj+eHnUhxyCHz4YbwjkyRJBcGEUJL0rw4Y3o2kd96idIXyAKxaBUceCa+8Et+4JElS/pkQSpJ26LD2ET74AGrWDJ/T0uCFLs/wwVmPxTcwSZKULyaEkqQcadYMPv4YGjSAo3mdx6PncMhTvZlyxK1EM6LxDk+SJOWBCaEkKcfq1oUPP4hy6y7DKc1GAHZ9+0UuuyCNjRvjG5skSco9E0JJUq5U2y3CPvNfYXqVo1lIPY7lNe59tBxdu8Jff8U7OkmSlBsmhJKkXNu5xs7s9/0r3NPtfX6lBgATJ0L79vDLL/GNTZIk5ZwJoSQpT0rvVIb/vlCbG2/MPDd9Olza/D2+m/hN/AKTJEk5VireAUiSiq5IBG6+OcwtPP982Dd9Fo/8egIcD/OuvIOGPVoBEI3CunVQtmy4Z7OqVSE1NS6xS5IkE0JJUgE491yoVTOD1M6nsmvGagB2vfN8uDNcjwAp27sxJQXmzTMplCQpThwyKkkqEEcfm0TSs6P5LVI15zelpcHy5bELSpIk/SsTQklSgWl4SnMY+WS8w5AkSTlkQihJKlDVmtaIdwiSJCmHTAglSQUqGo1teUmSVHBMCCVJBWrdutyVX78+NnFIkqQdMyGUJBWosmVzV75MmdjEIUmSdsyEUJJUoLLsM5gDGRmxiUOSJO2YCaEkKa4G9v2N1UtXxzsMSZJKJBNCSVJc9Zx2Kb/udTBLP1wc71AkSSpxTAglSQWralVISclR0XSS2Jd57L1uNhx2KB+9nRbj4CRJ0pZKxTsASVIxk5oK8+bB8uWbT0WjYTXRMmWyzjGcec9HNH3qalJYxw0ZNzP6mBTuuw/69Mn9XERJkpR7JoSSpIKXmhqOf0SA7S0+uv+TLZl9yP68ftnrPPnX2bARLrwQZs6E++7L/YqlkiQpdxwyKkmKqya9DuDUuTex//6Z5x59FE44dBW/fvFz/AKTJKkEMCGUJMVd7drw/vtw1lnhcxLpXDq1BxmtWjP78c/iG5wkScWYQ0YlSQkhJQVGjoSWLeHPywbSKfoaZEClXofxv1XfcvKVe8Q7REmSih17CCVJCSMSgUsugWPv6MiKSBUAhnMNp1y1BxdfDBs2xDlASZKKGRNCSVLCaXFFB/5+fzpPVruKm7gJgPvvhyOPhN9+i29skiQVJyaEkqSEVLtdXbovvp1TT8v8r+q996Dd/hv46dXF8QtMkqRixIRQkpSwypeH0aPh9tsh6Z//sa5cejlHDzyND097iGhGNL4BSpJUxJkQSpISWiQCV10Fr70GvXd6lgt5mDJs4LDnL2bYEW/y11/xjlCSpKLLhFCSVCQcdRQMeONg5pQPGxY+Q0+uffdI2rSBuXPjHJwkSUVUQiWEb78NZ58NDRqEYUJ77AEnnwzffLNt2ddfhzZtoFw5qFED+vaF1au3Lbd6dbhWo0Yo26ZNuHd7YlGnJKng1D64DvWWTuG5xv3owwggwtdfw/77w/PPxzs6SZKKnoRKCB9+GJYuzRwaNHx4+K1v69Ywa1Zmubffhs6doV49mDgRBg+GsWOhSxeIbjGdJBoN58aODWUmTgz3dO4M77yT9dmxqFOSVPDKVijL3k9256Eny1GuXDj3559w8slRxhw1kg1r3ZtCkqScSqiN6R94AHbbLeu5I46AOnXgv/+Fxx8P5/r1g6ZNYcyYzEUGKlWCbt1g0iQ47rhw7tVX4d13Ydw46No1nOvQAVq1CnVMm5b5nFjUKUmKnZ49o7RsCSedBPPnwxXcxWlvXsWXNR5nt3fGUrN1rXiHKElSwkuoHsKtk0GAatWgbt3QcwjhdcYMOOOMzMQN4MQTQwI3fnzmufHjYdddw7VNkpLgzDNh+vTY1ilJir0mTcIv4i47YjbD6AfAfms+YsxhD/PWW3EOTpKkIiChegi358cfYcECOOaY8HnOnPDapEnWcklJ0LgxzJ6deW7OHGjUKGuSB9CsWeb12rVjU2d2Fi6MMm1aepZzNWuGozCkp6dneVVisF0Sk+2SmLZul512gtsn7cMHJw3nkAnX8AkH0m/tANKPitK/f5SBA6OUSvj/7Yo2v1cSk+2SmGyXxLRlu0SjJWtLo4T+LzIjA3r3hrJl4bLLwrkVK8Jr5crblq9cOetKcytWhAVqtlcOYOXK2NWZnQEDIqxZk5zlXO/eP9Gnz8//fmMBm7XlpEwlDNslMdkuiWnrdqkw8DBe2/cprn30IDb+XhqiMGRIhNdeW8Ntty2ienXnFsaa3yuJyXZJTLZLYpo1axaLFy+OdxiFKqETwosvhjfegOeeg9TUrNcikZzVkdNysapzazffHOXgg7fuIaxOzZrV815pLqSnpzNr1iyaNm1KcnLyjm9QobBdEpPtkpj+rV2aN29O68vgrLOivPlm+Mf6iy92Yc5Jz1Ppita0ublzHCIu/vxeSUy2S2KyXRLTlu2ytITNAUvYhPCKK+DBB+GRR6B798zzVaqE1029eltauTJrL1+VKtmXg8yysagzO/XrR9h///h/8ycnJ/uPUAKyXRKT7ZKYsmuXmjXDVkC33w7XXw+d08dzddqtMBimvH45B30wnDLlE/a/vyLN75XEZLskJtslMSUnJxPJT+9PEZRQi8ps0q8f3H13WFm0d++s1xo3Dq9bzuuDMLx0zpys8wAbNw57GGZkZC27qYd+U9lY1ClJip+kpPB/yftTMrit9E2bz/828wcOPjSZhQvjF5skSYkk4RLCG28M+w8OHw6XXrrt9dq1w76Eo0dnTcomTIDffw97BG7StSusWhWubZKRAU8/HeqoVSt2dUqS4u+gdkns/s07fFqzC4uoS28eZfqMCC1awMQHl8DMmZuP6IyZpH0cXrc8z5Il8f4yJEmKmYQaM3PHHXDrrWHvv0MOgU8/zbxWtiy0aBHeDx0KRx8NPXuGHsQlS+Dqq8N+gJ06Zd7TqVM416tXSOxSU+HRR+HLL2Hy5KzPjkWdkqT4q7RnZdouHccTQ37l75srwnrYdfUSOvZtCKRtLhcBUrZXQUoKzJu37WR2SZKKgYRKCCdODK/jxoVjS3XqwKYFfzp2DD10AwaEBK1ChTDPcOjQrAu+RCJh38D+/eGaa2D16rD5/IQJcPjhWeuPRZ2SpMQQSYpw3vU1aHEsnHIK7LJgOeW2SAb/VVoaLF9uQihJKpYSKiGcMiXnZY89Nhw7UqFCWJzmwQfjU6ckKXG0bAkzZsCwU4DX4x2NJEnxl3BzCCVJiqUKFcL0BEmSZEIoSdIORaPxjkCSpNgwIZQklTjr1uWu/Pr1sYlDkqR4MyGUJJU4ZcvmrnyZMrGJQ5KkeDMhlCSVOFuuHp0TjzwCGzbEJhZJkuLJhFCSpB0Y8QgccADMmRPvSCRJKlgmhJIk5cDMmWHbituHZZC+Pj3e4UiSVCBMCCVJJU/VqpCSkqOiG0ul8GfZqkBYXObH/vcyp+phfP/2glhGKElSoUiojeklSSoUqakwbx4sX775VDQaEr4yZbLOMSxVtSqvrE3lrLPgj6lzGcK1lFuTxl9H7McLV79Lt6FtSPLXq5KkIsqEUJJUMqWmhuMfESC7xUf3AT76CCadNo3kF8Jw0bnsw2m3t6D95/DEE7DHHjGPWJKkAufvNCVJyoFSpeCE589g0dhpzCrXhjN5io2U5q23oEkTGDXKDewlSUWPCaEkSbnQ8OT9aPj7p3S9vvHmoaKrV8M558BDje/nx0+WxDdASZJywYRQkqRcKlM2wq23wscfQ4MG4dwRvMl/vrmYCgc15v3THiIj3e5CSVLiMyGUJCmP2raFzz+Hqy/fyMNcAMAu/MnS5z6gw+ERvv02zgFKkrQDJoSSJOVD+fIw/K5S/P3Ysywo24jfqMql3MP770OzZnDHHZDutoWSpARlQihJUgFocl5b9lg2k/+d/zarSlUDIC0Nrr4aDjogg/kT58c5QkmStmVCKElSASlboSx9RzRj+nRo2TLzfJvpD1D3+CZM6TCI9X+uj1+AkiRtxYRQkqQCtt9+8NlnMHQoNCyziCFcSxk20H7KTQxpMJKPPop3hJIkBSaEkiTFQKlS0K8fTHo1ysJdmgPwFU259edzadcO+vSB33+Pb4ySJJkQSpIUQ/WPqE+Tle/z3v/dx2XlHmEjpQF45BHYd1/439PriGa4RYUkKT5MCCVJirGkUkkc9vxFPDX/ALp2zTz/66/wx5kXMWO3Y1kyZWH8ApQklVgmhJIkFZLatWHcOBg/Prxvxwf05jFar3iDqh2a8NA1i9iwId5RSpJKklLxDkCSpJLmxBPh8MPh7eOnk/5eEslkMIlO/Of2ejz4GowYAQcdBCxZAsuXb74vGoV166BsWYhEtqiwalVITS30r0OSVPSZEEqSFAe77AJdplzON6MPY82FV3PJmnsBmD0bDj4Yrj5lCcPGNySyLm3zPREgZXuVpaTAvHkmhZKkXHPIqCRJcbRvz5a0+v1t+t+zOzvvnHn+7bHLsiSD/yotLUtPoiRJOWVCKElSnCUnwyWXwDffQLdu4dyJvBzfoCRJJYIJoSRJCaJ2bXjxRXj9tSj/V+aVeIcjSSoBTAglSUowRx8TYc/JI3J1T9StDCVJeWBCKElSAoqWLpOr8uvXxygQSVKxZkIoSVICKls2d+XL5C5/lCQJMCGUJCkhZdlnMAceewz+/js2sUiSii8TQkmSioGHHoZ99oGxY51PKEnKORNCSZKKiSVL4OZT5/B5pQ5888yMeIcjSSoCTAglSUpEVatCSkqOim4snUKkalUgyt1cTss/ptDwjP159oB7+eWX2IYpSSraSsU7AEmStB2pqTBvHixfvvlUNBpWEy1TJuscw1JVq/JuxVQevXIuhz32HgAZJHHrZ0fQZ2/o3x8uvxzKly/sL0KSlOhMCCVJSlSpqeH4RwTIbvHRCsCVj+7D96d9zS+nX8WnP6fyDY3gT7jhBnjwQbjl5ihnnZFBcpnkwoheklQEOGRUkqRipM7he9L2p5doOvkumjXLPP/TTzC511i+27UF0299nWiGK89IkkwIJUkqlg4/MpmZM+GJJ2D33aEM6xjCtTRIm0XrG4/lriZP8MUX8Y5SkhRvJoSSJBVTyclwzjnw7bfw8AVfUpUwH3EZ1Rj0TXdatoQzzwyrk0qSSiYTQkmSirny5eGch9qQNvs73mval5uSbmENFYhG4emnoUEDuPHyP/nj+1XxDlWSVMhMCCVJKiGqNd6Nw766n8u+6UPXrpnn162DMv8dRnq9PZnS+Q7+Xvl3/IKUJBUqE0JJkkqYBg1g3Dj44AM44ADYnR+5kjupHF1J+1ev5qK9XmfECNiwId6RSpJizYRQkqQSql07+PhjeGrwjywrXQuA6bTiid+7cMEFsM8+MHo0pKfHOVBJUsyYEEqSVIJFItDx2jbUWvU17/ccwfCqtxN2PISFC+H006HrPt/w2fWvuFWFJBVDJoSSJInS5Utz6DPn8+SSDtxxB1Spknnt3AXX0nbwicypcCBTR3wevyAlSQWuVLwDkCRJiaNcObjySujdG+6+G94b/hld1r4MQKO/prLfBaWo/jzcdhu0bUvYs+LXXyk3fz5kZBBNSmbdOihbNvQ+bla1KqSmxuVrkiRlz4RQkiRto0IFGDgQVpy6J1NOv4q20+/nRU5iNk2Z/Ta8/TacffgSHv+wIcnr02i0xb0p26swJQXmzTMplKQE45BRSZKUrSoNq9J+2u2smraA2acPo9QWv0r+8p3lJK1Py1lFaWmwfHlsgpQk5ZkJoSRJ2qGarWsx9OlazJ0bFppJSoIkXH5Ukoo6E0JJkpRje+4JTz8N33wDvQ/8Ot7hSFLhevttOPvssKFr+fKwxx5w8snhH8UiyjmEkiQp1xo0gL3vawqtc35PNLppQwtJKqIefhh+/x2uugoaNoSffoIhQ6B1a/j0U2jaNN4R5poJoSRJypN167JZQCYb69dD2ZhFI0mF4IEHYLfdsp474gioUwf++194/PG4hJUfDhmVJEl5UjaX2d1//gOvvx56CiWpSNo6GQSoVg3q1oWlSws9nIJgQihJkvIkksvxn59/AcceC61awQsvQPpGM0NJxcCPP8KCBdC4cbwjyRMTQkmSVKg+/xxu6j6b78vvywfnjmT9n+vjHZKkkurnn2HmTJg5k3Jz58LMmZRdtCjn92dkQO/eYcjEZZfFLMxYcg6hJEkqFKf3hHkvwdq10J+h1N8wj/ojz2X0/z5l5ZARnHdeWLRPkgrNiBEwaBDJQKN/TtUuWzbMCcyJiy+GN96A556D1NRYRRlT9hBKkqS8qVoVUnK4rExKClcMrsqSJXBr/z85OjJ586U7/rqASy4JU3Buuw1WroxNuJK0jT59YMYM0qdO5etnniF96lSWDhuWs3uvuAIefDCsPNq9e2zjjCF7CCVJUt6kpsK8eaT/+ivz5s+nYYMGJCUls349lCmz1RzDqlUhNZUqwPVDdmbNxd8x5fwR/PLOHL74uwUAv/0GN9wAQwdn8L+mt9Bk+JnscWi9uHxpkkqImjXDkZ7O30lJ0Lw5637+ecf39esHd98dVhbt3TvmYcaSCaEkScq71FSoVWvzD1KR5OQcbS2xy+670H7iVaSlwcNPwrBhsGnazuFrJ3DsZzeRftjNvFm/NxWfe5j994/pVyFJOXfjjTB8eDguvTTe0eSbQ0YlSVLcpKSEEVvz58Po0dCiBVzFHQAkk8HUhVVo0wYOOwwmTAjrN0hS3NxxB9x6K3TrBoccEjaj33R8/nm8o8sTE0JJkhR3pUpBjx4wYwbsfMcgplU7ljTKcj8XAfD++3DCCdCoEbx0/TTSVqXFOWJJJdLEieF13Dg48MCsR9eu8Y0tj0wIJUlSwohEoMWVh7P/skksfncxx55Tk9KlM68vnfcn7QcfxZrKdZhy+M0s+2Fd/IKVVPJMmQLR6PaPxYvjHV2emBBKkqSEtE/7GjzxRPgZ69proWJFOI/HqcQqqkWXUe3dseyxZxnOPrvIjtSSpLhzURlJkpTQdt8dBg8OSeHHF1Zm6XN1qJ3+PfdyCes3RHjySXjySWjXDq4+ZzmdelSkVMoWP+IsWQLLl2/+GI3CunVhH+ntrYQqSSWJCaEkSSoSdtkFjn7mDDY+dhofX/sS333aCT7NvP7hh3Dxh//h1/M/5duj+rLf/edTqdQaaNgQ0jLnHEaA7e6emJIC8+aZFEoqURwyKkmSipRSKaU46O7uvPXJTkyfDmecAaVLQ21+oBvjqJX+A+1f60/HRj9z6+XLsySD/yotLUtPoiSVBCaEkiSpyGrVCp56KowKve3M+axMqgrAZI7k83WNGDcuzgFKUoIzIZQkSUVejRpw5pMd2fX37/nogqd5vuGN8Q5JkooEE0JJklRslK1QloMfOp1HvjmEjz6Co47M3f3RaGzikqRE5aIykiSp2IlE4KCDoOVNwJs5v2/9eigbo5gkKRHZQyhJkoqtsrnM7uacfBNzRk61p1BSiWFCKEmSiq0s+wzmQMulE1h17uU0bw733QcrV8YkLElKGCaEkiRJW3iMXnz1FVxyCey+O/TsCR+MX0HGxox4hyZJBS6hEsKlS+HSS+GQQ2DnncNv9aZM2X7Z11+HNm2gXLmwsljfvrB69bblVq8O12rUCGXbtAn3FladkiSp6Jhe9yQmle+++fO6dfDss7C2aw9+KNeAKUcP4Zev7TaUVHwkVEK4YAGMGQPly8OR/7Iq2NtvQ+fOUK8eTJwIgwfD2LHQpUvW1cGi0XBu7NhQZuLEcE/nzvDOO7GvU5IkxVnVqpCSkrOyKSm0fu8uFvyyM489BgccEE7XYTFH8iZ1Nn7HIZNvoG3TtXTpEn4G2LgxZpFLUqFIqFVGDz0Uli0L7ydOhPHjt1+uXz9o2jQkj0n/pLSVKkG3bjBpEhx3XDj36qvw7rswbhx07RrOdegQNrHt14//b+/e42u68v+Pv05O5EIlEnFNhKhKSUIQxGUUHVTqXpcUaamq6U3LTFvtt/rTdkZVx6/tmK/R9FGXdhTj3viFaoMqVYpOG7doRytIKCGkWkLO/v2xJ4cjaIKcc2K/n4/Hfpzae+191vFpPPI+a++1+Oqr8r2miIiIeFhkJGRlwfHjzl2GYc4m6ud32TOGYWEQGUlVYNQoc9u5Eza9uIdTH1UjxDjJKnqS7YggewWsWGHeUjrm/tOkdD9KVPc73P7xRERulFeNEPqUojeHDsH27ZCS4tq+b18zwF0aIpcvh+Bg89il7/HAA7Btm3mt8rqmiIiIeInISGjZ0rnZWrXEv535eul+IiNLnBobC2OW9yTwRA5fPPEhn7Sc4HI8Jwdyp80jqkdjMqu2Z/VjH5Gf76bPJSJyE3jVCGFp7NplvsbGuu738YGYGPObvEvbNm1aMmg2a3bxeERE+VzzavbvN/jqqyKXfXXqmJs7FBUVubyKd1BdvJPq4p1UF+9jhZpUqlqJtm8Npi3w+H+KmD3bxty5NnJzbTzIXADift7MjH8cpt8sgz59DFJSDLp3B18P/bZlhbpURKqLd7q0LobF1p2pcIEwL898DQ0teSw0FPbudW3buPGV28HFqaTL45pX89JLNgoK7C77Ro/OYcyY3GufeJNlZma69f2kdFQX76S6eCfVxftYqSYDB5pzCmzZEEDhGzU5d8wPgIUM4dw5G4sW2Vi0CKpXP8+URtMJH9CQmnfX80hfrVSXikR18U6ZmZn8+OOPnu6GW1W4QFistOsKlWX9ofK45uVeecWgQ4fLRwhrUadOreu/aBkUFRWRmZlJXFwcdrv9t08Qt1BdvJPq4p1UF+9j5ZokJADjl3Hiuzw2/20bjbaFuMwn4JuXx4i8Z/HdUsTuwJZ8Mmkjg1P8qFnzsgtlZ5fpOcfSsHJdvJnq4p0urcshiz0DVuECYfXq5mvxqN6lTpxwHeWrXv3q7eBi2/K45tU0bGijdWvP//Db7Xb9I+SFVBfvpLp4J9XF+1i5JjXurEmfGUn0Afbsgfffhw8+gCGH5+GL+UVw7q/VePq5QP70P9C9Owwdas5JcNuJbPN5lLNnXa55xV/SAgLMSXJKGQrB2nXxZqqLd7Lb7dhuZPSnAvKqSWVKIybGfL30uT4Ah8N8fu/S5wBjYsx/lB2XrSNbPEJf3LY8rikiIiLW1KQJvPYaHDgAQ97uwMYGw/mFQObyIGAuVZGeDsOHw9Oh7/NN29ElwuBVnT3rMpIoInKjKlwgjIgwb8+YN881lKWlwcmT5v38xfr3h/x881gxh8P8xi4hAcLDy++aIiIiYm12O7QZm0jHHz6g6PBRfv/OYO66y7XN8POzaH5kjWc6KCKCF94yunix+bp9u/n62WfmF2FVqkDPnua+KVOgRw8YNgxGjzZvu3/mGXM9wKSki9dKSjL3PfywGewiI+Hdd+Gbb2DNZf/2lsc1RURERACq1q3KA4/AA4/AwYOwYAF8PPcInXZt8HTXRMTivC4QDhrk+udJk8zX+vWheMKfu+82R+heeskMaEFB5nlTprg+eG2zmesGTpgAzz4Lp0+bi8+npUHXrq7vUx7XFBEREblcvXrml87PPFOb7z/aycmX/i+tv3mv1OcbBljrCScRKU9eFwhLu+xHz54XRwyvJSgIZswwN09cU0RERORqGvVpytmwx6BD6QPhjBnQefzFORBERG5EhXuGUERERORW4u9ftvbvzTInsXsqYgnr7/o/fLdsJ4bDWgtpi8jNo0AoIiIi4kHXO8N978P/oPOGV7hjQBzPR7zPSy+Zs56X9m4rERFQIBQRERGpUO7pAQ2qHKML6wBwYGNObg9efRWaNTOXvZg4EXYt26eRQxH5TQqEIiIiIhXI5Mmw+1AQ2yYsYVP9oay29+IotZ3Hs7Jg5p+PETMoltqJA/gs4RnWrzO4cMGDnRYRr6VAKCIiIuJJYWEQEFC6tgEBEBZGYDV/2r7Wjw4/zqPL6RUsWwZDh8Jtt5nN+vARdhxEOA7i8+8ddOlqo3ZtGDkSPvoIfv21/D6OiFQsXjfLqIiIiIilREaaw3rHjzt3GQYUFoKf32XPGIaFme0vEVjZRr9+0K+fGfTWrIGCyWc48VUoocYJltEfgLw8mDPH3Eb7zeWhsI8o6tOf2Am9CK5frZw/pIh4KwVCEREREU+LjHQJejagjJOPAhAYCH37An3Hcu7MGNImLqDKqe7U+wQOHrzYbkDhfBJzPoaZS3ky9X/JuvsxeveGXr0gKuoqF8/OLhFaz50zZ0n9rdAqIt5LgVBERETkFuQb4EvdYbG8Gl+Tv/jAjh2wbBl8vLiArllrne2WOvqS8wl88gmMHQtNm8Kfw2fQsE8cMQ+3wzfA1wyD0dFw9qzzPBtwxRtdAwLMEU+FQpEKQYFQRERE5BZns0GrVub25z9X5Yc1uzjw9nLyt31Hzk/hLm1zd5+gz+4nsX/i4NjYGjw7+ADJzY/T45IweE1nz5ojiQqEIhWCAqGIiIiIxUR1v4Oo7s8AcDgH0tMhLc0cJez56yrsOADYa0QzZ2Eg3yyEHp7ssIiUG80yKiIiImJhdevCww/DihXmxDOPTW/KZ7GPccgeyUp6ebp7IlLONEIoIiIiIoA5KU2HJ1rAE/+L4fg7w78+T/DHkLUAyCz9dRxFhkYdRCoIBUIRERERKcHmYyOulR9xreBsZ6BD6c89ltiLrMZ9ODPuRdoPqUdwcHn1UkRulL68EREREZFr8i/jGhi1HEfotDeVB8f4U706dOoEf/kLbN9ujh6KiPdQIBQRERGRa3JZZ7CUttGKY9SkqAg+/xxefBHuTTjC8Up12NRgKOsfmceBAze/ryJSNgqEIiIiInJTffc/s9k5/HXatHENk91ZQ03jKB0OzKfw3bk0aAB33AGPPgqLF0PecY0eiribniEUERERkZvqjgHNuKNlS0ZgLkn46aewejXc/a9N8KvZ5lN+D8D335vbzJnwNG/zSOAHHI37PX4jhhL/YHMqV/bYxxCxBAVCEREREbm2sDAICDAXnf8tAQFm+0tOTU42N2PWTPYtfZycf2aQfzKJKtvhzJmLp3ZjDU1+3UGTrTt4aGs0855uTocOcPfd5taqpUElv2vcv5qdbSbQ/zIMOHfOfAbS5bbXsDCIjCzDX4DIrUuBUERERESuLTISsrJKhK3CQvDzK33YsvnYaDywGY0HNqMz8PdC2LrVHEFc92kR7TZtdrbN4G4KC2HdOnN78UXY4NOFwJAAfm51F4HjH6VFl2r4+f33hOxsiI52Ca02IOBKHQkIMD+PQqGIAqGIiIiIlEJkpEuAsgFlnHy0BD8/6NjR3CZNslOQk81XqRvIW7Od4J/ru6x9GEw+7R2fY89zcGHNp4SseQJHZWjfHjp3hqRax2hRmhFMMEPj8eMKhCIoEIqIiIiIl6hatyqtJ90Lk+7lHuDoUVi71hxBvJC+FfsRBwDbSOBnqsIv5rFPP4WtHGSFZ7svUiEpEIqIiIiIV6pVC+6/39ygOzlbsvlhzmd8sy+QRtnmZDTFEtjmqW6KVGgKhCIiIiJSIdRtW4+6bYfTAXgMOHwYPvvM3Jp/+AP8XPprrVkDMbUgPLy8eitSMWgdQhERERGpkMLDYehQeOcd6P7xH8t07oTnISICGjSA4cNhxbj17FuSieOCo3w6K+KlNEIoIiIiIhWe/3XOcHPggLm9wGM0fmsPpwjmuY6bqHdPDB06QJs2aC1EuaUpEIqIiIhIhWe7xvKEV9K2DezbZa6DGEoeTdkDQAC/Mmfj7ZzbaLbz9YWWzS7w+vnxVGqXQPh9idTv1rjM7yfirRQIRURERMRy/vEPmN4Mvv0WvllRwBezBxN1eCP7HQ04d8nqhRcuwNkdu+jMdMiEA6mRhIUeoG1baNsWEhPNUcSQkFK+cXY2gXv3gsMBdjuGAefOmSOcpV3PUeRmUiAUEREREUvy9YWWLaFlywbw8kIMh0HhrgL++S1s3AibNsHOndDW2OI850sSOXECVq0yN4B7Wcnf/P5ETkRbCroNoPaYvsTFmdd3kZ2NT9OmNL1kvUQbXBI/LxEQAFlZCoVS7hQIRURERKTiCwszQ1RpFqcPCDDbX8bmY6N+XBD142DYMHPf6dOwe1EH1i/8C4HffsmWX7pBget57dhMw8IsGu7P4o13apL0Tl8CA6FVK2jdGrrUzSI2zkb9kNP4lKZ/YH6O48cVCKXcKRCKiIiISMUXGWmOqB0/7txlGFBYCH5+1387ZlAQJI6KgVExALQx4MkfYcsW+PJLc2uzdRsYZvsvSQTg11/NUcaNG6ElrxLFPAqoQtWb8VlFbiIFQhERERG5NURGugQ9G3Cdk49elc0GUVHmlpxs7jt3+iN2Lv6GvP/3JTWM39HwG9i//+I5CWwDoCpnbnJvRG6cAqGIiIiIyA3wD/In9qE28FAb7vrvvp9+gm3bYPvWIk7NjObosVOEOo5TiQulvu7f/w51kgxa1/iRer9rgM1HU5vKzadAKCIiIiJyk9WsCUlJkJRkh0krADj8fgbhD/6+1NeYNRt+mn2YQzTkFEF8U70rS4YtIz4e4uMhJsa8HVbkRigQioiIiIi4Qd2Y0q5NcVELvgYgmNOczzvN3/528VilSnBfg+087phOUVw8tyV14vZBLalW7QY7mp1d4llMLY1x61IgFBERERFxg7IuZj/iQfh1x2mO7axJDeMn/k28y/Hz56HmdxvpyFz4z1zeXf4wCY+8S4MG0KKFOYqYWOcAd95RRETHBvj4+vz2m2ZnQ3S0y2ytWhrj1qZAKCIiIiLihcaOBVoOwzCGcfSbXFrsNJh8EL7+Gv79b/juu4sjiABf0wKAH380t2XL4O9MpTszKOA2pjWcweGuKcTFQVwcxMZCjRqXvenx46VbugO0NMYtQoFQRERERMSL2WxQK74OteKh6yX7Cwpg/5Kn+Cy9NbZv/02+vQP+/zFv7ywWy04AqvIzX+6vwcf7XS7NvwJSaFj5CAUN4vjp/qeJrQ1Ny/0TiTdRIBQRERERcYewMIyAAGylGYELCDCf0buGqlWh+YgWMMIcGewEXLgAe/eaI4hffw3GvNocPVabWo4jZBJX4hrtz64l/GwOnPiURjseIwjYUfZPJhWYAqGIiIiIiDtERuLYvZusL74gunFj7HY7hgGFheZsoTdjwhZfX/NW0NhYGD4cmLYQgLy9x/hnThiZOyEz09xyMvMI/yUHgDNUZj8NieffZXq/H9L3UKteNJVrVClzX8U7KBCKiIiIiLhLZCS/njhhzvhit2MD/N3wttXvrEGXO6HLJfecOi6EcPCL/eR8nMmx3ccY7O/D+S3Aj6W/btTE4TARNgbczWtdP6VpU2jSBPP1ToPgalo70dspEIqIiIiIWJCPrw/1OkVRr1MUAL0AYzuQUPZr/XQ2iPR0SE+/uO8+ljDd5ylyqjUlKz6ZkwNGceed5iSm4eFln3VVyocCoYiIiIiIAGUPaQcrRVH7/EH20KTEsabspo4jhzoncvhkbSueX3vxWJUq8Ez1WXTjEwqjovm1Rz9q3xNPs2Zgt9/gh5AyUSAUEREREZHrUu/LxZy/M45Be87S5ADs3g179pivTb7dBw6zXRbRLuedOQO3n1lLexZANoz6rB6zXoinTRvYtMl8FlLcQ3/VIiIiIiJy3SpVrkTjVpVo3AoGDLi4v6hwLtlfvMKRjF20NVpgP2KuY5+VBceOQTRZzrbFgXHrVjh4EKKi3P0prEuBUERERERETGFh5pIXN2FpDLufncjODYns3JA2lx07eRIOLf8Hn2/aRdGuvTSs3pTCnyApSWHQ3RQIRURERETEFBlpDuEdP+7cdbOXxgAICYGQkQkw0pzBpvMNdFlujAKhiIiIiIhcFBnpEvTctTSGeIaPpzsgIiIiIiIinqFAKCIiIiIiYlEKhCIiIiIiIhalQCgiIiIiImJRCoQiIiIiIiIWpUAoIiIiIiJiUQqEIiIiIiIiFqVAKCIiIiIiYlEKhCIiIiIiIhalQCgiIiIiImJRCoQiIiIiIiIWpUAoIiIiIiJiUQqEIiIiIiIiFqVAKCIiIiIiYlEKhCIiIiIiIhalQCgiIiIiImJRCoQiIiIiIiIWpUAoIiIiIiJiUQqEIiIiIiIiFqVAaDG5ubm888475ObmerorcgnVxTupLt5JdfE+qol3Ul28k+rinaxcFwVCi8nNzeXdd9+15P/s3kx18U6qi3dSXbyPauKdVBfvpLp4JyvXRYFQRERERETEohQIRURERERELEqBUERERERExKJ8Pd0Bqzhz5gwAO3bs8Gg/9u/fT9WqVdm0aZMl75H2VqqLd1JdvJPq4n1UE++kungn1cU7XVqX/Px84OLv77c6m2EYhqc7YQWTJk1i/vz5nu6GiIiIiIiUwv3338+kSZM83Y1ypxFCN3nggQcAiI6OpkqVKh7ujYiIiIiIXMmZM2fIyspy/v5+q9MIoYiIiIiIiEVpUhkRERERERGLUiAUERERERGxKAVCizh9+jSPP/44tWvXJjAwkDZt2rB69WpPd8syDh06xFNPPcXvfvc7brvtNmw2G+vXr79i29WrV9OmTRsCAwOpXbs2jz/+OKdPn3Zvhy0iIyODESNG0LhxYypXrky9evUYPHgwe/bsKdFWdXGPL774gh49ehAeHk5AQAA1atSga9eurFq1qkRb1cSzRo4cic1mo1+/fiWOqTbusX79emw22xW348ePu7RVTdxv7dq1dOvWjeDgYCpXrkzTpk1JTU11aaO6uM+IESOu+vNis9k4cuSIs63V6qJAaAGGYdCvXz8WLlzI5MmTWblyJVFRUfTq1Yu1a9d6unuW8P333zN//nwqV65Mt27drtouIyODXr16ERUVxcqVK5k8eTILFy6kX79+6HHfm2/mzJkcOnSIP/3pT6xatYqpU6eyd+9eEhISyMzMdLZTXdzn5MmTREdHM23aNFavXk1qair+/v4kJSWxYMECZzvVxLMyMjJYtGgRQUFBVzym2rjX66+/zubNm122atWqOY+rJu43e/ZsunfvTqNGjViwYAFpaWk8/vjjFBYWOtuoLu41ceLEEj8nGzZsIDAwkLZt21K7dm3AonUx5JaXlpZmAMbSpUud+4qKioz4+HgjISHBgz2zjqKiIud/F9dj3bp1Jdq1atXKiI+Pd2m/dOlSAzBWrlzpjq5aytGjR0vs++mnn4zAwEDjoYcecu5TXTzr/PnzRnh4uNGlSxfnPtXEc3755Rfj9ttvN6ZOnWrUr1/f6Nu3r8tx1cZ91q1bZwDGsmXLrtlONXGvAwcOGIGBgcbrr79+zXaqi+ctWbLEAIzU1FTnPivWRSOEFrB8+XKCg4Pp27evc5+Pjw8PPPAA27Zt49ChQx7snTX4+Pz2j9qhQ4fYvn07KSkpLu379u1LSEgIy5cvL8ceWlPNmjVL7KtRowYNGjRw/lyoLp7n6+tLtWrVqFSpEqCaeNpLL71ElSpVGDduXIljqo33UU3c77333gPgySefvGob1cU7zJo1i8qVKzNkyBDAunVRILSAXbt20bRp0xKhpFmzZs7j4nnFdYiNjXXZ7+PjQ0xMDDt37vREtyzn8OHDfP/998TExACqi6c4HA4uXLhAbm4ur776Kvv27XMGENXEc7Zv387bb79Namoqvr4llzJWbTxjzJgxzi9O+vXr5/L3rJq43+eff06TJk1YsmQJ0dHR2O12IiIimDBhgvOWUdXF83Jzc1m9ejUDBw503v5u1booEFpAXl4eoaGhJfYX7ztx4oS7uyRXkJeXB3DVWqlO5c/hcDB69Gj8/f15+umnAdXFUwYPHkylSpWoW7cub7zxBvPnz+eee+4BVBNPuXDhAg8//DCjR4+mbdu2V2yj2rhXcHAw48aNIzU1lbVr1zJlyhS+/vpr2rVr5/zFVTVxv5ycHL777jvGjh3L2LFjycjIYNSoUUybNo2RI0cCqos3mDt3LkVFRYwaNcq5z6p1Kfn1ntySbDabp7sgpaRaec6TTz7Jxx9/zIIFC4iMjHQ5prq419SpU3nuuec4cuQIH374IcnJycyZM4dhw4Y526gm7vXXv/6Vo0ePMnny5N9sq9q4R4sWLWjRooXzz506dSIpKYnY2FgmTZrE4sWLncdUE/dxOBwUFBQwf/58kpOTAejcuTOFhYVMmTKFl19+2dlWdfGcOXPm0KhRIzp16lTimNXqohFCC6hevbrzG49LFX/LcaVvQcT9qlevDnDVWqlO5Wv8+PHMmDGDmTNnMmjQIOd+1cUzGjZsSOvWrenduzfz588nKSmJJ554AofDoZp4QHZ2Ni+//DKvvPIKhmGQn59Pfn4+DoeD8+fPk5+fT2FhoWrjBSIjI+nYsSObN28G9G+YJxT/nRff1VAsKSkJgB07dqguHrZx40aysrKcI7bFrFoXBUILiImJYc+ePTgcDpf9xdPqX36ftHhG8TNrl9+f7nA42LVrl+pUjp577jnefPNN3nrrLUaPHu1yTHXxDomJieTn53Ps2DHVxAP279/P2bNnGT16NCEhIc7t4MGDpKenExISwvvvv6/aeAnDMJzzBqgm7hcXFwdQYomC4t/Dip9HA9XFU2bNmoXdbufBBx902W/VuigQWkD//v3Jz88nLS3Nuc/hcPDBBx+QkJBAeHi4B3snxSIiIkhISGDevHku4T0tLY2TJ09ecfFnuXETJ05k6tSpTJ06laeeeqrEcdXFO6xfv55q1apRvXp11cQD4uPjWbduXYmtVq1adOjQgXXr1nHvvfeqNl7gxx9/ZOPGjSQmJgL6N8wTBgwYAEB6errL/vT0dGw2G61bt1ZdPOjMmTMsWrSIHj16lPgd2LJ18fCyF+IGDofD6NKlixEWFmbMnj3byMjIMJKTkw273W5kZGR4unuWsWjRImPRokXGhAkTDMCYNGmSsWjRIiM9Pd3Z5tNPPzXsdruRnJxsZGRkGLNnzzbCwsKMLl26GA6Hw4O9vzW98cYbBmAMGDDA2Lx5s8u2Y8cOZzvVxX2GDh1qPP/888bixYuN9evXG/PnzzeSkpIMwJg+fbqznWriHa60DqFq4z5Dhw41XnzxRWPp0qXG2rVrjenTpxsRERFGUFCQsXv3bmc71cT9evbsaQQHBxtvvfWW8cknnxgvvPCC4evrazz66KPONqqLZ7z33nsGYCxevPiKx61YFwVCizh16pTx6KOPGjVq1DD8/f2NhIQElyAi5Q+44la/fn2Xdunp6UZCQoLh7+9v1KhRw3j00UeNU6dOeabTt7i77rpLdfEy06dPNxITE43Q0FDDbrcboaGhRvfu3Y2PPvqoRFvVxPOuFAgNQ7Vxl9dee81o3ry5ERwcbPj6+hp16tQxhg0bZuzbt69EW9XEvX7++Wfjj3/8o1G3bl2jUqVKxu2332689tprLoudG4bq4gkdO3Y0wsLCjMLCwqu2sVpdbIZx2Q3OIiIiIiIiYgl6hlBERERERMSiFAhFREREREQsSoFQRERERETEohQIRURERERELEqBUERERERExKIUCEVERERERCxKgVBERERERMSiFAhFREREREQsSoFQRERuWV27diUlJcXT3biilJQUoqOjiY6OJi4ursznHzt2zHl+dHQ0EyZMKIdeiojIrc7X0x0QEREprejo6FK3zcrKKsee3BwhISE8//zz2O32Mp8bFBTE1KlTAXj22WdvdtdERMQiFAhFRKTCKA5AxbZv387ChQsZMmQIrVq1KtF+9erV7uradalcuTJ9+/a9rnP9/f2d5yoQiojI9VIgFBGRCuPy8FRUVMTChQuJj4+/YrDy8/NzV9dEREQqJD1DKCIit6wrPUNY/Lzdli1bSE5Opnnz5nTq1InU1FQATp06xQsvvEC7du1o3rw5Y8aM4ejRoyWuXVBQwBtvvEG3bt2IjY0lMTGR8ePHc/DgwRvud25uLs8//zxdunQhNjaWdu3akZyczPLly2/42iIiIpfSCKGIiFjO7t27WbduHYMHD6ZPnz6sWrWKadOm4efnx4oVKwgPD+eJJ54gOzubDz74gOeee445c+Y4zy8oKCA5OZmcnBzuu+8+7rjjDo4dO8aHH37IoEGDWLJkCeHh4dfVtwsXLjBy5EhOnTrFkCFDiIiI4PTp0+zdu5cdO3bQr1+/m/OXICIiggKhiIhY0L59+1i4cCHNmzcHYODAgXTt2pUpU6YwfPhwXnzxRZf2c+bMYf/+/TRs2BCAt99+m4MHD/Kvf/2LO++809muf//+9O7dm+nTpzNlypTr6tv333/PDz/8wJtvvklSUtJ1fkIREZHS0S2jIiJiOfHx8c4wCOazhnFxcRiGUeIW04SEBAAOHDgAgGEYpKWl0bp1a2rWrMmJEyecW2BgIPHx8WzcuPG6+1a1alUANmzYQEFBwXVfR0REpDQ0QigiIpZTr169EvuCg4MBiIiIcNkfFBQEQH5+PgAnTpwgPz+fjRs30q5duyte38fn+r9vDQ8P5w9/+AOpqamsXLmS2NhY2rRpQ48ePYiJibnu64qIiFyJAqGIiFjOtdb9u9oxwzBcXtu3b8/o0aNvfueAcePGMXDgQNavX8+2bdtYsGABqampPPLII4wfP75c3lNERKxJgVBERKQMQkNDCQoK4ueff6Z9+/bl9j716tUjJSWFlJQUzp07x6hRo3jnnXcYMWIEoaGh5fa+IiJiLXqGUEREpAx8fHzo3bs333777VUXvs/Ly7vu6xcUFHD+/HmXff7+/s4JbYpvXRUREbkZNEIoIiJSRuPGjWPHjh08/fTT9OzZk+bNm1OpUiVycnLYsGEDMTEx1z3L6JYtW5g4cSLdu3cnKiqKKlWqsHPnThYvXkzz5s2dwVBERORmUCAUEREpo6pVqzJ//nxmzZrF6tWrycjIwG63U7t2bVq1asWgQYOu+9rR0dF069aNrVu3kpaWhsPhoE6dOowZM4aHHnroJn4KERERsBnFT8eLiIiI26SkpHDw4EGWLl2KzWYjJCSkTOcbhsHJkycBaNeuHf3797/uUUkREbEujRCKiIh4SG5uLu3atcPPz4/MzMwynXv8+HE6duxYTj0TERGr0AihiIiIB+zcuZPTp08D5kQ1iYmJZTq/sLCQbdu2Of9cs2ZNGjVqdFP7KCIitz4FQhEREREREYvSshMiIiIiIiIWpUAoIiIiIiJiUQqEIiIiIiIiFqVAKCIiIiIiYlEKhCIiIiIiIhalQCgiIiIiImJRCoQiIiIiIiIWpUAoIiIiIiJiUQqEIiIiIiIiFvX/AUFbZ+IsZg4VAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 7 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -290,12 +224,7 @@ "id": "485d63040c082608" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.154303Z", - "start_time": "2024-10-18T17:30:27.044433Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "ss316 = AISI_316LN()\n", @@ -309,29 +238,8 @@ "sc_strand.plot_Ic_B(Bt_arr, T=T_sc, T_margin=T_margin)\n" ], "id": "99e72df13124370d", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 8 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -340,12 +248,7 @@ "id": "868f3e5894a341c7" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.378235Z", - "start_time": "2024-10-18T17:30:27.187548Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# Define the range of operating current (Iop) values\n", @@ -382,19 +285,8 @@ "plt.show()\n" ], "id": "b81d77fe0194a673", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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ail69esE0TcyfPx+5ubmqxOiwww7DW2+9hbVr16JTp05o06ZNQuesd+/eeO211zBo0CC0adMGTz/9ND7//HPk5OTU67p69OiBESNG4M4770Tbtm1RUFCAJ598El9//XUgbCLR9fs5/PDDYRgGHnroIfz85z/H119/jRkzZngE7QcffIDly5dj0KBB6Ny5M7Zv3473338fvXv3rtdnaNWqFY4//ng88sgj6Nu3L1q3bo2HHnoIu3btUqV7yVJSUoITTzwRN954I2688Ub0799fhU1cffXVAOzv6uOPP8bmzZvRvn175OXlhbpyjfn78fOTn/wE//znP3H99dfjD3/4gwqb2LJlC+65556UjllbW6vKCWtqavDBBx/gpZdewtlnnx33Pcl8z8ny85//HA8//DBuvPFGjBs3DhUVFZg6dWpKs7nqc+8T/XeFEEL2FSikCGlmunXrhjFjxuDee+/1rO/Rowf++te/4u6778a8efNw5JFH4qabbsLPfvazRj3/H//4R9x1111Yt24dunbtihkzZniStW699VYUFhbiiSeeUH+AHXbYYRgxYkRK5zvvvPOwd+9ePProo5gxYwY6dOiAM888ExMnTqz3saTQ3L59O1q2bIni4mLcd999quTw5z//OT755BNcfPHF2L17N8aPH4/f/OY3cY9355134tZbb8XFF1+Mtm3b4uyzz8bFF1+Mp556qt7XNnXqVEyZMgXXXXcdWrZsibPPPhvnn38+Fi5cmPT1+ykqKsJtt92Gf/7zn/jXv/6Fnj174re//a1H0LZv3x6ffvopnnrqKezatQsHHnggTj75ZNxwww0pfYZbbrkFl19+Odq3b48rrrgCNTU1qvcpWQzDwD//+U/MmjULd955J77//nsceOCBOOWUU9Q+v/zlL3HTTTfh3HPPRVVVFe644w6MHj06cKzG/P34yc3Nxdy5czFt2jRcffXViEajOOqoo/DAAw+k3GP2n//8B4MHDwZg92l17doVV199Na655pq470nme06WQw45BHPmzMHUqVMxevRodOvWDZMmTcKtt95a72PV597X9d8VQgjZFzBEYxevE0IIIWS/5Pnnn8cf/vAHfPjhh2jbtm1zXw4hhDQp7JEihBBCCCGEkHpCIUUIIYQQQkgWU1ZWhttvvx0///nPccwxx6C4uDhuwM/SpUtx3nnnYcCAARg0aBCmTJmCysrKwH6VlZWYMmUKBg0ahAEDBuC8886LO8agOY/ZnFBIEUIIIaRRGD16NNauXcuyPkLSzJdffolXX30Vbdq0wUknnRR3v+XLl2Ps2LHo1q0b5syZgwkTJmDBggUYN26cZyyCEALjxo3DggULMGHCBMyZMwfdunXD2LFjAyNbmvOYzQ17pAghhBBCCMliLMtS8//+85//YOzYsXj00UdRUlLi2W/06NGwLAvPP/+82v/NN9/E+PHjMWfOHAwZMsRzjHvuuQcjR45U5xg9ejRycnIwb968jDhmc0NHihBCCCGEkCxGio1ElJWVYfXq1Rg1apRn/+HDhyM/Px+LFi1S6xYtWoR27dph+PDhnnOcc845WLVqFcrKyjLimM0NhRQhhBBCCCH7OOvWrQOAwHDsSCSCwsJCtR0A1q9fj8LCwoBAk8PJ5b7NfczmhnOkGsAPP/yAZcuWoVu3bmjVqlVzXw4hhBBCSNazd+9ebN26FYMHD0bHjh2b+3I8fP3119i5c2eTnuOHH34IPcfhhx+O/v37p3zcXbt2AYBnMLwkPz8fGzdu9Ozbs2fP0P0AoLy8PCOO2dxQSDWAZcuWpTTkkhBCCCGEJGb69Ok4++yzm/syFF9//TXOPGMIqmuMZjl/ixYt8Oabb+LQQw9t0HEMI7nrT3a/5j5mc0Ih1QC6desGwP4feu/evZv8fJZlYe3atSguLk6qFpakDu91euB9Th+81+mD9zp98F6nj3Te6w0bNuCGG25Qf2dlCjt37kR1jYFpNwv06pHec2/8Evjd7THs3LkzZSHVoUMHAK7jo1NeXq62y33j7Qe4blFzH7O5oZBqALKcr3fv3ujbt2+Tn880TdTW1uKoo45CTk5Ok59vf4b3Oj3wPqcP3uv0wXudPniv00dz3OtMbZs4vIeFI4vSe047Yrth9132HK1btw6DBw9W6y3Lwvr163HqqaeqdYWFhVi4cKEnDRAA1q5dCwAoKirKiGM2N/znG0IIIYQQQvZxunTpgn79+uHll1+GZVlq/eLFi1FeXo4RI0aodSNHjkRFRQUWL16s1lmWhfnz56Nfv37o3LlzRhyzuaEjRQghhBBCSJbz+uuvAwBWr14NAHjvvfewc+dOtGnTBqWlpQCASZMmYcyYMZg0aRLOP/98bN++HdOmTUNJSYnaBwBKS0tRUlKCm2++GRUVFejatSueffZZrFmzBnPnzvWctzmP2dxQSBFCCCGEEJIkprBginSfE6irtO+6667zLN99990AgIKCAuUCDRw4ELNnz8asWbNwzTXXIC8vD6effjomTZrkCXcwDAP33nsvZsyYgenTp6OyshJFRUWYPXs2Bg4c6DlPcx6zuaGQIoQQQgghJMuRvUZ1UVpampSrk5eXh8mTJ2Py5MkZfczmhEKKEEIIIYSQJBEALKTXkhLIHBeGuDBsghBCCCGEEELqSUY5UsuXL8dLL72Ejz76CGVlZejQoQOOPvpoXHvttZ45TZdeeinefffdwPsvvvhi3HLLLZ51lZWVuOuuu7Bw4ULs3r0bRUVFuPbaa3HKKacE3r906VLMmjULX3zxBdq1a4dTTz0VEydORF5eXuN/WEIIIYQQknVYsNLuSFl0pDKSjBJSTz75JCoqKnDVVVfh8MMPxzfffIM5c+bgpz/9KZ5++mkUFxerfXv27Ik777zT8/6DDz7YsyyEwLhx47B27VrccMMNKCgowNNPP42xY8di7ty5nsa25cuXY+zYsUo8bdu2DdOnT8eGDRvwyCOPZFRjGyGEEEIIIaR5ySghdeutt+Kggw7yrDvppJMwdOhQPPLII5g6dapa37p1axx99NEJj7dkyRKsWLEC99xzD0aOHAkAKCkpwejRozFjxgzMmzdP7Tt9+nQUFRVh5syZakhYfn4+xo8fj7feegtDhgxpnA9JCCGEEEIIyXoyqkfKL6IAoGPHjigoKMCOHTvqfbxFixahXbt2GD58uFoXiURwzjnnYNWqVSgrKwMAlJWVYfXq1Rg1apRn0vLw4cORn5+PRYsWpfBpCCGEEELIvoYlBMw0PyyR5rx1khQZ5UiFsWPHDmzZsgUnn3yyZ/2mTZtw/PHHY8+ePejevTvOP/98XHnllcjJcTP2169fj8LCQo84AqBKBNetW4cuXbpg3bp1AIA+ffp49otEIigsLFTb47F+/XrP5GUA6NSpEzp16lS/D1sHpml6nknTwXudHnif0wfvdfrgvU4fvNfpI5332v83FSGZSkYLKcuycPPNN6Nly5a4/PLL1frjjjsOP/7xj3H44YejqqoKCxcuxIwZM7Bhwwbccccdar9du3ahZ8+egePm5+cDAMrLy9V++nr/vhs3bkx4nb/73e8C60aPHo3zzjuvzs+YCitXrmyS45IgvNfpgfc5ffBepw/e6/TBe50+0nGvN23a1OTnaAgWRDOETZBMJKOF1G233YZly5Zh5syZOPTQQ9X666+/3rPf0KFD0bZtWzz22GO44oorPKEU9QmJSDVQYtq0aejVq5dnXVM5UitXrkT//v09zhtpfHiv0wPvc/rgvU4fvNfpg/c6faTzXufm5jbp8QlpLDJWSN1xxx144okncNttt+GMM86oc/+zzz4bjz32GD755BMlpDp06KDcJh3pREkHqkOHDgAQd1+5PR6FhYXo27dvndfYWOTk5PD/MNIE73V64H1OH7zX6YP3On3wXqePdNxrf0sGIZlKRv5Sp0+fjocffhh//OMfccEFFyT1HuE04emuUmFhITZs2BCotV27di0AoKioCIDbG+XvhbIsC+vXrw/0ThFCCCGEkP0TC4AJkdYHS/syk4wTUn/729/wwAMP4IYbbvD0RdXFSy+9BACeSPSRI0eioqICixcvVussy8L8+fPRr18/dO7cGQDQpUsX9OvXDy+//LJHdC1evBjl5eUYMWJEAz8VIYQQQgghZF8io0r75s6di9mzZ+PUU0/Fj370I3z88cdqW25uLo466ii8//77uP/++zFy5EgUFBRgz549eOONNzB//nxccMEFHveotLQUJSUluPnmm1FRUYGuXbvi2WefxZo1azB37lzPuSdNmoQxY8Zg0qRJOP/887F9+3ZMmzYNJSUlKC0tTdctIIQQQgghGUzzhE0w/jwTySghtWTJEgDAwoULsXDhQs+2goICLF68GJ06dYJlWfj73/+OnTt3okWLFujVqxduvfVW/OxnP/O8xzAM3HvvvZgxYwamT5+OyspKFBUVYfbs2Rg4cKBn34EDB2L27NmYNWsWrrnmGuTl5eH000/HpEmTUg6hIIQQQgghhOybZJSQeuyxx+rcp0ePHrj//vuTPmZeXh4mT56MyZMn17lvaWkp3SdCCCGEEEJInWSUkCKEEEIIISSTsYSAKdJc2pfm85HkyLiwCUIIIYQQQgjJdOhIEUIIIYQQkiSW80j3OUnmQUeKEEIIIYQQQuoJHSlCCCGEEJKYmldxcNvVgOgPIKe5r6ZZsZwhuek+J8k8KKQIIYQQQkhcRM2bMHZPQvcOgKg5DMi7uLkviZCMgKV9hBBCCCEkLqL6OW0p1mzXQUimQUeKEEIIIYSEIqwKYO8yd0Xu0Oa7mAzBFPYj3eckmQcdKUIIIYQQEs7eRQCiAIDqaAGQU9C810NIBkEhRQghhBBCQhE1r6vXuS22wKpZ0IxXkxkIuBHo6XrQkMpMKKQIIYQQQkgAYZUDe99RyxYEYH3djFdESGZBIUUIIYQQQoLU/BuyrM8Stidi5J7cjBdESGbBsAlCCCGEEBJAaGV8Ahb2xrrggJyiZryizMCEARNG2s9JMg86UoQQQgghxIOwdgG1/1XLFgQiOWWwov9rvosiJMOgI0UIIYQQQrzULIKcGSXL+gRiMKMfA20GN991ZQACgJXm9AeGTWQmdKQIIYQQQogHUfOa+xoWLCEgAOS0/FHzXRQhGQYdKUIIIYQQohDWTlXWJ4SAZUspRM2DcEDL45r56pof9kgRCR0pQgghhBDiUvMmABOAW1JmQcAwKmHF1jbbZRGSaVBIEUIIIYQQRbyyPkSqEa1d0lyXRUjGwdI+QgghhBACABDWD0Ctncynl/UJOD1SLYqb9wIzAKsZSvsslvZlJHSkCCGEEEKITc1C+Mv6TAhYAEyrHVrk7t+JfYTo0JEihBBCCCEAwsv6ALtHyrRyYVk/AChopqvLDCwBWCLNjhTzzzMSOlKEEEIIIQTC/BaoXWG/9pX1AQByvkVNzYvNd4GEZBgUUoQQQgghBKh5DYAFAEo8ybI+4QRORCKdm+3yCMk0WNpHCCGEEEIgal5Wry0IT1mfLaZy0arVqc10dZmDHTaR/nOSzIOOFCGEEELIfo6IbQGin9ivhZ3Rp5f1mRCIxjoBBv90JERCR4oQQgghZH+n5lX10lvWJ1RZn9FiK/ZUPY4O7X/ZTBeZGZiIwER60x/SHbdOkoP/rEAIIYQQsh8jhEhQ1meHoSsxZbRulmskJBOhkCKEEEII2Z+JrQVi6wG4ZX2mVtbn9khF0KbVGc14oYRkFiztI4QQQgjZjxE1r7ivnVI+yynrsxwnygIQix2GSKRjs11npiCEkfa5TiLNc6tIctCRIoQQQgjZTxFCANVuf5QUUPZreKLP0WIzdlc93RyXSUhGQkeKEEIIIWR/JfoRYG0DoKf16WJKlvU5wkrsab5rzRBMpD/8Id1x6yQ56EgRQgghhOyn+EMmZFmf8JX12WN6DbRtPbJ5LpSQDISOFCGEEELIfogQMaDmNXdZTY4Sjnhyo88tAGb0cLRscVhzXW7GYIkIzDT3SKW7J4skBx0pQgghhJD9kdrlgPUDAGiCSWb16T1SzusWm1FR/UbzXCshGQiFFCGEEELIfoiodtP6rARpfbaYMmDCQG3sy+a6XEIyDpb2EUIIIYTsZwhRA+xdqJYtraxPOVOOmDLh9kjltT65Wa43k7AQce5HOs9JMhE6UoQQQggh+xt7lwBOAp8QdjGf6WT1CSGUeBICjpgyIGLd0Lrlkc10wYRkHnSkCCGEEEL2M0T1S+q1BSu0R8oVUwYsGDBzylC5933kH1DSDFecOVgw0h5HTkcqM6EjRQghhBCyHyGsH4C9b6llCwKmVtZnOj1SgBRTBgADlmGhsvaDZrlmQjIRCilCCCGEkP2J6gUAYgCgBJPdIwUncCJY1qfmSOUe0xxXTEhGwtI+QgghhJD9CFHjlvUJWCqhT86PkmLKhCzrc1wp82Dk5f6oeS46g7CEAVMYaT6n/Q2RzIKOFCGEEELIfoKIbQKin9ivVdy55U3rc/a1xZQBAbtHKoZaVMc2NdelE5Jx0JEihBBCCNlPENXz3ddOxLnpc6IsAJZT1mfBcISVAdPYgx+q/412rYua49IzBssRluk9J0BHKvOgI0UIIYQQsh8ghABq9LQ+ofwnK0RM6WV9trAycEDLwua4dEIyEgopQgghhJD9gegHgLkVAJy4czetD4BypuzX3rI+U0QAqx06tB7cPNdOSAbC0j5CCCGEkP0A7+woofVICW2OlF3WBxhOWp8BIezXQhwA09qDlmjTTJ8gMzARgZnm0j5TfTskk6AjRQghhBCyjyPEXqDmNXdZi5hwE/rkHClvj5ScI2XmfIuyPc83y/UTkonQkSKEEEII2dfZuwQQFQB0waQn9MkeKW9an4Ad9y3Dt3NzOjXDxWcWqtQxreekG5WJ0JEihBBCCNnHEdUvqtcWLNUjZUGoOVIyZAKOC6WX9VkwANEaB7cZ3izXT0gmQkeKEEIIIWQfRlg7gb1L1bKlhUyo2VG+HimhiSkprAyzCwwjt5k+ReZgIQIrzV4E/ajMhI4UIYQQQsi+TM0CAFEAgCVk3Llll+sJ4ST0+cv67FI+04lABwzszdmKryqfSf/1E5KhUEgRQgghhOzD6Gl9ApZWyufGnbuBE960Pkv2Sgm7NygCOlKESFjaRwghhBCyjyJiG4Dox/ZrFXduecr6XJcKbkKf81BzpBABRAt0PoA9UpYwYIr0xp9baT4fSQ46UoQQQggh+yii2o0rF04vlKkS+oQKmJBx57YbBQCGSuuDI6RyzMPQMnJg2j8DIZkKHSlCCCGEkH0QIUzAP4QXQr32iynh9ENZWimfBUOJrOrIFny151Ucnj+qGT5N5qAcurSeU9S9E0k7dKQIIYQQQvZFapcB1jcAoOLOTSfyHIBypuzX8JXyyTlStmiQ/VJRs6J5PgshGQiFFCGEEELIPohe1icjzq2w2VECkH1RqkdKRCBUv1QEpvO6ywGlzfFRCMlIWNpHCCGEELKPIaxdQM0itWxpERP+2VEm3B4pt5TPcaaEK6xyYz3RtmVBs3yeTMISEVgizaV9gqV9mQgdKUIIIYSQfY3qV+CfHWX6ZkfZfVH67CjDmR0VUXOkLESUsKqKfIXtVf9tjk9DSEZCR4oQQgghZB/DW9bnnR0l1Ho3ZEKfHRV8bfdICQOoqN0I4OT0f6AMonnCJqy6dyJph44UIYQQQsg+hIiuBWKr7NdOyIQ+O8rUeqSkMwWZ0Of0Qllx+qU6tylpng9FEvLxxx/j8ssvx9FHH41jjz0WV199NdasWRO679KlS3HeeedhwIABGDRoEKZMmYLKysrAfpWVlZgyZQoGDRqEAQMG4LzzzsPSpUvTdsxsgEKKEEIIIWQfItnZUcI3O8ou63PnSJmIOLOkbFGVaxagQ6s+6f9AJCGffPIJLrnkEsRiMcyYMQN//etfsWvXLlxyySXYvHmzZ9/ly5dj7Nix6NatG+bMmYMJEyZgwYIFGDduHITWhyWEwLhx47BgwQJMmDABc+bMQbdu3TB27FgsX768yY+ZLbC0jxBCCCFkH0GIKFAzXy3LlD73tSumTEDFmstSPkv2SjlzpExhOILLQJXxHb6r+Qyd2/Zvls+WKZhChnCk95zxmDVrFjp06IC5c+eidevWAIATTzwRI0aMwKxZszBz5ky17/Tp01FUVISZM2ciErH9lPz8fIwfPx5vvfUWhgwZAgBYsmQJVqxYgXvuuQcjR44EAJSUlGD06NGYMWMG5s2b16THzBboSBFCCCGE7CvsXQpY3wPQy/q8s6OkRxBvdpTsAbJ7pCJKZMUMEztqPmqez0Xi8uGHH+LEE09UIgoA2rdvj+OOOw6LFy9GLBYDAJSVlWH16tUYNWqUEjwAMHz4cOTn52PRIjflcdGiRWjXrh2GDx+u1kUiEZxzzjlYtWoVysrKmuyY2QSFFCGEEELIPoKodv9VP97sKBl3DiWenNdOWp8q61P9Uu7rg1sdleZPlHkIRJx7kr6HSPAnezQaRW5ubmB9bm4uqqur8dVXXwEA1q1bBwDo08dbnhmJRFBYWKi2A8D69etRWFjoEUcAUFxc7DlWUxwzm6CQIoQQQgjZBxDWD8DeJWrZguU4UMnOjvLGnZsiAqEEVwQtrY44pPX/NcMnI4koLCzExx9/7OlHikajWLlyJQBg165dnuf8/PzAMfLz89V2uW+8/QCgvLy8yY6ZTVBIEUIIIYTsC1S/BMAu45Kzo6wkZkdZWjKfLbIiqkfK0l6bMFEZy77yq8bG7pGKpPkRv0fqkksuwYYNG3D77bdjx44d2L59O2655RZVKud3gAwjuf6uZPdrqmNmAxRShBBCCCFZjhACovpZteyfHWWp9cnMjoqoHil3ZpKBvcZubK58K/0fjiTkvPPOw8SJE/H888/jlFNOwZAhQ7B+/XpceeWVAIBDDjkEANChQwcA8LhEkvLycrVd7htvP8B1kZrimNkEhRQhhBBCSLYT/QiIrQfghkyY2uyoQI8UgMSzoww1O8p0Sv8AA+1zuzXHpyN1cM0112DFihV4+eWX8e9//xvPPvssKioqUFBQgK5duwJw+5j8vUiWZWH9+vWePqfCwkJs2LABluUdBLx27VoAQFFRUZMdM5ugkCKEEEIIyXK8bpQ3ZCKZ2VGekAmnR8rtnbJft7Dy0P2Agc3w6TILoYnOdD1sUZuY3NxcFBUVoVu3bti6dSsWLFiAiy++WG3v0qUL+vXrh5dfftkjZhYvXozy8nKMGDFCrRs5ciQqKiqwePFitc6yLMyfPx/9+vVD586dm+yY2QTnSBFCCCGEZDHCqgRqFqhly+mMEtDnRSWeHWV5ZkfZZX1Ci0EXMGBY7RC19qIlWjXTJyVhrFmzBosWLUK/fv2Qm5uLzz//HPfddx8GDBiAyy+/3LPvpEmTMGbMGEyaNAnnn38+tm/fjmnTpqGkpASlpaVqv9LSUpSUlODmm29GRUUFunbtimeffRZr1qzB3Llzm/yY2QKFFCGEEEJINlPzCiCqAUAlt+nzovQeqfrMjpIOlSzxq4x8i88rFuC4gy9K8wfMLEzYARDpPmc8WrZsieXLl+PRRx9FVVUVunfvjquuugpXXnklWrTw/qk/cOBAzJ49G7NmzcI111yDvLw8nH766Zg0aZInCMIwDNx7772YMWMGpk+fjsrKShQVFWH27NkYOHBgkx8zW6CQIoQQQgjJYkTVM+q1Bcs7iFcFTtRvdpSATIrzlvi1yemQ3g9H6qR37954/PHHk96/tLTU4xTFIy8vD5MnT8bkyZOb5ZjZAHukCCGEEEKyFBH9DIitsl87fVF6yIQZZ3aUtxcqAkuW8jmzo/QYdLuPKoKIyEWvvMHN9lkJyTToSBFCCCGEZCmi2nWjhCOaTKeUz9sX5Z0dJcWRgBs+4Z0d5e2jMhFBW6szcozc5viYGYUbCZ/ec5LMg44UIYQQQkgWIkQ1UP2yWpYhE/brMDFVn9lRwUj08sjXWFm+IORKCNk/oSNFCCGEEJKN1LwOiN0AoMr3ZOQ54A+ccGdHmZo48s6OcoMlZOkffOV+Bp0RWMKAJdJ7H9J9PpIcdKQIIYQQQrIQUaXPjrI8s6OsQF+UDJMA5MBdV1i5PVL+2VF6uZ8hclDIHilCFBRShBBCCCFZhohtAKLv269VyIRQIRNyEK8upmxXynBe+2dHGY7r5J0dpZf75VmH4YAWHZrvQxOSYbC0jxBCCCEkyxBVz7mvVciEpfqiTMB57Q2ZsLQSPl0oyehz+VqElPt9H9mCzyreQv8DhzXLZ84ULKefLN3nJJkHvxVCCCGEkCxCiFqg5gW17IadB4fv2mIqcciE2y+lvzY85X7SudoT25nmT0tI5kJHihBCCCEkm6hZCFg/AJAhEzLy3A2ZsIR87YZM6MESSiA5rpM7rFefHaWX+9nv65N3Yro/bcZhh02k2ZFi2ERGQkeKEEIIISSLEFVPqtfxQiYs+IbvQhNH0NZ7ZkfpvVNet8pEBPnmYeiQ26UZPjEhmQkdKUIIIYSQLEHE1gPR9+zXCUIm9NlRMmTCP2RX9UuFBEu4zlVEpfh9b3yNjZUfo0/+cc13AzIAd85Wes9JMg86UoQQQgghWYKoesp9rUr5kgyZ8LyOBIMlEHGO6J0dJcv9TABlezen9fMSkslQSBFCCCGEZAFCVAPV3pAJS1jua2e93helD98NDNzVQyZUsIS/3E8eIwLAQK+2/5fGT0xIZsPSPkIIIYSQbKD6VUDsBpBcyISpDdY1PQN33ZAJv+tk91G5JX6mJsLyra7o0vrwZvnomYQlIs0QNkHvIxPht0IIIYQQkgWI6sYLmRChrlPIHCnVIwWU4wdsr96U/g9OSIZCR4oQQgghJMMR0VVAdKX9uo6QCTk7Kl7IhOkLljChzY7yzJHyOlQxRLG+8hN0yytsrtuQETBsgkjoSBFCCCGEZDj+kAk7WCI8ZMJEEiET/n4pOUdKlfvpYsvdt+CA/VtEEaJDIUUIIYQQksEIazdQ87JatuzCPud1fUMm3EG8rusk3arwcj84fVVtxYHoecBR6fzohGQ0LO0jhBBCCMlkql8CRDUAGTKBFEMm4AuZcF2neHOk9ECKmAAqYjtxUItDmuU2ZAqWMJohbIKlfZkIHSlCCCGEkAxFCAFR7Zb1yZAJ4QuZMOENmVDBEp6QiRxNKLlzpOI5V1JsWcLuraowduPjXf9N/00gJEOhI0UIIYQQkqlEPwRiXwDQQyasQMiEFFMyZMKsZ8iE7lzJEr8wh+rgVl2a7VZkChYiMNPtSNH7yEj4rRBCCCGEZCii6nH1Woom0+mL8odMKAEVJ2QidChvaMiEoQVS2NstRNBatMWR7Y5J9y0gJGOhkCKEEEIIyUCE+Q1Q87paliETqqwPfjFlIBY3ZCIS2i/lDZkwnGO477OEO3OqtchDTMSa5V4QkolQSBFCCCGEZCLVTwOwhYsl7Dw+U0VMwJkjJV+7iX3BkAmjzpAJ6TqFhUzI7d/iGyz/fnHaPn6mIkVnuh8k86CQIoQQQgjJMISo9cyO0oMlwkMmZHCETOZzQyZiiCAYbe4VTXYqnBsy4Unu00oA2+bkpftWEJKxMGyCEEIIISTTqHkTsL4FAJXSp4dMmL6QCcvpkQor1fMHR+ghE1ackAk5O0oKLEsYaClyMSD/hOa6IxmD5fSPpfucJPOgI0UIIYQQkmGIqn+p1zLy3HJ6o+zXMqWv7pAJIQzE4oRMxOKETKgQCrglfh1wCFpGctN/MwjJUCikCCGEEEIyCBH9DIh+YL9WkecyZEIKKP21LYJUWIRfNHkcqPCQCSWa4DpQ7nYAMLBNbMd/v1/aHLcko5BOXbofJPOgkCKEEEIIySD0yHOhIs/tkAlv/LkMmXD7m3TRJAMnkgmZiLevW+InBZjZHLeEkIyEQooQQgghJEMQ1i6g+mW1LCPP7dfCcYdkPxQAyMhz+7UeMiFL+FINmbB8c6YiiOCY/OPTcRsIyQoYNkEIIYQQkilUzwNQAwBaMp/TGyVfBxL7vKJJ9jpZymEKhkzo8ejxQiaEr8TvUFGAdi3bN9ONyRwsRJx7ld5zksyD3wohhBBCSAYghOkp67NgOaJJ+OLPdQFlCx4BqAG8egmf3vdkIsfjMCUbMiEdqs1iGz7Y+UG6bwshGQsdKUIIIYSQTGDvUsDcCiA88jyY2GeLICWE9MAJvYQPkdCY8/CQiUggZEIKLAED39d+3zz3JoOwRPrjyC1R9z4k/WSUI7V8+XLceOONOO200/B///d/KC0txXXXXYcNGzYE9l26dCnOO+88DBgwAIMGDcKUKVNQWVkZ2K+yshJTpkzBoEGDMGDAAJx33nlYujQ8cSbZYxJCCCGENDai6jH12hVNbuS5HSxRd+S5FEgxzWGKIRg4ER4ygZCQCcAS9rpjOhyT9vtCSKaSUULqySefRFlZGa666ircf//9uOGGG7Bx40b89Kc/xdq1a9V+y5cvx9ixY9GtWzfMmTMHEyZMwIIFCzBu3DgI4Up2IQTGjRuHBQsWYMKECZgzZw66deuGsWPHYvny5Z5zJ3tMQgghhJDGRsTWA7XL7NdCwILliTy3AJ+YkiETbnCE/deKOydKqJAJ74Bebzx63SET0u0qQAE6terUDHeHkMwko0r7br31Vhx00EGedSeddBKGDh2KRx55BFOnTgUATJ8+HUVFRZg5cyYiEVsL5ufnY/z48XjrrbcwZMgQAMCSJUuwYsUK3HPPPRg5ciQAoKSkBKNHj8aMGTMwb948dZ5kj0kIIYQQ0tiIPY+6r1VKn8zrk4ET8rWb2OedDaUFTghvzLkMnNDj0fWQCSHih0zI92y1yrBm9xfo2+HIdN2WjMRCJO3hDwybyEwy6lvxiygA6NixIwoKCrBjxw4AQFlZGVavXo1Ro0YpwQMAw4cPR35+PhYtWqTWLVq0CO3atcPw4cPVukgkgnPOOQerVq1CWVlZvY9JCCGEENKYCGsnUP2iWrZDJiyV0mcGQiZkjLlddhdz3ChZlqfHnMt+KeVWaSETMbmvz83yh0yYjqgyIbBxz6Y03hlCMpuMElJh7NixA1u2bEFhYSEAYN26dQCAPn36ePaLRCIoLCxU2wFg/fr1KCws9IgjACguLvYcqz7HJIQQQghpVKqeQbzIcyA88twu9zM8TpMUUPqgXb1cz3WY3HI9tb2OkAlZCti3/f7tRgF20ISZ5ke6wy1IcmRUaZ8fy7Jw8803o2XLlrj88ssBALt27QJgl935yc/Px8aNG9Xyrl270LNnz9D9AKC8vLzexwxj/fr1sCzLs65Tp07o1Klx64hN0/Q8k6aD9zo98D6nD97r9MF7nT72iXstokDVvyD/TJaR5zJkQo88DwuZkP1O+hwo76Bd12GKiWCJXzIhE7LsrzMOQbdWBU1+v/1/UxGSqWS0kLrtttuwbNkyzJw5E4ceeqhnm2Ekp8yT3a++++r87ne/C6wbPXo0zjvvvJSOVxcrV65skuOSILzX6YH3OX3wXqcP3uv0kc33+sA276BnR7t9ISzy3IwTee46UK5bZGopfLJ3yu9WyX1jeiCFcLeHhUzIsr/vrd34z6dLcbBxYJPek02bWD5IsoOMFVJ33HEHnnjiCdx2220444wz1PoOHToAcF0knfLycrVd7htvP8B1oOpzzDCmTZuGXr16edY1lSO1cuVK9O/fHzk5OY16bOKF9zo98D6nD97r9MF7nT6y/l4LAeyaCsTsRQtWIPLcTeyzhZQUTnpZnin7mAKDdl0HyvQl8nncJk+JXzBkQjjHrEIt9hxSixEFRzfpbcnNzW3S4zcUC+kvtbPA0r5MJCOF1PTp0/Hwww/jj3/8Iy644ALPNtnHtG7dOgwePFittywL69evx6mnnqrWFRYWYuHChbAsy9MnJaPUi4qK6n3MMAoLC9G3b99UPmpK5OTkZOf/YWQhvNfpgfc5ffBepw/e6/SRrfda1H4IEbPdNCmg9MhzE8HIc11AxUTE7WfSEvssLXBCuVUix1PC599uQS/xC5b9yXP2zuvR5Pfa39tOSKaScb/Uv/3tb3jggQdwww03qL4onS5duqBfv354+eWXPTW0ixcvRnl5OUaMGKHWjRw5EhUVFVi8eLFaZ1kW5s+fj379+qFz5871PiYhhBBCSGMgqh5Rr92hu3bEhP06GHmul+3550SZIuhAmdJl0hwqM64DBcjgCde1ckVVR+Sjb/vidN6ijMTuGYuk9SEYNpGRZJQjNXfuXMyePRunnnoqfvSjH+Hjjz9W23Jzc3HUUUcBACZNmoQxY8Zg0qRJOP/887F9+3ZMmzYNJSUlKC0tVe8pLS1FSUkJbr75ZlRUVKBr16549tlnsWbNGsydO9dz7mSPSQghhBDSUIS5Dah5Qy1b2tQoXUAlE3ke80We2wIqR/U7xTzlfvEdKn2OlD6HSoopgQgqYpXo2KJDmu4SIZlNRgmpJUuWAAAWLlyIhQsXerYVFBQoZ2ngwIGYPXs2Zs2ahWuuuQZ5eXk4/fTTMWnSJE9ghGEYuPfeezFjxgxMnz4dlZWVKCoqwuzZszFw4EDP8ZM9JiGEEEJIQxF7/gU5VtcS9rPpF1OpRp5rgRP+fidT224KPYQC0GdGWVrIhHStvsUuLPl2BUZ3Py2t9yrTMJUgTe85SeaRUULqscceS3rf0tLSpJyivLw8TJ48GZMnT260YxJCCCGEpIqw9gDVz6hlb8x5uICKH3nult5J5yiGsMAJN6VPD6RIJmRCF1WdWx2U7ttFSMaScT1ShBBCCCH7NNXPA2I3gMSR51JMhUWe6yV8ZhwHSghXdOkCy+9QJRMyYcFAW7TBjw4c0Cy3jJBMJKMcKUIIIYSQfRkhYhBVD6llPfI8ODPK60a5JWWOGxUSeR5DSKS5JrC8Q3ftvic1R0q4rpW+Tji9VvloDwscliv7ydJ9TpJ50JEihBBCCEkXe98EzK0A9MjzxAN4Y7qD5Is8dx2mYKR5LERgSbHkfT88x5fvET4httn8Fq+XvZP+e0ZIhkJHihBCCCEkDQghIPY8oJalGyVT+rwzo2TkedCBkv1Oql9KRZ5H1L7eeHR/5Hmw98kbMmF41sl0QMBA2xZt0nnLMhIZSZ7uc5LMg98KIYQQQkg6iL4PROMN4A2KKbuvyVDzo2whFRyq603kk/vmBGdKCb0fKgIplvQQCj14QuhCy4qgFVripIOOTu89IySDoZAihBBCCEkDYo87wzJsAG/MKe8DbDfK1OdH+UIk/JHnultlu1n+yHNf7xO0Ej/hulZCW6eX9QkA3SKd0DqSm85bRkhGQyFFCCGEENLEiNh6YK89D9N2o8IH8MrIcxVdDsdB8kSe6/1OYel7buS5G0jhOlSJIs8965SosretNbfjzW/eTet9y0RkmmE6H6LOqyLNAYUUIYQQQkgTI/a4SX1CCai6B/Ca8Eae2+ETEdXLJAVPvMhz6TJ5ep8SRp5rc6Tke4UrqmqtaBrvGiGZDcMmCCGEEEKaEGF+C1S/qJYtWLCElfQAXv21FDl6Ip/rQCWOPNf7oaSTZanIc3jWCd86AQM5iGDwQf+X3puXgZhCunTpPSfJPOhIEUIIIYQ0IaLqXwBsJ0eKppgTMpHMAF7/UF1LluuFBE7ERE4dkeeyxA++OVSOWPO5Um7wBFAYORQdcts1wx0kJDOhI0UIIYQQ0kQIqwqoekIt226ULaHqM4DXlKIGbuS50NwmS9tuNiDy3OtGeUXVqujXWP79agw+ZECa72JmYQ8xTq8XIeh9ZCT8VgghhBBCmorq5wBRDsCZI9WAAbwxXwmfP33PW+6XZOR5SMiEFFq6GyX3/7r6+/TeP0IyGAopQgghhJAmQIgYRNXDalkO4LWcuVFhA3iVsJGOEdxIc+8AXj1wwhuP7kaeG457kiDy3BFaAAKR55bwiioDwMCDjkrfDSQkw2FpHyGEEEJIU1DzGmBuBaAP4LWcAbzSgfIO4LXL8YIDeGNaP5TeL6XcJhEWee4KsLojz10BJdf5RVVxzqE4tM3Bab6JmYedYpje8Id0n48kBx0pQgghhJBGRggBsec+tSzdKDkzyvs6/gBeN/7cHcCru0369rDIc92hSibyXAon2T8FuMl9a6PfYHX5l2m9j4RkMhRShBBCCCGNzd4lQGwtAN2NEo4bpYspKAfKng/lHcDrCY/QBvC6bpThE1iOAJMOlTCCJX5a5Hl4yIQuqtxQiigsrCzfnP57mWFYSP9AXqvOqyLNAYUUIYQQQkgjI/bMUa/dfihLCaiYGsvrzo8SunjRXtc1gFcXWKanBBDQ3SgpAPQeKTlsN17kud4jBRg45sDe6bqFhGQ8FFKEEEIIIY2IqH0PiH5ovxbCjjz3uVGAV0DFHOGjAiVk4ITsh0owgNcVYHAEmBMa4emRAuoTea5EmOUe4/Ccg1Hcrlt6byYhGQzDJgghhBBCGhFRGe5GuQ6UUIN5pZhS86N8859M5Gj9UGEDeF2BpQ/g1SPP/c5TMpHn0vmS4g4w8L1Zha1V36NHu0PSdzMzEHk/031OknnQkSKEEEIIaSRE9HOgdqn92uNG2aIqroDSHCj12jOAN8SNkr1PvgG8KvHPX+IXiDw36ow818XVTqsab3+7Ou33lJBMhY4UIYQQQkgjoSf1yS4oUxNTMZ+YkgJIulF2WR/gD4+QYimmCaOYVroXnsQnS/zCIs/d97qiKhh5LudOCWdbYbuu6bmRGYwlIrBEer2IdJ+PJAe/FUIIIYSQRkDEvrRnRzmYsGAKK+BGychzC0DM50YBcsCudKVyPP1Q7vZ4A3jduVBSiMkUQK/QgnY8zY3yz5HS9u+S0x4/OrAwnbeUkIyGQooQQgghpBEQe+ZCjta1hP2su1Gm0y/lDuP1pvOZQsafa/1OmliKIccjjMIH8PpCKITWdyWdKuF3r/yR5+4cKT25r4Void2xmvTeVEIyGAopQgghhJAGIsxvgOp5atmCpTlQfgHlDOCV/Uye9D235C4WIpaskMCJugbweiPPvWIqmchz4WzbFN2J+ds+SOdtzUjkbK50P0jmQSFFCCGEENJAxJ6HAEQBQOuBkqHndm+U342K6W6T0xtl76vPgvIP4IVHYMUfwOt1nuR+QokqQ3OjvHOk9Mhzf6lfl9b56b2xhGQwDJsghBBCCGkAwvoBqH5SLUs3yoIIKedz3SjpCLmx5K6AMp2gCE/vE4w4Asu7zk32Q7DsT4tB98+KckUVAk6VJQx0iLTBkEOOSuu9zUTc+5Pec5LMg44UIYQQQkgDEHseBkSV/VrYHpTuRsXvjYIbbw6vGxUTEeUCxeD2PkmB5XGjpOjypPMhsM4t2XOPF1bOJwWXcP56l4l9XXLapeeGEpIlUEgRQgghhKSIsMqBqsfUshniRplwk/rs14ZyoFwxpCXtxUnnU4EUfgfLWecRXaEDeCMBURUWeS63SQElRdXq2m/w/Nb303l7MxI5wyutjzQ7YCQ5KKQIIYQQQlKl6lFA7AGQ2I2SYsoWO343yvAIo5jI0USQN/5cD6TQE/1ckZRoAG945LkUYUpUiWCpnzxm65yW6bqzhGQ8FFKEEEIIISkgrEqIPY+oZROWU8InVFKf340yNTfJ7zbFHDcqnliKaal7MVXOl6NEjyr7E8EUPysgqrRUPp+oCk3uA3CA0RIjOvdN3w0mJMOhkCKEEEIISYWqxwBRAcB1o2KOG+X2Q2liClIsabOg4O198osl1RslDAgnhEL2WLlBEv4BvEZSA3i9yX1uGZ8eeS6fAQNFLTuhTU5uuu9yxiGSLcdrxIeoI/78s88+w9ixYzF48GAcc8wx+MlPfoKHH34YsVjMs9/SpUtx3nnnYcCAARg0aBCmTJmCysrKwPEqKysxZcoUDBo0CAMGDMB5552HpUuXhp67KY6ZLVBIEUIIIYTUE2HtcSLPbcLdKOGU8wXdqMAwXjUrShdLWhKfJrDMkHWWz43ShVZdA3i9cejByHPpqL1fvR2vfv1p+m4ySYqvvvoKl156KXbs2IGbb74Z9957L0455RTccccdmDp1qtpv+fLlGDt2LLp164Y5c+ZgwoQJWLBgAcaNGwchaz9hi/9x48ZhwYIFmDBhAubMmYNu3bph7NixWL58uefcTXHMbILx54QQQggh9aX6KUDsAlC3G+Up54M2C0rGnzvldTGPWNLEluqRirglgI7okm6FPoDXDBFHiQbwWn7hpEWeu8l99j6VsZo03uTMpDkG5CY636JFi1BZWYlZs2ahe/fuAICBAwdi8+bNeOmll3DLLbcAAKZPn46ioiLMnDkTkYjtpeTn52P8+PF46623MGTIEADAkiVLsGLFCtxzzz0YOXIkAKCkpASjR4/GjBkzMG+eO3i6KY6ZTdCRIoQQQgipB0LUQOyZq5aTcaPsfihoDpTW+4QcbVaUK5Zkn1JMiyt3HSQ3gU+lAEpRpSX7BR2tZAbwupHneq9UC0Qwsgt7pDKNaNQeBJ2Xl+dZ3759e7Ru3RoAUFZWhtWrV2PUqFFK8ADA8OHDkZ+fj0WLFql1ixYtQrt27TB8+HC1LhKJ4JxzzsGqVatQVlbWZMfMNiikCCGEEELqQ9UzgPUdgGBSny2Wgm5UDK4DpbtRsvxOF0ths6Jc0RVcp7tRXqGlx6C7ARKJBvAK4XezXDfq6NZdcFAr7x/rpPk5++yzkZ+fj9tvvx3btm1DZWUl3njjDbz22msYM2YMAGDdunUAgD59+njeG4lEUFhYqLYDwPr161FYWOgRRwBQXFzsOVZTHDPbYGkfIYQQQkiSCLEXYs/9atly3CjpQAkhEIvjRrmzouIJI710T8ajy/jziBYukeMJPPCU+HlmRflEFcJT+fQBvHqpn9rHssvKVuwpw7Jv1qO0a3E6bnXGkmmlfV26dMGTTz6J3/zmNxg2bBgAW8xcf/31uOqqqwAAu3btAmCX3fnJz8/Hxo0b1fKuXbvQs2fP0P0AoLy8vMmOmW1QSBFCCCGEJEvVM4C1AwBUOZ/rRgmPG2WFuFGmijd3k/hcsaQNz4W3dM8dthvuRqmyQd3d0gMkAHiEExIM4NUcK3854MbK71CK/VtIZRpbt27Fr371K3Tu3Bm//e1v0a5dO7z77ru45557EI1GMX78eLWvYSQnAJPdr6mOmS1QSBFCCCGEJIHdGzVHLcdzo2RWWQxBN0qW9cVzo0zkKNfILfeLqHX+kj3LSdXz9035h/KGCaewAbzq2dlHxaFbERgAhnQuauK7nPm4pZDpPWc87rrrLlRXV+P+++9XPVElJSUwDAP/+Mc/cO6556JDhw4AXBdJp7y8XG0HgA4dOsTdD3BdpKY4ZrbBHilCCCGEkGSoehqwvgEQ340CUKcbJZP4BCJumZ4WGuGKKq/oCkSiIwfePih3fpQVKqq8c6RC+6e0kAl7jpQrro5t3QWHte2YxhtOkuHzzz9HYWGhElGSfv36wbIsbNiwQfUx+XuRLMvC+vXrPX1OhYWF2LBhAyzL8uy7du1aAEBRkS2mm+KY2QaFFCGEEEJIHQhRnZQbZQnbkUrGjZLznvT0PilapBtlaq6VDKEwE7hRptMHBV1U6W5UIOo84gzdDQ7g9e/7SfW3WL1re1ruN0meQw45BOvWrUN1dbVn/UcffQTA7qHq0qUL+vXrh5dfftkjZhYvXozy8nKMGDFCrRs5ciQqKiqwePFitc6yLMyfPx/9+vVD586d1XEb+5jZBkv7CCGEEELqouoJT1Jf3W4UENP7oZSL5M6F0kv3VJBEIjfKiTUPc6P0cwjdjQoM13UG9mrhEjbBAbyWT1zVCgsrvtuMAQd1S9NNz0wyLWzisssuw69//WtcffXVuOyyy9CuXTusWLECc+fOxaBBg5TbM2nSJIwZMwaTJk3C+eefj+3bt2PatGkoKSlBaWmpOl5paSlKSkpw8803o6KiAl27dsWzzz6LNWvWYO7cuZ5zN8UxswkKKUIIIYSQBAirypPUZyblRmnlfI4bJd0jvUwvzI1SzlOIGxVesucKLX8MephwkoLJ7ZGKP4BX9Ug5648/qEfT3mxSb0aMGIGHHnoI9913H6ZMmYKqqioUFBTgV7/6Fa688kq138CBAzF79mzMmjUL11xzDfLy8nD66adj0qRJniAIwzBw7733YsaMGZg+fToqKytRVFSE2bNnY+DAgZ5zN8UxswkKKUIIIYSQRFQ9Dlg/AAibG1UfNypShxuliS4R7kZ5XCspgLTyQCtMVGlulHdGVNCx8qb76aV+wBGtOqL/gYem665nLALpd6REHeEWJ510Ek466aQ6j1NaWupxiuKRl5eHyZMnY/Lkyc1yzGyBPVKEEEIIIXEQVmVSbpTZIDcqooRN2DBevxtll/05x3OEUyyuG2X43CjA2yPldaM8c6Q8bpSBnbFabK+qSMNdJyQ7oJAihBBCCIlH1b8AsQtAfDfK7plKxo1CHDcqEnCjvM6TFn+uBU4E3SijbjfK8vdPwRVMgP3sDOD1l/p9Hd2DV7auTsttz2Rkj1S6HyTzoJAihBBCCAnBdqMeVMtxe6OAJN2onIAbZYa6UYaW6Ke7Ua4g01P5XOFkeNwoT4keXKEnt0kBpTtWflcKzrXLfY/IP6RpbzohWQSFFCGEEEJIGFWPBNyoWEI3yqiXGyVFl6WLLgEIRFQJoF6y55b4aULLca+8cea6cPK7UUFx5e+NAgxYjnOlz5Hq3jIPgw/pld7vgJAMhmEThBBCCCE+hLUTYo8byyzdKMsRUeFuFOrlRkmx5OmDCpkvZfrcKH3wbkx3o4QRIpy8bpSMVBd+wQTvAN4wNyrPyMWeWC3yW+zffz5mWvw5aT7oSBFCCCGE+BB7HgBEpf064EbZCX12mZ/uRkUawY3Sk/qCkeiJ3Cgz4EYh4DT5gyfC9rGsiPO54RFZn9X8gKc3fZymb4CQzGf//icFQgghhBAfwtwB7HlMLYe5UbJPCtDdKNTpRvmH7AbdKEO5UUIJH72XCkqkxbQYcynC1Gwpyz2+TOXzp/HZgskIuFFyWS/5g7PcpU27Jr33WYGQAR3pPSfJPOhIEUIIIYRoiD2zAdQAgDNkt/HcKDPEjTJ9bpTtHOW4IsmT1OfdXwoz6UZZSlS5TpM7eNdXxgdNMPli0V1h5Sb3HZTTGqcWFKfpWyAk86GQIoQQQghxELEtQNUzatmCBatJ3ShDiSXpRqkBvY5wioW4UbJvSjpNuhslY9D9bpTHsVKx6PDOkdIjz5194Tz3apWPiMFeHUIkFFKEEEIIIQ6i8h7Y8giwhOWbGwWYSkwBZp1ulJGEG5WTkhslS/88okoTTnHdqIBgCnejZAmgcKwtIYAVu7/B0xs/SefXkZHI7y/dD5J5UEgRQgghhAAQ0XVAzUtqWXejdAEl3ShZ2hd0o7zOUypulBXiRvlT/NwY8zCnye9GhYRKIIEb5Uvukz1SLQz+6UiIhGEThBBCCCEAROXfIOWG7UZB9UbZZXV1u1HCeY7vRkW0pL74bpTrQmglfkm6Ue7AXt2Nch0mK0QwCZ8bpeK2tV6ptpEWOLP7EWn6NjIXxp8TCf9ZgRBCCCH7PSL6KbD3TbVswtJK+7xulB0/Hu5G+Z2n4NwoOWsqmNQXdKNy6nSjrDrcKJHAjdLj1IPiSnOlnOdjDzgEeS1bNd2XQEiWQSFFCCGEkP0esfv/qdeWsGWG7kbF/GIKBqKaGyVFVJgbZYW6UfGT+urjRoWV8fnnRun9Trpg8seh+3uldDcKAnir/Gu8uuXzNHwbhGQHLO0jhBBCyH6N2PsOUPuO/VrY4RKmU9on3Sg39tyOovCU9mmzn6TzZPncqGiSbpQ7Nyo5NypQxucIInfwbkiYBJxlK3k3Si5/X1PdxN9GNiDFZ3rPSTIPOlKEEEII2W8RwoLYPV0tuxHnluNHuW6UFFNKLMHWGdKNshxBYyHiuEvytQGBiDeQImFSny/+vIFulL98TyC+GyU0N8q+P+5MqVwjgjMP4xwpQiR0pAghhBCy/1KzAIh9BgCOWHIFlAXhpPa5bpQdNCFDJeK5UfCtc0VSMm5Usr1RfjdKuk/JuFEijhslEN+VOqldFxzcum1TfyMZD8MmiISOFCGEEEL2S4SohaicqZalGxVL4EbZQseI60YJRBALuFGGz41y9ovjRqXSG+U6Ta4LFc+Ncp8d9wlA0I2yxRbgCqslu7bjv2VfNul3Qkg2QSFFCCGEkP2TqicBcysAvxtlJ/VZQsaea24UXDfK0two6SRJ0SMdp/huFBrVjXIH/Gppfgl6o3SRpR7wu1F6PLq9/bOd36Thi8ls3Jlb6Xw096cmYVBIEUIIIWS/Q1i7ISr/oZYtWMqNkn+zxgKlfbobZXjcKOkYuW6U1uskRZVyo7SQCo8bJR2qpnejpBCziedGGRBy+rCw/2gcXtC76b4UQrIMCilCCCGE7HeIPQ8AYpf9WghYsAMmpBtl1ulGuTHlrhtlBNwoyxEuYW5UIFTC2dZ4bpRbvqfcKPkeK1IvNwowMLB9ZxzevmPTfjGEZBEMmyCEEELIfoUwdwB7HlLLpuNGmRDOwF1vaV8MuhtlBNwo05knFVVzo9x1VgI3KiaCZX+N60YZWvCEK5oCbpTz7EadB90oAPh09w9Yu+tbHHVQlyb8djIfKWzTfU6SedCRIoQQQsh+hai8B0ANADhiSajhu5YUU7BdKFvMADHlRsmIc92N0iPOI2qddKNijgCSTpYZUsZnNqcb5Uvuk4N41bIjsnabMby1bVPTfjmEZBF0pAghhBCy3yBiG4Dq59SyBcvpgxJaap8/aMIXce5zo4Bwd8mNOI8oERPTHKrmcKOUMIL9nNiNcudJyecTOndvmi8mixCa0EznOUnmQUeKEEIIIfsN9vBde6yuJWRPlHSjpHASiCG+G+UJkPBEnPvdKCmYvH1VrnDybrOFW6a5UVAia0Dbjjj64K5N+O0Qkl1QSBFCCCFkv0DsXQ7sXWy/FraAkm6U6bhRsk8KgCOmfBHnIuLOkNJ6nZRwgr+ML+IZ1msLH104OU6TcpGa1o1yjQ3NjZLLUnxp5Xy6K1UZi+Hb6j1N8t0Qko1QSBFCCCFkn0cIE2L3X91lRx3ElB/lDt81AZiaMFJulCNu9JI9f6iEp4xPG8zrd6OsgBtlhLpRKh2wkdwoNRNKd6MEwp/tG6WeN1ZX4PkNq5vsO8oWZH9cuh8k86CQIoQQQsi+T/WLQOxzAK4bZQrLCXdw50XZqX3wlPaZATfKQBQ5vjS+iOs4QRNAcd0ow+dGIa4bJZMAG8ONUpHmsJ/hPCd2o9zn4gM7NenXREg2wbAJQgghhOzTCKsKovL/qWV9+K50o0zNjbJL+wxEoYdKBGdImYnK+DQ3ylTPem8UvG6UiHjf57hRQhM9zelGAcDhrfNQemjPxv+CsgwhpPBM7zlJ5kFHihBCCCH7NGLPA4D1jf1a74mCOy/KDZqw3SgZIOFJ2VPukteN0tdZjniRg3ldNwrwp/e5SX1OJLpzzkRulBRXzeFGdWzRGnvNWBN/W4RkDxRShBBCCNlnEWYZsOcBtewO37WU4RILiCk5fNedA+WfIeVN4zM8ZXyewbxSkMn9lYsUUX1PMeVGRQKCKdyNQtrdKCGA98u/w7/WfNrI3xAh2QtL+wghhBCyzyIq/4Z4w3dNCJieeVH2s7eMzzdDCjnOumAanyucIip8IqZKAYOOli6GpOCS4ieeGwXPPulzo+TzIQe0bcJvKztwv6f0npNkHnSkCCGEELJPIqKrgeoX1LIFM+Hw3RhcN0qKKc/wXeUuBfuf3Hj0HFccSUEmXOHkhlAArgvl9k0JJO9GiTS6URBAp9zWOKNnn6b5sgjJQuhIEUIIIWSfQwjhxJ3basByEvrChu+aCB++qyfv2QIrJ6T/SRdOsgRQd6O8ZX8mvGES6vgep0nvt8ocN6pv2w7IMfhv8EIXnWk8J8k8+L8GQgghhOx77F0I1K4A4MadW8IKHb4r3Sh/GV9U5GipfbZICut/8sSjyxQ+WcbnmQlleNwoWb4X87hRMnDC2cfylvgF3ShXXDWlGwUBLPm+DM9+sarJvjJCsg06UoQQQgjZpxCiBmL3nWrZLeNz487rHL6rRJLrLsn+J7kuipzgTCiZ6Kftryf1hblRXmcpohwuSygNA92FklHpcqaUlQY3CsJ+t6A1okRpus9JMg86UoQQQgjZt9jzEGBuBSDjzl0BpQ/f1ePO/cN3o2r4LrQI9BzlBrnCyXWVpJvkDt/Vyv78okq4EerKjZIlfrIM0AoGTvgFUsCNslw3SjpQjeFGAUBeTgv8pNcRTfOdEZKFUEgRQgghZJ9BmGUQe/6plmXceZgbJcVU2PBdd9Cu/Rze/wRvtLkazOuKIlM6TaqXCp6SQFsM+QMnkNCNkq5WqBul3qu5T86zWk7RjRp0YGfk5eY2/pdGSJZCIUUIIYSQfQaxewYgqu3XQg7drSPu3OckxYQebe4dvisQcR0nJZyCUei6exUQVY5g8ggnKaoSuFF+cZXYjdLL/BruRkEAb+zYhjc2rWuaLy6LEM73mO4HyTwopAghhBCyTyBqPwRq5qtl04k7t0MlvHHndkIfnF6n8OG7Mhwi6DhpwkmLQpd9T7LsT4VJQIoqKFElh/Cacd2okPhzEdH+sI7vRnncJ/iWU3Sj5HLZnsrG/dIIyWIYNkEIIYSQrEcIC6LidrUs487l8N264869w3fdMAk3sU93qCxNaMko9KgatBtR7pErqrzJfjJMwhMe4ZzXtLyBE64bFUzmS5cbBQHkGhGc2auocb+4LESWVqb7nCTzoCNFCCGEkOyn+nkgZkdzy7hzU7glfWFx50IXTtJ5gjv3SR++a6rhu3o8ekgUukrU87pYtqjy9zm5gsl1lIKBE+57XGEU6kY5YspGc6McwdZQN2pYp67odEDbJvsKCck2KKQIIYQQkt1YlRCVd7mLCQIm9LjzqHKjDEckRdzUPt/wXbdETxdObhR6TC/703qu/NHm0o0SmqgC3FlU+mBdSxNXoXOinGXTMjwBFAE3Cg13owBgUdnXeH/7tsb85gjJaiikCCGEEJLdVN0LWN8DsN0oNXTXeY4fd+4bpgt4QieiQivj00SXuw3uOsi4c0eUCai+Kd2pUj1V0lnSSvxcgeQNnFDiyudGAW5PlHSbbBrfjYJlC9CPd5Q1zXeYRSgXMJ2P5v7QJBQKKUIIIYRkLa1bfAVUP6qWZdy5CctxbIJx57bj5I07F5rzZEpnCm7cedjwXa8b5S37M+GKKjsd0C3/U/1WwpvMF3Sj3ONJIWRqbpQQdm+Up9SvKdwo5xEBMLTH4Y36/RGSzTBsghBCCCHZiRDo1uFhGM40KMvpiXIDJty4c7snyok7h1t6p8edCy0wIiZc4aRK9ALDdyNqW0yb7+SKKni2yT6nsPAIM9SNCumFEq6r5D4bocuN5kYJwICBwQd3Ru8DOzbVt5k1CGEADJsgoCNFCCGEkGxl7wK0a/UZADdgwtJL+7S4c1dMGYhJRwd1xZ27QROy7ykq9HW60xRRpXlSoEnBpJcGyn1MrWzQL5hMyxs4odwoSwo6eN6jl/rpoksuN4YbBQCfle/E5l07G/UrJCSboZAihBBCSNYhrEpgz51q2Z0TJbujoImpYNy5LMfzx51LkSTL79x1RqB8zz981woTVcId6Ct7qqQo87tR/sAJXVxZ2nO93CgnyU9YQEPcKAjgu9pavLFxfdN8oYRkISztI4QQQkjWISrvgWF9a78WApaW0CcDJkxAuTe2mHKFE6S75OuT8jpO8eLO3Sj0eMN3LSBuJHpMhM+J8gRO+J+d4AnpNiXtRoU8p+JGSY7vWtCAb23fgKV9REJHihBCCCFZhYiuA6oeUcsyYCJe3LknjQ+GJ/jBDYzI0eZEuQET/rhzSwuY8A7f1UWV4RFMJjRnCeHJfP7ACbkM2POhwobxqlh0nwhrCjcKAI7u0BHHdj20Cb5RQrITCilCCCGEZA1CCIjdfwbiBEyYIQETMdjR3XrceVTFncNXoud1qNx4dK18T5YHqrhzKZiCw3e9QRPaYF24yXyBEj9NKPlnSyVM5ov77O5XbzdKc6SiMQu7amoa9P3tK4TdpqZ8kMyEQooQQggh2UPNq0DtCgDBgAm3TyosYMIWTipMAm75nikH6/ocKt1p8m4Llv3FPHHniYfvyp4slcynBU4oceVzmRrmRiG+C6W5VaFuFNz3f1ZejmdWr2r875SQLIU9UoQQQgjJCoRVCbH7r2rZHzBhz4kKBkzIvijTFzChCyA30lwr26tn3LnrLCUevmu7Ud5kPteFinjfk7QbFRaL7n32i6hQlyqeFeIIsj4dD2rw95jt2C5emnuWpNAlGQUdKUIIIYRkBaLy/wHWN/ZrJ2DCTCJgIiZdIM2Nkn1PFiJuiZ7mUHkCKZKMOzcdR0nvwXLjzvVeKG+5nh44EdYLFXCjnBh03Y3S482FM6RXWL7SPb9wStaNsuzl3nntMKRnz0b+VgnJXiikCCGEEJLxiOhKoOpfalkGTJj1DJiwEHH7npzyvTCHykKqcefeZD4Bd5aUvxfKHzih+qcSuFHB0j/7Gc6xA6V7CN/udamQ2I1yXndt3RpR02zYF0nIPgSFFCGEEEIyGiFMiPJbIP+iTxQwYSKZgAm37ymqAiYiSmipuVKBbW7ZXvy484gKmkhm+K4URoHhu0pswV629Bj0kBI/37J8xBVIWu+UoZbju1EQwLJvvsWTK1c2/hecbSRKhWjKB8k4KKQIIYQQktlUPQ7EVgPQAyZsJ8rtiRJO6IQbMBFNEDARc8SRkEETHlEUHjChl/iFxZ2r9D7NjZIlfn43SrliMnAi4D5p8efSfXLEFJxjyWPK5TrdKPm+0JI+hJcAaq8NAB3btEn1WyRkn4NhE4QQQgjJWIRZZvdGOVghM6PCAiZiMOBN5guW70WFm8wX8wsnz3yo5OLOZQ+W7liZ9Ri+q9woS6b72YLKsnxOVgPcKOEXSvZNRbykPkMTXJ1bt8Zpffo0/pecZQikfyCv75shGQIdKUIIIYRkLKJiKiD22K+dmHMT2rPTE6WX9OnCKX7AhHSYctyACaHt72xLJe5cD5ioz/BdT6mfr5dJKLHlXUac7XHdKW19fdwoADim40FoGeGfjoRI+L8GQgghhGQkYu8SYO/ratmE6Qgny3FrhAqYsEv4/O5SooCJSFAUwQhsi/qH9Wpx57J/KZYg7lwKJNOKU+InxZYUZLIHCvC4UCqZz1kvt8tl3W2KF2semuAn3SgrsRsFAbz+1Ta88NnnDf1aCdlnYGkfIYQQQjIOIaohKv6slu2ACaiACXsAL5x1rhulQiFUYEQkILDiBUzAt006TKpfSrhizBVVenqf60a5rpY37txf4iddqIRx5w11o0LcKX/cOdQyQt0oW2iBqX3wlUem7aRpPh9JCjpShBBCCMk4ROU9gLnVfu24UKZwU/qkGyXT+tyACSNuwISJCGIJAib826Jwe6L0gAkpqnQXyh93HuY+hcedG6q3Kdnhu/V2o7T9PA+Z3JeEGwUA7Vq2xI+Lixrj6yVkn4COFCGEEEIyChH9DNjzoFqWM6P0gAl9ZpQdc+4GTMQSBEzERI7qhQok82nhE+EBE3pqH5QL5ZbnSYFUj7hzAPUdvutxo5wkPz2Jr75ulAGfKxVS/mcAGNa1C/JatWqU7zibEaIZwibSHm5BkoGOFCGEEEIyBiFiEOU3wY6PCJ8ZJWPO4wZMyBlQ8AdM5GhiyBVcesCElUTAhOxnksl+srfKNnkMj1CKOUIobtx5AjfKP3w32eek3ChPj5T+BbjulKGW7cdLm77Ckg2bGuFbJmTfgI4UIYQQQjKHqkdCZkbJnijbhYoGxJQTCuEIFukm6SV68Rwq03F5ZMCE6qWKEzBha5DgYF7LF3fuuk/BHqhEcef2sr3NP3xXBU7IdU4surB8fTsJXCmPG2Ul50YB9jkMGNiyq7xRv+6shI4UccgoIVVWVoYHHngAn332GT7//HNUVVXh0UcfRUlJiWe/Sy+9FO+++27g/RdffDFuueUWz7rKykrcddddWLhwIXbv3o2ioiJce+21OOWUUwLvX7p0KWbNmoUvvvgC7dq1w6mnnoqJEyciLy+vcT8oIYQQQgKI2BaI3X9Xy+7MKFOFS8j+KBNw3CBthpM+AwrwrIsXMCEDKfSACdPXC+UZyCsMdx8lroJx524CXzJx5/rQXftZBUrAWXae5XJYyV5wvc+NAsLFEtxtYW6U3Dc3EsFpxYUpfLOE7JtklJD68ssv8eqrr+Koo47CSSedhEWLFsXdt2fPnrjzzjs96w4++GDPshAC48aNw9q1a3HDDTegoKAATz/9NMaOHYu5c+di4MCBat/ly5dj7NixSjxt27YN06dPx4YNG/DII4/AMPgvAYQQQkhTIYSAqPgTgBoAUC6U6fRF6TOj9LQ+u0TPdaGiIQET/l6oqBYwYW9LHDBhOfv4E/lUOaCW3ufOgfKW+CWOOzdUSZ8/7jyl4buAK6g8w3bhEVrJulHSuTqt+6HozH9cJkSRUULq+OOPx/LlywEA//nPfxIKqdatW+Poo49OeLwlS5ZgxYoVuOeeezBy5EgAQElJCUaPHo0ZM2Zg3rx5at/p06ejqKgIM2fORMQZNpefn4/x48fjrbfewpAhQxr24QghhBASn+oXgFr7bwDbcZIzo0RgZpQMmAifGaX1ScFN1LOcRD7lLOnulUgcMGG/x9YVpkr285bp+QMmpPsk3SY37twIlPpJtynluHP4nhP1TMVzo7QyP0Mtu/sZAF7/chsu274DR3c7tEFfdbbD+HMiyaiwiUgjT8tetGgR2rVrh+HDh3vOcc4552DVqlUoKysDYJcUrl69GqNGjfJcw/Dhw5Gfn59Q0BFCCCGkYQjzO4jdd6hl2QsVswv5HEfKdaHizYyScef67KeoL2DC0wultiUOmJCOkh5zbmpx5zK9TwoiPe5cd53Uexo77jzkIQRcRykZN8r/rLlSBuxUwJgl8P5X2xr8fROyr5BRQqo+bNq0CccffzyOOuoonHbaaXjggQdg+obErV+/HoWFhQGBVlxcDABYt26d57lPnz6e/SKRCAoLC9V2QgghhDQ+YvftgLBDDIQQsGA5Ekrviap7ZpQSUI4ocnuhZNme27PkD5+IKocqGDChHCunLC/mCZZwBVJw2G6wxE+6SQE3ymqkuHME1+t9TqGOlN+N8jy7gssAMPjwHo3zpWc7cQRskz1IRpJRpX3Jctxxx+HHP/4xDj/8cFRVVWHhwoWYMWMGNmzYgDvucP9Fa9euXejZs2fg/fn5+QCA8vJytZ++3r/vxo0bE17P+vXrYVne/NBOnTqhU6dO9flYdSKFol8wksaH9zo98D6nD97r9MF7XU/2vgmjZoFalDOjTE1MBWdGGXFnRgm4JX1RZy6UtxdKi0f3hE+EBUzIEj9/wIS9nz9gQibwhQZM+Nyn4Gwpf9w5lJjyPiPw7HeSQnumPL1SWt93vOQ+9R4oYTWkoAt6dezQ5L9t/99UhGQqWSmkrr/+es/y0KFD0bZtWzz22GO44oorlOMEoF4hEakGSvzud78LrBs9ejTOO++8lI5XFytXrmyS45IgvNfpgfc5ffBepw/e67rJiezGkYf8CS1z7GU5MyrqzIyyHBfKHrarl/TJZL1EM6NkL5Retuf0VQlv+ISbyKf3S7miKhgw4bpOrmMFO7Y8pMTP9LlQrtiynTUrNO7cQFjceUI3KsSd8pf0eYSS77XHjbKCbtT6Hyqw+N330KlN60b7DYSxadOmJj0+IY1FVgqpMM4++2w89thj+OSTT5SQ6tChg3KbdKQTJR2oDh06AEDcfeX2eEybNg29evXyrGsqR2rlypXo378/cnJyGvXYxAvvdXrgfU4fvNfpg/e6HlT8DsZet6TPhAVTuIN3ZcCE5Svpizl9Tqo/ShNF7syoiFa2F9InpYdP6L1UIqLt6wgkGTChJe4FAyYM6PHm/hI/6R5ZPrEVCJSQ651nueyW9nnf5+9vCu2Z8qXvKbTSvdDyP7UPICxga2UVNloRjKwj7Kuh5ObmNunxG4yQ31f6MNJ8PpIc+4yQEs5/oXRXqbCwEAsXLoRlWZ4+qbVr1wIAioqKALi9UevWrcPgwYPVfpZlYf369Tj11FMTnruwsBB9+/ZtnA+SBDk5Ofw/5zTBe50eeJ/TB+91+uC9ToyoWQyx92W1LEv6Yk7cuV7SZ8ecw3Gm3DI8S7i9UYlnRmm9UJ7wCbdczxNvrpX06cl+pqdfKn7AhO46mVpEui62/CV+DYs711yo+sadAwHRlGjfHx3Wrcl/140dPkZIU7HP/FJfeuklAPBEoo8cORIVFRVYvHixWmdZFubPn49+/fqhc+fOAIAuXbqgX79+ePnllz11uYsXL0Z5eTlGjBiRng9BCCGE7AcIqxyi4ha1LEv6YnDdKFnSZ8IWDlEg0AvllugZymkKnxllOIEUjmCC7Sy58ejuXCk3tQ+QJX16aZ9VZ8CEW+KnAiacY6Un7hxBAaS7UnC3JetGyedjD+mI47rv39HnABovQKK+D5JxZJwj9frrrwMAVq9eDQB47733sHPnTrRp0walpaV4//33cf/992PkyJEoKCjAnj178MYbb2D+/Pm44IILPMl7paWlKCkpwc0334yKigp07doVzz77LNasWYO5c+d6zjtp0iSMGTMGkyZNwvnnn4/t27dj2rRpKCkpQWlpafpuACGEELKPIyqmAtY39mutpE8O3tVL+lwx5STrwfCEPqg+KaekL/HMKDd8IiZcMSZDIzzCS+uBko5VWMCEHm8eKPFTLlLTx527Yin14bt+N0oXWPL17pq96ND2gEb9PZCGceONN+KFF16Iu33ZsmWq3WTp0qWYNWsWvvjiC7Rr1w6nnnoqJk6ciDzfoOXKykrcddddWLhwIXbv3o2ioiJce+21OOWUUwLHb4pjZgsZJ6Suu+46z/Ldd98NACgoKMDixYvRqVMnWJaFv//979i5cydatGiBXr164dZbb8XPfvYzz3sNw8C9996LGTNmYPr06aisrERRURFmz56NgQMHevYdOHAgZs+ejVmzZuGaa65BXl4eTj/9dEyaNCnlEApCCCGEeBE1/wFq3D/6kinpMwF3WK4q34vYYQ1yXaBszzszKhp3ZpQUXvb+3plR7kDf8ICJYLx5ooAJwA2YMC1vCWBD484bNHzX70rB+2zAFnAffvMDnvloFa4ZfEKK3z5pCsaNG4eLLrrIsy4Wi2HMmDEoLi5WImr58uUYO3asEjrbtm3D9OnTsWHDBjzyyCPq710hBMaNG4e1a9fihhtuQEFBAZ5++mmMHTsWc+fO9fwN3RTHzCYyTkjJ/qV49OjRA/fff3/Sx8vLy8PkyZMxefLkOvctLS2l+0QIIYQ0EcKqqLOkz/KV9Nliyo47N5Uocsv3pGCyy/wioSV9gT4p38woN95cT+3zOlZuSZ8eMAFPz1N4wIShAiYsTwmeOzvKJrg9Xuy5Xyz5XSqZuhfXjdKf1b4IFVh+EdbroAOT+7L3YaSLmN6Txt902GGH4bDDDvOsW7hwIWpqajwJ0tOnT0dRURFmzpyp+tDy8/Mxfvx4vPXWWxgyZAgAYMmSJVixYgXuuecejBw5EgBQUlKC0aNHY8aMGZg3b16THjOb2Gd6pAghhBCS2YjdUwFrh/06Tklf1CemrDglfbKMLooWWiCE5lrBW9JXn5lRtkCKBN0nhLtP/hI/b8AEUg+YsJztKcWdI9yR8oslK857QrYVdmiHIX0Ob5TfAmla5s2bhzZt2uDMM88EAJSVlWH16tUYNWqUJ8xj+PDhyM/Px6JFi9S6RYsWoV27dhg+fLhaF4lEcM4552DVqlUoKytrsmNmG40upGpraxv7kIQQQgjJckTNv4Hq59VyQ0v6dDEUWtKn9UB5wyecfQIzo7TUPuETYE5pnwyYCHOf/CV+MiAi1YAJ/7MIXR+nZ0oKL3/ceR3Dd+OW+zkc3j4PptBW7K+E3fN0PJLkm2++wdtvv43TTjtN9SmtW7cOADxZAoAtZgoLC9V2AFi/fj0KCwsD6YlyvJDctymOmW2kJKTefvtt/OMf//Cse+qpp3Dcccfh6KOPxsSJExGNRhvlAgkhhBCS3QjrB4iKm9VyY5X0xZTDZCfx6SV9epKfHj5hC6XwmVEW4A2x0ASPO+DXcbA8ARMIBkwAocvJBkwkjjuXz4njzt39gu/1iyWPwAJCU/4WbdqOZz7goOlM58UXX4Rpmp6yPjkrVc5Q1cnPz/fMUt21a1fc/QB3HmtTHDPbSKlH6oEHHvAMqd28eTNuv/12dOvWDd26dcOCBQvQv39/XHHFFY10mYQQQgjJRoQQEOW3Atb3ajn1kj63jE4KpqiWxCedJZXkl+LMKK/75LpN0lUKzIxSARO+gb1KbDnvs9wSQE+vVLyAiZDSvfrEnSc1fBdwRVO8cj9tn3atWtXn699H0Xvb0nnO5Hj++efRo0cPHH/88cGjJBmgVp+gtaY4ZraQkiO1YcMG9O/fXy2/9tpryM3NxXPPPYcHHngAZ555Jl588cXGukZCCCGEZCs1rwB731CLDSvpMzyCSU/ikw6VLLULlvS5M6P0WHPbzQKk6wTpfGmOlZohhaDrFCzxs48lAyZUSZ/jHPlnQoUHTLgulAgTSf4SvhA3Kum480TlfvDu26VtG5zWtw9I5vL+++9j06ZNGD16tGe9NEB0l0hSXl7uMUg6dOgQdz/AdZGa4pjZRkpCqry8HB07dlTLy5cvx4knnqjqME844QRs3bq1ca6QEEIIIVmJMMsgKqaoZTOJwbthJX2xuCV9RkhJn79PKqIdx1vSZ7tRUgi5CX/xZkYBbkmfv8RPOViNEjCRIO7cJ7wCDlNcIZQgjMJ5Ha83Sr4eeGgntIwwpyyTmTdvHnJycnDuued61ss+Jn8vkmVZWL9+vafPqbCwEBs2bIBlees7ZbJ2UVFRkx0z20jpfw0HHnggvv32WwDA3r178emnn+K4445T26PRKEzTbJwrJIQQQkjWYZf03QSICrVsJTF4N6ykz4xb0mckVdLn7aVyS/pkWZ50vBLPjIJdmqe7TIHgCG+pX/z94Nk/GbFk30R5bxHqMNUZd+53oxKV+yG47wtrt+DVlYnH1Ow3hDmETflIgqqqKrz++usYPHgwOnfu7NnWpUsX9OvXDy+//LJHzCxevBjl5eUYMWKEWjdy5EhUVFRg8eLFap1lWZg/fz769eunjt0Ux8w2UuqROvroo/Hss8/ipJNOwptvvom9e/d65i9t2bIFhxxySKNdJCGEEEKyjOqngdq3Aci+KDMtJX2uqAqW9AXdJ+c9QoZQeGdGxTwzo9xo9EDABLylfiooQi373CcghYCJBHHnunvl/8M7Udy5X2wlikJ33r9nL8PEMpUFCxagqqoKP/3pT0O3T5o0CWPGjMGkSZNw/vnnY/v27Zg2bRpKSko8f8eXlpaipKQEN998MyoqKtC1a1c8++yzWLNmDebOndvkx8wmUhJS1157LS677DJccMEFEELg3HPPRWFhIQD7P5aLFi1CSUlJo14oIYQQQrIDEfsSYvdf1bLluE5R24dKuqSvrpQ+T1iE3EebJxWvpE8N6xVyEK8WZuGLMQ+bGeUp8fPMjPIGTAjh9Epp66VgalDARDzHIpW487BgCc/7oURU+1Yt8eP+2VmC1ajUwyVq1HPWwfPPP48DDzwQw4YNC90+cOBAzJ49G7NmzcI111yDvLw8nH766Zg0aZInCMIwDNx7772YMWMGpk+fjsrKShQVFWH27NkYOHBgkx8zm0hJSBUWFmLBggX48MMP0a5dO08qSEVFBS6//HIKKUIIIWQ/RIgoRPlEQFQ5y05Jn+NChZX0SRFV35S+mIi4UeWBkj5/L5XrdOklfVJkxTyCKPHMKFniJ4/pL+GTy4ESPrk+sF17lm4UEC6Y4gVMJBJNwiuIAvuqdd73uF+q/XRG7wLktWZqX6byxBNP1LlPaWmpxymKR15eHiZPnozJkyc3yzGzhZSEFGCnb4Qp3vz8fFx++eUNuihCCCGEZCei8l4g+qn9WgjEYMKSkedxSvpsMVW/kj5Rr5I+Qyvp09/jhlnIkj7oDhaSnBnlXwZSCJhAvQIm7Bvse3i+iDj9T2ECK07cub4tAgNPr9yM4cduwcnFhzfwV0LIvgGjVwghhBDSKIjaj4A9s9WypaLObQllhZT0RQEnZa9+JX3+lL54JX0yec9N6XPcLsd1cgMlnPf6SvvCSvo8aX4yYMKS6X6AjD+3n70zo/wBE8FlICCWEOyZ8vdGJR13Hm9fnRBhZZl2juGGb75P4pewjyOM5nmQjCMpR+qII46o9xAtwzDw2WefpXRRhBBCCMkuhFUJUT4JcnKrpdL57GdZyhfTxFQMcNwjf0mfVq6XYkmfN85cOlZhJX2uY5W4pA/asyO2HNEjZ0a5y7bjpWZJaSJJulWQzx6nCj4BlCBgQl/vF0P+gAm/K+VbFy/u3L9Pbk4EI/sWpvDrIGTfJCkhdc455wSE1KpVq7Bu3Tocfvjh6N27N4QQ2LhxIzZt2oQ+ffqgX79+TXLBhBBCCMk8xO6/AOZX9munlM8u6bOdKNkXZW8DTK2kzwqU9HnL9VJJ6Yv6SvrMkJI+03G+pKsk+6TCS/r0gAlvSV+yM6NkIIVyn2TAhL+kD97l0IAJmTadTMCE5b6uT9y5vg4Aziruji757er709jnUKI3rSdN8/lIUiQlpP761796lt955x28/vrr+Mc//oHhw4d7ti1atAg33HADbrzxxsa7SkIIIYRkLKLmDaB6nlo2nZK+qP3KKeWTjhRUSp+cERVe0hdBDBHEtJK+sMG7DSnpM6EFSAg5iBcIK+mTy2pGlH/ZeV+dM6Oc/aR48rhTQFAwKSHUwICJOsr94r7f2SfHMPDa51vx8+3f4chu2Tnzh5DGJqUeqb///e+46KKLAiIKAEaMGIELL7wQf/vb3xp6bYQQQgjJcIRZBlH+J7VsCXtKVMyZFiUFlAk4TpO3pE8GSkjBpJf0WU7pXXKDd1Mt6dNjzN0BvHpwhD+1r0Ezo3zbhQWPS+V/bpSACSB+uZ+OFbLNeb8ZE6iOmVi27st6/T4I2ZdJSUitXbsW3bt3j7v9sMMOw7p161K+KEIIIYRkPkKYEOW/A8QuZ9lJ51P9Ud6oc7ekz0BUuke6s+RbF08opVbSh4QlfYBb0idnPvlL/BptZpTPrbLxi6hgz1TKARNx4s7V+/Rn/bXPsTIM4OSinsn/QPZVwlzDdDxIxpGSkGrfvj3++9//xt2+bNky5OXlpXxRhBBCCMkC9twH1P4PgBRRpiOcLMePghYyYTs2sqQvEBYBGTZhi6OgUGqhxI0lIlpJn10CWFdJnxRC8Ur6VFCEDJLwlfS5pXq+ZcCzrESTs15uD50VpT0H/2g2giV9ei+V/4/rEMcJ8O3nK9cLvt+3zSeohvc+FH26HJTcb4OQ/YCUhNRPfvITLFq0CH/84x+xYcMGmKYJ0zSxYcMG/OEPf8B//vMfnHXWWY19rYQQQgjJEETtRxCVs9Sy5YRLRJ2ocxPBqHN38K7t4rgJfIbHWfL2PrlCSe3jcargGeAbr6QvmkRJnz84Qi/pk9v19XrJnr+kL/HMqJCACZ875emZks/+Mj34lvV9Q+LO6xswoQurFpEINn+/G99UVCb/I9lnSXPsuUeck0wipYG8EyZMwJYtW/D888/jhRdeQCRi6zHLsptLhw4digkTJjTqhRJCCCEkMxBWBcSu38KWSIAlhCfq3F/SJ8WUJdyoc1mmZzruj4BentdCE0r2sxRIMeETVXEG7yab0icDJTwlfPUs6TO1kj7dfYpX0ucuQ1uGEjnCEUKNFjCB8H3ivt/S/mx3tsViFjZ8vxsvf7gG1wwvqd8PhpB9lJSEVG5uLv7xj39g2bJl+Pe//42vvvoKQggcdthhGD58OAYPHtzY10kIIYSQDEAIAVHxJ8DappZNmFrUORxnSoopOK6RHXUuRZQQBmqdkj7pJFlwS/b8Qknu44oqb0mfLnii9Ujpk8uypE+W4iVb0udP5XNFlL7dW8qX9MwovyAKc5MSOU5SdIWV63nerz3D/37ve44+rGuin8d+gdEMPUtGms+XjXz99dcNev/BBx+M3Nzcer0nJSElGTx4MEUTIYQQsj9R/RxQ85paDEad26ESAlBR51FAuU/+qHMVLIEcJZRshyqiyvz8JX1WoKTPDaGIQfZJ1S+lT5b06al9yZT0xZsZ5S3pa4yZUY0TMJHIjQrs43OqftTtYBzXq6BePxdC0sWwYcMCc2/rw4MPPoiBAwfW6z0NElKEEEII2X8QsfUQu29Xy2bcqHO7P8oVU27Uud7PJEvqAol7juuk9oHhGcrrL+mTgsftk6pfSZ8/pc+sR0kfECzp85fwKZHkmx0VEEKJSvrCHCl/wEQCEZSwDyqum+Vuy4lEkGsYqK6NIq9NTn1+NoSkjREjRqC4uLhe76mqqsJDDz2U0vlSFlJff/01nn76aWzevBm7du2C8I14NgwDjzzySKqHJ4QQQkgGIUSN3Rclqp1lASsk6jyK8KhzS3efhDs/SqbzqTAITSjpM6Hs/SNOL1XEK658KX3STTKF1odVR0mfntKni666Svos6TRpjpJ0qwCvO+Up6QsTWp5lBARWnQET/pK+MIHkeX/Y+xBa5mfGLCzf/C2eXb4SVw47PqnfzD5LM5T2pf18Wcqpp55a78C7nTt34sEHH0zpfCkJqbfeegvjx49HNBpF27ZtkZ+fn9LJCSGEEJIdiIqpQGyN/VoIxJy+qBgsJ6fPjjrX+6ISR51H7BhzRNQwXgu2s6N6nLSSPr+oskMnIq7L5Is6l6V8UeUqJS7p00v3/CV9ntI937K/pE+6V64L5RVa4SIq2DMlXaSEJX2ARyzFLduro6QvIJ4s3/u093Y/iH/zkczkwgsvRM+ePev9vtatW+PCCy9Ely5d6v3elITUzJkzkZ+fj3vvvRcDBgxI5RCEEEIIyRJE9StA9VNqWfZFxRwJZSH5qHN/4p6lyvNcgRQQXp6SPsdlcgIk9KAKC9J1kiEUTgmfdKzUseo3eDcWp6TPilfSB2dZL+WTbhQQ0ttkNHxmVAI3KtA14hdd6nwILwE07XVFndpjSN9e8X4m+w9CRpKn+ZwkIVOmTEnpfW3atEn5vSnNkdq4cSOuuOIKiihCCCFkH0fENtkpfQ6W1hdlQWgDd10x5YooN1DCHrSrhU04IsofdR7zxJZLUSWdKimIWoSU9Ek3ydeHpZXu+VP5Uhm8q5f0iXglfcqhctZb8LhU8XqnEpbpwd2m3Cm/4xQnYEId039sob8vzjZn2TCAokPyYflaOQjJZt55550GvT8lIdWxY8d6xwMSQgghJLuw+6KuA8QeZ9meFeXvi5IlfW5flB11LvuiZImd6ovylOfpDpUuqiLaUF5Dlf+p9/iizmPCgF7KF/O4SgiU9Omleo2R0ldnSR+c52RK+jSXKmzQrnzdoICJZHuknHXCAl5d9RVeWLG6fj8iQjKMrVu3YtasWRg2bBiuvvrqBh0rpdK+UaNG4c0338Rll13WoJMTQgghJHMRFX+J2xdlaX1RltMXJUv6TOkKSfdJE0xu1HnENxMqPOpcd6r8UecyqMKfxhcV3hI9f0lfQwbvplzSF/bQhRV8wsZfzudsq09JX+D9yST36dudbYa2qlVLBj6H3tt0nJOkzN69e/HGG29g3rx5eO+992BZFnr16oVrrrmmQcdN6X8N5557LlasWIFf/epXuOyyy9CtWzfk5ASjMA899NAGXRwhhBBCmgdR/TJQ/bRa9vdFyYhz2RdlCXtelB51bmlOkup5clwnf9S5qZXfyZ4p/1Bed/CuExQB2SelhVo4wkyokr1IeAmfb1k+16+kD3WW9CGspE8/pv8hy/zipfLVVdIX4i553u+89rhSgOt0hQRMCGf/Lu3b4PSji+L+ZgjJND799FM899xzeO2117B7924YhoHRo0fjqquuQu/evRt8/JSE1BlnnAHDMCCEwJIlS+Lu9/nnn6d6XYQQQghpJkRsI0TFLWo5mb4oW0wZqNX6oiwhwyOk6+SNLY8Xde6KKvc9sUDUeQslqmQpnyXglPR5S/WskBK+4HIqJX2uexWvpE8KMb+gEbpo8pf0CZ+IAuKHSNS3pC+B8+TZ3wyuG1rYFS1bcIYUADpEGcwPP/yAl156Cc8//zzWr1+PvLw8nH766SgpKcGkSZMwbNiwRhFRQIpC6te//nWDJgcTQgghJDMRVhXErmvr3Rdlwu6LkiLKG3UuBVJES+Wz96mNG3XuOlWm8x7VLwXnuKovyu9YeUv65OBdN32vuUr6wgMnAiV90h2S1NkrVUdJX13iyS/EfO83ABgCeOq9jTjmqHU4/bgj4v18CGlWxo8fjyVLlkAIgYEDB2Ls2LEYMWIEWrVqhW3btjX6+VISUr/5zW8a+zoIIYQQ0swIIWwnKvaFWm5IX5QnbAI5wQAJp8dJiao4UefSbdKDKkzHRZLnUyV8qlTPiT53SvhiVviy3E+o93lL+uTx6pvSl3JJn9+NiiN04vZThYmhsGjzEEHlD5iQ62TYhAFg157qxD8iQpqRRYsWoUePHpg5cyb69u3b5OdLKbWPEEIIIfsg1U8ANfPVYip9USYMbcCu4XGdarUACW/YhDtg1+9UhUedG6okz3SEmQm3RM+NPneWHREUXPaut3QxBCmOvKV9yab0pVrSF3CUpBvlF0ECoeV+ATcqmeQ+fbu+Tm5y1rVv3RKnH1uc6Be0fxAmhNPxIHUyZMgQbNu2DRdeeCF++ctf4rXXXkNtbW2Tna9B0SumaWLjxo0oLy+HCJkrcPzxxzfk8IQQQghJE6L2E4iKqWrZbIS+KOVIIRLaFyWE3hclnSpfXxRcERWMOneOpUrzfKV6kNHnTn+Tb9lU4shZDinpk6V+6Srp88yMilPS57pFYX1USOxmJRJP8ZwtZ905R/dAuzatEv6OCGlO/vnPf+L777/H888/jxdeeAETJkxA+/btccYZZ6CkpKTRz5eykLrvvvtw//33o7KyMu4+DJsghBBCMh9h/QCx6zew/SXAEgJWPfqivPOi9L6oiKcvyvKn8IVGncMzUFc6VvGizmM+ESWXk4k610v3lFgCPOvdAb1Qz9KtavaSPn1dMgET+vvq2N+/rkUkgn+9sx6nHLcNxxcfFv/HtD8gDPuR7nOSpDjooIPwi1/8Ar/4xS/w4YcfYt68eXjllVfwzDPPwDAMLF68GD169EBhYWGDz5VSad/TTz+NmTNn4sgjj8T1118PIQQuv/xyXH311TjwwAPRt29fTJ06te4DEUIIIaRZEcKE2PVbwCpzlgVMrS/KLupL3BclY8vD+6IM1RdlOYLIE4fuc6pqfVHnlvOIF3VuOe6VUNcR8USbC00cyWV5vZYjdLylf3BK/HwiSTRTSV9cERRS7gf/+8Pe51sX5lTBu84AYEbtRqs1X30T/8dESIZx7LHH4i9/+QuWLVuG22+/HcceeyzmzZuHs846C6eddhpmzpzZoOOnJKSefPJJHHvssXj00UdxwQUXAABKS0sxceJEvPLKK9ixYwei0WiDLowQQgghTY+ovBuo/a/92knoE0Igar+yy/rq0RclBZIURFHZ44SIHTbhG6grE/wA+3jeFD6vg2U6oipe1LnpLEsxpCLLHZGkItF9JX3eqHMnWEJL71MzpGRJnxJRznNTlvT5RZC/3E/fVoebFbZ/oMxPuOJJrpM9Uq1aRDDs/xr+r/jZjiGa50FSp02bNvjpT3+Kxx9/HG+88QauvvpqVFVV4f7772/QcVMSUps2bcKpp54KACoG3TRNAEDHjh1x4YUX4rHHHmvQhRFCCCGkaRE1i4A996plCxYsYSHq5POZjgulh0zEUNe8KG9flBXiNtnBEL4EP+UyOaETsoRPE1G6UxUv6jyZkr54UedhJX1Q6+1nzyBeraRPhIkmaO/xP+pT0peMk1SXUEoxYMKA+0f8qGN7okvHdiAkm+nRowcmTpyIt956C//85z8bdKyUhFRubi5at24NADjggAMA2MOvJIcccgi2bNnSoAsjhBBCSNMhYhsgym9Qy5bqh7Ls/iinHyqqxJTdF2WX7nn7ompT6Iuq9fRFuYLMEzqhjgHoEeexekadm8L7LDSxpJf+WY4g8qT3+Ur8IF/7Svq8IgpNW9IXts3zfu0a9HXJlvnp60zpRuXgzU+/wuYdO8N+ToRkHZFIBKWlpQ07RipvOvTQQ5VQys3NRffu3fHOO++o7f/73//QsWPHBl0YIYQQQpoGYVVC7ByHsKG7plPOZzkiSjh9UZYIzouyZGIe/H1RkZT6olTohL8vShjueYWMNpfzo5yodeGIrZCoc1nSJ+dACXhL/+ySPq0UUCvpk+6Vp6RPCjInXEJoQiQtJX1hYkg/tn9byLqAU1XHur21JnZVRbHkkw3J/MT2bcIcxnQ8SEImT56MlStX1vt91dXVmDx5MjZt2lTv96YkpAYOHIg333xTLY8aNQovvfQSLrvsMlx66aV47bXXcNppp6VyaEIIIYQ0IUJYthNlbnKWg0N37X4ob7hEFLbQiDpOjwVDOUb+2U/+eVExzWWSosqqR1+Uvj7mL82TfVJIFHWeuKRPRp3brpQv6hzwLmtOk3yEiabmLukLrEvgVHnK+vSSPsvd3wAQMYBBfXuG/awIaXaeeuopbN68ud7vq6mpwdNPP42ysrJ6vzel+POrrroKgwYNQm1tLXJzczF27Fj88MMPeOWVV5CTk4Pzzz8f119/fSqHJoQQQkhTsudeYO+/1WK8obsmwsMlpPtkl/A5PUiQpXnx50VJN0l3qpLti3LnRaXeF6X3PcWNOnfcrLA+KCWeLDv6WmiCKKy0r7lL+uIKsZB1QrjiSYSILLluRL9u6FNwcMiPipDM4M0338SXX35Zr/fU1NSkfL6UhNQhhxyCQw45xD1Iixa45ZZbcMstt6R8IYQQQghpWkTNfyAqZ6nlREN3LS2pT8BN6HMdJG3oruM+xXOowudFSUGm90UZcfuizJC+KBllLvufQpfVM+AXS7Kkzxtt7gqoQNS56rGC9oyQEiwjXERlQEmf95zedcIMEVnOfq1zW2Dbt5XYubsKB3dg4ATJTBYuXIiFCxem7Xz1FlLV1dX45S9/ibPOOgvnn39+U1wTIYQQQhoZEdsEUT5RLdvhEhZiIUN3ZUmfGrorHaRAuIQURDkqZMKUQRFOcp8Jb7hEvHlRdl9UJG5flAl/X5Q798nbB+X2P1laiZ90ltzSP7ekT5b6eUWSN7UvbklfSD+Ux61KR0mfSCCUEjhVhvMW/35GyDlqqmP4bNsuzP/vZ7jqzJKwnxghzcq///3vundKQKdOner9nnoLqTZt2mDlypU466yz6n0yQgghhKQfYVVA7BwLiEp72RMuYTkTo7xDd/VwiZhWwiedKCmQomjhhE7kOCV7rsskXaGY8IqqxumLcgbravvL5XhR57E6os51p0m6VXpJnwgr6YO+rAssBEv6LHjFEdDwkr5EYgvwHcu7LpmSPv/x+h/eBQSc65SBFBQUpP2cKYVNHHnkkdi4cWNjXwshhBBCGhkhTIhdE+oMl4gJuzdKhkvIeVFhQ3dlap6d0KcP3c3xrLeUm+R1qiynDLCp+qJk6Z/uLCmxBNjLCaLO9ZK+sKhzT+S551FHSZ90oyQNLekL7I+69zehX4H9mwiZGaXWGe77TyjshGOLuoX+zgjZH0lJSP3mN7/Bs88+i3fffbexr4cQQgghjYjYPQ2ofVstxw+XECpcQhdRYUN3ZZmfpQ3dNVW4hBRVkThOlRtZrsSV7ItSpXvx50Ul6ovSS//0CPSGRJ1DF0+ekj6E7+Mv25PCShdGgFfwpFrSV9e6OpyqwH6mK6jUbnKOVMsWOCA3BzW1Mez3qJLOdD6a+0OTMFIKm5g/fz66du2Kyy+/HEceeSR69OihBvRKDMPA1KlTG+UiCSGEEFJ/RNXzQNVDajnZcAkZKuHvi5LOVOjQXbhOlCrhCzhVUpA54kr45kUBSDwvKrwPSi6bwieaZF9USlHn8BxDOA6WXzz5hZanpC+eiHK2GWqb9l59W0NK+hLFnuslfSH7iZBr2rs3irdWbccLSz/FJacdH/itEbI/kpKQeuGFF9Trzz77DJ999llgHwopQgghpPkQtR9CVPxJLbvhEqbTF2WHS0QRP1xCBjz4wyWiTriEN8pciirZS+UTVSpcooWnVC+sL0q6X4n6otx+p8R9UTJEItAX5Yk69wqvQElfaNQ5lNAIjTr3B07ohLlTzVzSFxBl2nG06j50Pag9CCE2KQmpNWvWNPZ1EEIIIaSREOZ2iF3jYU+Agi9cQth9UY6IEgnCJSzlOnnDJfxR5vrQXa+oisBN8HOdKL+Iivn6omKN2BfljT6XLpM/6tzbF6UfQ4qoYNR5uDsV6IuKV9KnO1dhzlOikj4zgbAKO4Yujuoo6QuEThiAMAUMAEWH5uOU/+sd51e3HxEmjtNxTpJxpNQjRQghhJDMRFhVEDt/BVjf2cvxwiU0J0oIW3KFhUuYIeESepS5pQkiWfbnH7prhvVBwRc2kWJflFz29kUB8fuiDFXqJ90of1+UcNL1koo69wkjT1+UiOc81SG6wkSRJNHgXf21Vr7n2HGBdfFK+nSRJkVUi5wI+nc/KPGPj5D9jJRT+15++eW42xcsWIAjjzwy5YsihBBCSP0RwoIonwTEPnOWBUyYTgmfHXIuwyVUP5QjolQsOeSg3EiCcIlIwKFKNHRXulByrpM7hBda/1NEPdtiS1929ks0L8rTF2Uk3Rdl6YII8BwjqahzC0pguQLE8Aok9QUlKOnzbwOCblS88r0E64QusuBdZwBujLfmWomQ0IlY1MLz/92IV95Zjf2eMPGcjgdJic2bN+PBBx/ElClTMGXKFDz44IPYtGlToxw7pdI+IRJ/m3VtJ4QQQkjjIypnAHsXqWUTllPC5yb0yXAJKab8MecyEMKdFxUvXCISCJeoFVJUeYfu6hHmlrZdhkPEHJFl90W54siUy5DLyc2LSq4vShdNcByoRow6l+6S+nK8j5RK+uqb0lfXugQlfQAgLNuNihgGLGeHnAiLmUh2IITAnXfeiUcffRSWZXm2zZgxA5dccgn++Mc/NugcKQkpwA6TiMeWLVvQtm3bVA9NCCGEkHoiqp4B9jyglsMS+vRwCRN2uITtNjmlfLqrhIaFS7giqoUKkfA4WL6+qFTmRcn9U+uLcoWXW6an9UWJkLI9f0lfiLuUdEmf5bhB+rbGKOlLUOYXbz/doXJ7wYRaZ8Xs1107HoCRJxTF+wnuNxhh309TQ4+i3tx///14+OGHceKJJ+Kqq65CYWEhhBBYv349HnroITz22GM4+OCDcc0116R8jqSF1CuvvIJXXnlFLT/44IOeZUlFRQU+/fRTlJSUpHxRhBBCCEkesXc5RMVktewm9FmBhD5/uERM9jY5IkQ6UTGtdE+fB1XfcAk1F0oEE/qiwlsOaCkR5SbyyT4oILxPKrl5UcG+KCmabLxzojx9UXppn/Du63GX9KhzYXj/8E0UZ54owU9f53eMQpylUKcKwXX+YwdK+gw4ZY2ayDIMnHbsYchtmfK/wROSVp5++mkcf/zxeOihhzwGUEFBAU4++WRcdtlleOqpp9IjpDZv3owlS5YAsP/HFC/2/IADDsAJJ5yAW265JeWLIoQQQkhyiNhGiF2/gS2L4LhNMqHP8iT02c9Q4RJuQl/EI4ik61MrwyQC86CkqKo7XEKFQTgiyzt0N+KGSfjWS5Fk90WF90mlOi9KHssjnqw4fVG+0r4GRZ37xZBvW6DcD779w9bFcaoSlvRZQcHkcajMYElfRACPvrEGRxUVYujxdKVI5vPtt9/iyiuvDK2ii0QiOP300zFt2rQGnSNpITV+/HiMHz8eAHDEEUdg+vTpOOussxp0ckIIIYSkjrB2Quy8BhAV9rITLpFKQp/dB+WUziHiJPJFNCcqx1Oap8r+6gqXEN5wCf/QXemESTHlH7prOiLJ3yfl74uKaSKqyfqikGRfVJiT5C/Hi7OtXiV9YeLI+Yy6KDOA0CG7IqxHyinpA9ySvpyIAStqv/52V6X/Z7h/wlK7jKdHjx744Ycf4m7//vvv0aNHjwadI6WOwX//+98YMWJEg05MCCGEkNQRYq8dc25ucZbtmPNkEvqkiJIJfXpJn3SmPK6S1ivlGbIbEi7hEVeh4RLxh+7qJX119UXJ9+mlf/bftrIvyu9aJdMXpZX0hZT2Cb/zFHCn4vRFxRU+QUFWr5K+EKdK1DF41wjpm5LviRghxwaUiMpvm4uRJcXBHyMhGcjVV1+Nxx9/HF988UVg25o1a/DEE0/gF7/4RYPOkVKha0FBQWBdLBbD4sWLsWvXLgwdOhSdOnVq0IURQgghJBw75vx3QPRDZ9ku55Miqj4JfXbanhsuIfufZImeXZoXUfHnbi+V16mqVcN1UwuXiMYJl5B9Ue7QXcdhAuxly4Bw0v0E4AmVsOqaF6WX9jlCq159UWHOlfqSEvQ+JSr3i1PSlzBUwu9U6ev0c9SR0meFlPTp288v7Y32bVtjvyeshDMd5yT14quvvsKhhx6Kc889F4MHD8bhhx8OwzCwYcMGvPPOOyguLsaWLVtwzz33qPcYhoFf//rXSZ8jJSF11113YcWKFXjmmWfUujFjxuDdd9+FEAIdOnTAM888g8MOOyyVwxNCCCEkAaJyBlDzmlq2YMESFqJaQp/eF2UCiInwhD4pomIe1ymCmDMvynKcKY+o0pwqAc3dkuKqznAJ6Si54RKJwiRMyy39E4ho7pImmgDVF6VHoCecF9XQvih/SZ/7hbiBE74yuzrL/fRj+PdPdl1YSV9oSp8Bod0Qf0lfxHC3t2rZAg8tWIOSYwdgQFHwH9QJyTR0gfTWW2/hrbfe8mwPy3tIi5BasmQJBg4c6Lm4FStW4Oqrr8aRRx6J2267Dffddx9uv/32VA5PCCGEkDiIqscDMee2eLKT+vSEPtkXZQo4YspxmZSrFJ7QV6v1PQUT+nxOlbCH5sYsJ7nPEUExX7hEzBMuEQyd8IZJyGW3T8rTLyVcseTpi5LhErJcTxNPDeqL0p69fVGG113StzmvlYjxi65EUedxjpGse6UOU2dKnxtxHjpHSuuR2rs3BgPAJ2u3UUiRrODf//53k58jJSG1Y8cOT3PW4sWL0a1bN0yaNAkAsG7dOrz88suNc4WEEEIIAQCImsUQFbepZT3mXPZESSfKH3PuJvQZXpdJm/ukJ/TFtIQ+N0zC61SZTriEGyZhiy3TEVd6uIRcHy9cwhsmgWCfVJyhu7LEz7JsIWZJl0kr2WvQvChnH8/2+syLCrhHRmIBFCgBdI9RV0mfbHGqb0lf2OBdfbvpCKrclhEMPb5PyC9z/4JzpLKDsFakxiYlIVVbW4vc3Fy1/MEHH+Ckk05Sy927d8e3337b8KsjhBBCCABARFdClE+A81d8IObchKV6oupK6JOzo9QwXV9CX8yX0CcdqVpfQl/UI65kmEREibBE4RKeZb9oChFRutOkD931z4+SokkPlwgTRinPiwpzrtSX5Ot9CogiI1wU1eEsJVvSJ9P66i7p0wRVWEqfJqgM7TpGn9wbXTu1ByHZxs6dO7F161YAQLdu3XDggQc2ynFTElJdunTBmjVrAABbt27F+vXr8ctf/lJt//7773HAAQc0ygUSQggh+zsitgVi5y8BUW0vq5hzCzGnnE/GnJuaE2XPjDJQC7eET819koETeh+UcB2qZBL6QkMjAn1R4fvFkgiX0OdLyR4nyzKUaBJwHCetLyps6G5Y9HlT9EWFia6EfVF+l6GuAIlE4RN6SZ/mQIX2TdUVOiF7pCKGep3XJhdL3v8Ko88ox2FdO2K/Rhj2I93nJPVmzZo1uP322/HBBx941h933HG4+eabccQRRzTo+CkJqR//+MeYM2cOLMvCJ598gry8PJSWlqrtn3/+OYMmCCGEkEZAmN9D7BwDWN/Zy07MuV3CJ2dFCcTixJzXxok5d2dHyTK/HFhOX1PMea3PigpL6IummNCnO1F1hUtY8A3ddY6d3NBd13GSfVF6uERq86Li90V5SvrCRFecYAj414XtZ4as00UWQoRXiGDyuFJW4h4pfY5UZeVe7AGw+H9f4IpzT/T/TAnJOL744gv87Gc/Q21tLUaOHImioiIIIbBu3TosXrwYF198MZ566in06ZN6uWpKQuqXv/wltm/fjgULFiAvLw933nkn2re3rd7du3dj8eLFuOKKK1K+KEIIIYQAwtoDsfMXgPmlvew4UWGzovSZUf6Yc39Cn+x3iiLHdahUmISe0GckndCnl/MpsQT32Z355IRLaKIpcbhE3UN3zbhDd/WSvpBwCc/DV/4XUqJX376oQEmfpDGizsNcLu2YhkcwhZQB6iLKEVx6SZ9+TDNmS96IAZz4fz1ASDYwa9YstGzZEk899RSKi73zz7744gtccsklmDVrFu6+++6Uz5GSkMrNzcXUqVNDt7Vt2xbLli1D69acM0AIIYSkihC1ELt+A8RWOctC9UHFmxWlx5xH4cacWyIYc17r6YuS4smf0Gc7VTKhT5UB+hL6TPiWVbmgoa2PeEIodNGUcriEI7i84RIhQ3d1B0qW7fnEU6P1RQEN64tK5ErVVeYX4lopB8pAwmG9gNeBMmP2ASLO9wQAp5b0QJ8eh2C/x/8bSNc5Sb1477338POf/zwgogCgqKgIP/vZz/DUU0816ByRBr077ICRCNq1a4eWLVs29qEJIYSQ/QJ74O4fgdplap0tohLPipIx51FHsNQn5lw6VJZwy/5cp0om88nkPT2hT5YAugl9aj/HaZLhEqYzODdeuEQ0lXAJrS8qNFxCF1qO+PKLJyU6As6Tsyzfk0xfVIK5TqF/gOu9UvoxwgSV+2MICCI9uc8IeY/qizIAaG5UmEgzNUElh/Tm57VG2Xd7UFFZ4z8zIRlJdXU1OnXqFHf7IYccgurq6gado9GFFCGEEEIahj1wd75aNoUbLJFoVlQMcFwkO+rcclykZGLOa30x525fVF0JfZE6Evq8fVEB0aSJKk+/lOp9amC4RMKhu/CW9sXti0Lj9EWFRZ0n60D5hJrHjQp5T7ztcnZUxDCUuIogRMxBL+kzUF5eg0/WfoeXF6/C/o4hmudB6kf37t2xZMmSuNv/85//oHv37g06B4UUIYQQkkGIPQ/GHbhryoS+OmdFOYl8luxvMtRMKEtEUJsg5lz1UjnhEm4ZoDc0wjNjqo5wiWhIQh8QDJuQJYTB0j9vuIQQyYdL6PsEwyV8JX3+8r3m7IuqQ2TpUeeeUkK5XZX06WLOPbl0mvSSv5yIe836tcp9AeCIXp1BSDYwatQovP3225g4cSLWrVsH0zRhmia++OILTJw4Ee+88w7OPffcBp0jpR4pQgghhDQ+omoexO6/quVEA3ftZ29Cn3dWlC2gTNjiJOb0Mek9UbpDpZZ9MefRRkjoCxNNskzPtORcKbdcTwZFmFq4hC2evKV+SYVLJD10FwExBMdRqrMvSnebGqsvqhGizgFAxFwHynJK+jzn1N6vl/SZUct9bdo7l/TrguP6Nexf8PcZ6BBlPGPGjMFnn32GV199FQsWLEAkYvtHlmVBCIEzzjgDV111VYPOQSFFCCGEZACi5k2IipvUcn0H7iaaFSVDI1yHSu+JShxz7neiwmdFaeV8sMv5bNFkJ/Qpx0lL6LPgTebzh0u4rhWgiydZ1pdsuIRn6G5If5R/m0dEKTHk7YsyhE8ohZXYha3TjhHYT19XV5kffAINCI0690SZm26ZnnSYItq+euiEFFGRiAEzau/bvm0rdGibi9poDG1yckBIppOTk4O//e1veOedd7Bo0SJs3boVQgh0794dI0aMwKBBgxp8DgopQgghpJkRe/8Hset6OH/BQ8acm0kO3FWR5HFmRcW0WVGyfE46VLZj5YgofXaUJ+Y8osr5YsjxiKOYEkvO8Sy3TC/ZhL6YJqIAN6HPHcLrCiIpmhocLiEFVgp9UfK1p5xO3z+JvqiAU+VfF3YOfZ12LWFR5x5BJdzSPEtzqDxulVYz6N8XAHZX1ODN/36JI4pW4sKzfhT8EROSoQwaNKhRRFMYKfVIVVVV4euvv467/euvv25wCgYhhBCyPyCiKyF2/Qq2LAL0gbsx6AN3LcQbuGvHnbsx57aIssVNcFZUUFQpp0rIgbyJY86lWIqp47mhFHJmlJ7QZ4m6E/qEElF66Z9XPKUlXMJyt8Xti4rrGNWvLyp0XSJXSoT0RWnbPSIqrORPE2keEWW6JX2OlkfE0PqltFLBTh3zsL/DsAkiScmRuvPOO/HBBx/glVdeCd3+y1/+EieccAL+9Kc/NejiCCGEkH0ZEdsA8cPVgNhjL2siSh+4G1MzooRK5/OX8yUauFv3rCg548lQokgXW1JkBWLORSQ05tyf0OcXUWEJffLZm9DnhkuY9QmX0OdGaQ5UyuESQAJh5dsW5jZ5joH4+yXbF6Un8yXabsBN6YM7D8pb8ueGTrh9URH1ukVOBGatCQAo6nkgTj6hEIRkKg899FC99jcMA1dccUXK50tJSC1fvhxnnnlm3O0jR47Eq6++mvJFEUIIIfs6wtwG8cOVgNhpL8cRUfagXfvZLu+zRUxUE1HxB+4anhhz6VAFZ0W5jlQMQXFkpwEmG3MeTOgTIvWEPsAt39PT9/RwibgiSn/o5X+JwiU873GoSyj5y+4s3/vl/gnK8xK5UoazKlDm5+uL8p9bKJFkeEv6HAvP0E4ltGQ+1SNlGDBrTRgADmiTi2OP4jBeAMHfR7rOSerkzjvvrNf+zSKkduzYgYKCgrjbu3btih07dqR8UYQQQsi+jDB3QPxwOWCV2ctOT5QIEVGyL8p2pOCU8TnJfL5yvuDA3RbQY8yj0GdDJTMrylnWwiX08IlgQl+IiIItorxhE5rz5CvbCyT0WQaEUybo75Ny3SVXaDU4XKK+Q3fjlPuF9UUh9L3wOERhYkyJJIQcBwj0RRnyTer35g7gFXoyXyzoVuklZMIJqACAqj21ePqVtejVsyfOHN4fhGQijz76aFrPl5KQat26NbZv3x53e1lZGVq0YI4FIYQQ4kdYP0DsvBIwt9jLTjqfdKIE3Jhzv4gyAUSluwQpamSwhIwzbxFn4K4UVZqDpYmsuhL6kok5l6LJU7YnoESULcQ050n4XStAOlCBUj9HdAXCJbR486YKlwibF+XfP6w8T5HivKjAe0xtnd/dAty+KMMVV6FpgHCjziMRA5Ys44tEENNizy0n9jxHC6MwTSvwmyYkUzjhhBPSer6Uwib69euHl156CVVVVYFte/bswYsvvoj+/fmvFYQQQoiOsCogfrgKiK23l5WIspQTJePN5awoE4DpiCjVz+SU5UXVsrecr1a0qGPgrndWVG2cWVG1CWZFWaJxYs5V7xMA24EKOk9+EeWNN3dFVEA8eUSUES6i6hsu4RFFRnwhJDHrEExJ9kXpZX5GAhFmwHWgZGCE6qcKEVRSRBmG4YqonIha37JFjpoj1fWQthh+8hHY7xHN9CAZR0pCasyYMfj6669x0UUX4Y033sCXX36JLVu2YOHChfj5z3+Or7/+usEDrgghhJB9CWHtgdh5NRD7zF5OQkTZ/VB2X1RMRBBzSvrc5D3NmXJmQtUGZkFJUVU/ESXFV/xZUU5yn0r2c3SBqEfMuc+1amhCn0dMef4ADQorJaKUsDK8f6zWFS4h3GMFyv08x3DfH+hxqse8KLVdc50CLhkAYbm9TpZWxiffF9EOrqfx6T1SltMXZQCIOq9btsjBWUN7oVUuK44IkaT0v4aTTjoJN998M+644w5cf/31nm05OTm46aabcPLJJzfG9RFCCCFZjxA1dsR59GNnWRdRlhJRwieiLCHDJaSIsoVT1HJcKDjx5SHpfP6eKJnqF2/grhtZrr1PiiUZiy6H/ApDOVDScUop5lz2PsEWRokT+tz+Jk+4hBJa8mbLR7D8TwkfXUTpPVCS+oZLhDkG8RyoMMEUJrJ8x/b3Uunx5qqMT+uL8rpO4VHneqiE5bxX/xd2vTdLmBbuf+JTFPbujYHH79/JfUbY993U0JHKSFL+Z4WLL74Yw4cPx+uvv44vv/wSQgj07NkTp59+Orp06dKY10gIIYRkLULUQuz8NVD7P7XOckSUPSfKHrgrhECtT0RFIRP6XBFlO1GuMxV1xE1M5GgR5a64UqIqILKkw+QO3LVT/3wDdy15fEOJKPlQZXo+56k+Med6Qp8dMuEM4w0p3wuIKK0/yi+elIjyuVOua6QLJc2mSSVcImzobliQhBUimOqYFyW07YH3wBVUEQOwQo5l6IJKK/kztV4o0xFaMupcvo5F7X6oljkR5UxtK9sFQohNg/zZLl26NCgykBBCCNmXEaIWYtd4oPZttc4UJkxHRJmOiJLlfCJUREU8IkqW9EkRZSJHOVQmIsqhsjyBFK6osre74ksfuCsdJ33grukIobpmRUVDRJTsl6pPzLlynoDECX1aL1VCEaW5UZ4yOV0U+dysZMIl1P7JDt1N1CsV5z11zYtCHJFkabOjdKfO3ddN4zM1t0qKqJwcN3SiZYsIonvt9fntcjFsMHukCJGk1CNFCCGEkDoQUYhd1wN7l6hViUSU/eyKKOUawTvzSZb0xTwiKkcFPkiHSs5pUuV48G6XTpNp6T1XOTCFf+BupEGzoiyPq1SfmHMjKKL0hD5nn3g9UIkT+owEoii+29Tgobvx3mNq2xHiWmllfKHlhCF9URHDUG5VTsT9nJ5ze5wtXVy5r6WIMgzgZ6OORH77NiCE2CTtSP3hD3+o14ENw8DUqVPrfUGEEEJI9mMCFTcAtYvcNUL2QiUnomq1gbsyoc9SIioHMT1wQrhlfrYYcpY1keVu10WTLXRMIWPRoUr2/AN3Vf9UvWdFNV7MuZ7Ql1hEGd7ZUIASRa6wiiOi4pXnJRsu4XeAQoIi4oqsEJGkl+Z55kXV0RclNEFlxoJ9UTmRiIoyzzHcOVIRw4BwTh7RLr1tq1w8/ORKHHdMfxT36QpCSD2E1AsvvJDUfobh/psKhRQhhJD9DhFDzwP/AaN2uVpliygLUWHGFVGiThGlOVNOWV7McYysgKgyQpb9ZXpun1NMC5eQ4il0ppSzLHuidOcpmVlRgD/m3FDiLTTmXCvRk45WaMy5/gjZ5g+XUKJI0sThEskIpnh9Uf5jGkD4vKg4JYOyzM8w4OuLcmZHaX1RLVvkaCl9bklfq5Y5qKzcCwPA+x9tppACgt9/BrB8+XLcd999+PTTTxGLxVBQUIDLL78cF154odpn6dKlmDVrFr744gu0a9cOp556KiZOnIi8vDzPsSorK3HXXXdh4cKF2L17N4qKinDttdfilFNOCZy3KY6ZLSQtpN57770691m1ahXuuusurFq1Cvn5+Q26MEIIISTbEMIEdv8RBx6QvIiKwRZRtQiKKP/AXVMLkbDFjiuipOhSjlWc7TGhi6agiIo/cFcTUXDfH9b7FG9WlEzfc8UTYCWKOZcOlT/mPCCiguV/8URUQATVJaIaKVwiVGSFzJDS0/jq6psS9eiL0uPNZV+UYUCJqEjEUCIqJ2IoEdUiJ4K91TFbUOXm4JRBRSCZx7x58/CnP/0J559/Pq644grk5uZi48aNiEajap/ly5dj7NixSuhs27YN06dPx4YNG/DII48oM0QIgXHjxmHt2rW44YYbUFBQgKeffhpjx47F3LlzMXDgwCY9ZjaRtJBq165d3G1btmzB3/72N7z++uvIzc3F1VdfjWuuuaZRLpAQQgjJBoSIQZTfCGPvy2qdFFGxBCIqXjmfPVg3J1RE6Ql8rqPkCC5NZLnb/SLKTfbzzoqKJ6Lile8FY8/rmhVlCTj9UMFZUUnHnHsemojybXNFiC6UtG6keAl9cp0msuKKKMRZV1cZX11zp/R485AyQTkvyoCvL0p7bVnS/9PEleccwd4qADBjbo9UzBFXAHDe2Ufg0C4dsN8jBXo6T5ngfF9//TX+/Oc/Y8KECfjFL36h1vvFyfTp01FUVISZM2ciErFjEvLz8zF+/Hi89dZbGDJkCABgyZIlWLFiBe655x6MHDkSAFBSUoLRo0djxowZmDdvXpMeM5toUNjEd999hylTpuDMM8/EG2+8gZ/+9Kd44403MGnSJLRv376xrpEQQgjJaGwRdQNQM1+t00VUrA4RZTZARLmzoow4223R5BVRhhJRsRARZTluE5xj6nHoevlenSLKCZCQrpXwiShdIFmqfA8BEWUlG3MemtBnBESR/aXBFVZhpXY+p8pdBzRYMMUJl/D3Rellfh7nSriiR2jn8ogobV6UdKNyDPfz6wEUOVpbRk7EUCIhx3BfH9yxLd5+Zwt2fFMBklk899xzAIBLL7007j5lZWVYvXo1Ro0apQQPAAwfPhz5+flYtMjt51y0aBHatWuH4cOHq3WRSATnnHMOVq1ahbKysiY7ZraRkpCqrKzE3//+d4wcORJPPvkkhg0bhldeeQW33XYbOnfu3NjXSAghhGQstoiaCNS8qtbVR0TJdD4RR0RZSYmoSGC7OysqEux1QouAiLJ8IkqfIaW7SW50uW92VMjAXcszcDeZWVFG0IlyBEZ8EYU6EvpQ/4S+usr9JGF9Tw0Il/A4YtBElOEr+dOEnV98AXpflG9eVMydF2XpPVLO/i1b5MCUs6O0161yW+C77yrx1bYKLF7yOUhm8f7776N379544403cNppp+HII4/EKaecghkzZqC2thYAsG7dOgBAnz59PO+NRCIoLCxU2wFg/fr1KCws9IgjACguLvYcqymOmW3Ua45UNBrF448/jjlz5mDnzp0oKSnBxIkTMWDAgKa6PkIIISRjESIKsWsisPd1ta6+Iko6UdE4Iqo2CRFlqe2GElG1cMWR7hbVxhFRqnxPGAERZTkiyj9wN6qJqPoM3E16VpQjoJJO6POJojpjzsNK9uIl9PlLq+L1PSUhmPR1uuvkKRkT7vtFTBdUshfKgCWdqZDjAvBFmof3Rcl5UZGIgejemBJd0ZqYWr+3Jqree9yxPbHfo9/vdJ4zDt988w2++eYb/OUvf8F1112HwsJCrFixAnPmzMH27dtx1113YdeuXQAQmmGQn5+PjRs3quVdu3ahZ8+eofsBQHl5udqvsY+ZbSQtpF588UXMmjUL27dvx5FHHonp06dj8ODBTXlthBBCSMZii6gJwN6Fal1jiCjhE1FBkRQUUbVqe45PRLXwiKigE+VGnLuDeN2kv7DyPf/sqLAeqGQG7iY1K8ofc64n9vlF1P9n78/j7KjOO3/8c6ru0nurtQshViFhEIuNQcgsMgFhHDteCNiOPV4SMoTBfL0E2bF/IR6S8W88A9gzIQxMnDDfOM4yju3MxB47DkOIIYkJdmzHBhwWsS+SQFvvfe+tqvP949Q55zmnTtWt23271S2dDy+9dOtsVbe7Je5bz/N8HguUdHQqH6LMiBXcQGKNgYxlIlXdNJew9wOmuYTRL8pRFyWjeNYZeXVRSUShKyH31K/fvONkbNroM48WmzjnmJycxBe+8AW85S1vASDqj1qtFr74xS/iIx/5iFpL3bWLVHbdfJ25VFQapD71qU+BMYYtW7bgzW9+M5588snCMBxjDB/60Ie68YxeXl5eXl6LSpw3wQ99FGj8rRrrFkRFFkSZkBQoSKIQxS2I4jkQJd35tIEEadjLNUQpe/QiiFJgFKj6poRjzg13i3tFgUBGFqza2pxzE6LMuiN07tBHgKhtVGo25hIGUGUBiDHouqhAw1XIGOIUgELGEKdEFTANVwFjSMD163SiEgaIoxhAaoGeRq/Wrh7CvlcmMTExg+HhfhzVWmQRqWXLlgEALrroImN8+/bt+OIXv4hHH31UrZFRJKrR0VE1L8/LWwfoKNJ8nLnU1FFqH+ccDz/8MB5++OG2az1IeXl5eXkdieJ8GvzgDUDz79VYGYvz7kMUUxAVWRDVLIAoGoFqJRqi5LyyLrcjTxZExQnT9VPcdOFTEMWzqX65DXfJOfm9oqzIVQFEZT7sFqXstUv3c8GPK2qUt2eW5hLtmu6C9JNKCFCZ/aJ0XZRM6auEgUrpq1ZCldJXq4ZoTpPXDWl7XsHe3aN4Zfcovv3XP8UvvWdpWlUfqdq0aRP+5V/+Bdyy9ktEESKCIFB1TE8++aSRUZYkCXbt2oXLL79cjW3cuBH33HMPkiQxapoef/xxdT8A83LmUlNpkPrjP/7j+XwOLy8vLy+vRS+eTIIfug5oPqTGFhKiuAFRlZIQ5Wi2i4DUQDFlMCEhKlMDhWwD3sSCn4hAlErj4x003LV7RVmgVNgrCshAF+MEXbj5ywAZV7ofUAxELhiz97iiUrx4v9kjql2EiuwjoCZNJBh0XVQQMGddVBgGui4qDBREBQFTEMUY0JxuqfM3+tS+RacdO3bgL/7iL3D//ffjbW97mxq///77wRjDGWecgbVr12LLli345je/iQ9+8IMKZu677z6Mjo7isssuM8772te+hvvuu0+NJ0mCb3zjG9iyZYsylpuPM5eaSoPUeeedN5/P4eXl5eXltajFk3Hwg78KtH6sxmIeI+ZJmso3/xAVtYGobDpfalCRApCCKAJkEqKiHIiSH8tlA14JZDQ1z4YoZSzBYTTczUCUjFA5G+6iAKLggCJmQhHIGa5okwE6FpTFDohqZx6RA1l2zZUZaYLbXIJEmmT9kx2tytRtAUb6n2mLruucjH5RpEcUfU3rogIw8PQm529dj9e99ngc7WL0+7ZQ4vnZfRdffDEuvvhi/M7v/A4OHjyIU045BQ899BD+6I/+CO95z3uwfv16AMDOnTtxzTXXYOfOnbj66quxe/du3HLLLdi6dSu2b9+uztu+fTu2bt2Km266CWNjY1i3bh2++tWv4rHHHsPdd99t3Hs+zlxK6ii1z8vLy8vL62gUTw6CH7gGiB5RY51AlOwTxXMhiuVAFCuEKAlLEqKU+54DomQaX0RroEiUymUkIRvwRqoBb3uIShKZvmdCFGC68UFGs1wQZcETTf+jc4WRJVhjc7Q5L0zjaxd1ckGSfZZ9PghEMSjnPlfTXQbRzyaRqYLQ2YABOZumFwbkliGpkapVQjSbaUpfNUQzde9bs3oIK0Z60WxG6O0N4bW49Lu/+7v4vd/7PfzhH/4hDh48iHXr1uGjH/0ofvVXf1Wt2bZtG+666y7cfvvtuPbaazEwMIArrrgCO3fuNIwgGGO48847cdttt+HWW2/FxMQENm3ahLvuuivT5Hc+zlxK8iDl5eXl5eVVIB6/Cn7wV4DocTVWFqKaMCNReX2i8iGqUghR1J1PNtt1RqIKISrMQFRCICpWEEXc+AhEASz9QC4gSs7FFkRJa3RXw12eA0qq3knOkciTCVGsGKJ4Dvw4aqYy/+yfB1kdOPQp0YgSrZty3Z9Gl6j9uctcwqiLCowaKdovStVFhdpEoloJldV5vVZRVuc1AlHVaoi9u0fx7W+N4qSTHsY73/l6HPVa6IhUG/X19eE3fuM38Bu/8RuF67Zv325EivI0MDCAm2++GTfffHPbtfNx5lKRBykvLy8vL68c8ehF8IMfAuLnxTXniJEgKYCoFkQ0YG4QVZzOlwdRSQ5ExRmICkpDVMylpXlgQFQsm+pyQNqe50JUusYFUQmxO3dHoqAhypGeVwaizIgVzChWTgTJPIfMAZlIUiYqZUGUBrd0ntY/0boodX/yKT3H2U8799nmEsUQVSEQVakECqIqYaAgKmC6jxQA4/Wy4V54eXkJeZDy8vLy8vJyiEe7wA/8MpDsFdcEoloQBhMuiHLVRC0UREU5EBWXhCgOF0QxJ0TZvaOSpACieE4kqh1EpesNS3NAAU5ZiGIWEAn4yYlU5UCUsxbKBVkuhz6aXkdT99qZS9jRLm7uA+BuugtiOsGgIIoxIEohijEgaui6qKilX4PraFi1EiKKIwDA5lNX4sKLNuNo12KrkfI6fAraL/Hy8vLy8jq6xFuPgO9/bw5ExUiQIJoDRMUpLMUFFuezgag4B6KikhClXPm6DlHMCVGylsquhXJClAU9eoyZnzDpGLdAh0JWyTS8UlEnO7XPeU+40wChISpgDDzRMCTPMD64G8/Is46Bctw6W7zW44j1mWGg7Tnq1RA8FhM99SpaDQFRy5f3Y+u56+Hl5aXlQcrLy8vLy4uIN38AfuD9AD8krjlHjJhAFFcQ1QRH3AaiFCwRiGql0NPiaQRJQlWmT1TWnW/xQBRSQwkz1c+wNLchijMyBzVHa6HyIIqm56noFGdZ+HHUPXXcK6qdzXm7+Zw0QGaDHZlnAJJYR44oXBm1Vdx8Det1YLzWsbGQ6a9VNQzAU2eKWjVEHAmrkJ56RdVF1aohZqaaYBDW6AdencCX/+hf8MB3/xVeXl5CHqS8vLy8vLxS8Zm/Az/wKwCfFNcKongGoorc+ShEtXjQBqL0uIaooDREteYBorgLotI1GqK0zbmqlyLGEAmBKEC+lj2jOoeobjbczUSFSkSiMql9IPP0TEeaHwNJ6bPvr9L1iM15jkOfTOkLGVPRJfFajFcCPV4JA+XoV60IWJKvW2lKH226W6kEaEy1FMg1SV0UjXBNT7dw1Isfpl9ei04epLy8vLy8vADw6b8EP3Q9gIa45hwRYsSco1kSolpIjR0MiApmAVFhBqJ4DkTl1UR1ClHUlS8iEAW5JnXumw1EJUkBRNHfOeCqk3JBlNFwN7e3FJwQxYD8XlE2ENlgRtfK+YJaKeNeQPb5xA+bTr8z+j9pQ4kk1kAloSgMGOJIg1NMGu0a5hKy6W6gTSRo010AiEm9VACd6tdTr6iI1THrB3HJpafBy8tLyJtNeHl5eXkd9eKTfwg+fou+TiGKRqJcFucxR/p7gJaMOkGASJSCUiKvS0CUns/2kWq2a7Y7R4iS/aBsiIqS9hBF0/k6hijjX9yl8UQORKkxAlF0LBd+HJEquU6qRK+ownlXVAtwOvQZz8K5M0JlmFKQ2qYk0ul/ylyCMdVQl7r4UXMJAGqcngOkUa30IXrqFcykkaneniqmJ0V6X/9AHe+86jT09FRx1OtwRIh8RGpRykekvLy8vLyOWnGeIBn7z6UgKk4hKrYgKmoLUSEihIh4uCghKk5YLkTFBRCVEIhSDXjtdL4UPoohyl5vQRRdL6FIfbNggJILfpwQZX8QzoOsWdqcq2vqykfrnPJMKRxAVcZEAsS5j0IXfR2QMyuBrp6qV0Mkskaqp6ogqloNFUQxBkQzLdz5+Qfxkx89By8vLyEPUl5eXl5eR6U4b4GPfgqYuluNif5QotmuDVFR+nsMICIQFSGthwJDKwkNiGrxEBECAVNcwFKcXisYMiAqMCAqKQFRLdInai41US6ISgogSvaFkqYRLoiSBhOzgihYa4psznMiSKUb7iZtIMk1X2BzzpBCVHovp805gwIjenaRQ58btLRznzoLpulENa2XAkRdVBTFAEQDXtp0dyYFJ8DsHVWriOa8APD0rr3w8vISWlQgtWfPHnz2s5/Fe9/7Xrz2ta/F5s2b8dBDDznXPvDAA7jqqqtw5pln4oILLsBv//ZvY2JiIrNuYmICv/3bv40LLrgAZ555Jq666io88MADczrTy8vLy2tpiydT4Ic+DMz8bzWW8LTBrmq2KyAqSiEqIhAVg6HFRS1UBFEH1UrCNCqVXvMQMcIUrkLEPEQioQoSqlgm3c82ljAhqqIgKuECopIOmu12ClEy62xhIAoO6OkiRNmv7T3cAUl5qXt5hhMlbc6FKx8nr8V4O4c+BsuVj8GwMOcisCSMJtLza8RcoloxzSWa0y0nOFVDHbHq662hkRpMDA3XccnlW3C0S8LuQv/yWnxaVCD13HPP4Vvf+hZ6e3vxhje8IXfdgw8+iOuuuw7HHnssfv/3fx8f//jH8e1vfxvXX389OOkGzjnH9ddfj29/+9v4+Mc/jt///d/Hsccei+uuuw4PPvjgrM708vLy8lra4vF+8IPvBxrfVWNxClGRgqhEmEvwdIxzxBxocSDiDE3OUogSANNMAvOap6l8SaiszSVcSYiSaX92up9s0uuGKG1NHnEbooKuR6ISzB2iFDy1hSiSnjdHiMpAmQuI7DEbfBzzhWl5JW3Oxc8ggag2Dn0BdegLGBLl0Beo9ZUwUKYT1Uqg6qIqYaAiTkHA0Gq6zSVCphMme3uqaDVFxKq3t4qp8RkVsfo317wOy5b1wcvLS2hRmU2ce+65CnD+7u/+Dvfee69z3a233opNmzbhC1/4AoJAsODw8DBuuOEG3H///XjjG98IAPjud7+Lhx56CHfccQd27NgBANi6dSuuvPJK3Hbbbfj617/e8ZleXl5eXktXPHoO/OA1QPy8Got5ggQJIh6rSFRCUvmkM5+oi5LOfLphbktCELSTnowyxSrKlDr5IRQghEDXUklnPx6gla5tSde+dhCV6Psmam7xpPMpUOoYotAeolxAJB39wHIhJ7PHhiQXULnmXZDE0dbm3KhzikiEikAUdeiTphBBwJS5RBgwxK1YvZYOfWHAVPSJMWYYTeSZS9RrFTSms+YSlUqA6fGG2jM42IMv//4/48wzT8PJp6zDUS//b+xeWGQRKQkwRdqzZw8effRRvP3tbzfWX3rppRgeHjbg695778Xg4CAuvfRS4x7veMc78Mgjj2DPnj0dn+nl5eXltTTFmz8B3/8uBVGcc0Q8RsxjtCyIoqYSdo8oClFNHhoQ1cyBqBZkeh9L0wCD9NqEKGo4ISGnmUJUZEFUKwNRgROiJIAtLYhi7SFKpjtlgCcnUkU/+LaDpJyGu/RZ7DRAhixEZfdpiDIiW5E+IyHRKu3Qx5C0yDhx5ZORKMB05eNRot5PwPS9ahViLlGvKoiqEXMJAEgIhPX31XBw3wQmxpv4/veegpeXl9CiAqkyevLJJwEAp5xyijEeBAE2btyo5gFg165d2LhxYwbQNm/ebJzVyZleXl5eXktPfOY+8APvB/hBcc25SN/jCVppKp/L3lxCVMJ1o90WgSjZeDdRwCPS+myIktcxD6x0vzB1/RNzkXTt4/pMnkIUl9EvHqjaKBOiAidEJTAhKrEgSqxdYIiyxroCUUXpfhSi2kGSa9567YKooka94jV3RqgMm/Mcy3P6GsY4jYoRhz7o86sVUS8FCFiSPaVqtQpmprQrX9Myl+CpOcXAQB2TE6K3Wq0e4sJLToWXl5fQokrtK6NDhw4BEGl3toaHh/H0008ba0844QTnOgAYHR3t+EyXdu3ahSRJjLFVq1Zh1apVhfs6VRzHxu9e8yf/tV4Y+a/zwumo/lpP/09g4j+AQfw9LSDKtDenjXY55yk8mRDFLYjiNL0vjRo104a5kbQth2kiQSNVHEGaFsgcECUAzIQoHalKEAjQSffGHUBUbEFUUbPdeYMoUgO1WCBKRaXk2qLUQHttXq+oxISoDLTlRbmIE1+RQ59UkP68AsIoQqb31araXKJaDc1mvMRoIgBDkj5If18Nk2OiLqq3t4qJ9DUAvPuDZ2PN2qF5/3vE/ky16ETTNhfynl6LTksOpKQYY+0XdbCu07VUn/zkJzNjV155Ja666qpZnddODz/88Lyc65WV/1ovjPzXeeF0dH2tExwz9GdYM/gtNWL3iOIWRBVFoiIrEhUbkShiU64gKq1rckCUrpkidVLKja8sRIn1NkS10p5QpSAqBaWjAqJcaXd5qX0FZ9lrGYkK0YhSLpTRyJY1LvaZkSt1X7ImAJCk54ckva8S6hqpMGQKooKAISIRJ07qpaoVDV71eqggKgiYkeq3enU/vve3T2P9hu9jeKQX86lnnnlmXs/38uqWlhxILVu2DICOIlGNjo6qebk2bx2gI1CdnOnSLbfcgpNOOskYm6+I1MMPP4wzzjgDYRh29WwvU/5rvTDyX+eF01H3tebTwNhvgDX/rxpKVCRKpPPZPaIkRMlGu9JYQkKUqF8KFETphroinc9spCuhKq1z4hSqUlMJZRYRGqATySa9KaA007onCVMJNyFKRZ4cEEVT9jqBqDjTbHeRQBTM3+l8W4hSz+RY2yFEqWsHODHolDsRoeLkNUkFpOt58euQMSSxNJQIEKcgVAlNh74oBSJhTGHVS6WvK2GgIkrUXKJWq6BBwEk9J4TRxCsvj+NVALufi7H9krMxn6rVavN6/lx1WOzIfURqUWrJgZSsY3ryySdx4YUXqvEkSbBr1y5cfvnlamzjxo245557kCSJUSf1+OOPAwA2bdrU8Zkubdy4Eaeffvoc31l5hWF4dHwQWgTyX+uFkf86L5yOhq81j/eBj/47oPUTNWb3iCpqtCt+T63MpblEWoOUgPR+IsYSCYk0NWU0SUWizHQ/ox4qjS4lyEKUjGKZEKUd+PIgKvVsIxCloWxRQRRdVxaiKBBZ820hCkAeRNEIUCai5IAoBhgNd0tBlKOeihFrc8OhjzEkMspEoCgMA/WaQlQQMAVRgOXQB/F9BIB6tYLGjEjp6yEQxRhDc1pDVG9PFTNTTQDAwGAPxg9Oqfucc/7J8/53SBnzMS+vxaAl95O6du1abNmyBd/85jeNHNr77rsPo6OjuOyyy9TYjh07MDY2hvvuu0+NJUmCb3zjG9iyZQvWrFnT8ZleXl5eXotXPNoFfuBdBkS5ekSJRruJarQbcbvRbhqFAksb6gYEqkLlwufuAUXTALM1UxKiYh4I2FFNestBVJQEorlvClGJBVGmecTsICohoDRvECXHugBRmXQ/qXaRprz6qNg8y5UG6IIvJ0QxZjbcJa9dEBUGGqJCAlE0yhTY1uYk+hRAPw81mqjXNURVKiFmSPQpgK69GhioYzqFqHpPFRMpRAHAz199Gk451VufG/C+kL+8Fp0WXUTqO9/5DgDg0UcfBQD84Ac/wMGDB9Hb24vt27cDAHbu3IlrrrkGO3fuxNVXX43du3fjlltuwdatW9UaANi+fTu2bt2Km266CWNjY1i3bh2++tWv4rHHHsPdd99t3LfsmV5eXl5ei1O88Y/ghz4K8DFxzTkSEoWiznztekSpqBMBKNoLSlqPS/vySEWTSMRK9oJKAaklf5fwJcGIy/Q+KGBq8cCAKVUHxaVbHxRERRmI0lGrpBCixO+JA6IkALWDKDq3GCEqAzv22iKISpWXBpjZZ7jvkUhURJvvlu8VFTBGbM41XAFQaX7ieTUEVULdU6pGzCVqtRCNKW0uETV0vVS9FqI51QIA9PfXlblEEDA0J3UfqZM3rcH+vROYnJjB0HA/vLy8FiFIffSjHzWuf+/3fg8AsH79ehVZ2rZtG+666y7cfvvtuPbaazEwMIArrrgCO3fuNAwjGGO48847cdttt+HWW2/FxMQENm3ahLvuugvbtm0z7lP2TC8vLy+vxSc+9WfgY/8BIjEPGXvzBOXtzcs22k1AIUpCFXOk+zGV7ifT/GRaXoQ0vS+FllimEDogKuLC6U/0hEIxRCU65U9bnIu1gh+yECUACZhXiJpNTVQRRMl5uV/KFWmyISppA1Hcvc8wiCD3czbcpel8pPmuu1cUVK8oAEZdk2F/Tpz7DLe+SoBWU7v1SYgKwwDNaQ1OIdMOfX29NUyNC3Cq91QxOa4d+ioBQys9e3hZH556bDeeegz4mzN+jKs/oMsgvLyOZi06kJL1S+20ffv2UpGigYEB3Hzzzbj55pu7dqaXl5eX1+IQ5xH4+OeAqS+TMeHMxzuwN5emEi1lDqF7RFEb86zzXtqQN02zi3iYghDpG0VqpWgjXGlUkai92so8IhEosUen8tmNdd0QFRgwlIGoRM+VgagkYUC6phREJebYvEFUPEeIovvKQFReNMsBUXmARns/8YiCkx5nOecwDlXvVAl0jVS1EqLVFLBUqQQKogAgJimAtYq2Q6cQFQQMjSmd6tefzgFA/0Adowcm1JfphJNX46gXx4KbTfAFvp9XOS06kPLy8vLy8iojnoyDH/oY0Px7Naad+TREdRqJatcjKiapejEnaX8qRY+lUSYJUSTNT0GUjExJdz3aT6ocRFGjCdk3SpyX9oSyIEpUABdDFN236CFqrpGovNS+DiFKpw9a8JOOu5z9nOsh13PDHEMqIM59tFdUpRIoiAoChpiYTgTkiJ56VTXgrdcrCqIAEmWDcOiT5hK1WgWTY9PqGd5w6Ul43fknw8vLS8iDlJeXl5fXkhOPngc/+GtA/JQaK+vMp+3NA7TAFETZ9ubSga+THlE63U+fSRvtyr2yES7tDSUjUspYgqT3uezMM259nNQ/pWfHc4Ao/XqRQpScVz8A1rxxvuO1K6LEHdDTBqLEOHkQG6K4dZ74YXU23GVp415Ap+0xZG3Oda+owDCdsG3O5Ry1OQ/DwLA5r9craE6LGqmBAQ1RjAFxpBvvnrxpLdas6UerFR3xzp+l5CNEXvAg5eXl5eW1xMQb3xORKH5IjcU8TeHjsYAnJCk4CVe+GOJzr7Q3b0lTCMvePAapczLszYt6RJn255012pXzup4qUYAVGql78gyn0YSEqIQpmLIhSjXa5RDNdj1E5UNU3AaijNc8J0IF872qZ+XONfTZqRkFBaKQ9IpiDIhbJPpEvia1aohmmt5Xr1eMxrpJU6f99ffVMDkhDCV6equYOKQd+ur1irJAH17Wh6d+9jKe/tnLOHbDBrz1XVvh5eXlQcrLy8vLa4mIcw5MfVnURDlMJco782mAkql0UQpQqs5JWpJn6p0kVJk9onS6n65xShyNdk2ICo2IlIYokt5HUvekEx+HaTQh1/I0TTAh9VMJdHQqF6JS+OLpB3FhJsEg66UWPUTZ88b5jtfdgKgkH6Lo2dl76vUs3S7ACapeKq/hrp22Rw0oaMPeer2iHPoqFdOtr8IYUjd09PfXlblEpRJgZlw79A0O9mB8dAoA0NtXw+i+CTXXN1CHl5eXkAcpLy8vL69FL86b4GM3A9NfI2PZeqgI0kxibs58ut4pMOBKQ1QgaqmMdD8JRiFplksiXSSdL9sjStQ2yUa7NkTlGU1IiEq4+GgeJwSakEJUogFLzslGuxK+nBCVQK1bFBDlMpagUNNNiEKHEGWtcUIUec1g9pDiFJxk891A94oSjn46bY+RVMIqga16TUMUY8y0Oa+GaEyLub6+muHQR1MCh5f1KXOJsBKgSUwoTj17LS687HQc9bJhfqHu6bXo5EHKy8vLy2tRi8f7wA/dALR+pMZmayrBwYiLXllnPmoi0Zm9eaz2QgFTUaNdmbJn1z+5jCbiEhAVp1Dkgii5rzREJenv6kOkhKgsEKmxMhBl/e6cdxlLzCdEtXXw4znjbgBjgOotFZCeUGGge0WFgYaogOnmu0AarUqfOWBAkt6D2pxXCSilf0jU696eKqbTFL56vYKpCR19qldDVSM1ONRjOPRVGUMjEsmhG05ahQt2nIwgUHfw8jrq5UHKy8vLy2vRird+Cn7wBiDZo8Y6N5XQ9uZuUwnhohc7U/W0Mx9HkGnEW2RvrnpEJQJsinpElWm0S2ukKETJOc5Ty3OYECVqo4IUjNpDVJKCkJyj0SeQ9cbcEoIo+jxtIcp5XgmIIucHgNV8NwtRQcBUOh8A1VtKPJuGqJCss3tFUcvzSiBADAB6e6uYTtP2wjAwYKu/r4bJtAFvb18N44em1Jd4oL+OyVHh2Dcw1IsXnnoFd//nvVizej0uvvwMHM2ScLzQ9/RafPIg5eXl5eW1KMWnvg4+9u8BiIJ3zjkSAlCxhKgCUwlR/5Q1lUhIjZTs55RN1Sty5mNGI94oY28eKpMJjnY9ojpotGvUOxH3PTDlYSAhSq8pB1Gqbmo2EGWvLwtRGVApAVFzMJYw0u/keR1BlHmGHKfPIqJPuvZJglMeRDEyLu6rXxt9o8IAUWouUSUQxVh+r6ieHg1RjAE81oA2MFDHxKFpdd70xIz6Eg8P92F0v6iLqtUrmBqfVg8yemASXl5eQh6kvLy8vLwWlThvgo//R2Dqz8iYNpVoIRFAlZPKR6NQwplPm0pIpz5a95RN1QtUql55Zz6dztfqwN6880a7HUBUOmdDFE3XE2YSKUQleqwQoqwUPwk8auxIgij7Nc/2fmLkHAErxRAVMAJRgGEaQW3RaSTLbrgbkegT3V+vVVTEqVarYIak8IVBgLglTFr6CUQxxgyb82Uj/Tj06riaY5wjiUV065jjl+GSt5wJLy8vIQ9SXl5eXl6LRjx+FfzQ/2PUQ3HOEZF6KA6RwichSphLaIjq3FTCTtUznfnsHlKdOfMFyoXPdOZrb29u9ojKb7QreEE68mnAUml6WGCI4iQJaalDlDHOCfy0gyim0vMoRDFiLAHoyJU6X0IUgS0KUaGr4W66x3TrM00iaK+ovr4aJlOIAoBqJUCzIeaGhnsVRKm14yK9b+WaIVz9a69Hb5937ZM/Xwt+T69Fp+BwP4CXl5eXlxcA8OaPwfe/0zKVSBAhRswTw5kvJpEoCVEtiEhUI41EtdJ0uSYPRfSIGEPIKFSU/op5iBYPSU2UgKVmWjMVqR5Sem/Mw9TgIbVKV6591N5c1mMFygxCrs8685k1Umaj3UBBEk39E4yQNtolECUiUgCsCJaCqGSBISqBG5LIPFsCEAXMBaIA0NonK9Il328lCAhEBQqibOe+SiB6TQGmW18QmG59vT1VNFKIqtcrmBrTbn19fTUFUf39dYztn1RzQ8t6FUT19NYwNTqN//bp/4tHf/QsvLy8hHxEysvLy8vrsEr3h/pPEDElGPVQMYipRFoPJcwkeFoH5a6HcplKyLqndqYSqpbKcuYToGQ684keVNSZT9zX5cwXk30y6lTG3lz1iOJmo10KSZzM5TXaVRDFLYhyOPKBAlZCxyyIUpCUD1H54FMwLzUHiOrInW8+I1EQP6iuezGuI1MV0nC3EgZokdonkBQ+2l+qViNufRLW0vfd21tVzXir1RAN0ph3YLAHE2mvqHq9gslRHaUaWdGPg6+OAxAW6IhiTKcw9vhPX8SZ556Mo1kK+hf4nl6LTx6kvLy8vLwOm3gyCT72W8DM/9FjBfVQeU12I6SOfJBNdUn/JglVJJpkQlQ5Uwlpje4ylZAgFMuaKN6ZM99s7c3zekS5IEq/7qDRrgSmdIyCS1uIcuwxokR580cARDGGXAMJA6IAZYtOISoMmDKWAABG0v6qlRCtNOJUq4ZoTuvoU8iBJBELe3trmJ4UNVJByBCTKBWFqDAMjAjWyMoBHHxlTH0LeutV5d43tLwXl7z1bHh5eQl5kPLy8vLyOizi0VOiHirapcba9YeS9VCcl2myyyxjiNCIMkm4Mk0lKFQFCs5ok908UwkZ0ZqtM5+ukdIRqTL25h032u0iRDG1Xn4DUQxJHUKUdgNc3BAlwIkYSETkXgSC6Puj4EUhyrZCz4OoajVE07Y8bwrTCBGJ0m59LOEKsChEMQZUGEMz7RU1PNJnQNTwsj6M7RN9pfoHe/CBT1yIkZUDOOrla6S8UvkaKS8vLy+vBRef+Q74/qsMiBJpfGY9lOwPReuhYi4M0YvqoSQItSBqoJpJBa0krWtKoUpGk2S0qWHUQ4n+UBEP0EoqYl/q0CcATPaM0s58EQ8QJaGKSEmIUo6AXEadAqe9uVyTgagkAFf1Uxqi5O/SAdBw5suDqKQkRCXIhyh+BEIUR4cQxQyI4qQOitN72RDFs9ErG6JoDymaQlaraoiy+0ZV01Q/AOgh6XyArrkCgL7+uoIoQNRPNWdEyt7AUA9G9+tmvMuXD2A0hahKJcSyZX3401v/Hi889Qq8vLyEfETKy8vLy2vBJKzN/zMw9WUyplP5ZH8oO5WvXX+ocvVQGq7idK2yQbfqoaRznwlBJF2QRKKkhXnWmY9EpjJRp7k78+XZm6toEoEo3SOKQJQLpiREkXkKQQqi5B4pClY2+MixvHn52jVWBGTyNUpCFIfZMNcFZUBJYwkCThSiiiJRDogKCUQxZkJUCJ2mV6tW0JzRZhJG36hqiGZaI1WvVzBDIKqezgGi4e5Uah4BiD5Sk4ekmUQVE6QZ74pVg9i/e1S9r76+Kl565lUAwIP3/gwnbFoHLy8vD1JeXl5eXgskHr0APvoxoPWwHiPW5pEjla99f6g07U4ClISqkvVQkYxgcQZtfx4oAFO1TTJShaCNqQRTKXwxz0adpOFEJr0vNaMQNU9ijppKiBQ+GFEqOSa+jl2yN6dzZSAqD5KoS988QpQzJS/nHqzd2YBZy1QinY8TM4lOIapiQBSMZrkhWVerhgqiGLFFB6RbnwAnw3QCQA+Bqp7eqtFwd3CwB+MHp8RzVEO00qgUYEIUACxb3o+De0W6X62nggvetAVe8Kl2XgA8SHl5eXl5LYD4zL3go58CuK6/SFJHvpgTV74ciJL1UK02/aE6q4dyNdmVNU6hmZ4nASxNm9PNdTVMzcZUwunMR+ZMZz4oiKKmEpwjC1EuZ752EOWyN6frXRBFx1wQVTQvzzDOAnJBZz4hKrUfL4QoFEBUh+l8NBIFwGiqGwYalmpWHRSFLQpKlUpgrOvtrWJ6vJECVgUzkw35zgyICsIAjHNEacPdkRUDBkQJqDoEQADXe2+8AOtPWAkvLy8hXyPl5eXl5TVv4ryFZOxz4IeuVxDFOUfMY0Tpr5awl0DEE0QpRElnvpjL/lABmlY9VMPRH4rWQ4loU5iph4pVPVTgqIcKU4gKFCjJFEFZD9VKAsQpqNkQFalaKpaBqFhdkygXceazActw5ktT+Vz25jZEJSqVz7I37xSiSI1UKYgiqXxtIUpCmnEWsmsJRLGFgCiOfIhKZg9RIal9CoPAaKpLo2BGDykLoujZFKKCgCkrdADo76spiApJs13AhCgAqNdEs18AGBzuxcG9YxqiVg8piGIMOPXMDfinbz+BA8SM4miV/Blf6F9ei08+IuXl5eXlNS8SqXy/DrR+osfauPK5UvmEtXmaegdt7JCQCJKuaXL3h+pGPRRN4cvWQzHVZNcVdXKl9ykrc5KuJ+ugOGT0SXxcjykccRJtIpGmOdmbuyBKjsn6J/VNRD5E5c0DCoaMlKgiiLL3wXUPfV5XIErCEtBViIpbiX7dzIMohji1PK9UTDMJ+j5pHZRdV9XfV8Nk2nA3CBh4lCClbgwO9WL8gG64299fw+S4SPfr669jnDTjXblmCPteOqi+TSvXDOOR7z8NALjvf/0I777+Unh5eXmQ8vLy8vKaB/GZvwYf/U2AaxewTlL5xO/51uYGGKkGuqEFSMX9oXQ9VGD0fOpGPZRtKmHWQ7UzldAQ1amphIw+yXWlICodozABMNNlT31joSFJGj20gyjr9+xZMMDDAKe4HQilz+AYny1E2UDlhCiG0jVRJkRp6MlCVKIgiqb9BQB4LA6v1yqqqS5j4j3IdX0EouTXJE6L6AaHegyIohbotXoFjdQqHchC1JpjlmHvCwfEmYzhjPOP7ma8Xl5UHqS8vLy8vLomzmfAx/4jMP0/yRhHkgJUDA1RwoVPW5zHEKl81FTChKjQcOWT0SYVZbLMJkyIcveHirnDrjy3HiqvyW4alSJRJ3WdRpioqYTq+YTyphI2RGlI0hDldOYjwJOxNydgZUCUHCP3AGCCVSGAFECW+qFAMUTlzS8ARDEXRGF2EKXT+cpCVGg0x81A1JSGKMYBTprvThGIqlQCRGnKXv9AD8YP6HS+gaEe5dBXqYZAlCBJ+0gtXzlgQNRqAlEA8PZfOxebz9qAo172Pwos1D29Fp08SHl5eXl5dUU82gV+6GNA9IQeK0jlo9bmGqCgISeTyqdBqJlJ5ZOAFKp0PwVVxLmPp7VUyrZcOvalkBNJQwoZKUoBTcKUXQ+lgcly4Us/fkeJTv2T+2Q0KSaNdGdtKsExS2c+PU9T6YrszQshqgxk2WcBzihQxxDlSAN0AZo+pz1EAXOPRFFjifyaKDsSlQNRdR2JknPSGr2nt4rpcQ1RtVqoekP19dcxeUhD1OBQD8ZTiArDABXG0EhrqIZH+nFgz6j6Nq1atwyvEIh6/fbNOLhnDFMTMxgc7oeXl5cHKS8vLy+vOYpzDkx/RUSioC2W5zOVr5y1uXbss63NIwlgqe34gtdDcQbOAwFHgDaVgPhdRa1SEKJpgDSq1D1nPrghSq1jcEINz65ZMhBluQe6olizi0TBjEQVGEvQmig7nS9JnfxsiAoZQ5K67PX21nIhqqe3iqnRaTU3tKwPYwdEqi0LGOrVirJEHxjswei+cfVtWn2MCVHrT1iBf/7bRwEAm077Pq781UtwNOtwmD94s4nFKQ9SXl5eXl6zFk8Oilqoxr16zNFgN6HRpzmn8rkASafyuazN4/RcmQpIbccjaUiRRobkPWiz3TL9oYrqoWg0Kc9UgoJWV0wloFPsCp355FgZZz6gvb25C6Ic4FYaoizgSZ/YGGcWULnW5EJUDoAxwGq8q8dB7Mpnn86XQlTYAUSl7n9AFqLqtRCNFKLqPVXMjOu6p+GRPozu1/WKffUqplKjid6+GibHpiFNKexI1DHHr8BLu/aq6/UnroKXl5eQBykvLy8vr1mJN74HPvpJIHlFj3XcYFek8rld+XTKnoCovFqnIJPKJwGJc2mXnv6SYMWZOlebQ7B0DVMwVdQfinNhhZ4bmSqohxKfvVP4WmBTCcCCqHamEhYEmTVMOZAlzzDOgrl2jhAl/4WeuVz9rDUUYkpFsaAhSsCRBipEORAFlIIo6uJXCQPDupxCVK1WBFFmOl+9XkFjuqlfTxCIWmZC1EB/HZOj02JtbxWt6SZ4GuFasXoIr76oIWrdhuV4+SkNURe983Scs/1UeHl5CXmQ8vLy8vLqSJw3wSd+F5j8Q9C8rZgnylSCpvLFaT2Uy9o8r8FuuVQ+06UvMiJQureUTtMrZ20uYUq69ZWph6IwNvd6KDpvApFpKkFS+Uj0qbSpRKcQVeTMB5iRqsxZMNfScRuWeBaWKODQ3lEstsDJscaGKAO6XPfleg9jyIcocj8GDUAUohiD2WyX6Wa7ee58AqJCNKfcENXTo5vtAg6IIvA1NNyL0QMaogYH6pg4JCCqVq+At2JErRgAsGzFgOobBQBr1o9g9zP6H0lef8lrsO6EZYijBJWj/dOj+nO3wPf0WnQ62v8oeHl5eXl1IGEo8QkgelSPOVL5XIYSwqUPaUpfgJaEpRKufNpAon0qH0/NIYpS+Uxrc+3GZ1qbC5BSkSkHMFGnvkVXD2XNU2jpijNfXqRK/WDADVF2Gh4FJ7m3XaNdzA2imAVpRvqf4dSHthAFzpUxRRFE0bS/aiVEq2H2iXK58wFZiJoh0aaengpmpgRE2RGsweFejB2clE+JobSPFCCa/SJJVD3V4LI+HHpV10itOXYEe599VV0ft3EN/vlvH8U//y2wbu16XPFLb4CXl5cHKS8vLy+vEuI8Aab+BHz8VgANNS4NJRKeoGUZSuRHobQjn4g6mQ12Z+PKVy6VjyEi1uZJCkcxl72kXKl8RdbmVmSKk9onCVFdq4eCgihqfd5tU4lCiOrEma8kRLWtmYJ73GlvDuRDlNE4Nwei0m+STu1LU/aYaGyr4Cox7yH3mBDFwGOd2hcA6oxKJTAhKo2AATnGEilE1TMQVcXMlPizWLUiWINDvRgnEDU8LNL7GIRbX8CAxpSAqIGhXmGHnoZDV683IerYk1bj+Sd2q+tK1X90BODNH7wAeJDy8vLy8mojHu8BH/000PxHPebsDQVlKmFEojjQAtIokzuVTwKUrHkq02DXNp3IT+VL+0VlUvm0EYUymyiRyueqh5JRq7n2h6KRps7roQogaq6mEu0gyxXVkq+RlxoI835zgSgrWpULUXlRLApR5CwDopiGK3tPwDREBYGOPAFmyp6dzsckYcNstiv3SXc+dyRKQFSlGqI11TIiUSZE9SqICgKGaiXAzKTY2z/Yg+nxaVUjtXLtMF55TkPUuuNX4sVde9T16duOx8W/cDa8vLyEPEh5eXl5eeWKz/w1+OhnAD6qxwoMJfJ6Q8WpoYSEpjiNANmpfEkaYVJwBF0nVdRgt50rX34qn9jvSuVT0SgCX1mnPj1HU/q62h+KRppyISprKkGhAGAdm0pQCJt3Z75uQRR15gMURDHyGkApiGIAgShmpOkZEAWouicbohhJ+7MhCgSialY6n4h8iUkboup1nc5XrYZoTedD1NBQL0b3T6pnq9dCTE8IiOob6EFjqoFYNuNdNYh9L2mjiTUblhs1Ume+4RRceNVmhJUQR73kz/hC39Nr0cmDlJeXl5dXRjw5BD72O8DM/9FjnCOBjkSVMZRw9YaKuHbpU+l5KUQ1U8ChhhIqxQ46lY822G0pgJKRqQJXPmRT+SRI5aXyFVubW/VQOal8IiI1P/VQcg+dNyCK67WH1VQib02BMx9dk28M4YAoC4goUNmpedR8Qu4Xe4hrnwVRjDynNKMIQ90XCtDRJhuiVO2UhKiqmZbH0nsAWYiq1UJlLFGrVQrT+YaGejB2gEBUvYJpaXneX0dzuoGoqY0mDuw5BJnet+bY5dj73D511nGnrMHD//AYfvrAv2Ll0Bpsu+IseHl5eZDy8vLy8rLEG98FH70Jtq15nEahRC1Utw0lbMe9QKXpxWSvhqq8BrtFrnwSsjpz5es0lU98zp+ltTlncPeHghOinKl8gGkqYafykfl5NZWg4wXOfLOGKGOvG6IyESbDlCLHEp0YTlCIklEiBVEcKkoVhiwDUXJdtRKgpSBKpwoCKUSlESXGGJAk6uvggihpDlGrV9AkaYADQz0YP2RGoihE9dQrqm9UT18NrZmmgqjh5f0YfXXUhKgX9quz1p+4Cs8/9rKa3/uinvPyOtrlQcrLy8vLCwDAkwnw8c8B0181xqWhhB2FykAUp7VQ7Q0ldG8os3murJNKOFnLtUufXsNUKh+1HM9rsFvWlc+VyicjXHYqH9ANa3PMoh6KpPI5IkpqLMdUQrn1FUFPEWSBnDdbZz7L9ME1XtQjyglRFpgxsodToIrdbn4y2iQjR4nRkNeMShlOfS1dRwUSbQoDDVFBoK3PAROi7JTAnnrFhKiqhijbkGJgsEeYRaSyIape0xBV760iarQMiBrbP67SCFcdM4JXXtyvoGntcSvw8lN71PX6U1bi0qu34qiX/Y8JC3VPr0UnD1JeXl5eXuCNfxKGEslLesxhay7NJGLIOii7HkobSuhIEjWUSEEn11AiNADK1RtKWp9HNEKVl8rXtsFu+VQ+uq4ola9ja3OO/P5QORGprvWHAjKmEoWQ5YpqyddwRK26UQ8FmBBVYG/eLoqVgSj7LAJ01LWPQg5iDW5BwBBHuj6KE1AKmY5ShWGAmDTerYaBgig7JdCORFXCAM1GDkQN2RBlpvPVqiGmJ9JIVG8NUdOEqPED49poYt0w9r18QEGVcO97RV2fdPp6XPmJC9E30AMvLy8hD1JeXl5eR7F4Mikszaf/zBhvF4Vy2ZpHYKr2Kd9QomKl6RUZStDeULK2StY3hRqGuIx+6YiRBC1Zk5VAR5TmJ5UvNbIwrM0DI5VPrU1BxWVtTlP9OuoPJSGKQlA7iHJCTwmI4sie4Vq7kM58duTKAVHgEKl5kBEmfRZNxaN7XBAl19E5G6ICQAGWbTJRCZiyP89AVN2EqCBgiFoRAGF/3iBzg0O9RjrfwEAdYwem1PNQd77e/nomnW/8wLiyVl++Zgj7dx/SEHXsCF59Yb+6Xnv8Srz64gHcfu1fYO3X1uP08zbiaBbj5OdtAe/ptfjkQcrLy8vrKBVvPAg+9ptA/KIeK4xCQRlL0ChUka25NImQoOTu+1TeUKJdbygOncpHYUqm8skzsml7eZGpcql81HlPApb4eiK/Hqpb1uZ0bZfroWB/YCwLSAsJUWV6ROU12gXMWikDoqBASaX2kedO0j5RoZWyxziQpM9nN94NAERpZKpaNefqtQpmJhvqfuBAEgnw6e2tYXp8RkeiBs2aqP6+OiYPTSs4CwOGRurs19dfR3O6iaglzhoaSdP50kjU8tVDOLh3VEHTmmOX49UX96n5NRtWYP9L+9CcEUD3yINPHvUg5eUl5UHKy8vL6yiTqIW6FZj+c2O88+a60LVKVhSKp1BFQSniISIu3fqkWYSEKl3DRCNVpqFE+95Qsg4rslL4dCpfoNL2JDDlN9gVxhUZSEK3U/naQZRlbQ5owOlyPRSgzzWiUPa+dufZa+bZmQ9A6R5RqlaKAYis1EBowJJufEHAkDRJVIpz8PQ8G6KQ6Lls412uaq6EcYSeq1VDZX8eBEz0j0rrknr7apge0xDVP1DHxKhO5+vrrWJqbBqA6CkVcI5m6uzXN1BHY1JbnA+N9GF8/5iCphVrh3FgjxWJenGfilStWj+CA7v3K4gaXjWAn3uXr5Hy8pLyIOXl5eV1NKn5j+Dj/76DWigzClXUXLfI1pxGmGSdlIYqZkSq9Jq0PxTX5hEZQ4n0WaideSSjTtCRJtlbypW254pMyfuUduWbp1Q+GbGi8xQiALbw9VB5gDRHUwnjHLgALAeiaBQrc2/3HgYoiGJAxhJdwlDALIgikTAKUbaleSW0ekbFiXqOWq2C5ozu/VQh9VKVSoCoGakve19fDVMEovr6a5hMoQkAenuqmB4X6XvVWgWIYjTT/X0DdUxPNFRkaXhFP0ZfGVOAtnLdMuzffdCoiXr1hf1q/Yp1y3Bo70EFUavWj+D9n30Tlq8exlEv9edzge/ptejkQcrLy8vraFByCMctuwts9AFz2IpCcdDokzsKlddcl6O9rTmNQsUk0qT6O1mGErJOSkaNYh6olMAiQwm7N5Tbgc8dmaKNdZXJhJXKV8aVb1apfOT1rK3NJTAURZkWYz0UUABgPPP+2+7PsTcXXwtSK2UZTqjUPiALSumygDGzCS81mQiYSt8Tc7HuGZVamKu1DIjSqFW1FqKVOvMBWYjq7asp9z3GGOrVADNpc91avYK4ESFOUwH7B3swOTqtoGlk1SAO7j6ozl65bhn2vXxQza86ZgSvvrBPu/etHzEgavmaYYysGMSf/vvv4NTNp+K4TcfAy8vLg5SXl5fXES3OOdD4DjD6O1jRv98YX7golGlrrt3y2tuaU6OHCKECnfkwlNBRLB096kqDXY7Zp/JR2LHHclL5aKQpH3qsc+lrkPMORz0UMCdnPrE/395cAZUDomiPKJmGZ1uTi/5RaX1UyBCTiFXAtBW6cOqLdD+pqpnOF3Bdg1WrV1Q6HiBqogyI6q0q970wDBBwoDGVOvn1VtGcbCgIGlrWh7H9E+qs5asHceBlC6JeOqCuRSRKQ9SaDctxYPcBBVEja4YQtyI88eNnAQD/+M0f4bgbj26QYuBgnLdf2OV7ei0+eZDy8vLyOkLF473gY78NNO7VHxaho1DlHfncUSgJL2YUSkaYzOa6MsIUEwArtjVnKjrltDWXEaw0aqWiRBA1VHmGEnmRqVhFsjQkKZMJIB3TtVKdNdiVESkY84cjla8tZGXOg3keHe+0HqoMRNE14ofVCVcs79402gQ3KNlzbXtERWZqn7yXbWkOUgNVqQSICDRVSb2UjFLJz+H1nooyhgBSaCLGErVaiOnUfa9SDcFbMaK0hqm3v45pUi8lmuuOq+t2ELX2uBXY88wrKjK15rgV2P/SfrQaOhLVmmli/IAwtqj1VHDBW18HLy8vIQ9SXl5eXkeYOE+A6f8JPn4bwCfI+HxHoRhavOKMQhU1143Ts2kUKs/WXBpS0JQ+CVsqekUMJWRtU7GhhNUbiut6JzOVT9ZFdbHBLnldKpWPgJD+xqJ9lKlsKh+5nwEp9nn286ENRM2XqYSxnxs1UNwAL9PeXNZAMcbA83pEMROi6BkiEhXre5G5aiVAi0BUGDC00rXCCl1HqXp6qpiZaqgvfU+9gunxhnq2MIBqxFurV9CaainoGRjqxcQB/ee7HUStXj+CV17QUel1x6/E7qf2qOu1x63AK8/vU+mBI2uG0JxqqD5VvQM9eP9nr8D6jWvg5eUl5EHKy8vL6wgSbz0JPvZbQOtHxnjMEyQdRqGKHPna1ULNJgoloUyCUST38/a25rJ5b3cNJaBS+SgsSYhyGUpocGL5USiQ15zMz9KVr22UqV2kCuZ5GVByntdmDXIgyEizI+u6bCrBCVDl2ZszQEFUkb25s0dU010fZUMUg+4nVa2GaE3r+igbomqVADNps90wDJDECaJWGnmynPsGh3sxTtL3lq0YwKFXxtT1itVD2P8yiTxtWI49z+1T1xmIOn4F9jz7qmE0MT02rdwA+wZ7cfbFm/BP/+unuPjyN2DluuU4qqX+IWSB7+m16ORBysvLy+sIEOcN8Im7gMk/gIglyXGOGHEKSmlKXwpOMTgizpEAbftCzaYWqkwUqkxzXerEJ4Eua2ue7QOVSECyI1NkXaeGEuJrquczqXyJHpNAJTYBs2qwCza7VD76mkSw5mRtPheIohBE96bPadQwwfFe2kCUAWcElDK1Uur96vQ9ahyh5rhO7bPtzRM5Z6X2hQFTECXrquR7qdUqaKb25gBQr1c0RDEghN5brVXQmtGpfv0DukcUAAwN9xo1UMuW9xsQtXLNkJG+t+ZYG6JWGBB1zImr8PKuPaKeEsDqDcsxvm9C1WT1D/dieKQf//i//xkA8Ld//j28+9ffCi8vLw9SXl5eXktevPGPohYqflaPca4iUAm4MwpF0/hEFIohAlPA1L4vVHeiULNtrls2CmUDGY0iSehyRaGA+TSUgDuVj0SD2rry5ezrSiqfPd5pPVQeBOU9D9AVUwkjqiWjUgy5znwMyICSijzZgEWArRIGypkPABjnKkoVhgHilp6r1yqqRxQg0vsaqbFEEIpoV5II6OrprWJmUkepBgZ7MHFwyohEZSCKpPOtWD2YMZLY+zyBqOOWY/dTe9X1ho1r8PxjuhXCuhNW4cDug6pma3CkH729Vbz05G7xPhmw5Q2bcLSLyT9nC3xPr8UnD1JeXl5eS1Q8fhV8/D8BM980xhMjChXnAxR0FComUSgFPBJ+IEAlShvgylqmxRiFUv2drMhUGVtzAU7FhhKcMzUGMqYjUuKcrKEEfZ01lAAOXyofBTEDVhzzzjUg4/Re6euO6qGAkqYS3ISwMqYSgFHLBA5laR4wplL5INdG7e3Nxb0T9VzVamg04a1WAgOiGKD6RFVrIVpTOtWvt6+mokAAMDBgQdSQmc43bEHUyMoB7Cc1UavWDVs1USsMiDpu01o897MX1fUxJ63GK8+9iqgpaqSGlg+gWmHY+9yrAIAgDHDVpy/DqeeeDC8vLyEPUl5eXl5LTJzHwPRXwMc/D/BxMq7NJGLIWigoQwlXLZSIEDFVCyWAKevIF6koFG2km9cXqpNaqLlGoYA4CYjD3uxtzZMkvYcjCsW5BiwaUco3lABmlcpH5ufqygcsklQ+wISeMvVQgGkqYdRTFezJNZVAxplP7rMhCgXOfAxQ9uZ2jygbokIGneoXpk596Vy9p4rGRENDVH8N0+Maovr765g4pCFqYLAH48RYYnCoxzCWGFrWi4N7DqnrFWuG8OpLGqqOOX4FXqaRqFPWGBB17Mlr8NJTe1WN1PI1w0haEfalZ1SqIXa87wKM7hnHzGQD/UN9OKol/7wt9D29Fp08SHl5eXktIfHWo+Bj/x5o/dQYtxvryigUt6JQwspc/NJ1T2YtlDSXEJEiHYVSUSTp0AdiUw5WEKky+0J1MwqV11yXw7Q8l/VMcTLbKBSFpDkaSlAYIgCUm8qXV+tE93eSypcHSHNJ5cuDqLznET+0en8H9VCu8xjQxlSCWqK7TSUYS89I54KAmfbmia6dsu3NK2FgQBRLEiTps9mNdnt6qpghENWT2p1L9fZWMTmqa6J6eyqYOCjsx1nA0FOvYPzglHrG3t4qxvaNq/lly/uxf/chdd6a9SMGRB178mo8/686ne+4TWvx/GO7Id0AVx+7HNNjkyqFsNZTxYmnHYu/vvu7AICTTjkR7/jwm+Dl5eVBysvLy2tJiCej4BP/BZj6c9BPxZ1amutaKOnIx0jvJ10bReudWjxMIcY0mqBRqBbt/VSiL9TcHPnmIQrFUWxrDqiIktNQYi5RKLk2cUShKEAVRo0KIlXWeQaAwQFlNuAB7tQ+uAGs41S+LtZDyTkX/DAG8BaFLT1nO/Mxrmun7NS+SmhCFAMQpTVRsgmv/PrU6xVVDyWvKUTVaqGqiWIMqFZCzKT252EYgIFjeiLtIVUJAXJdrVUQMo7JtI9UtVZBT08FB1PjCRYwjKwYUKl5AHDMCSvxwuMvq+sTTzsWzzz8vJ4/aTUO7TmAyVHh1tfTX8cxJ6zC4z94Sq1ZfdwKeHl5CXmQ8vLy8lrEEj2h/hf4+C0AP0jGtZlEDK4BCp1ZmksIMtP4pJFEmMJNADvFT9ZNuSJVRVGoiICW7gula6jmUguVF4VyNtflEBBF1ndka25DEgGZjg0lKDCpbzDaG0pYoDWrKJQTytqsQU4UygY5kNcUenIiVIY1Orf2lK2HyoleMVgQRaJhATMhisKXndoXMIg+UEjBLNbn2PbmtZoJUdVKgEZqbw4maq1kj6hKJUASJWhNi+tqrYJopgmePmdPXw2NqaZKvevtr6M53UArrbfq6ashiWOMpz2fKtUQfX1VHNgt/s5gjGHNhuV4aZd26zvp9GPx9E81RG04ZS32PL1HPVP/UC9GVg8ba37ul7fivDefjaNd3mzCS8qDlJeXl9ciFW/9TLjxtX5sjOs0vs7MJKKOGuuGBkDJFD+ZksdzI1V6LpF1UCTCRXs5SXBa8CiUAq38KBRN1ZuNrbkELxusZmMoQfcVRqHyIGournxlIcoFYJlzcyCK7gdMaGpTD0XnVOQJUCYS8gzVV4ox8NiMPMnrgAnLcjkXOlL7ktTVr1IxXftsiKpUAjSlhTkDQq7rpSqVAFErRpxCUb1e0YCFtF4qjQgBaePdNLUPkD2kxo356YlpxJGGLCSJSs2rVEOMrBrE7qd1et8JrznGAKTjTz0Gz//ri0jSZrzLVg2ip7eGF0n06m3XXYplJ/ZB2qR7eXl5kPLy8vJadOLJQfDx/wpMfwXqn/aBwjS+BOiSmUSRpTmpmyKRKmGJLmubAl0DlYJQBGkwoZ3yVC0V15Gx5HBHoaABTMEQR3dszQEFFR0bSlCAKKiXUlEhep4dvbLPK1pjX2OWqXwQP5BuuCqCMDdEMSC3HgqwIComUSkGA6Lo+Xb6HkO+qUSlEiJq0vooZkAUg3bmCysB4kYE2XvKTvWzG+0ODPVg4oCGpqFlfYbd+fDyfoy+Mqqul60YwKFXdQ+poZF+TI9PqZqsnr4aavUQrxAL9A0b1+DZR7TRxElbjsVTP35GXa86djmSKMaeZ1K3voDh7De+Bn91x3cAAKtH1mDH+7fjqJfnSS94kPLy8vJaNOI8St34/ivAR8k4T4FJRJ9cPaEoQAkjCSBG1kxCp/G1N5Nob2nO0nukxhKc1j2lqX12FMo4K9BQk66PjSiUGXFScMVnG4USYFk6CpXCSke25jLiRCJWbaNQQDlA6oahhD3erVS+3P1FEaWi/bOrhzKgKaIOfmYDXh6Z0GT3izJS+6gTX8gMiGKcI2rQ/lGR+jbYTXjtHlF9/XVMEVOJgUETojI9o1b049Be/ffCyrXD2EfszpevGcLB3QdVOuDAcB+iRhOjr4p0v7AaYtW6ZXjh8d1qzylnHYcn/lnXP63fuAbj+8cxti+NZtUqOPX1J+KH9/xErZHphF5eXh6kvLy8vBaFePP74GP/AYgeN8bLpPElgHLk01EomsaHtAeUTuOTkSZlNNHGTKLY0lzWPdHGujS1DypVT69nal669SU8SD9rmz2g5FiSQtKs+0LNRxQK1jyBLAoPc4pC5a0pYygB8rpN9CkTbSqCqC6n8un7WKCUUw8l9zmjV0BxPVTkhiZXvygKUa4olbynsD7Xznx2ul69x4So3p6qAVG9fTUjfa9/oAfjBKqWLe8zIGrFmiEDolatW4ZXX9BRp5HVQxjbN6YiYz39dfQP9KgoEwBsPHODAVEnnHYs9jy9Vz1nva+G4zavw8MP/EytOfOyU3Hp+y6E1+LSQw89hA984APOuQcffBDLly9X1w888ABuv/12PPHEExgcHMTll1+OG2+8EQMDA8a+iYkJfP7zn8c999yD8fFxbNq0CR/5yEdw8cUXZ+4xH2cuFXmQ8vLy8jqM4vFL4OO3AjPfNsdnmcYXgSlgkjVILWUOoQEodqTodcNMIs/SXNujyyiUBiJlLAENPCriBGpzrs+zIWi2jnzzGoWi+1xRKBcg5QGPA866HoWi90yVgavDkMoHAKzI2tyqh1IpgFY9FDhXa6VRhITNMGAKkgARaYqbqS16IOBLfj0qlRDRjE7lsyGqWgkNiKpWQzSmtDNfJQgwnTr3qeux6XSeoV6vKCc+xkRPqUOv6PS9ZSsHDHvzVeuGDYhas2E59j77qqplGlo+AB5FqllvEDBsOGUNnvzh02rPxrOOw9M/eR5JWrc1ONKPFWsG8cQPdqk1l39wO7b8/EZUqv6jY+YfMBbonu20c+dOnHvuucbY0NCQev3ggw/iuuuuU6Dz0ksv4dZbb8VTTz2FL33pS2BM/NRyznH99dfj8ccfxyc+8QmsX78eX/nKV3Ddddfh7rvvxrZt2+b1zKUk/6fBy8vL6zCIJ5Pgk38ATN4NQP9L9Wzd+FxpfCqKBKbBKU3jc/d7CshYXqSKmkOYZhLFluYy4kTT80JdB4UUBBPTYMIYk+OdRKGSgACXhiU5Nq9RKDAzZU99k/WvQkDqtqFEWdBCfiqf01WvA1c+5zMChdbmRdErw6WP1kMBpqlEyXookRKYqCiVbSoh+kdpiApDZkBUyBha6XwQMPAkUfNhJUDSiBFxEeWSznytljB4qNUriFqRighVayFYwjFxSESmgoChz4KqlWuH8OoL+9X1uhNW4mXizLfymBGM7RtDU7oB1itYsXoIzz7yglqz+ZwT8fj3dWRq5foRVKsBniFrzn/r6/B/v3Q/7vmjv8PIN1dg65tfB6/FpxNPPBFnn3127vytt96KTZs24Qtf+AKCIAAADA8P44YbbsD999+PN77xjQCA7373u3jooYdwxx13YMeOHQCArVu34sorr8Rtt92Gr3/96/N65lJScLgfwMvLy+toEucJ+PT/Bt/3JmDyTlCIEtGnGBGP0UIsXiNBxBO0eIImT9DiHC2IGqgmgBZnaCJECyIdr8UDtJIQDR6ilYJTxEM0UUWLh2gmFTSTMHXsCxHxEA1UROpf+quVhGjydCyNWjV5iCYPESUhWryijSl4gBaviDnOEKUg1UwqaCVyfeoAyAPESYAoCVVvKVmrFSWBSuVzjcl7JTK1L53XqX4CbKI4jXbRuURDFAUvCUNJwjREpZElDUkahkDGlKFEwjRESYCS0alYgJIzlU+OW/vM1xTErDUUaGICOUn+a+N8exz6zNxUPE7GuXWW+OHOhTMmnxGOPYY5BF2XhSgJSTTyBIg/EIycydOIWMCYiGYRwMrUQ6X3D8PUBj1dG4YmRAWA6h/FmLhPnEJQpRIAcSJMKSAgKIliVa9U76kinokgQ6K9/TW0phpqvm+gjuZUE0lLO+9FM03l/FfvraJSCTCRRqrCMMCykT7se/GA+tFae9xyA6LWnbAK+186oCBqYFkfBgbrhnvfa8492YCoY09Zi7jZVOcwxnDeFWfjwb/6AZJYvJ8XSDPfo1b8MP2ag/bs2YNHH30Ub3/72xXwAMCll16K4eFh3HvvvWrs3nvvxeDgIC699FI1FgQB3vGOd+CRRx7Bnj175u3MpSYPUl5eXl4LJN78IfiBd4GPfhJIXlHjCeeIeCQAisdopgDVSsGpCW4CFE8BigdoIkghSoBNg1fE6zRFr8krAqp4iIaCI/F7k1fQlFBE9htQxUM0U+BScJWEAmJ4iCYqyiRCQpK0RRdwFaS25YEyq5DrE4iUvFZC1lhjUUIMKGQkK52TUSgJS3EKQ3GcRr9IFEoCGAUknuhfAqZgwBQFJxDw4hSWuA0VTMCVBCEKPBYg5cMPgSw41sA8MzfC5AKfvDOtcXVukpOKZ+znZv2SBWfG/jwYkkCYvjWWcAQOKGOAAC8CW4h0uh59DgZk66FSaAoCZpwTMoa4oaFJRKlilSKIOFHW6JVKIPo5pVBUq4UCsNLvTb2nolzzAKCvr4YGacLb11+37M17MEWuB5f1YXp0UkHW0Eg/mtNN1d+pp7+OapUptz7GGFasGcLup/XfKetPXo3dT+1VZ6w8ZgRJq4UDaUpgEAY45ezj8a//9KTac9KZx2Hfi6+qNZVqiDMvfg0e+tYP1ZoNp6/Dm37lEngtTn3mM5/Baaedhte//vW4/vrr8cQTT6i5J58U3+tTTjnF2BMEATZu3KjmAWDXrl3YuHGjAUcAsHnzZuOs+Thzqcmn9nl5eXnNs3j0vKiDavyNOU7qoGIkqRsfVC2UTOOL4U7jczfVzbrx5afx6f2uNL7ISPHTNVY0jU9Gg2j902zNJEybc+LWl0Z0JESp1DwgrY/SJhLaUAIAtFMfJ1BTvi8Ufb2wtVCAYw09z97rWm/vpaBDIQUFUSh6P7j2c3cUCpi1K9/sU/msWik5ZxlMBIxlolJxkui1Ma2HClQUCnDUQ1VDNKesJrxT2t68Xq9gitib9/RUMZVGlQCgf6COiYP6enikD6PEznxk5SAOErvzoZF+jB8YV89Y66miXgux7yUdmTr25DV48QntzLf+5DXY8/ReRE3x3PW+GlYfsxxP/vAZtWbz60/Ck/+8C3HaR6pvqBfHbV6Hf7nvYbXm3De/Fr/w6UvQN9gLr8WlwcFBfOhDH8K5556L4eFh7Nq1C1/84hfx7ne/G1/5ylewadMmHDp0CIBIu7M1PDyMp5/WNXOHDh3CCSec4FwHAKOjo2pdt89cavIg5eXl5TVP4sko+MSdwNSXITAoHS9pZy6d+GJIV74ALaQGDcrOPIUhA4Dm5sYnAayoJxQFnRbXUKaMI4iNuW0mQY0jnAYTXANWGUtzNZ8CTl4tlGEkIWGLjIOATqm+UBKsQFPwrDQ+C5BcwNJRLZQ9XnhmmzWpMi5+HRhKpO8+H8KADAwZZ8zClU9BkwJE7bzHGFSkCem1HZVKODGciBJtOBEGKgoFAJXQhKgwsOuhoPpHMSbuL1PxGGNgnCvTCRYwBABmJmbUfK0WYpJEovoH6gZELVsxYEDU8tVDOPCyBqah5f2YHp/G2AFxRlgJsXLtsAFRx29eh2cf0Y13h1YMoFat4AXSaPf0bafgkb/XznwrjhnB4Ei/Ea264pcvwf9z56/i4Uc0WB3NykSHF+ieeTrttNNw2mmnqetzzz0X27dvx1vf+lbccccduP322/U5jLmOyN6v5Lr5OnOpyIOUl5eXV5fFeROY+nPwif8G8EPGnLYzT5wA1c7OXMBK2r+pdFNdaVWe58ZHI1UuNz4gSSNc7p5QusGu7AmVkAjVvJpJcGaCkYStoihUomHLFXnSERiZ5kfnl0gUCo7XJQ0l2u/nmTVtIUz88BdEr7gRObP3qbnEikoRmGOASvNTawlgZVz6aP0TYwZEif5QEdmbIA0CCeBq6ia7lWqIqNFS3/5qLURruqXeQ72nisbkDOJ0Qa1eQasRqchVWAkQcI7J1FSCMaB/sMdotLtizRD2k6jTymOWYd8L+5UzX99gDyphgL3Pafe+4zatNSBqzfErRY+o1KwiCBhec97JBkQdu2kdmtMNPPPT59TYv/mtq/CBm9+FJEngtXR0zDHH4JxzzsGPf/xjAMCyZcsA6CgS1ejoqJqXa/PWATqKNB9nLjX5GikvLy+vLkkYSfwf8H1XgI///w2IknVQrYI6qCitg2pxXQfV4gEaadPbFgT8NHkFLWUEoeufmunvLR4iSuuZWrxCaqMEIDWSCppJBTE5o6GMKipoJZaZRGpUESdBaiih659apCZK1UEl0lZdpxq24rArZhKxbSaRBIaZhEjzCywziUCZSSSxBVmcwaiFSsGJcwYem/MMabqaNJlY6FooC3TamUs4jSYoRLlqmYr2ix/yLAyl61gCs66Jkz1xXg0VV2YQkHOOfQDEvyxYNVb0NWJ3rZToHUWiVBwGRIFzJLHuD4U4UWepeqhU1WooeksRKIpIlKqnt4rWVEvN9/bV0JiYUV93aSohz+ztryGeaapIV62nimo1xMSh1P48YBge6TMgatX6Ebz6/D4FUSOrh9CaaanmvWElwLrjV+K5R7Xr3omnH4v9L+xXtVg9fXWcePp6PPIP/6rWbDrnJBzccxB7nxW9poKA4WP//Vp88LfffURGEuYkDvmvMQv4axaPybmqSZJ1THYtUpIk2LVrl1HntHHjRjz11FMZeH78cdHncNOmTfN25lKTBykvLy+vLog3HgLffxX46K8D8Yt6nHNEXFgeN3mMJiK0FJbbhQAAietJREFUkCACJ0YSqRsfpxAlAEo48gnIaSSpOx5Eb6hW6rgnTCOqaCXaKELOtTgxnkgqaKSOedKNTwKYdOOLJEAl2o1PpAmKX80kVKDUTAEqSkJlFiEd+iQYSeMICkummUSobdAtgHKZSSS8vZkEdd6jhhKmmQSQbyYhgYqm7EGECeVaCkJSBKC65shn73XBjQVEeaADEACLTQDqiqEEt/YAbkMJMlfKiALIdeUTcKuNKRhgQBMSrkwiRNpfomzRg4AZ76kSBkiasdorrM91Sm4lDFQqHwBUqwGa07oeqlYLMTOuTSV6eqqqPxQgTCYMU4nhXkyTeqnBZX1oTjWUqUS9t4pahRnpfstXD+KV53RT3TXHrcDB3YeUuUX/UC+GR/rw8i6d3rfxrOPxzE+fR5Q6DC5bPYRlK/ux68fPqDWnX7AZu370tGoK3NNfx83/65N4y7U74LU09eKLL+KHP/whzjrrLADA2rVrsWXLFnzzm980YOa+++7D6OgoLrvsMjW2Y8cOjI2N4b777lNjSZLgG9/4BrZs2YI1a9bM25lLTT61z8vLy2sO4q3HwSc+DzS+a46X7AdVpg5KWJUX1UExJGntkpzT5hLINN4VtU4iupSgXFNdmcanI0+zS+OLScSpKI1PfSbn0r58NmYSBJzQQWPdnHQ8DV7I1kKVqXNayFoo+xruPbkAZVxz9/2B2RlKALkNdgFYPaW4AVjcTuWLi1P55LlBwAQkpUuDgCnAAlJr84auhwK4gihZdxVFkTpL9IeK1Fk85sp0ggUMAeekHkqYUEwRqOofrGN8/7i6Hl7ej9F9+npwWZ+Yl86A9SpqtVA11QWAY05chZd3aSvzlceMYHzfKCb2N9RzHX/qMdj1Iw1M605ajfF9o9j9su49ddb20wxTiRXHjOCz3/w0Nr72RHi5pf5BYoHvmacbb7wRGzZswOmnn47BwUHs2rULf/AHf4AgCPDRj35Urdu5cyeuueYa7Ny5E1dffTV2796NW265BVu3bsX27dvVuu3bt2Pr1q246aabMDY2hnXr1uGrX/0qHnvsMdx9993GvefjzKUkD1JeXl5esxCPXgCfuB2Y+QboJ+A8IwnpxBer31WmUurGlzbShWyoCwFJKUCpdDhSBxXxABxBpkaK1kGZjn1pbZWsiaKRIAJhug4KKWhpw4mYNNXtxI0vJqAUq2iSCVi2G1+S6PnEYSaRqGsdTVJRJa7BqSuNdW0QkvM2WFnwNS+1UICVAofMMzsByjq3raEESSdy3bO0oURC3qPDlY+m2JlGFATgABWFEvv0nAQs02BCr2UcBkSBcyTKMp2BR7GqdxKGEyQKVQ1FA950vloLDWvzWq2C5lRTPUe9p4rGxAxiOV8XVuhGPRQ4JtPIDyDszylEjawaxMHdGpiGlvdjanQS45PT6oyRVUMGRB1z0mq8/MTLKt2vp6+O4RX9eOZhnd638ezj8cxPnkWUvr9aTxWnvO5EA6JOOut4fPabn8aqY1fAa+lo8+bN+Na3voU/+ZM/wfT0NEZGRnD++efjhhtuMJzytm3bhrvuugu33347rr32WgwMDOCKK67Azp07jfRNxhjuvPNO3Hbbbbj11lsxMTGBTZs24a677sK2bduMe8/HmUtJHqS8vLy8OhCP94NP3gVM/RlsJz4ObWdexkhC/J665AGpS55pJJEQSIo4I9El8ZqTORmxEv2agtRcooydeerGxwOdSse1gQQ1lZApdQrYSGRpVm58nESb1Dgxk0gCsh4atnKiUBqmYMyXMpMAMvCzJKNQ5MwyUSi1zq5Pct0/XTdbQwkn/GX2cdPNjzwLYzAMJRiQsT03olJRoq7tKFQYmr2jMql8lQCRkcpnQlS9XlGufADQ21vD9LiOOvX2143Uvp6+GmbGphRkVWsVgHNVDwWIyBSFqBVrh7HvRR096hvsAUu40Yh3/Umr8OLjuknuinXL0JxqYO+z2njiNeedjJ997zF9n1VDWLZyAI/8va6ROu/nX4vf/POPe3vzJahrr70W1157bam127dvNyJFeRoYGMDNN9+Mm2+++bCcuVTkQcrLy8urhHgyDj75P4Cp/xfgU8acduKTEahEAZQAJB2Fkv2gBLwwlbKn3fKoEx9ElMnZD0r3ipJjwoqcQJLh2CeBqL2duahlkml/0lFPuveRBrkQUCPqo5gx1rkbX7s0PhhjYsAEqtw0PpDX5KxMGp9cJ8dVTZIFUBSsXBGjsmucZ6L9mXC8nq8oVB6EZc7gGdiic+WjV/paRZrU/SzAKnDlE72iqCsfMql8FKIAGBDFODeszxmQsT6nEFWtBAZE9fSa9VH9gz2YPDihrgeGe1U9EgBU6xUEnGPUsDsfNCBq+ZphHNw7qowqKtUQy1cP4kViZb5h0zrs3rVH1UNVahUct3mdAVHrN67F1NgUnn1ER6vedv2bcP1//WWElRBeJUT+nC7oPb0WnTxIeXl5eRWI82lg8k/AJ78I8FFjLuEcCWIFUKoOKo08OeugOER9kp2yJyEH0t48VCl4NLqkIlZt+kFxLizTZf1URCNJ3KyDoil3MXTNk10HpWqjCCzFjjQ+ndpnRpzoHncaX5pOmAROgJKvJTRRkCpM4yOvXWl8ALSBQ14aH5AfYUpcrwvW0DPLpugdjiiU9fzzGoUCTIc+0hvKPsdO3QOs64QjSUjvKM6RSDizUvmCUACXVFgJEDcida+MtXkalVLW55UQUaOJVtrINgwD8IRjZqKhzuzprRoQNbSsD2MH9PXgsj6M79OGEpVqiHq9YtRDrTluBfY+o00mhpYPIG62lMMeAJx42nojlW9o+QB6+qp46sdPq7GNrz0Rzz3ynDK0CMIA1//XX8bbrn+Td+bz8pqFPEh5eXl5OSR6Qf2FSONLXrXmzBQ+AVBw1kHJCJQAKQlQTKXxRW0b6urokmkkwXRUSgFOCNVQF7KmSUajZGSoTB0UA4d2wYu4BiNXzVNhHRQ07MQkTU+m8dGmuknadFc7/mab6kKeVyqNj74mZhKzTOMDdIQpH3bcEa4MQDngSJwP8gzInlVw7qKOQtkQlWMooa6ho1LGWs7BraiUBiwBSfI6k8pn9Y4KQ9E7SspuwFuthkYUitZDAboeSqqnr4aZcX0t6qOamEkjVYwB9d6aAVHDy/uNKNTgSD8mD05gYlqAGAsYlq8eMiBqzYbl2PvcqyoyFVZDrD52uQFR605cjUN7D2L0FQ1jp79hk5HKNzjSj5v+4ka87tIz4NWZpNPlQt/Ta/HJg5SXl5cXEectYPqvwCfvNGzMxZwGqBiyDkoAFHXiE6l7IgIVpaDSzokv4aJPlDSB0DVPgTFX3kgiXZ+CTkIgTPZlog58qiaLz7UOiqUwNE9NdeeUxqfHKTCUTuODfE3HrTV5QOQ6E47Xc4lC5ZlB5IGa+OK4z07Pm00UCtQmvWgfYBpKcF68tiAqxQDwlpmqRyEKCUccmVGquEVyDIkrX7rAgKgAMCCqWg1MiOo1IUpYnU8a843ppnbyCxj6+mpmKp/VdLdvsAdJFGP/SxqG1h2/Ai/v2qOuh5YPgHGO3cR44uSzjsNTP35aORzWems44fRjDYjacOp6/M5f/QaOPWUdvLy8Zi8PUl5eXl4AOI+BmW+AT/w3IH7emqMAxREhBgdKWJl34sSXRqBIeh6dMyJWHRpJxOoM0qtJ2aHr6BEFL1oHlXCkqX2zr4NSn9G5bJpbXAdFAambbnwATeNLxztJ43PCzuyjUCbQ5QAYARtjD3IiSKVAjR/eKFSRoURmLQScqXuaaX9ISF8pBmF7DhKVauneULYrn0zlU72j0lQ+KZnKJ+8nXfxkKh8LGAJAARIgUvkoRA0u68X4AX3d219Hc3IGk4f02JDVdHf5mmEcIKl9lWqIoZE+A6KOOWk1Xnlun6rtCgKGE05fj10/fEqfs24E1WqAxx/SDVPPveJs/Oaffwz9w/3w8vKamzxIeXl5HdXiPAFmvg0+8XtA/Iw1V9wLqoyVuQAV04nPhiTa58l26eNGHZUr1Y+k5JU0kqBApmAGxN7cgiXV+ylnzIgokflMHRTXjnwuO/NsGl+a5jcbNz4JJ7NJ46PjDviSr5lxz5JRqLz1QBaU2oCZK/VvVn2hrGfoehSqja25AUku8wkJWBKS6HWLGEgwhiQ2DSWo7TljMCAqYAwxSeULwyALUcS1T7j06dqnek8VjckGceELETcjlcoHCGiiEDU00ocx0mC33ltD3IqMprsr1y0zXPmGVw5iZnzKAK0Nm9bhhX/VTn39w33oH6jhKdJk9/jTjsXeZ/dimkTKrr7xF3DNf3ofwtCbSsxJ5M/xgt7Ta9HJg5SXl9dRKRGB+g745H8Dol3WnAaoTnpBGfVJDoDSkBQocwjVy4nCFUnVcwGUuo+KNsnoU/uGum4jCaZqqcTnY2bAkhzLrYMiACTroKjNebYOSoITMnVU8sOCrIPSMAUFXnaEaV7c+CBf0/GSa6hmaSYBuEEpfQeZPcy+v/M+3DwjLxUw86w23JhzhVGokrbmAExDCcC0NXfURtmOfgmNSkUJpCEEYww8jtWPi4xSKcOJgCGJYsRJmnjKAMTcgKgwYE6Ikurtr2F6VLt51nqqaM20DCe//v6aAVHLVg7g4J5Dek+9inpP1YCo1RuWY+/TOm2vWq9gZOWQAVFrjl+JQ3sOYu9+nSa4+fUn4TEShar31nDj3dfjkvdcAC8vr+7Jg5SXl9dRJQFQf52m8D1lzeUDVKe9oJJMBKq8lbmCJcuJjwJU1okPAqpsJz5opz7dPFdHkTo1kqDOfxSA7DooCUpFdVB0XnwDTKAqVQdF9mWiTaCRJEag4vCk8anncYEZHK/bmUkA+RGkzNncfXbRGcCCRaGYda6yNQdErY+CIgFJhs15bEWlqO15CklSYZim8qXXdu8oGoViSBvwTjcRp0uCMACPExOq6hUDovoHezBJrnv6amhON42x4eX9BkQNrxjA2P4JNKdSkwnGsGLtkAFRy1YNYWZiBq88r/tDnXDaejzz02fVda23hg2nrDUgat1Ja3DzX34CJ515PLy6I+b6R5MFuKfX4pMHKS8vr6NCAqC+nQLU09acjDbFcwIoo4aJAJSEpNkCFFdRLAlooQk8BU58EpZsJ775M5KQqXvzWAdFXmcACshASidufOI1HS9YY0eBAHJPR3TIjk7Nl6W5sc4GGPNeiykKBcCsjQKyzXZzolByPmN7HhGrs4QjjszeUBSiwoAZUahqLURrqqnm7ShUtVZBa7qBxqQ4gzGxhgKTbW1e760iacU4ZPSLGsKB3YfUdd9gD3gU41UCTOtPXoOXntht3HvNccsNiFp57HIkUYxdJL3v9W86C5/+049iaPkgvLy8ui8PUl5eXke0hAvfN8En/zsQP2vNmRGoGGkj3VIAxQzTCBOgmAFJ1CDCdNijvaCQUyuVtTKnhhAxtL05R3knvjxYcjbZJbBFI0uqRxSgolOqH1TiBihnHVSi5wQc0XmgrZ05jRKVBSg6ngdH9t7MuP3DhkIzCScoUQBqYyZBn7HI0lytA899hkJDisLo1fxFofJgTEah9DUDJ1GoTJTKFYVqakOJsCKuuTWfRPp8xDFaU8QavRKYqXx9NUyPaWCq99bQmJzBzKSMKAE9PVUDogat+qhKNUS9p2pA1MpjRrDv+VfB0z80QRhg1foRA6JG1gwjajTwwmM6ve/ELRvw/L++aIDhL336nfjg77zb10N5ec2jPEh5eXkdkeK8CUz/JfjE7wPJS9bcwgNUApLyBxqVogAlo1KBAiiXlTkFKGllTmufZD2UBCdO9pVx4puLkQQn8+JrnQdQMOugEgJQkAe7YapUHZQCACuNT8GKtTdxvT58aXyZ/encnC3N886Q+1zAl87lR6/se/I5RaEyfaMi69p28LOjVCQKxQAjlS8IgJjARiUMEJF5Owolok5NRGnPJhYwhAwGRPUN1DE1pmuhRD+paUyP6/sMDPUYECVT+SZntLnFirXDePW5V9T10PIBRM2W0UNq/Slr8NLjLylr8yBgOP70Y/H0vzyr1vQP9+GTf3QD3vD2c+E1X+JQ/0K0kPf0WnTyIOXl5XVEiSdTwPRXwSf/EEj2mnNzqoEqD1AJD9AqBCiynptpfe17QclzoUBKpwvaVuaAdPfLtS13AJRw8MsCVELMJ1wA1bmRhHwN0DS/wn5QPDu+UHbm6l70TApKIHu6ncaXdx9jHbfAKOeMzHPw/K8JLPCh64BsJKmoL9ScolAwbM1l8928KJQ0lJD75Xwiv4/p/iii9VUwIKpWFw14pUQD3mlIPArCAAzcgKiBoR5MkIa7vf11NKYaGCdjI6sGcXCPTu3rG+xB3Iqw7wWdyrfmuBUGQAVhgLXHr8CL/6p72w2tGERvX82AqI2vPRGf+eqNWHfSGnh5ec2/PEh5eXkdEeLJGDD1p+CTfwTwg+bcAgJUJFPrjB5PboDSaX0ugKK9oPIAirj0lQQolxNfpJz2ggxAUSe+skYSGYAC0n/AdRlJEEAiAFPaSMIe511I48tb4zoTjtdzTeOz9szWTILeq7SZhHWmCT70OXnWptyIWJmQVBiFcpxlR5l4Zj6/ForBtD23IatSCRAR23PRG6qJREJZyJC0EgOiarUQjQliaz5QxzQBqLASIkBiQNTQSJ9ha17rqYIBBkQtXzOM/S/uI+cEWL562ICoZauGEEctvPTEy2psw+ZjsPeZvRh95ZAae8u/vQzX/+4vo9ZTg9c8y/47YYHu6bX45EHKy8trSYvH+8Cn/l9g6s8APmnO5fSBsm3MEwCR+Cw3R4ASbnrtIlB5ABVzHb2SAKVdAE3bceoMqGAG7XtBUYByWplDAw+FqnJOfDDGxYADoLgeVx8OHDA1ZyOJTgFKng84QUedWya6VBa04Hgu+Rx5KXiW8UM+CFlnwNpXZCaR9/yAw8mPu59HnlU2CgWAx3OPQinHv0Csl1EoMAAxNyAqDBii6fwolDSUaE5FxhoKUf2DPZg8OKH6SQVhgGo1MCBqeMUARl/R10EYYHikz4Co4ZWDmJmYwasv7Fdj6zemqXzpHzDGGI4/fT2e+clzak29t4aP/vdrseP92+Hl5bWw8iDl5eW1JMWj58En7wam/xJAw5zjKTSlABVB9JApaqRbzsa80xqo2QKU7A9VHqDkOcoYAhSggFx781JW5siAU3ec+ICOjCQA9YG/NEBBvobznI7qoGxQQg40LeE0PpeZhL6HDV+Oa+hrZgOWHbFawCiUbXseVsR1HElAEe+1aaf2TepmtvWeKhpTDTQJeNXrFUwe1FGogeFejO8fR5z+lcQYMDDcZ0DU4Eg/pkYncWD3QTW2esMKvPKsjkKF1RAr1w7hxcd0Kt/g8gFUaxUDok44fQN+839+HCecvgFeCyjyZ2NB7+m16ORBysvLa0mJtx4Fn/wiMPM30IUx6ZwBUAkiJG0BymykW9wHarEDlPhMzDoAKDGfB1BAJ1bmJjhRgBKvywBUuma2RhL2eB7cdKsOynku2QO4QQtdTuOzALEUCDnO7MjSPKFfD55Zy/L2Wtd5USgFYwyFjnyZKFTafDchfzUwzk2ICgPExORBGkqAzrciJ0RJSUMJaX0OCNOJ8f3j6rp/qBfTY9MY30/qo1YP4cBLOuJUrVfQ199jQNTImmFMjU5gzzPaeGLD5mPw8pMvI2rp9/6Wf3sZrvsvH0JPXx1eXl6HRx6kvLy8Fr0450Dze+CTfwA0v5eZT9IUPgpQCWDUQCUEoAQwiQhUbAGUrDeSaX3ZRromQIn9nZlIdCOFbzYApezPCwBKfr4uA1BOK3MO8MQBUEgPLgIonl3TDSMJ+5x5rYNCwTo47inXd9uND9Y+o76JPlebNL5OolAZGLPWzioKle/IB14uCiWfT9qcx2QNYzAgyo5CVWsVtBotA6Js6/OefgFVk4d0avHQSD/G9mmoqvVUEQAGRK1YtwwHdh/C6JSOVq0/eTVefFy7jIbVEMectBrP/+wFNdY31IuP//6v4Y3vvgBeXl6HVx6kvLy8Fq04b4kmupN3A9FjmfmEJ2kNlLQwFwAlTCVg1EDZABVBp+tJIFJ9lwhARSk0tW+k67Ixb28i0Q6gVLpeFwBKAlIRQAlziSxAie9HHkCl4+0Air4uA1D2OG+TxpcHR2XWGD94mJ86KCBTe8XsKJjzDK6f23H+rNP45tNMwuXmB3qdmF93AkwySiXnRZRKA5TtyCfnaRQKVvPdMGCIGzQKFaI13VLfniAU9VRGFKrXbMAr6qcapvX5YB2TBzVA1Xqq4HFiQNTIqiEceFkDFGMsNZk4oMZ6B3pQ7wkNiFq+bhkakzN4/mc6vW/zuSfjN//8496V7zCLuf7eWIB7ei0+eZDy8vJadOLJBDD9FfDJLwHJHnOOc3DIFD4KUNR5T6fwJSlARTyNOkl4UvBjAhSFKgE5nQBUADOKxUyAUg1yA3LG/EegJPRQkwgXQIkmugCX9+kAoHQ0qg1Apa8XHKDSfS6YMc4tExmy9+eBVqrO4Me+5uZcGzc+fT+efS/kzE7T+JzP4zqrHVCJBma5QOaOUpEvZiKiUkbqH4GsTC2UikLpM0QUyoIqEnGqVEJErciAqJ7eKmbGtcFEraeKqNEyIMqOQoWVAD29NQOiBpb1ozk1g/0vaYhadexyvPr8K5g6pJ/x2E3r8AKxOmeM4eqdb8OH/sO7Ua1V4eXltTjkQcrLy2vRiMcvgU9+GZj+C4BPmHM8tSuHdOGTzXRJ2h4cKXwSllLQkAAV8RARGLhqfMsE/OSk5sn0PmMshS0NULLWSrvnuftAza+JxGwAigO6T1RHACVfHwaAgnxNx8nrnFQ/J0DZ+2Gf5dhftA75Uaw510HxRZLGZz9vu7Q+2/LcvndsRaVIiClgIm3PrpWSb1/WVtEoVMBgRKGEzTmJQqVntIgjn10LVa1VEDWaBkT1pS599NyAMQOiBkf6MfbKIUxM67OWrx3GgZcOquuwEmLZygG88qzud9c31Ive/poBUauOXYFPfukGnH3JFngtEom/HBf+nl6LTh6kvLy8Dr9aP0Ey/iVg5h4AsTFl94ASESjdAyoGR0R6QMkIVMyBSAGU7Okk+zrpMQk3MReNbV1gFPHAAC5ZKyXGSAqfSscLFbTIXlLqXC6MHCSIyb0xASgNa7MDqDI1UN0EKBmN6gig0vGuARTQ1okPsNJjbMhBdo9zPwWlMv2g5Lkd2JnnnT+fbnzGdZyfxmeclV7b88Y1MZNQ8+m9GOAwm0gMzkXCkVDHPpi1UgEDkpaeF1GoFlQgi4kzopmCKFQ1RGTVQtV7q2gQgKrWKohbUcalb+KAjkoxBvQP9mKM9HfqHegBksSAqJE1wxjfP4p9xP583UmrsefpV4zzL756Gz7236/F4MgAvLy8Fp88SHl5eR0WcR4BjXtwyso7wQ494Zh3W5i7ekDZTXRjMAVLKtpEDCS0y16gIkc2GEWkH1SizqBjJAKlokmhATYJMaKQABWTCJOMXAnYKdNI1w1QZVz4jiiAArIAk2c2YYOSPQ7H6/k0ksg9wwIox3N0palu5n1zY21HaXyuCJb9XHNI42OAcOxLr20zCdlXKiFnMnBnFEpKQhaNQlVrFbRmmuaeRsuAqN7+GqZGdW1UWAlRCZkBUQPL+jCxfwzj+7V5hB2FYoxhxbphvPq8dumr1CpYvmYYLz+5W99voAc3/N412PGB7WCM/Jnw8vJaVPIg5eXltaDiySFg6qvgU38CluzGgOXcm6SRp4QL9z1pIJHnwFe2B5SKSKVwI+HJBKPAACMVTcqs0wAlz6Iwk6R1VBp2NCCpVD6wwwJQs6+BOkwAlRNdygMowJHq5zzX8dqOOpVZl8r1bO36QYkzeP4cMLc6qLz3AORAzzyl8aXnGdcFZhLS8pxz80y7bxRvacc+CUjyyymb8UYzek+lEiByABOFqHq9gobl2hc1WwZEySiURDrGRLrf+Kuj+pzeGgIGA6IGl/ejMTFtQNTKY0Zw6JVR7H1WW51vufBUfOL//TCOOXktvBanGKy/Wxbonl6LTx6kvLy8FkQ82pXWP/0vADPmXAcGEtSBL+LCdMHlwGdbmNMeUBqMdIRJOutJ4FIQlukVRWHIBKiY1lERgJJ1Twrq0j0SmihASSiKZbQIKACo/Ea68nN1no25+LovbYACUN6Jr6xBRBFAOc4uBKiiM9Q3IG+uKJLFC742XD1DW/gC2rrxdTONT9+v2EyCRqnyHPt03ygxHxu25wxxk1xXAsSNFqJIR6EkRElV6xW0phpGX6gey+Y8rIQIGIwoVN9gD6YOTmCC9I9atmoQh/ZoqAKE1fm+FzRABWGAVccux56ndX1UtVbBL3/2l3Dlx9+CMAzh5eW1+OVBysvLa97EeQw0vgs+9WVn/6f29U9AxGVEKmsgYTvwUQMJGoEqcuDTr5keI7VLqoZJRqDS9D6eXnMyTwEqtiNQ6R6jlklGpVKASrg+0x2V0vN5ACU/cnqAIme3M4goC1Ag+zPPo8/qBkAZ9ygDUOl8efjiKtoFMu88C+iKG192Xn+z7Ma7srGu8e20HPsCxpC0NPjIRroxKbMMGBCTiJNM44saGs6q1QAtEoWq1StoZGzOezB1aMqIQvX21zF5QAOUjEJRiBoc6UdjctqAqOVrl2H8wLgBURtfeyI++aUbcOKW4+C1BET+LlnQe3otOnmQ8vLy6rp4MgpMfw186k+B+MXs/Czrn4SteNZAgtY/2QYSpjU5ceezLMxNowmmACq/ia6ELuGup6BKPgtPzSdyAEqZSHQKUAR+6FgCDUh2ryjxWn7t59mFLx0vBVDyvLz6JleqXTcBqtN1yI9iOSNAOQClzpmLkYQ1n18H5Qao3GfqtKluOzc+wIhS2REmADm1UlaqX8uqlWrFSCwXs7hJIk6VAFGjpdbIMiOaxif7QrVIpKpmp/bVK0haMaYOaajqH+7FxP5xTB7UEa3hlYMY3WtGoZavGcJ+0oCXBQwrjxnBK8+Zkalf+tQ78b7f+kVva+7ltQTlQcrLy6tr4q2fCXia/gaAhjmXpu+1a6Dbrv4pIe54IlVO1hfJ1LtQRY4iGTmiPaDUmHTgc62jDnydW5hnm+gyNZbntudK65PnU/gRkSZibQ4oUJoLQMmxto1009fzHYEC5gZQxjp7D1k3a4CS7zfPic8434aV7L3npQ4qs5cXPJOjDipztgVJ7dL47HlHGh86SOMTexIkpCeU7BMFdYaIZEUz1EwiRIsYTrCAgXGeab47MzFjpPb19tcxPaYNJ4KQoVoJMbFvjOyrIYliA6IGlvVhZnzKgKiR1cMYPzBuQNQJWzZg5//4MDa//mR4LS35hrxeUh6kvLy85iTOm8DMdwRAtX7smOdppClW6XtF/Z9oA90kjT7FsMwiStY/Uevx2RhItHPgo1En28Lc7AEFJ0DpdVZUiuu1NkApYwlkAcqALgJKCdeRJgVBwNIGKLmHnj0PAJU3VxqggML32E2Acu4Ful8HNdc0PqCc5XlBGp80k4iNZrzMcOwLKwKyWsRwolaroDk1o+7FGANj3OgTVe+pojHZMCCqf6gHE/vHjeYMdgNexhiGVw7g4O4DaiyoBBhZPYx9L2ib87AS4pc+/U689zev9FEoL68lLg9SXl5esxKPXgSf/gtg6isAP5id7zB9z9X/iUMCCkTkCFY0KY0wSfChqXdl659sAwlhg97eQKJbDnwJp2mB4j1rKAI4ASz1edqYLwYoCkcUpJYsQBmftnP223vIum4AlJoDct6HA6BKgxg3XftsMCsBX/Z16Tqodml87QCKcwVlSjRFDykwWXuKLM9ZwMCtND4GgLc00gQhQ9KMEJNIVRAwK9UvRNRooklsz2s9VTQmNCyBCdBqTJKGvPUKeBQbZhJ9gz2YmZgxIGpoxQDG9o0aEDWyZhijr4waELXxtSfixrv/HTaefSK8vLyWvjxIeXl5lZYwj7gffPrPgcYDMEMC6EL6HlORJtljSfaFyvZ/kml5ov9TIoELoYahLtU/zcVAorwDHxREFVqYU4AiKX7KQEJdlwQo9SHb/ToDUA4AmVUfKBuAZmkiYayz93S6DvlzhQClnrFDgDLW8gLwcwBU4V7e/TooCkj2eYAzjY9GoTJ1UIAJWWlPKDuNj1twlLS0AYWMZFFbdOnGR45GGDBEM6T5bi1Ea7qJxgR16KtjZnwazUjXUPUN1I3muIyJ/k60XiqsBOjpq2F070HjGfqGenHgZQ1V1VoF7/utq/DuT74dlar/6LXklf6jwoLf02vRyf9p9vLyaisev5KaR/xPINmTnS/pvscBA6AiCSezTt+TAJVN30ucfaLs+ieaqmdZmkPDTLb+CQqgJAgp6EJ5Awmd3qchp20PKKQApdL1igHKhCg9frgBKgs5ixSg7OfIhSTeVYACHNDTDYByPHPxeeR9AaXszA2Asswo8s0mSBQKAKeNd9M0voS68QVAQiJOslmv6cYXojXdMNLxKpUArSkCVdUQcSs2Uvuk7Tk1k+gf7sXkgUkDooZXDODQ3oOYIJC2Yt0y7HtxP0aJqcUZF78GH//9X8OGzevh5eV1ZMmDlJeXl1Ocx0DzH8CnvgI07oP6dKfmZfSJpwAlGugK8wcJUDp9L+I0+oQ0IkTrlnT6Xp77Xrn0PQJImf5P4h52/6fESt8ToBeoqFF+/RO5tiJNtq05BTbbQKJTBz6Q+VwDibkAVJIdnxNAyfscpQAFmPO5AGXNd+TEx+nXt1OAst4HMOc6KACzMJuQUSkTxHiL1kGJxrvUcIIxIImyfaOoG1+1XkFzcgakzAq1ehVNAlVBwBCGAaZHJ419jAOTpHdUva8GHsU4RKJQvQM9AE+w70VtMNE/3Idrb3k/rrjm5xAEAbyOMNl/R3kdlfIg5eXlZYjHe4Dpr4NP/QWQ7M7OZ8wjRDSqbPPcOHXfoxEkt/temfS9QMFKxO0xd/pep/2fNPjMrf7JNpCg0aXZGEiUtTCHtWZWAGWPdwJQDsihdVLqHm0AyoANe0+n65A/1zWAckBSJwAFOPYCZoTIBVDI3susqXIAUScAZUWYxB4rwtQmzY8BbrMJWivFAE5S9qQbXxxl+0bRHxm7b1Qoa6OIG1+9t4bGxIwBUb39NUwdmjSiV/1DvZg8OAmqwWV9GNs3aowtWz2Eg7sPGmMXXXU+Pvy7v4IV60bg5eV15MqDlJeXFzhvpbVPXwUa98OOPgFAkqbtCfMIEX0Sn+Ham0fo6JOreS7LAJJK1+PCfU+l1XXovudK30uMNMC89D0ZaXL3f6JOezQ9r6v1TzkAlahrGXECQNbKsTkZSMi1ib6PEUFSPzj6V2FkScGAB6hCgMrbK/cfTic+Z5pfNkWvKEol4Yhb96U9o2SKnhnc4uAklBSEAZJWhJjsq1QCRDNN428uuzYqrARIogSNCdJ8NzWcmDqk0/h6B3owPTplQJRI7ZswIGpw+QAmD04YELXm+FW44feuwflvPQdeXl5HvjxIeXkdxeLRc+DTXwOmvw4k+7LzVvTJsC8viD6JVD4dfaK9n1zmERJs3FGlkPRtIk56mTQ/0gcqx33PMJPg3Uvf48jWPxlOewUAlaRRJAVb6RnAHAwk0jVdc+CTAKR+MFAuNc8BUBmIsM91rSs8u2AdYDz3UgIogOyfC0CVMZJwNNTV94cAJvtMy0jCACjACVkZs4k41vdiAI85kkwfqci8F+eGDbpM44tIpKqS1kaRLD5RL2X1kgoDhsY4NY4IEQQM06N6rFqrIAiAif26d1SlVkG9p4KxV0eNvVff+At4329dhZ6+OryObPk+Ul5SHqS8vI4y8WQKaPwN+NTXgdb3nWs6iT5RgMqLPsUqfY846TmjT8XmEXHat6m4ee5s3fdmk75nmUpY6XtyX0Lg6LDXP9mghAKA4sbH3s4BKjfKtQgAqowLn/pmHD6AUtftAGqWTnyZ5wVZU8ZIosDu3HWGStmzHf3ItQQosw5KpPbZ1/GMBVAzTaM2qtZTRXOygRYBtHpvFTNjU0b0SkahjNS+4V4DoABgeOUgDu09hNa0jnSdcfFr8NE7/y2OP20DvLy8ji55kPLyOgrEOQda/wI+/XVg5v8AfMq5Zja1T7ONPumokhl9stPuaO8n8xxmnKNT6fR9VGod4EjfK3bfc6XvUagyf9npe3rfbNL3FrT+qR1AdQJGrnXyeWznuczZeswZtTpaAUoCkHre2QGUeV53AMp+jswa260vSgxgAiDqqegay848CJlheQ6IOqik1crsa02bUBU1WmiSnlAyjW9mTO/t6a9jZmzaiEL1DfZg8tCEAVH9Q72YGpvGob2H1Niy1cP4t//532DHB7aDMesfHbyObLnMVRbinl6LTh6kvLyOYAnjiG+AT/8lED+dnc9x3hOfjx21TzzPea84+iRrn2JH9Ik68+noE03PC0CjWDGJTtm9n6RxBY0+5TfP1XsoCJVz34O6b5n0Pdu+PJu+RyFsftP3AMzdQAIwAapdlIrcO3u2HiuEM7hf2+fngk+3+kAtMEA5wU6e51qPvOfJg58OAApo3y/KtSYpNpLQa7K1UkY/qFA02aXNeQMGxCTVjwUMjHNEJI0vrIiaKprGV61VhO35mLY9r9YrSKIYkwd1o91KrYJKNcQE6ScVhAHe/uEr8IGb34WBZf3w8vI6euVBysvrCBPnDWDmXgFPzX+EyziiTN8n3UDXNo5wO++1iz5FSWCCkjP6RNPzQmf0idYtZaJP0ONlzSMoKOnmuQVQxfV4sfseFkf6HmAAgDE+FwMJe3/ROufZeswZtSoCLcf5eVEsZu+ja9XzlwCoXBBbGICi80sJoGADVJyY2zgXtVJ0TcKNqJQEqJik+oWpuYTZI0pEobgxFhhpeIwxVGshmlPa2Q8M6OmtYnrMjNQPLOvH+P4xtDRr4aw3no4P3/4rOHHLcfDy8vLyIOXldQRIpO79CHz6fwMz3wL4hHNNAhF/EpGmJDWO0NCko1BF0ads3ydOx3Jqn2I55qx9YrOKPtnW5TRlT/SLsqJEBlR1Zh4h79FN9z352gCouaTvUVBygE1h9AmYtYGEc13u2Tl7APKcJSJVqfKiWO0ASkGGfY+lBFA2MM0HQM0GshJu1Eq5AIoBoubJSgekkStZK5UBKMtcQhhORIhImmC1VkFzagatlt6rbM9Jz6negTqmDk1gukWb7/Zh4sA4xklq38r1y/Frt30A29/1Bp/G55X9e26B7um1+ORBystrCYtHzwMzfyUAKn4hO09S94RxBEeM2ICmBByRZRwh6p7SiBOYqn2KadSIjOX3fdLRp8iKGs259ikn+qR6N80q+lTSPIKLVD2AmEeAuO+p+ihAp+/Nk/se0PX6J7W/Xf2TXD8XAwnXGdw6z1Ff1RFAOSAnA1cOgDLuuZQASvyg5gOUbRIxW4ByrUkd/ugaWhcl0vqspruAE7zMWinRiNd254saLcSNLEA1CRhJgKK25z19dUyPTxq25z19dbQaTUwcGDf2vusTb8fVn3gbevt74OXl5UW1JEHqoYcewgc+8AHn3IMPPojly5er6wceeAC33347nnjiCQwODuLyyy/HjTfeiIGBAWPfxMQEPv/5z+Oee+7B+Pg4Nm3ahI985CO4+OKL5/W9eHl1Kp4cBGa+Az79V0DrR+41mdQ9EYHiKGcckYBZ0ae0ZomMyX5MymjCmZJXpu+TjGLpeqVu1D7lRZ8UaFnRJxGB0rbnC2IeYUAUDLASixYofc+xPwsqOVEquc55th7LS70rDVrIn8sFlzIAVQRicOwtAqjEOrsIoEq68Bn7uwRQ5p75Ayi0ASgJTObZZq0UYwxJTiPeaEavk459zUkNRtVaBa2ZlgFQlVoFSSvC9BipeaoEqFZCTI+bqX0/994Lcc3n3ofVG1bCy8sQ+fO9oPf0WnRakiAltXPnTpx77rnG2NDQkHr94IMP4rrrrlPw9NJLL+HWW2/FU089hS996UsqPM85x/XXX4/HH38cn/jEJ7B+/Xp85StfwXXXXYe7774b27ZtW9D35eVli/MG0Pg7AU+N+wGjQ4pcQ1P3EgVPwrEuSUFJw1OUfq6zo09ljCPkuKsZbozAiEjJFMBYmjqoiJQELSBRUSwNMeWc9+ZS+1Q++qRS+SA/m5rRJ1krpaNPJoQBOtKkm+fK1zDWgL4GigHKAQyHJX0PIPcvkZbnWJeZo8+cM9dpD6jce9gAZQOQvd4CHBdAmZBhp711CFBlgOqwAZS1To6lKgIoejoDwCPr7zVuRqUYY+BJgrhJm/MyJK0YrakWGQuAhKM1reugWMBQCRmaU6TgCcL2fGp0EnFD7z/1vI34d//lQzht22Z4eXl5FWlJg9SJJ56Is88+O3f+1ltvxaZNm/CFL3wBQRAAAIaHh3HDDTfg/vvvxxvf+EYAwHe/+1089NBDuOOOO7Bjxw4AwNatW3HllVfitttuw9e//vX5fiteXhlxHgPNfwKf+RYw89cAn3Ssae+650rdo3VPrtQ9ATpZ44goTd2T9U5FxhFmjVSoI1JphMvo+8TNKJer75M2mZC9pBho36eyznsmVLlqnw5j9AnyZtnUP3tcfSiepfseUABQ85W+VwZ8KDzlzHVqYZ73PJ2l4NGvAc/Cl/EcebBDrun6dMxlY248j31muxqo+QIocPEXSDuAiu3okgOgnKl+HDyiNhIStKwxBsQzTWNM1ktR1XoqaExMg67sG+rF5MEJTI3qv1fXHL8Kv/If34s3vvsN6jODl5dLDFyb1CzgPb0Wn5Y0SBVpz549ePTRR/GpT33K+Avx0ksvxfDwMO69914FUvfeey8GBwdx6aWXqnVBEOAd73gHPve5z2HPnj1Yu3btQr8Fr6NQwjTip+Az/0f0e0r2O9cl3BV5crvuZeGJqV9m3ZMeM2qbOkjds5vmqrRAClKqV1NAQCmEyzhCpuFRE4nESPEr6vtUAFUF0ad2tU9djT4BmBfzCNA1BdEnua5L6XuZffRZ6RzIvi4aSIi1FqR0AlByr8s9ENCg0i2Ass+TY50AlAvKZJToMACU2Dc7gGIMSFomBAnDCbOPVBgyRDNNY6xaDdGaaSG2G++OT6MxYfaNmh6bwiSxM+8f7sP7fvMX8fYbrkCtpwYvLy+vslrSIPWZz3wGH/nIR9DX14fzzjsPH/vYx7Bp0yYAwJNPPgkAOOWUU4w9QRBg48aNah4Adu3ahY0bN2b+BWrz5s3qrCKQ2rVrFxKrm/uqVauwatWq2b85h+LUIjaO4zYrveaqBf9aR08AjW8DM98CS150LrHrnqRteVHD3Nm67sW5PZ/cqXuJ095cpubR1L72xhEJAbGY28YRujaqqMbJDVXdd96b3+gTQIGrY/OIdml59v68dcYPoWOOQkWb/k+5c8ifm1P9k33WHBz4ikHOATvp16NTgCoEqjIA1Q0Xvlmk8Il9idM0ohRAWWMSoBLyV3AYMuHYR5z4qrUQrekWWgSgaj1VNCanMTPeMsaaM03D4jyshPiFf3c53vubV2JoxSAA///XIi3k/xftz1ReXotVSxKkBgcH8aEPfQjnnnsuhoeHsWvXLnzxi1/Eu9/9bnzlK1/Bpk2bcOjQIQAilc/W8PAwnn76aXV96NAhnHDCCc51ADA6Olr4PJ/85CczY1deeSWuuuqqDt5VeT388MPzcq5XVvP5ta6FezDS+yBG+r6H3moRPEnHvYTAk7vuyTSNENAhG+bq6BCte6KpeTpiZIKSiEJl9xT3fJpL6l454wirxsmCKvsXQOAIrE3fJ8DtvEchTIxDfr5sF32ywGgu0Scx1z59zxn1KbvOdTa9ts/LPIfeX5S+l3fOrNL35Hw3DSTy3rtzP2/rwGc+j7WfnkH3LDGAAgPgqoGK2wOUbNZLASoImHDsIwAlU/ha08RwohaiNd0wIlBhJUASJ2hMasMJADjrTadhx69dhJUbRvD0C08BWdNTrxwtxGeQZ555Zt7vMSfJdI+FvqfXotOSBKnTTjsNp512mro+99xzsX37drz1rW/FHXfcgdtvv13Nle33MJe+ELfccgtOOukkY2y+IlIPP/wwzjjjDIRh2NWzvUzN29c6fglofAdofBss+plziV33RPs9JapJroanbtQ9uSNKso+TtYdEqXhqFpHnuidS6kTEan5T9+i4hj07anSkRp/Enpy0PAMO2kSpjB9Ex1weFOXBRuYZ9fGzSt9TazRMZN4HYDxbOQBCBl46cuArAVAmkNn3zwMqXnimeO4COCqzBug6QFF1GoGidwjDAFGjiZisc1mey95StuEEY0BETCQA4JzLz8Ivf/Y92PjaE+HVmRbyM0it5lMsvZaGliRIuXTMMcfgnHPOwY9//GMAwLJlywBARaaoRkdH1bxcm7cOcEe1qDZu3IjTTz99Vs89G4Vh6EFqgdSNrzWPXwZm/hp8+ttA5P6XvNmaRtDIUwyGCLCgh9Q9kXQ73adJgke7uifm6A1lpv/FGdc9AmI5rnvZnk+dpe7NxThCzJkpfrIvk4QqvTYLQNm+T4Az+mRB0qKPPrnOsJ8vc765f17T9+BYW+osnvv1UfO8TfQq3W9EglxAhDkAVKKB5WgCqHYpfBKgqOV5WAkRN5uIZjQsMcYQVgK0LBOKU8/biGs+9z6cfckWeM1NC/EZZLGbfTBO/k5ZwHt6LT4dMSAFiA+j8g+frI168sknceGFF6o1SZJg165duPzyy9XYxo0bcc899yBJEuMP7+OPPw4Aqu7Ky6usePQi0Pgb8Jm/Blo/da9xwJOKPBF40vVPpO6J55tGJFz3gOKZdD4zoqTd9wIVURJ7kOPOJ17LKBZNraO1VNK9T0WkuIaqmEaZDNe9zlL3dM8nnbonjSCKUvfUmk6NIyBMJ8T3riD65HjdTec9oFz0qe0644fRMVe0r0zaHYWnnLlup++5z7LhxX32fDjw6edtA1C54GOd4XTpc0BPuzWue80GoOSYrTIpfAEDj0yACkKGuGGn8IUirW/GjECJPlJmtKlaq6A53UBC/CpOPOM4fOh33oNtb3v9nDJPvLy8vFw6YkDqxRdfxA9/+ENcdNFFAIC1a9diy5Yt+OY3v4kPfvCDCpDuu+8+jI6O4rLLLlN7d+zYga997Wu477771HiSJPjGN76BLVu2YM2aNQv/hryWnHj0goCn6W8D0SPuNQSeEuW4Fyt4au+4R/s9zc40ghPoMqNMZA8HuKPuKUZI0vO0+15x3ZMGKbvuSUek2rvuGY10CahR1z0jNQ/pZ9X5St2DvIEJRjZ8ZSAJ+dGnts57kK/p/eYp+uSaawda9NyCczp138u9T+FZ7QFqzvVPsK7p89A9RWdSqLHfszqDO+47C4ByrWnTSFeMlQcoe2+2LoqJMUcNVOwAKJrCF4QBkigLUDK1rzmtDz120zp84OZ3Y/u7ti366IaXl9fS1ZIEqRtvvBEbNmzA6aefjsHBQezatQt/8Ad/gCAI8NGPflSt27lzJ6655hrs3LkTV199NXbv3o1bbrkFW7duxfbt29W67du3Y+vWrbjpppswNjaGdevW4atf/Soee+wx3H333YfjLXotEfFoFzBzD/jMd4DoMfeaLsCTNo2wm9yaY9IUwg1PGpQyRhPOFD+HaYTRIJeRJrq67kmepRrw8vZ1T3bDXBdUyXubUaPO657E9wQlU/eAORtHpOea8GSe1c2+T851xg+kY65oX4fRp8w51vtvD2I8C1d5Z9F5EPiyn9mCM1ckTN/PAT+Hw0BiNgBVFrIS7gAjbu4DF+l69pgdpVIwZq7j1l5pIkFXKYAi68JQRJsMgAoYkihGHJkAVamGaM00EREXuTXHr8K/+czV2PH+ixFWfAq81zyJ/L2zoPf0WnRakiC1efNmfOtb38Kf/MmfYHp6GiMjIzj//PNxww03GO5727Ztw1133YXbb78d1157LQYGBnDFFVdg586dRoifMYY777wTt912G2699VZMTExg06ZNuOuuu7Bt27bD8A69Fqs450D0M/CZ/yua5MbP5K5beHhiSHjo7OGUhSfTFIKm8LU1jeA61c9tGmFGnAQQhTqyROBpdpblKZjRcWg4mrXrXpq6R9dTsBLfWOs1rDXqf64dRp8yH/jbpOTRM1z3AUy4cc0V7SsNPfoWs4o+qTUaJIqeaS7ue53tT69tO3Lj+Q+TgQTgrm2y1zgBKnGk61l7XWCUe352Hc9AFTIOfk6AqgSIm5HRByoIAyStCLHViFcCVIsA1OrjV+J9/79fxI4Pbke1VoWXl5fXQmhJgtS1116La6+9ttTa7du3G9GnPA0MDODmm2/GzTffPMen8zrSxHkMtH6YwtPfAMmenHV2zdNCwVM5u3K7PiqRPZ5KOO5JQNJjJjzlmUZo44kg/XyYbxqRV/dkg9Fs6p4AZozLT3Vzc91Lz+ogdU98qC0RfbKAYEGiTza4IPscrnvMOfqUdx/7LDqP8gA1X+l7hWe4rtvVPznXmJAyJ8hK7LP0M6ixMgAl15Bx7kr9s+8JqIa7FKAULFkpfHGrlYlAKXc+C6AufN/r8cu/+W/Q01uHl9fCyPFnYyHu6bXotCRBystr3sVnMNTzQ2D8q+CN+wB+yL2sDTzp/k4LCU+A6rtUmOJnRqZo9CdO4UlGiETNlG6Qa8OThBQKT7K+ifZympVpBGdIknQcAI1OifIO+Vruld+bcnVP8v+H3AIZBVz0NT2LA3NN3VNg5QIN46yCKBX0Gvv8bOpb/vWCRZ/EF9G8T85ZxnwuqLjPdkWfxHncvJ/cs4Dpe+3XWDDTTQMJG6AcYJQLUBYslQUocJ6plZLpekncHqCCkCFuRkYK39oTVuE9n3onLn3/xXj0Z4+gWvMfZ7y8vBZe/m8eL69UPDkINL4LPnMv0HgAJ69oADOOdRl4ynfbiyE+s0TchCgKTwJMFgs80Tqm2cOTTL+jfZ3E57wcsOImPHXNNAKwgKm7dU/qw20Z1z2yLjclz4KDORlHyH3kPDegWa9z7tE2YpQBFnutBolFE33i0Ol7OaDTlfQ9WGsk4GTWWDAzW8iarQPfXADKYVKh1tkAlbE7DxA1LYBi4j0lUQzCT9iw+Ri851PvxM+990JUqhXEsZn25+Xl5bWQ8iDldVSLR88Bjb8V8NT6EeQnSGav47reSYATzzTJpfBE+zy53PZUfyceFLjtzb7maWHgSdZCueApm85HXfW67rgnf7dT7+haFzwBmLe6J8DYP5ueT9k5+wfTMVe0r5O1cLxXct3t2id1bay3wcXez4v3A3BHiuiZ3Alcc3LfKzi3K+l7LoAqYyCh0vzoOp6FLHCH0QTAbVc+BvEXnAugXIATJ1m782aEiAAUCxh4nIhfZOuJZxyH9/3mL+LCX9zq+yh6HXYx19/HC3BPr8UnD1JeR5VEvdNPRLrezD1A/GzBWmoWIdP2THiKQdP3YKTvRdK8AQROwDQkWX2e2lmVu9z2zDqlIH2mbM2TK21PfP7pLjzlpvM54Kms4567Wa64l9M0gpvjPD24nGkEmSPnAvnwJD7MulP3yrruqTNKgI25h1zbz5h3TtHaVHlRrHzgsZ/JhojsMx+u6JN4Xm4+v/NMxxmAA8p4do39LGUiSxylIct8n7x79U8565wGEpZtuuwVVSbVLwhYmsKnx4PUrY/H5trXnH8K3vOpd+L8t57jbcy9vLwWnTxIeR3x4skE0PwH8Jn7gMZ9AB9zr+M8/dyUqHqnBBwREnACTAk4onStTtWTr1PbcICk7ZEIlISU9LW0KqfRpISAlrvPk4QcbVVexm1v/uFJj3cPngAJQ4XwhJKOe5A3tOCJpvfxPHiy59zw1BaKDHjJuZe8dp1P19J1ufdwrHWlB9rPCvO89iDG28zrc+zapU5rnzLvGwXwM1/mEXZqnvMM7rg3+aLPBbK6Xf9kjfMi+LK28zgWsJVK9IrKwhcDkMSRE6DsFL1z3/xavOc33oEzLnqNb6Trtfjk+nO0EPf0WnTyIOV1RIpHz4p6p8Z9QPMHELjjWMddZhEaohKuzSLslL0EsvZJ1juBQJOGJ6N2SY2LaFJkAVEMGo0idVJJkNYZ0bPcfZ44t8ZRzm2v2DAC4G3S9lw1T3JNmbS9WcETZ+CJXl8MT9n9peCJrJsNPNlg0HHdE7fWOu7RLdc9ej0n4wjrXrl9m5xRnuz5Cx59kmOudL52zXPpGjVmw0ibNer+3PGMJBJEgc0+q5v1T46oEjjPpPqpZrv20vRMTgjMBVBBwHDxu96A9/zGO3DyWSfAy8vLa7HLg5TXESHOm0DzB+CN+4HGvUD8YsFaWu/EUWQWYQKTaRYhIk/UGCJQxg868hSSaFEWiOLUhpyCTSLT/0hvJ1UHpVIBQwIwZF8KT3aT3KI+T5znw5MRYUI5wwi55oiHJxRElKwP/W2tzY0fUMec4x4d1T2RcTVn76Xg0/Ys3gau0CH8uM+fL+e9/OfIu+bmGMgaoLvmEeIPUBay5jN9Dxy8VJNeOA0kxNrEGGKMIYmjzJkyBZACVE9/HT//q5fhnR/9eaw9YTW8vBa7WKL/gWgh7+m1+ORBymvJise7gcb94I3vAo3vwWmxlyrhIu4km+Pa9U4CjvLrnUTKHpQxBDWLcDvtUYOHgMwLoHJFq4Qrn2ksoaNUQMKlyQTArX5QsUy9I/AkgSk2IMlskqua4IJAFG/f50lGvSTYzM5tDzgs8ETWmdGYOcKTdT0fphG55xStTZUXmZqP1D3j3nR/zvug5zMDjsg8kA86c3XeA+Y9+mTso7/L13Nx31ug9L3SvaKiGJxn1/LENJBYvm4E7/zIz+Mt116GwZEBeHl5eS01eZDyWjISUacfgTf/Hpj5WyB+umBtfsoerXeiKXuiBslM2bPrnWKrxom+dplFJGlqnTaLoNGqbG2UNIuIrcgUVz2dTHjKpu1R6JLRocCIQEnoyoWknPFO+jwB+W574vvTBp7kMx5OeDL2zC886Q/1Je6B/Dl1TVRY9+Q6y3jfFkDIeStVr23qnut+BGQyzwNzPgNcjjNckMPoPH1u69x5jz6p8601rvs5o0+O8+30vdwIF88AUEfpe/ZaBqdTn1hr9opiaa8oe+1JZx2PKz/6Fvzcey9EtVaFl5eX11KVBymvRS0evQg0/z6NOj2IoqiTy6Jc/A64LMo5yqfsCUgC7JQ8mbIXJ2LMVQ+VrXeSaXKdmkWkEJRGlwT8kfoqCkky9Y7UTFFIss0i3OPWGgVGKRCWaJJbbFUOZODJilQd0fBE17a5h3Ot/f6Iulr35JwvgDG63563zm+bumfvaWccATuiZT8Hdz9rGee99Ae9YzCiv7vuZ4zZ5+nvg143u/Q9Z/QJ6Dh9D/atFGjpCZHWFxsOfIwxnP8L5+AXP/ZWnLn9NG8g4bXE5fhztxD39Fp08iDltajEk0mg+X3w5j8Ajb9rW+sk/6NRpzgdS7js6eR22Uu4aVFuRJi4bphrQFAHKXv0rLx6JwpiIrqk652oWURevVMZp708eMrt/eSAp4Rb45Cf39LzCTwpwFKfIUmqH4GODDwpUAMU7ECMmx+056HmCWTOdYZ13VV4grW3k7VEnaTadQ2e4NjvOF/cn+c+j76nfUZ6PUvjiPbn8uyazB4+OzBa6OgT0rNc0Sfn8+j3q4Y7Sd/LWWun7/UO9OBNH7oE7/jIm7F+4zp4eXl5HUnyIOV1WMV5AkSPAY1/AG98F7z14zSZLm+9GXWitU6xijrZr1N44mZj3DiFDBkJckKQgqdszyaa0icgCWjX34mm7CUGJLlT9sQz2/DkrndKZCqfqndKISnJGZdgpJz9xGv5Oa4Qnshe+RowQYiOy09WMrpUCE/Wntn1eTLPO5w1T8ZaoBw8pfN5dU1Sndcp2eu5ms+Dp+LzeO7Xi54/K9c915i1h1n3yTunTOqecXYHphDGPte6wxV9suELcEefOBfNc8tEqnjqvkfGZfTJXnvspnV4+4ffjB0f3I7+oT54eR1RIn+PLeg9vRadPEh5Lbh4vAdo/GMadfp70L5OdrIHjTpFPO3nNIeoU75RBIyoUmREnWg9VDuXPdNAglqU56XsaXCSKXuMrJXQReFq/uqdeLoGEpwAfU/5WU85+QEUntqZRUiAgjpHr3PCE/1cWhaeDCjKgScOzMaq3F47X/DU7j7dhKfZncfzn119/bpU9+TYU9Z1z1jTjdQ9oHx9VHqWeT7vfu2T9YwdmUe40vdy1zqiT+kZNlSd++az8Y4b3oxzLj/LN9D18vI64uVBymveJRrifh+88Y9A834gfr54PYk66Z5O5WqdBDjRWqdyUScTnvKiTiRtzjKV4ASYYrmP631lXPa6lbLnGs9L2aP1TvIjZPl6J/MsFzw5zSKALBTR14ACLtc6M3JirmM4jPBkQQMFqSMCnujXyHrv7eDJOKOdZbljT2HqXlE0aqFS91zrjPOtc21YKeu810n0yQY3udZlNOGIPoGnRhOW+x533Gtw+QCu+OVL8JZf2+HT97y8vI4qeZDy6ro4bwKtn4A3vgc0HgCPHgVD9l8z9XoZXbKtycs57Gl7cu2wZ9QnQRpBmL2U8qNO2tihW1EnZRQBUUNkGkWYr1XEJ33+RI5bFuWJMq2QgKRT+cxzxGv5ma50yh5g1DsZgAR9reCJm1DFORRI6Q/VbpDKRKesdQsKT5k5+wc25xz7HkXXADKRHOs+SwWeOj9D/rAWR4hmXffkBCMLgrqZupeum1XqXifRp7nUPnUUfcqaRxj3I3rN+afgF657Ey6++nzUe+vw8jpqxLn++20B7+m1+ORBymvO4jwGon8FGg+CN/8evPkjMDTVfFG6noQnna4H0L5OKnWP2+BkOexxalNuOuwJ+/HQctgTrnm2fbkAKqaBimeBqn3USUe5FjLqRC3SORhQkLKXtSgHilL25Fl21Iim6pWrd9LXTngi62bttEevjTl6HozaqEJ4ytzDHPfwZJ1B1rjgyfmc1rkGuMzBsjyzr1uue/oPETmfl0/dy4tSUXUSfcqrfSoZfdIpge2jT70DPbjkPRfgF/7dm7DxtSfCy8vL62iWBymvjsU5B6JdQPNB8Ob3gOZDAJ9U825wAgGncul6simuBCcZdZIpdtKG3J2uR2EnVGt1VKqdw55dF8XSZrkWXCUUruxaJxpponVPBFpyo05WdKlgvPsue+ZZNvi4U/b0Oht8TPgCOqp3AkxAgrUGDiCyXxsgkHM/oGvw5FzrOgvFZx1t8JT/LNx8P3Ksg7onc8z6BnQSobIhi1tnmn+IrHuUNI6w4EXBU4noUy48dSH6dMo5J+Gt1+7AG99zAfoGe+HldVRL/tlf6Ht6LTp5kPJqK+Hq9Exa5/Q9oPk9UIOIvD1Zd710DDpdL5lDup4yYiAgY6fr2X2fnNGl3AiVvl8infk4VI2Uy2FPni1rsnQPp8MVdQJMs4hOXfaAjEU5MIeUPfP6cNY7qbWue8ABC4X3KVhLz0LBPDlnycOTuj/P3TOv8FQmqjSbuidjzDrPBU92RCcX0njm/vnGEcg8Z37qnmMtT9Lnbx996hvsxSXvuQA/f+1l2HTOyfDy8vLyMuVByisjAU7PAs2HwJsPika4/FDbPXadk2yKS931RMTJdNeTr6MUjiIDgExw0rVLDDEPtVlEiXQ9Vx2Tma5He0TR6JKOXJlpfNphT0WuUmiZm8NeQa3TYow6AXBalANpNGJ+U/ZskOlWvZNzr/0+yPrZwFMm7c+AAnu9BQ6u+5HnWtTw1O4ce023TCNcZxWsy0BW13o+Qa+z4Slvf1njCPs50zFpXZ45wwGQZ1z0GlzxKz+Hi646H739PfDy8vLycsuDlFcKTk+lEad/ApoPAny07R7aDDfOAScz+mRHnGT9EgUmmraXdddz93SSpg8m9OQ1xNXuebohLufudD19XrFJhPjclYJNOk77OnEODWAJgSIIQwpOok62w56KOhGHPTvqJO4tPxPNV9SJXJPXnUWdxPl6rlzUKQtL+WvnK2XPOCvnvdEIV15Knw08uWc7gcT9fIXwlPM1LA1P7SJGuc9qnWvtW6zwZN6Dz67uiY5TOUCp49S93EiVuRaAdt1zRbCssZE1w9jxgTfiil+5BBs2r4eXl1eBOFTa8oLe02vRyYPUUShhDvEE0PwBePOf0hqn8TZ7THCSznoucHIZRMzOltxd52Sn1hXVOVHDCXfUSbvrccOtj6lxuSYvXU/s7TxdjwMpVOl0PQo74vOabqjbucOenstEneRzdlrrBBhnAOgo6mTAk/WhvhCI1Idlef4sUvbos7XbywvWuubhOItct03ZU8/Gs/P0fvQ90Gc19vDsM2TO4Ma9O3Xby6xBHpQVnOPY11V4mu06G0oWHJ6y9+oozU+tbR99qtYqOP9tr8ebPvhGvP5NZyOshPDy8vLyKi8PUkeBhB35o0Dzn8GbD4K3fgjGp9vsKQKn9s56eeAkwUcCUj44maYP7QwiZGpd9jy7zolBp9/NpqeTrpHSQJUHSHNP1wNkOUZeuh5A4alcU1yg0GEPMK9d66zrhY46OdcaP8D2Xph7yZr5SNkrBU/qmrvnrefJjWQBWTjJuUdbAKNfF3ruQsBTwdmHBZ7oerrfBTTO+5BfajhnP7f2AzpqZKf5udZyDnSQunfKOSfhTR+6BJe85wIMrRiEl5dXZ2J84e3PF9xu3auUPEgdgeLJhOjj1PxnoPmP4K1HwdBS88y157CBE40QZcEp4bpmKmsQkQ9OPNeWXAKVjBbZdU7URU+DUyfuepxDn9/GXS8vXY+nzy0/F805XU/uKZmuR89wzWUc9ihUFdQ6GUBEP9A7ruct6pR3Ft3vei6QvcZzmme2i2KJMZ5d43i+2dY70TUZeCL/L85Gr9IXRU1y0zGW937oPp7dp37AXV8HYx93PAN5+CMRnuTaEq57GpJy6qyss1dtWIFL33cxLnv/xTj+NcfCy8vryNanP/1p/OVf/iUuvfRS3HnnncbcAw88gNtvvx1PPPEEBgcHcfnll+PGG2/EwMCAsW5iYgKf//zncc8992B8fBybNm3CRz7yEVx88cWZ+83HmUtBHqSOAPF4L9D8YQpOD4LHT6cfyYWKwCnhurapkxqn+QEnMT57cMoaRFBwchtEWIDUgS25+IzIMnsL65xA3fXkfvk9sdL1CsBJfwaba7pe9gwAOl0PyACDkcqnPjgvcNSJjJeBocMWdVL349l5+nz2szrP5OYe+2sg1xSdAZAP8eaeDAQ57s3sNbzE2UB33fbmsk6NWXvmA55c0STHvQzQcp5NngscXDruGaAkx80f3N6BHlx01fnY8f7tOHP7aQiCAF5eXl2Q4x8rFuSeJfXggw/iO9/5TgZi5Nx1112nQOell17Crbfeiqeeegpf+tKXwFj6+YRzXH/99Xj88cfxiU98AuvXr8dXvvIVXHfddbj77ruxbdu2eT1zqciD1JJTAkSPgzf+Bbz5faD5fSDZZ6woBqdiVz3xe545hAlOTqDpMOJk72sHTipCVdJZT7wHvd6ELqAInGRkSa4Xn2ncBhH5tuTpurnUOZEIlgE9clx+VitK1zM+UM9Tul76ulsOe+o69z70PRVEmexrFMyjeJ7lnWffHyWjTmTcfj/qDMceG3pmXe/kGoO5b1bwxPX44Ycnbr63JQdPQKFluXVGpRrivJ9/HX7uly7E+b9wDuq9dXh5eR09mpmZwWc+8xl8+MMfxp/92Z9l5m+99VZs2rQJX/jCF9Q/rgwPD+OGG27A/fffjze+8Y0AgO9+97t46KGHcMcdd2DHjh0AgK1bt+LKK6/Ebbfdhq9//evzeuZSkQepJSIePQ+M/nucue4HYAeb9v9nzbXcbIAr+zipXk5cglS+HXnEReRGQRPKuOoFiFQdUmiCSxtziHY1TnaqXl7ESYITjTjNNzhJoJk9OIk5yHVtwEld54KTfT1LcMqLOlkf5tVczjnZ6/wUwQw45Z1F96ZjhyXqRJ6vXdSp3Jk8d4+9ppThBOiadKyTeie6L+cZ20OR46zZuuiVXbdY4SnvfhzmmFxf0jSCMYYtF52KS997ES666nwMLfd1T15eR6t+93d/F729vfjQhz6UAak9e/bg0Ucfxac+9SkjQn3ppZdieHgY9957r4Kee++9F4ODg7j00kvVuiAI8I53vAOf+9znsGfPHqxdu3ZezlxK8iC1RJQc+ghY9DOEjswMXd8ka5kScMgaJzGvo0yOBrjp79SOnEZ/pEmDnb6XpH2cbBe8Mq56nZhDaGvy7qbqLQg4wW0QUQ6cgLnYksvz8+qcDPiwz+AlwclxThE42YAzF5OIwmvHeic4OfZ3Jepkvb9uuOwVAhhZ4zyXnmPf23rujs0iiuBJXjvrosgX1lrH6LgFGB3Bk3y+zH2WPjwBwKlbT8El774AF199PlauXwEvL68FkuvvpoW4Zxs98sgj+PKXv4w//dM/RaWS/Yj/5JNPAgBOOeUUYzwIAmzcuFHNA8CuXbuwcePGTErw5s2b1Vlr166dlzOXkjxILRHF0VPqm2XXN0ljCBllkrAkjCO0MUQCE5x0mh5xqlORJxmFSkEJsu4pcKTeQY0X93HK2pFH8nXad0ndU0IIQgU/7c0hJOAUg1OZGqdZp+rlgJOAL/n5SZ93WMAJmP86J9Bre56IfDi3zwVyIlaus8rcyz6PXHer1qncmTx3Tx7g5AJY5r1z8/1bz6v2kTNnW++UeYbMPm6eT3/vdB25bway5tLnyflM2bWLCZ5OOut4XPLuC7D93W/AuhPXwMvLywsAoijCTTfdhKuvvhpnnXWWc82hQ4cAiLQ7W8PDw3j66aeNtSeccIJzHQCMjo7O25lLSR6klohmeANV3gIkLJH6JmkGIX7PM4bQ0SZZ36SiTwqcXPVIQSaCZPZlknVPBLrstDoSbVKNcxMCXFYDXHUecbxLuOjjpACN23bkulaqrKte1hxiDuA0q1Q9oGvgBOT3c7Ln1AfvNuAEem3Nk3PnAk42JOSm69F513p7HsXnMXptn2c8P4cBawX37EatUyGAkTUGyGSeh+fuy7Mod43NKmWvm/CUt842VegUnux/1S2Cp6LIlTFM1s4DPJ189gm4+KptuPjqbTj2lHXw8vI6zOJQadILes8C/Y//8T+wb98+/Pqv/3rbo6T5Q7fWzdeZS0EepJaIpniMGhcW5q40PZHilpemJ2BJp+mBGDPQ6JBufmuCEI1ImdEmbkSnaASLRKossKKmEvocRoCKKdBKCGhp6AJ0M9zUqQ7MiCDl2pGTiJPTVY8Akfg8WeCqBxOc9OfLfHDiPNDXiwGc0rWlwYmjc4MI17zrmeXrnDmgYB5k3nG/Oafr0ffreJZZR53I83Q96kTuWTrqRMdy+jsZY+1S9lzn0TVl4MmGkRyYcQJR4TNxI8LUUZNcuX6eDCMA4JTXnYgLrzzfw5OXl1dbvfzyy7jjjjvwW7/1W+CcY2xsDACQJAmiKMLY2Bh6enqwbNkyADqKRDU6OqrmAWDZsmW56wAdRZqPM5eSPEgtEU0lHC3EAPJtyBPInkvMqDWSMMW7lqYnACcbnXLXN7Uzhihy1OMy2gRmNbkla5BtgJsBqqIGuCBABB2JgkwT5Po+nbnqpfvauurZ19molQuqFgqc1Fl5a+kPqrXPCU55852sR/vzZpuulzmT7JlVup51n84BjDven/nMSzbqBJgwpvZzfY69nxyhYCgvaoTitbOGp05S/DgHhSdurDfPP23bJlx45fm48MrzfNqel5dXab3wwgtoNBq46aabcNNNNxlzu3fvxrnnnovPfvazuOiiiwCIWqQLL7xQrUmSBLt27cLll1+uxjZu3Ih77rkHSZIYNU2PP/44AGDTpk0AdG1UN89cSvIgtUQ0yUNU0//nzibaJG3IYwI52kQiyAeuRFuWJ47zZMQoL01PRptE9AigqXm01olClytNT0aVbGOIXKBStVJQ0CZfG5EowJxLsusy4JSeJ0SgR0bHFhM42XPGB2hyzWHNe3BynU0/KLdL16Nr8p7NOMf1fDkOe8bZxnuwnsk1ZpzPi88H3FEneS59zfXvnUed5O85a40x7l7rvFd2bTE8ke+FvbYjeHIAG71HqiAMcNYbT8cb3n4uLnzned4wwstriYhxbv59uED3zNNrXvMa/PEf/3Fm/Nd//ddx3HHH4WMf+xhOPPFErF69Glu2bME3v/lNfPCDH1Qwc99992F0dBSXXXaZ2rtjxw587Wtfw3333afGkyTBN77xDWzZsgVr1oh/7Fm7dm3Xz1xK8iC1RDTJq6jwKgBtS27DT5Sm18U8SF/PLdpU3PhWggbLgJsdbdL3ZblpegkxiSiTpqeeg8txR5peuq69MQRSiGLpZ6I8YwhAwpF6TcDJ6OnkACWxSF9nAMu+hp0OlgNO9gd4OWd8ODfXzKnGyTVP75U338l65MyTr0lZW3Jwbt4T9tfVOst+DjXHzee270PWZADMPidnn/7+8Nx9pR324Bhb6KgTXTsXi3IbfArvZa0F0HG9U956jsw9C+HJEXWq99bw+ivOxgXvOA9b3/I6b1Xu5eU1Zw0NDWHr1q2Z8Xq9jpGREWNu586duOaaa7Bz505cffXV2L17N2655RZs3boV27dvV+u2b9+OrVu34qabbsLY2BjWrVuHr371q3jsscdw9913G/eZjzOXijxILRFNJTWEqAHQ5gr5FuTZ2iZtCKHrnFy1TTotcDbRpvReBdGmdjbkCQGi0ml6KQDRND35ka19mh5Qtr4p3xhCr3XBkB21Et/E7D4bKOy1swInFdGw1gK59z1iwMle4/j6tAWnovdG1sxLuh657iRdzzyfu9ehzDqy0BXFMsatteRMY91cjSLynqtbKXudrJc1T8i5L9HImmGc/9bXY9vbXo/XXnoGevp8k1wvryUtx98LC3LPLmjbtm246667cPvtt+Paa6/FwMAArrjiCuzcudMwgmCM4c4778Rtt92GW2+9FRMTE9i0aRPuuusubNu2bd7PXCryILVENJH0oArRiDfbtykwolSyXsoGLgNwHFEtuqaod1NebZMchx1tUlGo8tGmhKQCcrUegJWmp+CoXbSJwJH5D9sLkKYHaHAiRg027NCUvkyanvydztsf3l0f9ula5M0T0fdln2c/N8+5tte3ud+sUvWMPbMAJ+e5XF87vq50zVzBKbMOZkSok4a4mTE7XS/32Tj5eqs/EGQPL78OmHutUzeiTkVnGI9M1s1TvdPqE1fiknddgDe8/Tycel62b4qXl5fXfOu+++5zjm/fvt2IFOVpYGAAN998M26++ea2a+fjzKUgD1JLRJNJDRX0ApAOfTRFT0eSzB5LWUMI2WSXczsVL5vqV+ikJx3sSLSpqLYpsaBH1zaZ9zFqm9JoE62Bmmu0qTBNb67RpvScvLUdp+kBZjRKrYW1175uk9YHcm3dy5ijZ9Oz8s5udz+g8PnL1jjNW8QJ9vdofsGJrnPWObnG5FnGGDfXoZN15GGKolNt1s57rRPQedQJ5OtI13crZc96zmqtgrMuOR1b33IOzr3ibOwZfRlnn302wjCEl5eXl9eRKQ9SS0RTSR0BhP25XddEocmsg4KqlWrftwkpXAHtUvS4MS5qtmDAEW1mC/VcHNmeTrR+SVuQa7iSc/qzTtnaJgBdjzbptfI1gMOTpmeflXPfUml6QPH+MuvtZyXrXGcUA479rDx7H3om+ZosRKqe8Tx0H3efVQROxl7jvRWMdROcAHckybW26ExXU1wKMsbanAgQVbuoU6dRpLLrwTuyKF9xzAjOe/PrcN7Pvxbn7DgTvQPpP3bFMfb8y8vw8vI6QuX4+2BB7um16ORBaoloOqmDpX2kaOqeACikdVBpmp5y2pMpetqlj4MhBjTsGDBWbD9OU/TcESWgGyl6gC6l4BBOekm6J9dJD8wAKtC/49KIlQQaDsxPtEl8czJr5zXa5FoPPW/DU9s0vbz7l7mf/DrYZ1r7cs9Uz2TCQS44uc4FXcPz3z9dYz33fEacXM81F3Ayn4U7noUsNN6H60xrrX2mGufW83Jzj3EG+ZV3r7y1QPuokyuK1HGtE0dpl72A4bQ3bFbwdNKZxx9xzSW9vLy8vMrLg9QS0URSR4gmAAuaMoYOprW5TquDNqEorGsioGRFlNrXNek1tv04XWcAFeYhRY9Em3S6nriXC5ToXG60qV1tE7mer2iTPT/raBPdzwvWcLhBi5ybm6ZXdCbdgxLg5Di3nI249TUha9oCWM6+boCT81kBN7x0Ck7yHPqarG0bneLIcddz7CsDTu3u10m6nuOe3ap1yswRrVy/HK9/09k494qz8dpLz8DgyAC8vLyOcvmIlFcqD1JLRNNxFUAPAHQRmgDposetuqaYU6ACYEWhXDVSPD3PrmuS1/nQxGBHlORnogw0EUhaiBQ9wEwXKwVN9rwNFLnXOZBlf+Cn++z5zIfzovvl7Hfdr+iZuP4alU7TK1yTBaZy6XXZr81iAKd5jTjRPW3PdOxxrc0FJ9e9yK/M2WQsZ70R+ekYhHj7Z1R7slGq3FqnehVnXPwanHPZmTj3za/FCadv8FEnLy8vLy+nPEgtEU0ldTAei38whlWLVDrShHmEJmZcA2ZdUzloAvLrmtLnNSAKxnUuNFnnuCJRTmii5/LOoEmdRefJ2V2xIC/Y77y/dVZH0Sby/IctTS9nnx7XcNVJA1xjjFtn5TxDx3bkAMgfCPJ+ra+P8SzccSZZWARDnaxdQHASw47xdiA0j+l6AHDiGcfhnB1n4ZzLz8IZF52Keq+3J/fy8ioQB/nH1wW8p9eikwepJaKpqArGxLfL7hdF7ciNmiYCParHUmF63tygSdQxFUMT/dzWFprUh9pZQhNdy7Nri+qaxGoTdujaUtDUyXr5+PYHfxuCXIDAHdfWWbONNhWZN2T3zEO0yfWe1D6euUeZs5ymE8YH9KLzrb2lxnj+PaDnjypwst+3cQwvBifor1P2/HLW5CvXL8drLzsDr7v0TLz20jOwYt0IvLy8vLy8OpUHqSWimaSKhIt/JXW553EuI1AgPZ8oGAF5zW8TlT5XrqapLDQVRZo0JOnnWnBoItel6ppyzuoaNNnAAOvanqfXdAxwgxVZtyC1TfI5XOca+3LS9Iz3wPOfEXA/Ex3jjjH6DGRdqfqm3Hvw7LrcvfR+5EO/2kPvXXItvb8aJ3PGftcZ5Bcdt+9XsL7rtuSF4ER/UKw5ooFl/Tjrjafh7J87A+fsOBPHbjrGp+t5eXl5ec1ZHqSWiGbiGhIeA4CzT1MRNMl0Pmltzsm6fPc8AGDGnP6MoqNaSO9tfC61oUnBS/pcc03Po2t5du2ihCZ6H/K6U2hSZ9I9rjPJPZ3RHevs3FRBNVZgQe7Yd7iiTRkoo2Nkbds0PTWXHVNr07PLwJl8XuNMOQZzPh/GSq7NBSduPNuiBCfHc+paJp4aYTjubT1vT18dWy46FWdfcgZee+kWnHz2Cb6fk5eXV9fEOAcDb7+wy/f0WnzyILVENBNXkCDUkZ8UjICCtD0JRpCfqyQM6dQ/8ZmF1jNBQxgHFGhxFzQx47rIPU8ZRpD5Imii8GXOdyE9T314X1zQlHum8cHecSa9J9xr5pqil3tv630YzwtHRMfxdTCiTa73QM8i5xjn22M5z5GJNrnOy7sHoD7ku6JN5vNwx5kuaCixltw3s5anh9jPCHOtvia/8p4pc4Zen0mXy8Ba9tlnBU4SmvIiTtbz1ntrOP2CzThz++k4+5It2PT6k1CtVeHl5eXl5TWf8iC1RDQTh0i4+GBg243TVD+eztFIlRGBMlLzoNICKaCJzydWnya5Tj6QBU0JARgJZi7wAZhZoDlb9zy5ns6DwJn8faFrmnJgYVbQZO3pGjQZ5+ZAk+ve1vh8pui5z+LWWe3T50qbQtj7JRDkrc2cycl7yn7Qz0aJSqwlr3PT9Iz11j5jT859jDN45nUGXNqAkwKtWdc40R8Yx/1T9fTVcdobNuHMi0/HmdtPw+bzNqJW9+Dk5eW1QLL+rlq4e3otNnmQWiKaiSqIkYJUUibKJMfk5xczNU+AD9Pr6OfEdql56uaO5rYWJBkART9XuaCJ3Mf8IF8iNc+etz/M0/lO16v3k3c/c25RRZqMZ1kAaHLdr5OzkN1b/PyuezjOgwPE6JgBX/RTvn5Z2oa8XbSJrgFZ4wQ3+/mss40zKIzkPFOb9XPq4zRHcCoyh+gb6sXpF5yKMy8+DWduPw2bzjkJlar/35eXl5eX1+GV/z/RElErCtGCyPGffZQJ1uviKBNtZmtGmcS8DUn0ek6peeS6qJ5JzdvnFV23W6++FtbvZd3znM9YYo9134WCpv+vvTsPcrI8/AD+TdiDvbLJ7mbv+wSWY5FL7kssVqYookxHa20VRjymeKA4rVUrHRSoWrVQax2sxzhUalUoVaocOi1qhTIO0HJMEWTEqiywIvxgN3l+fyR59z2T991N3uRdvp+ZNcmb58rjspvvPs/7JtKX1dCkaQs62+ditSWV60VoivSrN5aox4RxP7LXYhywdAKDlbKy+y51G4lYbdIpb7hNz6jfSB294GQU8BR1zF+OvKDUi8ETB2LIhIEYPHEA6oZU8xwnIiJKOQxSDnEukIau8MUmut8Hmj2XCVJoCh2QB6budkIFlVv6lIFJ/bg3q0zd9TUhB1DeijiuMqnf4Ov0FTMAqfvUCw1GdVT9KkKFrF3DMKL7ehK70mQY0qy0pR6r0GlPXhY6wSxyTDMX2mPKfoR+WejV1yurChjxLKsOVlCWVZYT2nLqPtXljVabdNuBtjxgvE1PZ6zS+U1G/cvHJ1MzqBKt41oweMJAtI5vQVl9Ca+qR0SpS/Xzki5cDFIO0dnZvSIVa1te93ubaJcZB3p+LpO6fBJWmdTtRX1ePm5tv/rbwGL1qaxjuBKlbhPQtKsJI3pjkcqoQodB2z0KTXr9p3Jo0u1HGJfVtKlXVi8sGJTVKx+tXfkvXuU/WsU8dpcX2vKGY1SWT9hqU6w60OlfNebMrAy0jG7EoLEtGDS2Ga3jWuApzAMREZHTMEg5RFfAjU4R3toHAKpteUK+UpSwbXmqupoAkaRzmdRv7qP2CXN9yssY1YnSr2Gw0WsXyjKh8YjYZdRt6xwLlRNRx9R9LEnnNMHhoUnxeoTOcwZBJE6hKXRI6B5PhdWmkho/Bo5txsAxTWgdPwANw2p4fhMROZviTZVtnarecFAq4G8zhwgE+qErHKR6FpjUjw3K6tVVvbHWC1hWtuXptacNK8qx9/pcJs3447TKZNBuIrbmxWwbsrY0/w/Ux3oZmlTtWw5Nqv9vKbU9T6+8UNbThCZoy0jHFH0KaMKG3hj1yspXm/SCi5WVI706qv57utrUNKIegy5uDoWni5tRWOYDERFRX8Qg5RCBLjeCQTcApGxgAlQBSKc9bcDp3bY8RR2jNmKFimj9xmjXMLCpXz9guMoUbYzGgUzVltF8yFajer3KpD5uUN+lV1/xWuVv3mX15aHDqJ9IfXVZRbt6IcWgrF55oaxnOTQpyguD8ShDS9TQFOX5HocmozrQjtnoEuQulwvVAyswYHQTBoxpwoAxjagbXI1+abwoBBERXRgYpJyiyyUFqRCdLXmKN9R9MDBpxqBqw0oddaBAjDKRW4PVK209c+cyRY6lxCqTfFx6r0FVV719MGqbinnSCSKxQpNhWVVIEdr7pi5RHnlNiuASPqjuH6p+pPJCOx71GOVlVfdtDU3QqaM3Bhl/VSEGjG5E88hGDBjdiKYR9cjxZIOI6IKj+Au1bZ1ya18KYpByioAbiKxIKd6YMjBFraMugxhlIq+pt4FJXs/0CpbQHVe8A5PUR7TXoapvaZVJr6xhsNEZaw9XjqIGrGhta9oyCCGxgo76fpSyCdueF6fQ5PV70DyqAS0jG9E8sgHNI+tRUMotekRERHIMUk7RGTofSiILOOrzh9Tb9xRhCbKy6ueNAoZeAIgWsGBhS55sXKkemHT7l9dTtWV8YQlZmIn6errLmQpg6tcgencuk+lVJnUIUh+Xl4fB1ryehiC98uq2FX0K1TgNQkisoGM4Tm1ZJ4SmphH1aLqoHo0X1aNlVAP8lYW8/DgRkZFkrUhRymGQcghXwAVXMPzGRvr324PVJcgfq58P1zG4Sp5hYJL/2zYIFZo25GViBQqjMjBRJtJfIgOTYfvC3OuLFZhUbfYmMEXaVK5Imd+Wp+xLGPelbjdWCFIHJvU45GWilO/11jy9MRn2rQwsumElRgDS3c5nJWhBVcdoHGEefy4Gjm5G4/A6NF1Uj6YR9QxNREREPcQg5RSdLkC4Yq4uASm2HS9Wu5qxx+hbr4yANpzpjLe7niwsGY1Rr3291wvEPzDFYYVJXj/qtrxogUk+HnVf0cqq25aNSbovtPdtWWWS2tAJLEb3jVaZorUVLfz0ZnUKQjFGowtBAEB5Yykah9ehYVgtmi6qQ93Qahw+9ina2trQrx8vCEFE1GNB1ZsXWwiAP7pTDoOUQ7gD6F6RgomtdUL1OA6rS4p25Y9lZWzZjmembcDSVfJitg/ELzAZBB55/UQEJsO+VGWi9Wc5MMnLS3MVJbgI7f1eByb1/ahjVYadnqwySfV6ssokf42Rx+pxqMaenpmOuiHVqB9ag4ZhtWgcXov6YbXIzstSvNxAIIDDx0BERERxwiDlEO4uF9zyrX3h24Scu6RpU1XGRKCRygDmVpdUbZvfjieMxyAvF2s7nlRXJ3AYjUPonFsULTDpBRsrgUlo2+3xljxAf2VO3W5PApOZ8kaBSXFMHZxkxxT9Rgk7hmPVlu31uUw9qQdV/chzeq8PQGG5D/XDalE3uBoNbbVoGFaDyuZyXnKciIgoCRikHMLV2f2mOxFhST+UhZ8zsyIVpf8+tboEmA9g6sAkzWfsAKMOMqYCk+Y1CWVZeXlVf70JQH0uMKnnJFJHXt7yKpP8NQptm6r++2dnoqa1EnWDq1E3pAb1w2pQN6Qa+UUeEBFRsglABGMXiyfFmyJKFQxSThEA3AHZ1j5A9aZYdpuEsGTUtrWVq+5Oo4cqVfvyY4oxqYKFUTlVSLNldUnTV4K244X7jL0apRcKoB/GzJSHzpa8VA1MsjH0PjBFygnlOPReHwB3PzcqW8pRO7gatYOqUDe0GnVDqlFWXwK3W/65cURERJRqGKQcol8AcAUQeudrJSzpBAPAQlhSvCFPbFhSllOOK9ZWPMVrUIdCQPUGvwfnLkXGLfSPR6uvWV1Szanp1SV1f6o+E74dL1p53aBkIjDp3Y/afyoEJnk/5gKTy+VCWX0xalqrUDOoCrWtVagbUo3KlnJkZKaDiIgcRO93VuI7tbk/MoNByiFcnS64DM/VsRBm1AEKqsd6gao3YUnenqacsow8LCn7MGhPqI/1MiyJ7jYMw5JeG3phSa9dGIQoC2FJ83p1y+sFgu7XphhjrPLyccrb0F1d0juuc2u0uqR+XnarG1LMBh+90GSmXk9WmNwulDWWomZQJaoHVqJ6YAVqW6tQNaAC/bMzQURERH0Hg5RDuAIC7kD4gSZAdN+aOV9KuaIT+nKr6um1b/kCD2bDWKRNQHcbnmHfPQpLQipvZ1iSjtsRlmT99mx1SfV8lJWdXq8uqcrH5bOYDOurxh6pKw9NJgNTemY6KpvLUD2wAtUDQoGpemAlKpvLkNE/A0RERNT3MUg5hLsTcEeChTq4QPlYN1BFCx26IStKWNILYzH76B5g1FUlQLONznoAU9aPGpZU4+qTYSnyWBGWwv+R3xoFDjOrQYb9a28trS7Jb6ysLqnrSWUi9UIFRLQxA/AU5qFqQDmqB1SiakAFqgeUo7KlHKV1xfwsJiKiC1VQKH/n2YEXm0hJDFIO4Q50b+3TC0pRV5XUj4HoQQnGYSd6WVWA0amv6dsofEnHjAKYOowYvCbZuEyds2S0LdF0+FH12duwZFReXUfnvqu3YSlyG6+wpNuechxRP+jWbD2Lq0tp6f1Q1lCKqpZyVDaXh25bQre8Sh4REREZYZByCHeXQD/V1j4g2vY+YbKc8ph0nbA4nasUc1VJr00grqtK2vHqBCijsCQ/Li8LK+V7GJbMrESpA1SUsJGyYUl+P9r4pbryeY4SlmT3XS4X/FWFqGgqQ0VjKapaKlDZUo7K5jKU1Pj5OUxERGSe+veVPZ3a3B+ZwSDlEK4uhK7ahyhBSRUMTK9aSccSG5SUK1VCKq97DlOMcYUuTBEjhGleh07wkZVP9BY8S3WC8r5VYchsWDIKNvJyumNxQFjSGxOAoooCVDSVobyhFOWNpahsLgs/LkFmFi/2QERERPHDIOUQ7q4g3IHoQccwKGnebJvfepfUoCQvGysoyY8LqFZrdPpUPB+rvPYNe7xWlUKvQV5GKF6fdExVJ2aw0YzXuLylCzzIb4zCmpWQZWYbnuy+y+1CSXURyhpKUd5QiorGUGAqbyhBWUMpr4xHREREtmGQcoh+XS7060LonbrZQKMKWboXktAra9BuzK136lUygzaiXibd6LgiRKgCjt5nUyFKeduDUviAYVCSPR/PoBS+tW1VSX4TJSzprirJ7mdmZaCsvgSl9cUory9FWUNJaIWpoQQltX6kZ/Bzl4iIKMnUv5MTzRW7CNmPQcohXJ0CroD6zTy0QcnKapLhKpWQysdcTdJpw2gcmjbkP4P0jktv2mV9qsejLg/oBCuD0KNT54INSuryivuq16CpqwxKum3L7rtcLhRVFKC0rhgltX4gK4i2sUNR2VSOsvpi+Eq8cLn4G4OIiIhSG4OUQ7i7BNxdyjfFeiEJiLWVTyjKG64mqYMDerGaJD8udMKNJpxB8SZeOXa9YBUr9PSyjnp7m11b78K3loNS+MZ0UJLXjRayZIEp1rlKXr9HCkqltcUorQutJpXVFaO4xo+MzNCqUiAQwK5du9DW1sbLiRMRkTPo/U63o09KOQxSDtGvC+jXJSDt7UtkSLKyaqTqN2pIko9HbyzqcarbitSP2YdBHfkxw4s5BBVzK91RjVvTR1B2mUOjMjrHhLxeb1aUoo1H/TrUdaVb80HJV5KPktpilNQUoaTaHw5NoeBUXF2ErJz+2vkgIiIi6kMYpBzC1RmEq6s7JSk++FUTkrTBSXOpbKhDjLqNMLMrSZF2TawkSXViBZ6e1NGMWd6GMF5NUhyPEWjkzxmVD99aW00K3xFGQUdWL8pKVKiYMhjJb4XQCW+y++5+/VBSXYjimlAoKqn2S/dLa/3wVxXyCnhERHThCgb1/4Ca2E5t7o/MYJByCHengLtL9Y9IWDsnKeZxxcpG6MbqxRu66xgEhTitJGlCUrRw0dOQFCMsRfuQV0srQvFaTVKPy+A1eApzUVwdCkb+ykIUVxcpvnylXm6zIyIiIoqBQcoh3F3B8Na+MMsBKXxAE3hgEJ6MQlWMsGOmjryeXrBStCE0r0m6I2DQdu9Ckuay3uryUYJO1JWkGHWlvhX9KccSKyTlerPhryqCv6oQ/opC6X5RZSGKw7e8RDgRERFR7zFIOYSrMwhXZ1A6Rap3AUlZx/SWOaNVJJMBydIqkhD6benVkz+nV152XzonyUzwUoyndyFJG4CEon2h3iKgMz5vsQf+ylAYKiovCN1WFIRCU2UhCisKeG4SERFRoum9D7GjT0o5DFIO4e4MwN0VCD1Qrc70bgUpfLBXAUnoXCZd3V+CA1IiV5Givh6jVSR5+9HPScrMykBhRRGKKgpQVFGAwjIfiipCwShyrKDMy89PIiIiIkohDFJO0RmAK3yOlNVzkBR1FLey9tWhKnxfubqlXF1S3Amqw4R6LAYBKcZWNSkgmV5B6n4NSQtI4cdpGWkoKPOFw5EXheUF4S9f6LbMi6KKAmR7svm5SURERE7CFSICg5RjuDoDcHUGpK19QBzCkVSmh6tH8ltFnybDkcHz0cYkzKxomQlXir6tBaT+uf1RUOoNrxT5UFDiDd2WeVFY5kNhuQ8FZT7kenMYkIiIiIj6KAYph3B3dinOkYq+cgT9IKKoZxBgAN3wENdwJI3LQjgyek2RlxRt9QiACApZeZ3Vo/R+8Pg9KAqHIF9xPs7h/zBw2AAUloWOFZR64Sv18mINRERERMQg5RSiswuu86ogBeivGknHowQjvfKy+7qX59ar25uVI6v1rYajjDR4/R4UlIVWjLz+fPhK8uEr8YZuS73SffXqUSAQwK5du9DW1sZLgRMREVG3oFB+tIodXDb3R6YwSDnF+U6gU+eKc/LHZoJR5LlYoctKMAK6L+ZgZtVIFYgi94VRaAtzAcjJz4anKA+F5QUoKM0PhyMvvMUe5Ps9UjDyFucjK7c/t9YRERERUUIwSDlFVxdEZyAUDFJtxShqMJIfVwUjVzgYFebCW+JFYakX+UWhQOQtzofXL7tfnA9PYS5Xh4iIiCiphAgqz6e2qU9KPQxSDiHOnQc6A4pcEn4mnE/CocVodcp0MNIPSIZXwJPJyExDri8X+X4PCsKrQpFglF+UF771wFOUB6/fg1xfDoMRERERETkSg5RDiM5OiHNdoWWcKKFIEapihqLIcW0oyszKQG5+Tij0FHukUOQpzAuFo6I85BXmIb8oD57CPHgKc5GZxYswEBERUR8nknCOlJvnSKUiBimHKCjKwbFPv+4+IJ1zFH2lKC29H3I82cj15cBTmIt8fz584W1ynsI85BXkhoNQKAzlFYS++OGvRERERETGGKQc4t4/3IZVi9bgxFcnUFRWBF9JeIWoIBe5vlxFCJIf4yoREREREVH8MUg5xKCLm/Hrvy/lJbmJiIiIkkl+TrqdfVLKcSd7AERERERERE7DFSkiIiIiIrOCwe7Pz7SzT0o5XJEiIiIiIiKyiEGKiIiIiIjIIm7tIyIiIiIyS+h/BmfC+6SUwxUpIiIiIiIii7giRURERERkkhACwuaLPwiuSKUkrkgRERERERFZxCBFRERERERkEbf2ERERERGZxYtNUBhXpIiIiIiIiCziihQRERERkVlBEfqyu09KOVyRIiIiIiIisogrUkREREREpgUBYe/lzwG7+yMzuCJFRERERERkEYMUERERERGRRdzaR0RERERkkggCwuaLP9i+k5BM4YoUERERERGRRQxSMqdPn8ZDDz2E8ePHY+jQoZg7dy7ee++9ZA+LiIiIiFKFCCbni1IOg1SYEAK33HILNm7ciDvuuAPPPPMMKisrcfPNN2P79u3JHh4REREREaUQBqmwrVu34sMPP8TSpUsxd+5cjB07Fo899hiam5uxcuXKZA8PAHDs2DE888wzOHbsWLKH0udxru3BebYP59o+nGv7cK7tw7lOXTt37sSNN96IiRMnYsiQIbj44otx/fXXY9u2bZqy7733HubOnYuhQ4di/PjxeOihh3D69GlNOSu7tBLRplMwSIW98847yMvLw/Tp06VjbrcbV1xxBXbv3o0vvvgiiaMLOXbsGJ599ln+ELMB59oenGf7cK7tw7m2D+faPpxrGSEggvZ+QRhf3KKjowN1dXVYsmQJfv/73+Phhx9GRkYGFixYgL/85S9Sue3bt+Pmm29GZWUlnnnmGdxxxx3YuHEjbrnlFghZ+1Z2aSWiTSfhVfvCDh48iMbGRrjdymzZ0tICADhw4ABKS0sN6waDyr2rfr8ffr8/7uPMyckBAAQCgbi3TUqca3twnu3DubYP59o+nGv72DXX6vdUFN2UKVMwZcoUxbGpU6di2rRpWLt2LS6//HIAwIoVK9Dc3IzHHntMer+bn5+P2267Ddu2bZPaiOzSevrppzFjxgwAwJgxYzBnzhysXLkSf/rTn6R+EtGmkzBIhZ08eRK1tbWa4/n5+QCAU6dOGda95557NMfmzJmDuXPnxm18QGiFLLJMu2vXrri2TUqca3twnu3DubYP59o+nGv72DnXhw4dSmj7vZaMiz9Y7C8tLQ0ejwfp6ekAgC+++AJ79uzBkiVLFIsG06dPR35+Pt555x0p9ETbpbVs2TJ88cUXKC0tTUibTsMgJeNyuXpUb/ny5aivr1ccS8SK1M6dOzF16lRs2bIFF110UVzbJiXOtT04z/bhXNuHc20fzrV97JzrjIyMhLbfVwWDQQSDQbS3t+PVV1/Fp59+Kv2x/8CBAwCApqYmRR23243GxkbpecD8Lq1EtOk0DFJhXq8XJ0+e1ByPrERFVqbkzp07ByAUwNTfGMePH8fx48fjOsZDhw6hq6sLhw4dQnZ2dlzbJiXOtT04z/bhXNuHc20fzrV97JzryIpU5H1WqunMsH9cZvpctGgR3n77bQChbZi/+tWvMGnSJACQ3uPqvZ/Nz8/Hf//7X+mx2V1aiWjTaRikwhobG7Fp0yYEg0FFKNq3bx8AoLm5WVPn6NGjAIDFixfbM0gANTU1uP/++23r70LGubYH59k+nGv7cK7tw7m2j91zffTo0ZRaafT5fMjKysLx0s+S0n9aWhp8Pp/h84sXL8ZNN92Er7/+Ghs2bMCdd96JZcuW4Xvf+55UxuzuKyu7tBLRplMwSIXNmDED69atw+bNm3HJJZcACC2Rvvnmmxg8eDBKSko0dSZMmIAVK1agsrISmZmZdg+ZiIiIqM85d+4cjh49igkTJiR7KArl5eXYuHEjTpw4kdB+2tvbdfuoq6tDeXm5Yb2qqipUVVUBAKZNm4aFCxfi4YcfxqxZs+D1egHAcPdV5HnA/C6tRLTpNAxSYZMnT8aYMWPws5/9DB0dHSgrK8Orr76K//znP3juued06xQUFChSPhERERH1XiqtRMmVl5dHDTOppK2tDZs3b0Z7e7t0HtOBAwcUATUYDOLgwYO49NJLpWNmd2klok2n4edIhblcLqxatQozZ87EihUrsGDBAhw+fBirV6/G2LFjkz08IiIiIiLTPvroI3g8Hni9XpSWlmLw4MFYv3694vLymzdvxqlTp6TdWEBol1ZHRwc2b94sHdPbpZWINp3GJUSUT/giIiIiIqKUddddd6GiogKtra3w+Xz46quv8MYbb2Dbtm24//77cd111wEIfXjujTfeiJkzZ+Lqq6/GsWPHsHz5cjQ3N+MPf/iDdA6TEAI//OEPsX//ftxzzz3SLq233noLzz33nGKBIRFtOglXpFLI0aNH8ZOf/AQTJ05Ebm4uXC4Xtm7dqlv2rbfewujRo5GVlYXS0lLceuut6OjosHfADmZ2rl944QVcc801aGhogMvl0nzgHcVmZq47OjqwdOlSTJ48GSUlJcjLy0NbWxuefvppdHV1JWfgDmT2+3rRokUYOnQovF4vsrKy0NTUhMWLF8f9SqN9mZWf1xH79+9HVlYWXC4XP/PIJLPzPGXKFLhcLs3XbbfdZv+gHcrK93RHRweWLFmChoYGZGZmori4GDNnzkR7e7u9gyYAwPDhw/Hhhx/i5z//OW644Qb84he/QCAQwOrVq6UQBQBjx47F6tWrcfjwYSxYsAArVqzAzJkzsWrVKsWFIKzs0kpEm07Cc6RSyMGDB/HKK69g+PDhmDFjBl5//XXdcu+++y5mzZqFq666CsuWLcPhw4dxzz334N///jfefffdPnlVlHgzO9cvvvgi/ve//2HixIk4e/asvYPsI8zM9ZEjR/Dkk0/i+uuvx913343s7Gxs2rQJixYtwkcffYQXXnjB/oE7kNnv646ODvz4xz9Gc3Mz+vfvj507d2Lp0qV4++23sWPHDukDHMmY2bmOEEJg/vz58Pl8OHbsmD2D7AOszHNTU5PmZ4UTP5cmWczO9alTpzBp0iScPXsW9913H5qbm3H8+HFs2bIF58+ft3fQBAC47rrrFIEpmsmTJ2Py5Mkxy+Xm5uLBBx/Egw8+mJQ2HUNQyggEAtL99evXCwBiy5YtmnIjRowQbW1tivKvvfaaACA2bNhgx1Adz+xcy8uNGDFCTJ482YbR9S1m5vr06dPi9OnTmrp33323ACCOHDmS6GH2CWa/r/WsXr1aABDbtm1L0Oj6Fqtz/dvf/laUlZWJJ554QgAQ//rXvxI/yD7A7DxPnjxZDBs2zL6B9UFm5/qWW24RFRUV4vjx4zaOjig1cWtfClF/qK+eo0ePYseOHfjBD36gKD979mz4fL6YfxWlEDNzbaUcGTMzhzk5OcjJydEcHzVqFIDuz2yj6Hrz/VpUVAQAXI0yycpcf/7557j33nvx61//2rGX+E0W/gy2j5m5/vbbb7FmzRrMnz8fBQUFNoyKKLXxJ5TD7NmzBwAwePBgxXG3243W1lbs3r07GcMiSoitW7ciLS3NsZdFTXVdXV349ttvsX37djzwwAPSx0BQfN16660YP348rr766mQPpU/bt28ffD4f0tLS0NLSghUrViAQCCR7WH3Kjh07cPbsWZSXl+P73/8+cnNz0b9/f0yZMgXbt29P9vCIbMcg5TCRk8H1/hJUUFDAEz2pz3j//ffx7LPPYuHChSgsLEz2cPqc3bt3Iz09Hbm5uRg3bhyqq6vx5ptvcgUgztatW4dNmzbhN7/5TbKH0qdNnDgRTzzxBP785z/j9ddfx7hx43DvvffipptuSvbQ+pTPP/8cAHD33XfjzJkzWLduHV555RV88803mDZtGj755JMkj5DIXrzYhEPxghLUl+3duxdz5szBRRddhEcffTTZw+mTGhsb8c9//hNnz57Frl278Mgjj2D69OnYunWr7jZLsu7kyZO4/fbb8eCDD6K2tjbZw+nTHn74YcXjWbNmwePx4Mknn8Sdd96JIUOGJGlkfUvks4IqKyvx2muvoV+/fgCACRMmoKGhAcuXL8dLL72UzCES2Yp/enSYyF/m9S5T3N7ezj3L5Hj79u3D9OnTUVZWhr/+9a/IyspK9pD6pP79+2PkyJGYOHEibr/9dmzYsAEff/wxfve73yV7aH3GT3/6U+Tn5+NHP/oRTp48iZMnT+LMmTMAgG+++YYfWZFg1157LQDggw8+SPJI+o7Ie5AZM2ZIIQoA/H4/Ro0ahZ07dyZraERJwRUph2ltbQUQ2pZz6aWXSseDwSD27NmDq666KllDI+q1gwcPYtq0afB6vXjnnXf4hwEbDR8+HJmZmdi/f3+yh9Jn7NmzB/v27YPf79c8N2nSJFRUVPBCKgkkhADAC1bEU2RlLzK3csFgkHNNFxwGKYeprKzEyJEj8fLLL2PRokXSD63169fjxIkTuOKKK5I7QKIeOnToEKZNm4bs7Gy8++67KC4uTvaQLij/+Mc/cO7cOTQ2NiZ7KH3GE088gZMnTyqOvfXWW3j00Ufx7LPPSn8Yo8R48cUXAQAXX3xxkkfSd5SXl2PMmDHYtGkTAoGAtCr15Zdf4uOPP8Y111yT5BES2YtBKsWsW7cOQOjKOACwbds2fP3118jJycFll10GAHjkkUfwne98B9deey3mz5+PI0eOYPHixZg6dSq++93vJm3sTmNmrvfu3Yu9e/cCAE6cOIGzZ89K9UaNGoWampokjNx5Ys31l19+ienTp6O9vR3PP/88jhw5giNHjkj1GxoadP+qT1qx5vr999/HsmXLcOWVV6Kurg5dXV3YuXMnHn/8cTQ0NPDkfAtizXVbW5umzqeffgoAGDlypO7zpGXme/rRRx/FnDlzUFNTg9OnT2PdunV46aWXMH/+fAZWC8z8Xly5ciWmTZuG2bNnY+HChThz5gx++ctfwu12Y8mSJUkbO1FSJPuDrEgJgO5XTU2NotzGjRvFyJEjRWZmpvD7/WLhwoXi1KlTyRm0Q5mZ6wceeMCw3Jo1a5I2dqeJNddbtmwxLMO5tibWXB86dEjMmzdP1NbWiqysLJGRkSFaWlrEXXfdJb766qvkDt5hzP68lluzZg0/kNeiWPN84MABcdlll4ny8nKRkZEhsrOzxYgRI8SqVatEMBhM7uAdxuz39JYtW8T48eNFVlaWyMvLE5dffrn45JNPkjNooiRyCaGz0ZWIiIiIiIgM8axAIiIiIiIiixikiIiIiIiILGKQIiIiIiIisohBioiIiIiIyCIGKSIiIiIiIosYpIiIiIiIiCxikCIiIiIiIrKIQYqIiIiIiMgiBikioj7uqaeeQktLi/S1a9euHrWza9cuRTtPPfVUfAdKRETkIGnJHgAREen78MMPcf311yuOZWRkoLi4GKNHj8ZNN92EhoYG0+3dd9998Pl8qK6uBgC0tLSYrrtv3z5UV1dj+fLlOHHiBJYtW2a6LhERUV/EIEVElOJmzZqFSZMmAQDOnTuH/fv349VXX8Xbb7+N9evXo6KiwlQ7l1xyCSorK6XHy5cvVzy/Y8cOrF27FvPmzcOIESM09QsKCjB79mwcPXqUQYqIiC54DFJERClu0KBBmD17tuJYTU0Nli5dir/97W+44YYbetSuus1AIIC1a9eira1N8xwREREp8RwpIiIH8vv9AID09PQkj4SIiOjCxBUpIqIUd/bsWbS3twPo3tr3+OOPw+fz4dJLL03y6IiIiC5MDFJERCnuqaee0lwhr7GxES+//LK0MkVERET2YpAiIkpx8+bNw8yZMwGEVqQOHDiA559/HgsWLMALL7xg+mITREREFD8MUkREKa6mpgbjxo2THk+dOhWjR4/GvHnzsHLlSjz++ONJHB0REdGFiRebICJyoLa2NuTl5eGDDz5I9lCIiIguSFyRIiJyqEAggPPnzyd7GERERBckrkgRETnQ3//+d5w5cwatra3JHgoREdEFiStSREQpbu/evXjjjTcAAOfPn8fBgwfxxz/+Eenp6Vi0aFFyB0dERHSBYpAiIkpxGzZswIYNGwAAbrcbXq8X48ePx4IFCzB06NAkj46IiOjCxCBFRJSixowZg3379sWtvY6ODrS3t8Pj8SAtTfvjf86cOZgzZ45h/a6uLnR0dKCjoyNuYyIiInIqBikiogvElVdeCQBYu3Yt2traLNffvXs35s2bF+dREREROZNLCCGSPQgiIkqczz77DJ999pn0eOjQocjNzbXczunTp/HJJ59Ij6uqqlBVVRWXMRIRETkNgxQREREREZFFvPw5ERERERGRRQxSREREREREFjFIERERERERWcQgRUREREREZBGDFBERERERkUUMUkRERERERBYxSBEREREREVnEIEVERERERGQRgxQREREREZFF/w+WlxlwPIC9/gAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 9 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -403,12 +295,7 @@ "id": "bd0790ca6b1bf080" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.462088Z", - "start_time": "2024-10-18T17:30:27.395341Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "Ic_sc = sc_strand.Ic(B=B_TF_i, T=T_sc, T_margin=T_margin)\n", @@ -432,28 +319,8 @@ "bluemira_print(f\"cable area: {cable.area}\")" ], "id": "24b8d416381cb290", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| cable area: 0.0010339726336685993 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 10 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -468,12 +335,7 @@ "id": "98f43735d63a31df" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.598151Z", - "start_time": "2024-10-18T17:30:27.517927Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "aspect_ratio = 1.3\n", @@ -482,36 +344,11 @@ "bluemira_print(f\"cable area: {cable.area}\")" ], "id": "90fb56d6ed67d713", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| cable area: 0.0010339726336685993 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 11 + "outputs": [], + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.866024Z", - "start_time": "2024-10-18T17:30:27.626561Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# optimize the number of stabilizer strands using the hot spot criteria.\n", @@ -528,57 +365,11 @@ "bluemira_print(f\"Adjust aspect ratio: dx_cable = {cable.dx}, aspect ratio = {cable.aspect_ratio}\")" ], "id": "1e0815dd39475200", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| before optimization: dx_cable = 0.036662848004065086, aspect ratio = |\n", - "| 1.3 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Optimal n_stab: 514 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Final temperature with optimal n_stab: 237.7118981259334 Kelvin |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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IxG0agiEiEgUZM2Zk9erVlCtXjsOHD1s6HRERERGRWE9DMEREoihVqlR06dLF0mmIiIiIiMQJGoIhIiIiIiIiIjFOQzBEREREREREJMapACEiIiIiIiIiMU4FCBERERERERGJcZqEMgxXrlxh2bJlODs7Y29vb+l0REREREQkDM+ePePChQt4enqSI0cOS6cjIh+gAkQYli1bxurVqy2dhoiIiIiIRNDw4cMtnYKIfIAKEGFwdnYGoFmzZri5uVksD4PBwLVr18iWLRtWVlYWy0PiLz1jEtP0jElM0vMlMU3PWOx3/PhxVq9ebfr5XURiNxUgwhAy7MLNzY3atWtbLI+goCBOnDiBi4sL1tbWFstD4i89YxLT9IxJTNLzJTFNz1jcsHr1ag2bFokjNAmliIiIiIiIiMQ4FSBEREREREREJMapACEiIiIiIiIiMU4FCBERERERERGJcSpAiIiIiIiIiEiMUwFCRERERERERGKcChAiIiIiIiIiEuNUgBARERERERGRGKcChIiIiIiIiIjEOBUgRERERERERCTGqQAhIiIiIiIiIjFOBQgRERERERERiXEqQIiIiIiIiIhIjFMBQkRERERERERinAoQIiIiIiIiIhLjVIAQERERERERkRinAoSIiIiIiIiIxDgVIEREREREREQkxqkAISIiIiIiIiIxTgUIEREREREREYlxKkCIiIiIiEjknDkDdesa/xYRiSAVIEREREREJHJ++gm2boWff7Z0JiISh6gAISIiIiIikbNnj/nfIiIRkNDSCYiIiIiISCx38yb8+y8Al67bsvn3YrgRRKU//oBjx8DKyrhfunTg5GTBREUkNlMBQkREREREwufpyd09pxnJUObRgUDGkoAgbr/MSNpixUL3q1QJdu+2XJ4iEqupACEiIiIiIu/l7w+TU09nElnwJ5kpnoqHWBMYuqODA3To8OkTFJE4Q3NAiIiIiIjIO16/htmzIWdOGL6+gFnxoSK/sYeKpOahMVCtGly4AI0aWShbEYkL1AEhIiIiIiImBgNs2gQDBsDff5tvK5LGj/H3v6UqO7HCEDr3g7MzpE376ZMVkThFHRAiIiIiIgLA/v1Qpgw0bGhefMiaFZYvDea4VTGq8StWDilg6FBIkcJYsVizBoKDLZe4iMQJKkCIiIiIiHzmLl6E+vWhbFnw9Q2Np0oFU6YYR1e0aPCCBFkyQb16xsCIEca/69WDLFngxQvL3YCIxAkagiEiIiIi8pm6e9dYR5g3D4KCQuOJEkGPHtC/v3FuSWPQHg4dAmvr0B3TpjWO1wgKMo+LiIRBBQgRERERkc/M8+cwdSqMHw///Rcat7ICDw8YNcrY1PCO9xUZVHwQkQhQAUJERERE5DMRHAwrVsCgQeDnZ76tcmWYMAFcXS2Tm4jEfypAiIiIiIh8Bvbsgd694c8/zeOFCsHEicaVNEVEYpImoRQRERERicfOn4dataBSJfPiQ4YMsGiRMabig4h8CuqAEBERERGJh+7dg+HD351g0t4e+vaFXr2MX4uIfCoqQIiIiIiIxCMvX8KMGTB6NDx9GhpPkADatIGRI43dDyIin5oKECIiIiIi8YDBABs2QL9+cPWq+baqVWHSJON8DyIilqIChIiIiIhIHHfoEHz/PRw4YB7Pnx8mT4bq1S2Tl4jImzQJpYiIiIhIHHXjBrRoASVLmhcfHB1hzhw4eVLFBxGJPdQBISIiIiISxzx7Zlw6c8IEePEiNG5rCz17woABkCKF5fITEQmLChAiIiIiInFEcDCsWgX9+8PNm+bbGjWC8eMhe3bL5CYi8iEqQIiIiIiIxAG+vtCjBxw+bB4vWhR++AHKlrVEViIiEac5IEREREREYjE/P+M8D6VLmxcfMmQAb29jTMUHEYkL1AEhIiIiIhILPX9uXDpz/Hjj1yHs7KBPH+jbF5ImtVx+IiKRpQKEiIiIiEgsYjDAunXGAsP16+bbmjY1FiSyZLFMbiIiH0MFCBERERGRWOLPP6FbN/DxMY+7ucG0aRpqISJxm+aAEBERERGxsHv3oEMH44SSbxYf0qaFRYvgyBEVH0Qk7lMHhIiIiIiIhbx+DXPmwLBh8PhxaNzGxrjixeDBkDy5pbITEYleKkCIiIiIiFjAb79B9+5w9qx5vFYtmDwZcue2TF4iIjFFBQgRERERkU/o2jXo1Qs2bTKPOzvDDz9A9eqWyEpEJOZpDggRERERkU/gxQsYPhzy5TMvPiRLZux4OHVKxQcRid/UASEiIiIiEoMMBti8Gb7/Hv75x3xbmzYwdiykS2eZ3EREPiUVIEREREREYshffxmX1dy1yzxeogTMnAnFi1smLxERS9AQDBERERGRaPb0KfTpA4UKmRcf0qaFxYvB11fFBxH5/KgDQkREREQkmhgMsGqVsfhw+3Zo3NoaunY1Lrfp4GCx9ERELEoFCBERERGRaHD6NHTpAn/8YR7/8kuYPh0KFrRMXiIisYWGYIiIiIiIfIQnT6BHD3B1NS8+ODnBmjXw228qPoiIgDogRERERESixGCAFSuMwy3+/Tc0bmNjXPFi8GBImtRy+YmIxDYqQIiIiIiIRNLp09CpE/j4mMerVIEZM8DZ2TJ5iYjEZhqCISIiIiISQU+fGrsbXF3Niw+ZM8PGjfDrryo+iIi8jzogREREREQ+wGCAtWuNxYc3V7ewsYHevWHQILC3t1x+IiJxgQoQIiIiIiLh+Osv6N7dOJnkm6pUgZkzIU8ey+QlIhLXqAAhIiIiIhKG589h1qyMrFiRgNevQ+MZM8IPP0DDhmBlZbH0RETiHM0BISIiIiLylp9+gsKFE7BkSQZevzZWGaytoVcvY0dEo0YqPoiIRJY6IERERERE/uf6deNwiy1bAEIrDOXKwezZULCgpTITEYn71AEhIiIiIp+9169hwgTIly+k+GCUMuVrliwJ5v/+T8UHEZGPpQ4IEREREfms7dsH330HZ8+GxqysoF27YJo0OUuFCoU03EJEJBqoA0JEREREPksPHkDbtlC+vHnxwcUFfH1h9mwDKVIEWSw/EZH4RgUIEREREfmsGAzg7Q3OzrB4cWg8WTKYNg2OHAF3d4ulJyISb2kIhoiIiIh8Ns6fNw63+L//M483amRcWjNjRoukJSLyWVAHhIiIiIjEey9ewODBUKSIefEhe3bYvh3WrVPxQUQkpqkDQkRERETitd27jV0Ply6FxhImhD59jEWJJEksl5uIyOdEBQgRERERiZfu3YPvv4cVK8zj5crBnDlQoIBl8hIR+VxpCIaIiIiIxCsGg3Fyybx5zYsPKVPCwoWwd6+KDyIilqAOCBERERGJN/76Czp0gD/+MI+3aAGTJ0PatJbJS0RE1AEhIiIiIvHAq1cwYoRxksk3iw85c8LOnbB8uYoPIiKWpg4IEREREYnT9u2D9u2N3Q8hEiaEfv1g0CBInNhyuYmISCgVIEREREQkTnr82FhkmD/fPF66NMybBwULWiQtERF5Dw3BEBEREZE4xWCA9eshXz7z4kPy5MbVLfbtU/FBRCQ2UgeEiIiIiMQZN25A587w44/m8QYNYPp0yJjRMnmJiMiHqQNCRERERGK94GCYNQvy5zcvPjg5wZYtsGGDig8iIrGdOiBEREREJFY7dw7atYMDB0JjVlbGTogxY4xDL0REJPZTAUJEREREYqVXr2DcOGOR4fXr0HiBArBwIZQsabncREQk8lSAEBEREZFYx9cXvv3W2P0QwtYWBg82rnxha2u53EREJGpUgBARERGRWMPfHwYNghkzjKtdhChTBhYsMK58ISIicZMKECIiIiISK+zcCe3bwz//hMaSJYPx46FDB0ig6dNFROI0FSBERERExKIePoRevcDb2zz+9dcwZw5kymSRtEREJJqpACEiIiIiFrNxo3E1i3//DY2lSWMcgtGkiXG1CxERiR/UyCYiIiIin9ydO9CgATRsaF58aN4czp+Hpk1VfBARiW/UASEiIiIin4zBAMuWQc+e8OhRaDxTJpg7F776ynK5iYhIzFIHhIiIiIh8EtevQ82a0KqVefGhY0c4e1bFBxGR+E4dECIiIiISo4KDYd486NvXuMxmiFy5YOFCqFDBcrmJiMinow4IEREREYkxly5BxYrQqVNo8SFBAuOqFydPqvggIvI5UQeEiIiIiES7oCDjShYDB8KLF6Hx/Plh8WJwd7dcbiIiYhkqQIiIiIhItLpwAdq0gQMHQmMJE8KAATBoECRKZLncRETEclSAEBEREZFoERQEU6fCkCHw8mVo3MUFliwx/i0iIp8vFSBERERE5KOdPw+tW8OhQ6ExGxsYOhT69TN+LSIinzcVIEREREQkygIDYfJkGDYMXr0KjRcrZux6KFjQcrmJiEjsogKEiIiIiETJ+fPQqhUcPhwas7WFESOgd2/jvA8iIiIh9M+CiIiIiETK+7oe3N2NXQ/58lkuNxERib1UgBARERGRCAtrrodEiWDUKPj+e7C2tlxuIiISu6kAISIiIiIfFBRk7HoYOlRdDyIiEjUqQIiIiIhIuC5cMM71cPBgaCxRIhg50tj1oLkeREQkIvTPhYiIiIiEKTgYpk2DgQPh5cvQeIkSxq6H/Pktl5uIiMQ9KkCIiIiIyDsuXzbO9bBvX2hMK1yIiMjH0D8dIiIiImISHAxz50KfPvD8eWjczQ2WLoWCBS2Xm4iIxG0JLJ2AiIiIiMQO//wDVatC586hxYeECY1dDwcPqvggIiIfRx0QIiIiIp85gwG8vaF7d/jvv9B4oULGrgdXV4ulJiIi8Yg6IEREREQ+Y7dvQ+3a0KZNaPHB2hoGDYKjR1V8EBGR6KMOCBEREZHP1Lp18N138PBhaCxvXmPXQ4kSlstLRETip1hVgPD19WXr1q38+eef3LlzBwcHB1xcXOjWrRs5c+Y02/ePP/5g+vTpXLx4kWTJklG1alV69epF0qRJzfbz9/dn8uTJ7Ny5k//++488efLQrVs3ypcv/ylvTURERCTWePAAunSBNWtCY1ZW0KMHjBkDiRNbLDUREYnHYtUQjNWrV3Pnzh3atGnDggUL6NOnD1euXKFBgwZcuHDBtJ+vry8dO3YkU6ZMzJs3j549e7J9+3Y6deqEwWAw7WcwGOjUqRPbt2+nZ8+ezJs3j0yZMtGxY0d8fX0tcYsiIiIiFrV9u3EyyTeLD9mywe+/w5QpKj6IiEjMiVUdEMOGDSN16tRmsdKlS/Pll1+ydOlSxo4dC8DEiRPJkycPU6ZMIUECYw0lRYoUdOnShf/7v//jiy++AGDv3r0cOnSImTNnUqVKFQDc3d2pX78+kyZNYuPGjZ/u5kREREQsyN8fevWC+fPN4+3bw6RJkCyZZfISEZHPR6zqgHi7+ACQKlUqnJyc+PfffwG4c+cOZ8+epU6dOqbiA0ClSpVIkSIFu3fvNsV2795NsmTJqFSpkimWIEEC6taty5kzZ7hz504M3o2IiIhI7ODjA0WKmBcfMmQwdkPMm6fig4iIfBqxqgARln///Zfr16+TK1cuAP7++28AcufObbZfggQJyJUrl2k7wKVLl8iVK5dZoQLA2dnZ7FwiIiIi8dGrV9CvH5QvD1euhMabNoUzZ6BGDcvlJiIin59YNQTjbcHBwQwePBgbGxtatmwJwOPHjwHjkIu3pUiRgitv/Ov6+PFjsmXLFuZ+AE+ePAn3+jdu3OD06dNmMUdHRxwdHSNzG1EWFBRk9rdIdNMzJjFNz5jEJD1f4Tt5Elq1SsDp01amWMqUBmbONNCkiXHOLL114dMzFvu9Of+biMR+sboAMWrUKHx8fJgyZQoZM2Y022ZlZfWeo8xFdL+wTJ8+nenTp5vF6tevT8OGDaN8zqh4uwgiEt30jElM0zMmMUnPl7mgIFi+PB1z52YkMDD056DSpZ8wZMg/ODq+5sQJy+UXF+kZi72uXbtm6RREJBJibQHCy8uLVatWMWrUKGq80R/o4OAAhHZCvOnJkyem7SH7vm8/CLuL4k1hLdf5qTsgTp8+TaFChbC2tv4k15TPi54xiWl6xiQm6fl615Ur0Lp1AvbvDy08JEliYOJEA+3bJ8XKqoAFs4t79IzFfn5+fpZOQUQiIVYWICZOnIi3tzcDBw6kcePGZttC5n74+++/KVu2rCkeHBzMpUuXqFq1qimWK1cudu7cSXBwsNk8ECFLeubJkyfcPDJnzkyhQoU++n4+lrW1tf7RkxilZ0ximp4xiUl6vsBggMWLoUcP42oXIUqVgmXLrMiVK+odoaJnLDb7mG5nEfn0Yt0klD/88AMLFy6kT58+pnkf3pQ+fXoKFizIjz/+SHBwsCm+Z88enjx5QuXKlU2xKlWq8PTpU/bs2WOKBQcHs23bNgoWLEi6dOli9mZEREREYtjdu1C3Lnz7bWjxIWFCGDMG/vgD/jePt4iIiMXFqg6IRYsWMWfOHKpWrUqxYsU48cYARVtbW/Lnzw9A7969adu2Lb1796ZRo0bcvn2bCRMm4O7uToUKFUzHVKhQAXd3dwYPHszTp0/JkCED69ev56+//mLRokWf+vZEREREotW2bcbCw717obF8+WDFCnBzs1xeIiIiYYlVBYi9e/cCsHPnTnbu3Gm2zcnJydTJUKpUKebMmcP06dNp3749SZMmpXr16vTu3dusDcvKyorZs2czadIkJk6ciL+/P3ny5GHOnDmUKlXqk92XiIiISHTy94fvv4cFC8zjPXrA2LGQOLFF0hIREQlXrCpALF++PML7VqhQwazb4X2SJk3K8OHDGT58+EdkJiIiIhI7HDwIHh5w6VJoLFMm8PaGSpUslpaIiMgHxbo5IERERETkXa9fw/DhULasefGhWTM4dUrFBxERif1iVQeEiIiIiLzr77+hRQs4fDg0liIFzJljLECIiIjEBeqAEBEREYmlDAbjPA8uLubFhy++MHY9qPggIiJxiQoQIiIiIrHQvXtQrx60bw/PnxtjtrYwaRL89htkyWLZ/ERERCJLQzBEREREYpkdO6B1a7hzJzRWsCCsXAmFC1suLxERkY+hDggRERGRWOLFC+jWDWrUMC8+9OgBR46o+CAiInGbOiBEREREYoGTJ6F5czh7NjSWIYNxec2qVS2WloiISLRRB4SIiIiIBQUHw5QpUKKEefGhXj3jRJMqPoiISHyhDggRERERC7l1C1q2hN27Q2P29jB9unEOCCsry+UmIiIS3VSAEBEREbGALVvg22/hwYPQWIkSxokmc+WyWFoiIiIxRkMwRERERD6hZ8+gQwfjEIuQ4kOCBDBkCPj4qPggIiLxlzogRERERD6R48fhm2/gwoXQWJYssGIFlCtnubxEREQ+BXVAiIiIiMSw4GCYNAlKljQvPjRrZlz9QsUHERH5HKgDQkRERCQG3b4Nnp7mE00mSwazZxuX3dREkyIi8rlQAUJEREQkhvz4I7RpA/fvh8ZKljRONJkjh+XyEhERsQQNwRARERGJZi9eQJcuULt2aPEhZKLJfftUfBARkc+TOiBEREREotHp08a5Hc6eDY1lzmycaLJ8ecvlJSIiYmnqgBARERGJBgYDzJoFxYubFx8aNTJONKnig4iIfO7UASEiIiLyke7fh7ZtYdu20Ji9PcyYAa1aaaJJERERUAFCRERE5KPs2QMeHnDrVmjMzQ1Wr4Y8eSyXl4iISGyjIRgiIiIiUfD6NQwaBJUrmxcfevUCX18VH0RERN6mDggRERGRSLp61TjR5KFDobF06WDpUqhWzXJ5iYiIxGbqgBARERGJhDVrwMXFvPhQvbpxokkVH0RERN5PBQgRERGRCHj2zDjRZLNm8PSpMWZjA5Mnw88/GzsgRERE5P00BENERETkA06ehKZN4a+/QmO5cxu7IdzcLJeXiIhIXKIOCBEREZH3MBhg5kxwdzcvPrRsCceOqfggIiISGeqAEBEREQnDgwfGIRdbt4bGkiaFOXOgRQvL5SUiIhJXqQAhIiIi8pZ9++Cbb8DPLzRWtCisXm0ceiEiIiKRpyEYIiIiIv8TFASjRsEXX5gXH77/Hg4cUPFBRETkY6gDQkRERAS4dcs4tOL330NjadLAsmVQo4bl8hIREYkv1AEhIiIin71ffoEiRcyLD19+aVz9QsUHERGR6KEChIiIiHy2AgKgTx+oWRPu3zfGEiQwDsPYtQsyZrRsfiIiIvGJhmCIiIjIZ+nqVWjaFA4fDo1lygSrVkG5cpbLS0REJL5SB4SIiIh8djZsAFdX8+JD7dpw4oSKDyIiIjFFBQgRERH5bLx8CZ07Q6NG8OSJMWZrCz/8AFu2QOrUlsxOREQkftMQDBEREfksXLwIjRsbJ5YMkTMnrF0LRYtaLi8REZHPhTogREREJN5bsQLc3MyLD02bwvHjKj6IiIh8KipAiIiISLz17Bm0aQMeHsavAezsYMEC42STyZNbNj8REZHPiYZgiIiISLx07pxxrodz50Jj+fIZh1wUKmS5vERERD5X6oAQERGReMfbG4oVMy8+tGoFR46o+CAiImIp6oAQERGReOPZM+jUCZYtC40lSQJz5oCnp+XyEhERERUgREREJJ44c8a4ysX586GxggVh3Trj0AsRERGxLA3BEBERkTjNYIDFi6FECfPiw7ffwqFDKj6IiIjEFuqAEBERkTjrxYsEtG5txYoVobGkSWHePPjmG8vlJSIiIu9SAUJERETipDNnwNMzL1evhjZ0Fi5sHHLh7GzBxERERCRMGoIhIiIicY63N5QqlYCrVxObYu3bw8GDKj6IiIjEVuqAEBERkTjj2TPo3BmWLgWwAsDe3sD8+VYaciEiIhLLqQAhIiIiccK5c9CokfHvELlyPWfr1kTkz29tucREREQkQjQEQ0RERGK95cuheHHz4kObNsF4e/+lIRciIiJxhAoQIiIiEmu9eAHt2oGnJzx/bowlSQLLlsH8+Qbs7AyWTVBEREQiTEMwREREJFa6eNE45OLUqdBY/vywfr3x76Agy+UmIiIikacOCBEREYl11q2DYsXMiw+ennD4sLH4ICIiInGPChAiIiISa7x6BV26QJMm8N9/xpidHSxaZFx6097eoumJiIjIR9AQDBEREYkVrl6Fxo3h6NHQWO7csGEDFC5subxEREQkeqgDQkRERCzuxx/Bzc28+NCkifG1ig8iIiLxgwoQIiIiYjGBgdCvH9SuDY8fG2O2tjBzJqxeDcmTWzQ9ERERiUYagiEiIiIWcesWNG0K+/aFxrJlM65yUayYxdISERGRGKIOCBEREfnkfvsNXF3Niw+1asHx4yo+iIiIxFcqQIiIiMgnExwMo0dDlSpw964xZm0N48fDli2QMqVF0xMREZEYpCEYIiIi8kk8eAAeHvDLL6GxDBlgzRooX95yeYmIiMinoQKEiIiIxLjDh6FRI7h+PTRWsSKsWgXp0lkuLxEREfl0NARDREREYozBALNmQdmy5sWHwYNh504VH0RERD4n6oAQERGRGOHvD+3bG5fTDJEyJaxYATVrWi4vERERsQwVIERERCTanT8PDRoY/w5RvLhxic2sWS2Xl4iIiFiOhmCIiIhItFqzxlhseLP40KmTcclNFR9EREQ+XypAiIiISLQICICuXaFZM3j2zBhLkgRWrjTOA5EokWXzExEREcvSEAwRERH5aDduQOPGcPBgaCxvXti4EfLnt1xeIiIiEnuoA0JEREQ+yq5d4OZmXnxo3Ni49KaKDyIiIhJCBQgRERGJkuBgGDUKqlWD+/eNsYQJYdo04zwQyZJZNj8RERGJXTQEQ0RERCLt4UNo0QJ++SU05uQE69ZB6dKWy0tERERiLxUgREREJFKOHzcusXntWmisUiVYtQrSprVYWiIiIhLLaQiGiIiIRNiiRcYOhzeLDwMHwq+/qvggIiIi4VMHhIiIiHzQixfQpQssXhwaS5ECli+HWrUsl5eIiIjEHSpAiIiISLiuXIGGDeHPP0NjRYoYl9jMmdNyeYmIiEjcoiEYIiIi8l7bt0OxYubFh1atwNdXxQcRERGJHBUgRERE5B1BQTBsGHz9NTx6ZIzZ2sL8+cZhGIkTWzY/ERERiXs0BENERETMPHwIzZvDjh2hsSxZjEMuihWzXF4iIiISt6kDQkREREyOH4eiRc2LD1WrwrFjKj6IiIjIx1EBQkRERADj0Iq3l9gcMsQ4D0SaNBZLS0REROIJDcEQERH5zL18Cd26wYIFoTEHB+MSm19/bbG0REREJJ5RAUJEROQzdv06NGgAR4+GxooUgU2bIEcOy+UlIiIi8Y+GYIiIiHymdu8GNzfz4oOnJxw4oOKDiIiIRD8VIERERD4zBgOMGwfVqsGDB8aYjQ3MmQPe3pAkiUXTExERkXhKQzBEREQ+I0+fQqtWsHlzaMzJCTZsgJIlLZaWiIiIfAZUgBAREflMnDsH9erBxYuhsS++gLVrIW1ai6UlIiIinwkNwRAREfkMrFsHJUqYFx9694Zdu1R8EBERkU9DHRAiIiLxWGAgDBgAkyaFxpImhSVLoGFDy+UlIiIinx8VIEREROKpu3ehaVP4/ffQmLOzcYnN/Pktl5eIiIh8njQEQ0REJB46dAiKFjUvPtSrB4cPq/ggIiIilqEChIiISDyzYAGULw9+fsbXCRKAlxds3AjJk1s2NxEREfl8aQiGiIhIPPHyJXTpAosWhcZSpYI1a6BKFcvlJSIiIgIqQIiIiMQLN25AgwZw5EhozM3N2PWQLZvF0hIREREx0RAMERGROO73343zPbxZfGjVCnx8VHwQERGR2EMFCBERkTjKYIApU4zDK+7dM8ZsbGD2bFi8GBIntmx+IiIiIm/SEAwREZE46NkzaNsW1q4NjWXIABs2QOnSlstLRERE5H1UgBAREYljLl0yLql55kxorGxZWL8e0qe3XF4iIiIi4dEQDBERkThk+3YoXty8+NClC/z2m4oPIiIiErupACEiIhIHBAfDqFHw9dfw+LExZmcHS5fCjBlga2vR9EREREQ+SEMwREREYrmnT8HTE7ZuDY1lzQqbNhmX2hQRERGJC1SAEBERicXOnzfO93DhQmiscmVYvRrSpLFcXiIiIiKRpSEYIiIisdTmzVCihHnxoW9f+OUXFR9EREQk7lEBQkREJJYJCoLBg6F+ffD3N8bs7Y1Lbo4fDwnVvygiIiJxkH6EERERiUUePYLmzY1dDiFy5TJ2QxQsaLm8RERERD6WChAiIiKxxJkzULcuXL4cGqtZE1auBAcHS2UlIiIiEj00BENERCQWWL8eSpY0Lz4MGQI//qjig4iIiMQP6oAQERGxoKAgGDTIOLdDiGTJYNkyYzeEiIiISHyhAoSIiIiFPHwIzZrBzp2hMWdn43wP+fJZLi8RERGRmKAhGCIiIhZw6hQUK2ZefKhdGw4dUvFBRERE4icVIERERD6xtWuhVCm4ejU0NmKEsfMhRQrL5SUiIiISk2LVEIw7d+6wcOFCzp07x/nz53n+/DnLli3D3d3dbD8PDw8OHz78zvHNmzdn6NChZjF/f38mT57Mzp07+e+//8iTJw/dunWjfPnyMXovIiIibwsKggEDYOLE0Fjy5LBiBdSqZbm8RERERD6FWFWA+Oeff/j555/Jnz8/pUuXZvfu3e/dN1u2bIx/c8YuIE2aNGavDQYDnTp14sKFC/Tp0wcnJyfWrl1Lx44dWbRoEaVKlYqR+xAREXnbgwfG+R527QqN5c0LW7YY530QERERie9iVQGiePHi+Pr6AvD777+HW4Cws7PDxcUl3PPt3buXQ4cOMXPmTKpUqQKAu7s79evXZ9KkSWzcuDHachcREXmfkyehXj3zIRd16hhXukie3HJ5iYiIiHxKsWoOiAQJojed3bt3kyxZMipVqmR2jbp163LmzBnu3LkTrdcTERF52/vme9i0ScUHERER+bxEqAPi1q1bH3WRNGnSYGtr+1HneNvVq1cpXrw4z549I3PmzDRq1IjWrVtjbW1t2ufSpUvkypXrncKG8/96Xf/++2/Sp0//3mvcuHGD06dPm8UcHR1xdHSMxjt5v6CgILO/RaKbnjGJaZ/zMxYUBIMGWTFpUui/QcmTG1i2LJivvwaDwbiPRN3n/HzJp6FnLPYzGAyWTkFEIiFCBYiKFStiZWUV5YssXrw4WudbKFq0KF999RXZs2fn+fPn7Ny5k0mTJnH58mW8vLxM+z1+/Jhs2bK9c3yK/00x/uTJk3CvM336dKZPn24Wq1+/Pg0bNvz4m4iEt4sgItFNz5jEtM/tGXvyxJpBg7Jz8GDokhbZsr1g0qTLZMr0ihMnLJdbfPS5PV/y6ekZi72uXbtm6RREJBIiPAdE5cqVTZ0DEfX8+XOWLFkS6aQ+pEePHmavv/zyS+zt7Vm+fDmtWrUyy/NjCidhrZbxqTsgTp8+TaFChcw6O0Sii54xiWmf4zN2+jS0a5eAK1dC//2pXduAt7ctyZPns2Bm8c/n+HzJp6VnLPbz8/OzdAoiEgkRLkBUrVqVWpFcI+zRo0csXrw40klFRe3atVm+fDknT540FSAcHBx4/PjxO/uGdD6k+MBi65kzZ6ZQoULRnmtkWVtb6x89iVF6xiSmfS7P2IYN0KoVPHsWGhs+HIYMsSJBgvh//5byuTxfYjl6xmKvj/llo4h8ehGa9bFJkyZhDmX4EDs7O5o0aRLuPAvRJWT815v/E8qVKxeXL18mODjYbN8LFy4AkCdPnhjPS0RE4r+gIBg4EBo1Ci0+JEsGW7fCsGEQzXMsi4iIiMRJEfqRaMSIEVHqBEicODEjRowge/bskT42srZu3QpgtjRnlSpVePr0KXv27DHFgoOD2bZtGwULFiRdunQxnpeIiMRvjx9DrVrwxhRE5MkDhw9D7doWS0tEREQk1onwEIxr165Fqgti0qRJ9O7dO9IJ7dixA4CzZ88CcOTIER49ekTixImpUKECR48eZcGCBVSpUgUnJyeePXvGr7/+yrZt22jcuDG5c+c2natChQq4u7szePBgnj59SoYMGVi/fj1//fUXixYtinRuIiIibzp3DurWhb//Do19/TWsWAEfGOUnIiIi8tmJcAGidevWrFmzJkJdAxMnTmTx4sVRKkB0797d7PWMGTMAcHJyYs+ePTg6OhIcHMy0adN49OgRCRMmJEeOHAwbNoxmzZqZHWtlZcXs2bOZNGkSEydOxN/fnzx58jBnzpxoXZVDREQ+P1u2gIcH+PuHxoYMMc75oCEXIiIiIu+KcAHi0aNHtGrVipUrV5IqVar37jd58mQWLVpEyZIlo5RQyPwM75M1a1YWLFgQ4fMlTZqU4cOHM3z48CjlIyIi8qbgYBg5EkaMCI0lTQrLlkG9epbLS0RERCS2i/DvaGbOnImfnx/ffvst/m/+uucNkydPZsGCBZQsWZJ58+ZFW5IiIiKxwdOnxiLDm8WHXLng4EEVH0REREQ+JMIFiLJlyzJ58mQuXLhAhw4dePXqldn2KVOmmBUfEiVKFO3JioiIWMrFi1CyJGzbFhqrXt042WSBApbLS0RERCSuiNQo1apVqzJ69GiOHz9O165dCQwMBIzFh/nz56v4ICIi8dL27VCiBJw/Hxrr3x9++glSprRcXiIiIiJxSYTngAhRr149/P39GTNmDL179yZLlizMnz8fd3d35s6dq+KDiIjEGwYDjBsHgwYZvwZIkgQWL4YmTSybm4iIiEhcE+kCBICHhwf+/v5MmzYNKysrdT6IiEi88+wZtG4N69eHxrJlM65+UaSIpbISERERibsiXIBYsmSJ2Ws7OzsyZcrE/fv3KVGiBKtWrTLbbmVlRatWraIlSRERkU/p6lWoUwdOnw6NVawIa9dCmjSWy0tEREQkLotwAWL8+PHv3TZ9+vR3YipAiIhIXPTbb9C4MTx8GBrr0QMmToSEUeobFBERERGIRAFi2bJlMZmHiIiIRRkM8MMP0Ls3BAcbY4kSwfz54Olp0dRERERE4oUIFyBKlCgRk3mIiIhYzIsX0KEDLF8eGnNygs2boXhxy+UlIiIiEp+omVRERD5rfn5Qrx4cPRoaK1MGNmyA9Oktl5eIiIhIfJMgIjudOnWKx48fR/rkQUFBnDp1imfPnkX6WBERkZi2fz8UK2ZefGjfHvbsUfFBREREJLpFqADRpEkT9u3bF+mTP336lCZNmnDq1KlIHysiIhKT5s+HL7+Ef/81vk6YEObOhXnzwNbWsrmJiIiIxEcRGoJhMBh4/Pgxt27ditTJnzx5gsFgiFJiIiIiMSEgALp3NxYbQqRNCxs3QtmylstLREREJL6L8BwQY8eOZezYsZG+gJWVVaSPERERiQn//gsNG4KPT2isaFHjZJOZM1suLxEREZHPQYQKEF26dPmoi2TWT3UiImJhx45B3brGSSdDtGhhHIqROLHF0hIRERH55Pz8/KhUqRIAefPmZevWrRE+dtOmTQwYMAAAT09PBg0aFOFjP0kBQkRExJJWrYK2beHlS+PrBAlg0iTo0QPUqCciIjGhf//+PH36lNmzZ5vFDx06hKenJ0eOHCF58uQROlfFihXx9PSkVatWMZBpxM2YMYPdu3d/8MPq++49Pjt06BDe3t6cPn0af39/smbNStu2baldu7bZPp6enu8cu337dnLmzGl6/euvvzJt2jSuX79OlixZ6NmzJ1WqVAn3+hcuXGDUqFGcOnWKFClS0KRJEzp37vzBEQne3t7kzZvX9Dqs7/HRo0fp2LEjderUYfDgwdSsWZNy5crRtWvXD74vb9MynCIiEm8FBUH//sZiQ4iUKWHtWvjAv+MiIiIiEfbnn3/i7OxMu3btSJMmDXv37qVfv34kTZqUihUrmu27Y8cOkiZNanqdKlUqs/P07NmT7t27U7lyZXbv3k2PHj1YtWoVRYoUCfPa/v7+tGnTBnd3dzZs2MC1a9fo378/SZIkoU2bNuHm7eDgQMqUKd+7fe/evXTv3p22bdvSrVs3AOzs7LCzs8PGxuaD78vbIrQKhoiISFzz6BF89ZV58aFAAThyRMUHERGJXX799Ve++uorChYsSMWKFVm8eLFpm4eHBzdv3sTLywtnZ2ecnZ3fe54ZM2bwxRdfULBgQcqWLcvo0aNN2ypWrMisWbPo1asXrq6ulC1bluXLl5sdf+vWLb777jtcXV1xc3Oje/fu3L9/HzC23c+cOZO//vrLlMemTZvCzGHz5s389ttvpv0OHToEwL///kuPHj0oXrw47u7ufPfdd/i9MTayf//+dOrUiblz51K6dGmKFSvGzJkzCQwMZPz48ZQoUYLy5cuzYcMG0zF+fn44Ozvz888/07RpUwoVKsRXX31luuan0rFjR3r06IGbmxtZsmTB09OTcuXKsWvXrnf2TZ06NY6OjqY/1tbWpm1Lly6ldOnSdOjQgZw5c9KhQwdKlizJ0qVL33vtbdu28erVK8aNG0eePHmoWrUqHTt2ZMmSJR+1KMSPP/5Ily5d6NWrl6n48LFUgBARkXjn3DkoUQJ+/TU0Vq8e+PrCGx2OIiIiFnfmzBl69OhBzZo1TR/4pk2bZvpwP2PGDNKnT0+3bt3w8fHB582ZlN+wY8cOvL29GTFiBDt37mT27NnkyZPHbJ9FixaZCgcdOnTAy8uL/fv3A8aVDzt37syTJ09Yvnw5S5Ys4caNG/Ts2ROAmjVr0qZNG3Lnzm3Ko2bNmu/k0aZNG2rUqEG5cuVM+7m6uvLixQs8PT1JkiQJK1asYNWqVSRJkoRvv/2WgIAA0/EHDx7k7t27rFixgv79+zNjxgw6dOhAihQpWLduHU2bNmX48OHcvn3b7LoTJkygdevWbNmyBVdXV7777jsePXoU4e/D0aNHcXV1DffP3DeX0IqA//77DwcHh3fidevWpWzZsrRs2ZKDBw+abTtx4gRl31qWq1y5cvz555/vvc6JEycoXrw4tm+sI162bFnu3r1rVuCJjJUrVzJgwADGjBkT5rCRqNIQDBERiVe2bYPmzcHfPzQ2YgQMHmyc+0FERORT2bt3L66urmaxoKAgs9dLliyhVKlSdO7cGYDs2bNz6dIlFi1aRP369XFwcMDa2hp7e3scHR3fe63bt2+TJk0aSpcujY2NDRkzZqRw4cJm+7i5udG+fXvTdY4fP463tzdlypThwIEDXLhwgd9++40MGTIAxg/1X331FadOnaJw4cIkSZIEa2vrcPOwt7fHzs6OgIAAs/02bNiAlZUVY8aMMc1L4OXlRfHixTl8+LDpQ7eDgwODBw8mQYIE5MiRg4ULF/Ly5Us6duwIQIcOHViwYAHHjx/nq6++Mp2/efPmVKtWDYDhw4ezb98+NmzYQLt27d6b65sKFizIli1bwt0nRYoUEToXGAtCp0+fZuTIkaaYo6Mjo0aNokCBAgQEBLB161ZatWrF8uXLKV68OAD3798nderUZudKnTo19+7de++17t+/j5OT0zvHhGyL7KIQly9fZuTIkYwZM4Y6depE6tgPUQFCRETiheBgGDMGhg4NjSVNCsuXG1e/EBER+dTc3d0ZPny4WezkyZP06dPH9PrKlSum1QhCuLm5sWzZMoKCgsza88NTvXp1li5dSuXKlSlXrhwVKlTgyy+/JGHC0I98Li4uZse4uLiYWvsvX75M+vTpTcUHgFy5cpE8eXKuXLnyTjEjss6ePcv169dxc3Mzi7969Yrr16+bXTPBG78xSJMmDblz5za9tra2xsHBgQcPHpid581CT8KECSlYsCBXrlyJcH52dnZkzZo1wvuH59ChQwwYMIDRo0eb5Z4jRw5y5MhhlvOdO3dYtGiRqQABvDNxpMFg+OBkku/b/qHjwpI+fXqSJ0/OwoULKV++PGnTpo30Od5HBQgREYnz/P2hVSvYuDE0ljMnbN1qnPdBRETEEhInTvzOh9o7d+6Yvf6YMfpvypAhAzt27GD//v34+voyYsQIFi1axPLly8OdLDDkA+r7PuRGV37BwcEUKFCASW9OzvQ/b07C+GbBJCS/sGLBwcHRkleIo0ePfrBbokOHDqZOjPc5fPgwHTt2pH///tSNwG9AihQpwrZt20yv06RJY5p3I8TDhw9JkybNe8+RJk2adzokQgo0b3dTRIS9vT1LliyhTZs2eHh4sGzZMtKlSxfp84TlowsQ//zzD/fv3ydPnjwkS5YsOnISERGJsCtXjB0Op0+HxqpUgTVr4I2fZ0RERGKlnDlzcvz4cbPY8ePHyZYtm6n7wcbGJkIfuO3s7KhUqRKVKlXim2++oUaNGly8eJEC/6vGnzx50mz/kydPmn4jnytXLm7fvs3t27dNXRCXLl3iv//+My0RGdE8wtqvQIEC/PLLL6ROndpsBYjoEjIPAkBgYCBnz56lefPmET4+OoZgHDp0iI4dO9K7d2+aNGkSoeueP3/ebKiKi4sL+/fvN1tyNWQejfdxcXFh6tSpBAQEmOaB8PHxIW3atGTKlClCebwtRYoUeHt707ZtWzw9PaOtCBHl0bC///47lStXpnr16rRo0YIzZ84AxkpLlSpV2LFjx0cnJyIiEp49e6B4cfPiw/ffw/btKj6IiEjc0KZNG3x9fZk1axZXr15l8+bNrFy50mz5RCcnJ44cOcK///7Lw4cPwzzPpk2bWL9+PRcvXuTGjRts3boVOzs7MmbMaNrn+PHjLFiwgKtXr7Jy5Up27NhhmmCwdOnSODs707t3b86ePcupU6fo27cvJUqUoFChQqY8/Pz8OH/+PA8fPjSbPPJNTk5OXLhwgStXrvDw4UNev35NrVq1SJkyJd999x1Hjx7lxo0bHD58mNGjR7/TFRIVq1atYteuXab5C548eUKDBg1M26tXrx7mihQhQoZghPcnrAklQxw6dIgOHTrg4eFB1apVuXfvHvfu3ePx48emfby9vdm9ezfXrl3j77//ZvLkyfz666+0aNHCtI+npyf79+9n/vz5XL58mfnz5+Pr60vLli1N+6xYscLsda1atbC1tWXAgAFcvHiRXbt2MW/ePFq3bh2lIRghkiVLxuLFi0mZMiUeHh7R8n2KUgHi0KFDdOnSheTJk9O5c2eztpzUqVOTJUsWtm/f/tHJiYiIhMVggOnToWpVCPk5LFEiWLYMJk+GhBpgKCIicUSBAgX44Ycf2L59O7Vq1WL69Ol069aN+vXrm/bp1q0bN2/epHLlypQqVSrM8yRPnpz169fTrFkzateuzcGDB5k7dy4pU6Y07dO6dWvOnj1LvXr1mD17Nv369aNcuXKAcVjDrFmzSJ48OS1atKBVq1ZkzpyZqVOnmo6vVq0a5cqVw9PTk1KlSvHTTz+FmUvjxo3Jnj07DRo0oFSpUhw/fpzEiROzYsUKMmbMSJcuXahZsyYDBw7k1atX0dIR0atXLxYsWECdOnU4evQos2fPNhvacfXqVf7777+Pvs77bN68mRcvXjBv3jzKli1r+tO1a1fTPq9fv2b8+PHUrl2b5s2bc+zYMebPn0/VqlVN+7i5uTFlyhQ2bdpEnTp12Lx5M1OnTqVIkSKmfR49esSNGzdMr0MKBXfu3KFBgwaMGDGC1q1b07p164++r6RJk7Jw4UJSp06Nh4fHO6uPRJaVIQqDejw9PfH392fDhg08efKEUqVKmWZvBeNSMVu2bOG33377qOQsZdu2bfTp04eJEydSu3Zti+URFBTEiRMncHFxifDkMyKRoWdMYlpMPGOvXsF338GSJaGxjBlhyxZjN4R8PvT/MIlpesZiv9jyc3tcULFiRTw9Pc1a++MDPz8/KlWqxJYtW8iXL5+l04kzouN98/DwIG/evAwaNCjCx0SpA+LMmTPUrl3bbHbSN6VPn/6diTNEREQ+1u3b8MUX5sWHUqXg2DEVH0REREQiq2nTpjRt2jRSx2zbtg1XV1eOHj0a6etFqUk1ODg43JlUHz16FO52ERGRyDp8GOrVg1u3QmNt28KsWcbhFyIiIiISMenTp2fnzp0ApokrI6pixYqmISGRXYgiSgWIHDlycOzYsffOKvr777+TN2/eqJxaRETkHUuXQocOxuEXANbWMG0adOoEHzG3koiIyGdjz549lk4hRmTKlIkLFy5YOo04J2HChO8sERtRSZMmjfK8HVEagtGwYUN+/fVX1q9fb5qA0srKihcvXjB69GhOnDhB48aNo5SQiIhIiMBA6NkTWrUKLT6kTg27d0Pnzio+iIiIiMQlUSpAhKwpO2TIEKpWrYqVlRW9evWiWLFirFixgnr16mkSGBER+SgPHkD16vDDD6GxwoXh6FHjPBAiIiLxjbOzM7t37w53n/79+9OpU6cIn9PPzw9nZ2fOnz//selJFIS8/87OztSpUydSx27atMl07JgxY2Iow08rSgUIgEmTJjFjxgxKlSpFjhw5SJEiBeXLl2fatGmMHTs2OnMUEZHPzJkzUKIEvLmYUqNGcOAAZMtmsbREREQiLLKFAgAfHx/Kly8PvL9wMGjQIMaNGxdteYJxTL+3t3e0njO2e/PD/Zt/XoW0XIZhxowZYR7j4uLywet5e3ubvcczZsx4pyBx9OhRihUrxqhRozAYDNSsWRMfHx9cXV2jepuxTqTngHj16hW//PIL2bNnp0qVKlSpUiUm8hIRkc/U5s3g4QHPnhlfW1nB6NEwYICGXIiISPzm6Oj4wX0iO+mfvF/SpEnZsWOHWSxRODNbt2nT5p0VI1q1akWhQoU+eC0HBwdSpkz53u179+6le/futG3blm7dugFgZ2eHnZ1dvFrgIdIdELa2tgwePJhz587FRD4iIvKZCg6GkSOhfv3Q4kOyZLB1KwwcqOKDiIjEbR4eHowePZoJEyZQokQJypQpw4wZM8z2eXMIRqVKlQCoW7cuzs7OeHh4AO92Vvzxxx80a9aMYsWK4e7uTocOHbh+/Xqk8rp58yZeXl6m3+iHOH78OM2bN6dw4cJUqFCB0aNH8/z5c9P2ihUrMnv2bPr27Yurqytffvklu3fv5uHDh3z33Xe4urpSq1YtTp8+bTpm06ZNFCtWjN27d1OtWjUKFSpE69atuX37diTezehhZWWFo6Oj2Z/w2Nvbm+374MEDLl26RMOGDT8qjx9//JEuXbrQq1cvU/Ehvop0AcLKyooMGTLg7+8fE/mIiMhnyN/fOMRi2LDQWO7ccOgQ1KplubxERESi0+bNm0mSJAnr1q2jT58+zJo1i/3794e57/r16wFj676Pj887xYoQL168oHXr1mzYsAFvb2+srKzo3LkzwcHBEcppxowZpE+fnm7duuHj44OPjw8AFy5coG3btlSpUoVt27YxdepUjh07xqhRo8yOX7p0KW5ubmzevJkKFSrQt29f+vbtS+3atdm0aRNZsmShX79+psULAF6+fMmcOXMYN24cq1evxt/fn549e0Yo3xBz587F1dU13D9Hjx4N9xzPnz/nyy+/pHz58nTo0CHSv2Rfv3492bJlo1ixYpE67k0rV65kwIABjBkzBk9PzyifJ66I0jKcdevWZdu2bbRs2TLSa4aKiIi86coVqFPHOO9DiGrVYPVqCKdTUUREJM5xdnamS5cuAGTLlo0VK1bg6+tLmTJl3tk3VapUgLF1P7zfzFerVs3s9dixYylVqhSXLl0iT548H8zJwcEBa2tr02/3QyxatIhatWrRqlUrU76DBg3Cw8OD4cOHm4YqlC9f3jQsoXPnzqxevZpChQpRo0YNANq1a0eTJk24f/++6fyvX79m6NChFClSBIBx48ZRs2ZNTp06ReHChT+YM0DTpk1N13ifdOnSvXdbjhw5TF0f/v7+LFu2jGbNmrF161ayRWDCqYCAAH788UfatWsXoXzDcvnyZUaOHMmYMWMiPUFlXBWlAoSbmxu7du2ibt26NG/enMyZM5M4ceJ39itevPhHJygiIvHXb79B48bw8GForE8f8PICa2vL5SUiIhIT3hzeAJja+D/G9evXmTZtGidOnODRo0emToPbt29HqADxPmfPnuWff/7hxx9/NMUMBgPBwcH4+fmRM2dOwPye0qRJA2B23dSpUwPw4MEDUwEiYcKEFCxY0LRPzpw5SZ48OZcvX45wAcLBwQEHB4eo3Rzg4uJiNnmkm5sb9erVY8WKFQwePPiDx+/cuZNnz55Rt27dKOeQPn16kidPzsKFCylfvjxp06aN8rniiigVIFq3bm36etSoUVi9NTDXYDBgZWWlpV5ERCRMBgPMmAHffw9BQcaYnR0sXAjNm1s2NxERkZiSMKH5xy8rKyuzoQlR0bFjRzJkyMDo0aNJmzYtwcHBfP3117x+/fqjzhscHEzTpk1Nc0+8KUOGDKav37ynkM+Fb06aGBJ7+z7f/gz5vtj7zJ07l3nz5oW7z4IFCyI8PCJBggQUKlSIa9euRWj/9evX88UXX0Ro4tD3sbe3Z8mSJbRp0wYPDw+WLVsWbtdGfBClAoSXl1d05yEiIp+JV6/gu+9gyZLQmJMTbNkCHzGEUkREJF4J+RAfFFKpD8OjR49MbfwhH7Q/NO/B+6719pwR+fPn5++//yZr1qyRPt+HBAYGcubMGVO3w5UrV3j69Ck5cuSI8Dk+dgjG2wwGA+fPn49Q18iNGzc4dOgQc+bMifD53ydFihR4e3vTtm1bPD09430RIkoFiHr16kV3HiIi8hm4fdu4ysXBg6Gx0qVh40ZIn95yeYmIiMQ2qVOnxs7Ojn379pE+fXoSJUr0zhKcKVKkwMHBgbVr1+Lo6MitW7eYPHlypK/l5OTEkSNH+Oqrr7CxsSFVqlSmuRtGjBhB48aNSZw4MZcvX+bAgQMMGTLko+7NxsaGUaNGMXjwYBImTMioUaNwcXExFSROnTpF3759Wbp06Xs/jH/sEIyZM2dSpEgRsmXLZpoD4q+//mLYGzNir1ixgl27drF06VKzYzdu3IijoyPly5eP8vXflCxZMhYvXsy3335r6oRIH09/MIr0KhgiIiJRcfiwscPhzeJD27awZ4+KDyIiIm9LmDAhgwcPZu3atZQrV85s6c0QCRIkYOrUqZw9e5avv/4aLy8v+vbtG+lrdevWjZs3b1K5cmVKlSoFQN68eVm+fDn//PMP33zzDfXq1WPatGkfNeQghJ2dHe3ataNXr140adKERIkSMWXKFNP2Fy9ecPXq1Y8eRhKep0+fMnToUGrUqEGbNm24e/cuK1asMJuD4tGjR9y4ccPsuODgYDZv3kz9+vWxjsYJq5ImTcrChQtJnTo1Hh4eFlmW9FOwMkRh0NGWLVsitN/HTMhhSdu2baNPnz5MnDiR2rVrWyyPoKAgTpw4gYuLS7Q+3CIh9IxJTAt5xk6fdqVjxwS8emWMW1vDtGnQqRNEYriniBn9P0ximp6x2C+2/NwuEbdp0ybGjh0bpaEicZGfnx+VKlViy5Yt5MuXL0rn8PDwIG/evAwaNCias/v0ojQEo3///mFOmPL2pCFxtQAhIiLRIzAQpk7NxMqVoQ13qVPD+vXw5ZcWTExERETkE2ratCn58uVjzZo1ET5m27ZtDBs2jJcvX5I3b94YzO7TiVIBYtmyZe/EgoKCuH79OqtWrSJx4sT06NHjY3MTEZE47OFDaNIkAbt3h47dLFzYONlk9uyWy0tERETkU0mfPj07d+4EwNbWNlLHVqxYkSJFigC8M/9HXBWlAkSJEiXCjJcqVYp69erRsGFDzp07R8mSJT8qORERiZvOnoU6deDy5dDOuAYNwNsbkia1XF4iIiJiWfXr16d+/fqWTuOTSZgwYZRXEkmaNClJ49kPTtE+CaWtrS21a9dm1apV0X1qERGJA7ZuhZIl4fLl0NiIEcGsX6/ig4iIiMjnLEZWwbC1teXff/+NiVOLiEgsFRwMo0ZB3brg72+MJU1qYNKkSwwaZNBkkyIiIiKfuWgvQNy9e5c1a9aQKVOm6D61iIjEUv7+0LgxDB0aGsuZE/bvD+aLL55YLjERERGxqIoVK+Lt7W3pNN7h7OzM7t27P9n1PDw8GDNmzCe7XmwVpTkgPD09w4w/ffqUK1euEBAQwLhx4z4qMRERiRuuXjXO93D6dGisShVYswZSpIATJyyWmoiIiMU8ePCAadOm8ccff3D//n1SpEhB3rx56dKlC66urjF+/RkzZrB79262bt0a49eylMuXL1OzZk3WrVtnmqwRoFGjRpw/f54jR46QOHFiAAICAihevDgDBw6kSZMm+Pj4kCJFio/OwdnZGVtbW3bs2IGTk5Mp3qlTJ5InTx6pz8UzZszg559/5s6dO9jY2FCgQAF69uxpdm/nzp1j2rRpnDp1Cn9/fxwdHSlcuDBDhw4lVapUH30/MS1KBQg/P793YlZWVqRIkYIqVarQvHlz3NzcPjo5ERGJ3fbsMXY+PHgQGuvVC8aNg4QJISjIcrmJiIhYUteuXQkMDGTcuHFkzpyZBw8e4Ovry5Mnsasz8PXr19jY2Fg6jSjJmTMnjo6OHDp0yPQh/dmzZ5w/f57UqVPz559/Urp0aQBOnTrFy5cvcXd3B8DR0THcc0fmfbGysmL69OmMHz/+I+4GsmXLxtChQ8mcOTMvX77E29ubNm3asGvXLlKlSsWDBw9o3bo1X375JYsWLSJZsmT4+fmxZ88eXr58+VHX/lSiNARjz5497/z57bff2LRpE5MnT1bxQUQknjMYYMYMqFo1tPiQKBEsWwaTJhmLDyIiIp+rp0+fcuzYMXr37k3JkiVxcnKicOHCdOjQgS+++MJsvyFDhlC6dGkKFSrE119/ze+//x7h6xw6dIiGDRvi4uJCsWLFaNq0KTdv3mTTpk3MnDmTv/76C2dnZ5ydndm0aRNg/I396tWr+e6773BxcWHOnDkEBQUxcOBAKlasSOHChalWrRpLly41u1b//v3p1KkTixYtomzZsri7uzNixAhev35t2ufBgwd07NiRwoULU7FiRbZt2/Zxb2QEuLu7c+jQIdPro0ePki1bNipWrGgWP3ToEOnSpSNbtmyA+RAMPz8/nJ2d2b59Ox4eHhQqVMiU+8aNG6lRowaFChWievXqrFy58p0cWrRowbZt27hw4UK4uQYFBTFy5EiKFSuGu7s7U6dOxWAwmLbXqlWL0qVLkzlzZnLnzs2AAQPw9/c3nffPP//E39+f0aNHkz9/fjJnzkypUqUYNGgQGTNmNN2ns7Mzvr6+1K9fnyJFitC0aVOuXLkShXc3+kXpR8QjR46QM2fO97Z4PHz4kMuXL1O8ePGPSk5ERGKfV6+gc2dYtCg0ljEjbNkC+t++iIgIJEmShCRJkrB7925cXFywtbV9Z5/g4GDatWvHs2fPmDhxIlmyZOHSpUskSBCx3xEHBgbSuXNnGjVqxJQpU3j9+jWnTp3CysqKmjVr8vfff7Nv3z6WLFkCQLJkyUzHzpgxg++//56BAweSIEECgoODSZ8+PT/88AMpU6bkzz//ZOjQoTg6OlKzZk3TcYcOHcLR0ZGlS5dy/fp1evbsSb58+WjcuDFgLFLcuXOHpUuXYmNjw+jRo3nwZptkGLZt28awYcPC3WfEiBHUrl07zG3u7u54eXkRGBhIwoQJOXToECVKlKBYsWIsW7bMLPeQ7of3mTRpEv3792fs2LHY2tqybt06pk+fztChQ8mXLx/nz59nyJAhJEmShHr16pmOc3Nz4+rVq0yZMoV58+a99/ybN2+mYcOGrFu3jjNnzjB06FCcnJxM79+bAgICWLt2LcmSJcPZ2RmANGnSEBgYyK5du6hevTpW4czwPXXqVPr370+qVKkYNmwYAwcOZM2aNeHe/6cQ5TkgJkyYQK1atcLcfvDgQXr16sX58+c/KjkREYldbt+GBg3A1zc0VqoUbNwIGTJYLi8REZHYJGHChIwbN44hQ4awZs0a8ufPT4kSJahZsyZ58+YF4MCBA5w6dYrt27eTPXt2ADJnzhzha/j7+/Pff//x5ZdfkiVLFsA4JCFEkiRJsLa2DnOowddff03Dhg3NYt26dTN9nTlzZv7880927NhhVoBIkSIFQ4cOxdrampw5c1KhQgV8fX1p3LgxV69e5Y8//jCbj2HMmDFmx4elYsWKZnMchCV16tTv3VaiRAmeP3/O6dOncXV15fDhw7Rt25bixYvTt29fXrx4gbW1NSdPnmTIkCHhXqdly5ZUrVrV9Hr27Nn079/fFMucOTOXLl1i7dq1ZgUIgF69elG7dm2OHj1KsWLFwjx/hgwZGDhwIFZWVuTIkYOLFy/i7e1tVoD4/fff+f7773nx4gWOjo4sXrzY9It/FxcXOnbsSO/evRk+fDiFChWiZMmS1K1blzRp0phdq2fPnpQoUQKA9u3b0759e169ekWiRInCfQ9iWpQKEG+2iYQlMDAwwpU7ERGJG44cgXr14ObN0FibNjB7tnH4hYiIiISqVq0aX3zxBUePHuXPP//Ex8eHhQsXMnr0aOrXr8/58+dJnz69qfgQWQ4ODtSvX5+2bdtSpkwZSpUqRY0aNUibNu0Hjy1YsOA7sdWrV7N+/Xpu3brFq1eveP36talYEiJXrlxYW1ubXjs6OnLx4kXAOCFkwoQJzc6dM2dOkidPHm4uSZMmJWnSpB/M+X2yZctG+vTpOXz4MLlz5+b8+fOUKFGC1KlTkylTJo4fP46trS0vX76kZMmS4Z7rzdwfPnzI7du3GTRokFnhIjAw0KybJESuXLmoU6cOkyZNem+nQZEiRcy6FlxcXFiyZAlBQUGm99Xd3Z0tW7bw6NEj1q1bR48ePVi/fr2pCNOzZ09atWrFwYMHOXnyJGvWrGHevHmsWLHC1CkBmH0dUoR68OCBaaiGpUR5lO772j0CAwPx9fUlZcqUUU5KRERil+XLoV074/ALAGtr+OEH41CMcLr/REREPmuJEiWiTJkylClThi5dujBo0CBmzJhB/fr1sbOz++jze3l54eHhwb59+/jll1/44YcfWLJkCS4uLuEelyRJErPX27dvx8vLi379+uHq6oq9vT2LFi3i5MmTZvslfGuSJysrq3d+OR3esICwfOwQDDB2QYTMfZA1a1bTh/WQuK2tLU5OTmTKlCnc67z5vgQHBwMwatSodzo03vfL9m7dulGtWrWPWt4zSZIkZM2alaxZs+Li4kLVqlXZsGEDHTp0MO2TMmVKatSoQY0aNfj++++pV68eixcvNpsE883vVcj3JOSeLCnCBYh58+aZxrNYWVkxaNAghr654Pv/vHz5EoPBQIMGDaIvSxERsYjAQOjfHyZPDo2lTg3r1kHFipbLS0REJC7KlSuX6cOps7Mzd+7c4erVq1HuggDInz8/+fPnp0OHDjRp0oSffvoJFxcXbGxsIvyB89ixY7i6utK8eXNT7Pr165HKI0eOHAQGBnLmzBkKFy4MwJUrV3j69Gm4x33sEAwwdg2MGTOGnDlzmoYdABQvXpyVK1diY2Pzwfkf3pYmTRrSpUvHjRs3wi1+vClDhgw0b96cKVOmmIbFvOntgs7JkyfJmjWrWVfJ2wwGAwEBAe/dbmtrS+bMmXnx4kWEcrS0CBcg0qZNS4ECBQDjJJROTk7vPAhWVlYkSZIEFxcXWrZsGb2ZiojIJ/XwITRtCrt2hcYKFYKtW+Ejfk4SERGJ9x49ekT37t1p0KABzs7O2Nvbc+bMGRYuXEilSpUATBMlduvWjf79+5MlSxauXLmClZUV5cuX599//6Vly5ZMmDDB9IH+TTdu3GDdunVUrFiRtGnTcvXqVa5du0adOnUAcHJyws/Pj/Pnz5MuXTqSJk0a5mSYAFmyZGHLli3s27ePTJkysXXrVk6fPv3BjoE35ciRg3LlyjF48GBGjRqFtbU1Y8eO/WCnx8cOwQBjAeL58+ds3LiR0aNHm+IlSpRgwIABJEiQIEq/IO/atSujR48madKklC9fnoCAAM6cOcPTp09p3bp1mMd06NCB9evX4+fn9878F7dv38bLy4smTZpw7tw5VqxYQb9+/QB4/vw5c+fOpWLFijg6OvL48WNWrVrFnTt3qF69OmCcH+Lnn3/mq6++Ilu2bBgMBn7//Xf++OMPxo4dG+n7s4QIFyDq1atnmmijYsWK9OrVy/Qfj4iIxC9nz0KdOnD5cmisQQPw9oaP/BlBREQk3rO3t6dIkSKm1SICAwNJnz49jRo1omPHjqb9ZsyYwfjx402TDmbNmpVevXoB8Pr1a65evfre32wnTpyYK1eusHnzZh4/fkzatGlp3rw5TZs2BYxzUOzatQtPT0+ePn2Kl5cX9evXD/NczZo146+//qJnz55YWVnx1Vdf8c033/DHH39E6r69vLwYPHgwLVq0IE2aNHTv3p3p06dH6hxRkTlzZpycnLh586bZSozp0qUjQ4YMXL9+/YPzP4SlUaNG2NnZsWjRIiZOnEiSJEnIkydPuL9sd3BwoF27dkyZMuWdbXXr1uXly5c0atQIa2trWrRoQZMmTQCwtrY2fT8fPXqEg4MDhQoVYuXKleTOnRswdtAkTpyYcePGcefOHWxtbcmaNSujR4+mbt26kb4/S7AyfGhGyc/Qtm3b6NOnDxMnToxwu01MCAoK4sSJE7i4uITbliMSVXrGJCxbt0KLFuDvHxobNQoGDYr8fA96xiQm6fmSmKZnLPaLLT+3i0jERHkSShERiV+Cg2H0aHhzHqikSWHFCmM3hIiIiIjIx4hyAeLYsWPMnz+fkydP8vTp0zBnPz137txHJygiIjHP3x9atoRNm0JjOXMauyH+N/2PiIiIiMhHCXv9kA84fPgwLVu25NSpUxQpUoTg4GDc3d1Nk6Pkzp3bNPmJiIjEbleuQOnS5sWHqlXh8GEVH0REREQk+kSpADF79mzSpk1rWi8WjLN9rl27Fm9vb27evKllOEVE4oA9e6B4cTh9OjTWuzf8/DOkSmW5vEREREQk/olSAeLMmTM0atSIlClTkiCB8RQhQzDc3d2pV68eP/zwQ7QlKSIi0eDMGahbF86cwWCAadOMnQ4PHxo3J0oEy5fDxImQUDMEiYiISCzg7OzM7t27LZ2GRJMoFSACAwNJkyYNAIkSJQLgv//+M213dnbW/A8iIrHNTz/B1q283LKD1q2hRw8ICjJucnICHx/j6hciIiIi/fv3p1OnTpZOI8bt3LmT+vXrU6xYMVxcXKhTpw5btmwx22fevHk0aNAAV1dXSpUqRadOnbhy5co752nbti3u7u44Oztz/vz5T3gXcUeUChDp0qXj9u3bgHH92ZQpU5oVHK5evYqtrW30ZCgiItFjzx5ukYEKE79m6dLQcOnScPQoFCtmudREREQkbnr9+rWlU/goKVKk4LvvvmPt2rVs27aN+vXrM3DgQPbt22fa5/DhwzRv3px169axZMkSgoKCaNu2Lc+fPzft8/z5c1xdXendu7clbiPOiFKTrZubG76+vnTr1g2AL7/8Em9vb2xtbQkODmblypVUqFAhWhMVEZFIunkT/v3X+LXBgO//BVCfY9x5msG0S7t695kx6RWJ0jtZKEkREZH4ycPDA2dnZ2xtbdmwYQM2NjY0bdqUrl27RvgcT548YeTIkezfv5/nz5+TPn16OnToYJpv786dO4wfP579+/cTEBBAjhw5GDZsGEWKFInQ+Xfs2MGsWbP4559/SJw4Mfny5WP27NksWrSIzZs3A8budoBly5bh5OREpUqVmDp1KqtXr+bEiRMMHz6cihUrMmrUKI4ePcqTJ0/IkiULHTp04Ouvv47U+3Ht2jUGDRrEqVOnyJw5M4MGDYrwexVV7u7uZq9btmzJli1bOHbsGOXKlQNg0aJFZvt4eXlRqlQpzp49S/HixQGoW7cuAH5+fjGec1wWpQJEs2bN2L17Ny9fvsTOzo5evXpx+vRpZsyYAUCuXLno169ftCYqIiKR5OlpnGUSWExrvuNXAjAOm0vIa6bTjY6b52L1tBJobKWIiEi027x5M61bt2bdunWcOHGC/v374+bmRpkyZSJ0/LRp07h8+TILFiwgZcqUXL9+nZcvXwLw7NkzWrRoQbp06Zg9ezaOjo6cPXuW4ODgCJ377t279OrViz59+lC5cmWePXvG0aNHMRgMtGnThsuXL+Pv729adCBFihTcvXsXgEmTJtG/f3/Gjh2Lra0tAQEBFChQgHbt2pE0aVL27t1L3759yZw5s1kxJLz3Izg4mK5du+Lg4MC6devw9/dn7NixH7yPuXPnMm/evHD3WbBgAcUi0OppMBg4ePAgV69eDbeTIWT6gRQpUnzwnGIuSgWIwoULm5bcBEidOjXbtm3jwoULJEyYkOzZs2NtbR1tSYqISBR07MjrY6f4/slQZhL62wVH7rKBhpRnHzg4QIcOlstRREQkHnN2dqZLly4AZMuWjRUrVuDr6xvhAsStW7fIly8fhQoVAiBTpkymbT/99BMPHz5kw4YNODg4AJA1a9YI53bv3j0CAwOpUqUKTk5OpnxD2NnZERAQgKOj4zvHtmzZkqpVq5rF2rZta/raw8ODffv2sWPHDrMCRHjvx4EDB7h8+TJ79uwhffr0APTs2ZN27dqFex9NmzalRo0a4e6TLl26cLf/999/lC9fnoCAABIkSMCwYcPe+z0yGAx4eXlRtGhR8uTJE+555V2RLkC8fPmShQsXUqRIEVNLCoCVlRV58+aN1uRERCTq7n3RiMYF6rD3QOicPK4cZwt1ycINqFYNli2DtGktmKWIiEj89eYHegBHR0cePHgQ4eObNWtGt27dOHfuHGXKlKFy5cq4ubkBcP78efLnz28qPkRW3rx5KVWqFLVq1aJs2bKULVuWatWqRei3+gULFjR7HRQUxPz589m+fTt3794lICCAgIAAEidObLZfeO/H5cuXyZAhg6n4AODq6vrBXBwcHKL8HoSwt7dny5YtPH/+HF9fX8aNG0fmzJnfGZ4BMHLkSC5evMiqVas+6pqfq0gXIOzs7Jg3bx5Dhw6NiXxERCQanDhhXHHzn39Ciw9NWc0i2pLE6iVgBc7OKj6IiIjEoIRvrWttZWWFwWCI8PEVKlTg999/Z+/evRw4cIBWrVrRvHlz+vXrh52d3UflZm1tzZIlSzh+/Dj79+9n+fLlTJ06lXXr1pE5c+Zwj02SJInZ68WLF+Pt7c3AgQNxdnYmceLEjB079p0JKsN7P8J6X6ysrD54H9ExBCNBggSm7pF8+fJx+fJl5s+f/04BYtSoUezZs4cVK1aYFUok4qI0BCNLlizcv38/unMREZFosHYttG4NL14YX1sRzDj60yfFAqy694Hp0+HxY1izBqZOhQRRWhBJREREPoFUqVJRv3596tevz5o1a5gwYQL9+vXD2dmZ9evX8/jx4yh3AFhZWVG0aFGKFi1K586d+fLLL9m9ezetW7fGxsYmwvNJHDt2jEqVKlGnTh0AgoODuXbtGjlz5oxwLrly5eL27dv8+++/piETf/755wePi44hGG8zGAwEBASYvR41ahS7du1i+fLlHyzQyPtF6afOb775hvXr1/PkyZPozkdERKIoKAj694emTUOLDylSGNieqzt9613C6uIFGDECLlyAevUgS5bQHUVEROST69u3L5MnT37v9mnTprF7927++ecf/v77b/bu3Wv6UP/VV1+RJk0aOnfuzLFjx7hx4wa//vqr6UP7qVOnqF69Ov+GrIj1lpMnTzJ37lxOnz7NrVu32LlzJw8fPiRHjhwAODk5ceHCBa5cucLDhw/DXW4zS5YsHDhwgOPHj3P58mWGDh0a6V9Yly5dmuzZs9OvXz/++usvjh49ytSpUz94nIODA1mzZg33T3jdIvPmzWP//v3cuHGDy5cvs2TJErZu3Urt2rVN+4wYMYJt27YxefJk7O3tuXfvHvfu3TNNCArw+PFjzp8/z+XLlwG4evUq58+f5969e5F6H+K7KHVA2Nvbkzx5cqpVq0a9evXIkiXLO+N7IHQpEhERiVmPH8M338Avv4TG8uWDrVutyJ3jB3hzYuC0aWHTJmPFQhMGi4iIWMzt27dJEE4noo2NDVOmTOHmzZvY2dlRtGhRpkyZAoCtrS2LFy9m/PjxtG/fnqCgIHLmzMmwYcMAePHiBVevXn1v4SBp0qQcOXKEpUuX4u/vT8aMGenfvz8VKlQAoHHjxhw+fJgGDRrw/Plz0zKcYenUqRN+fn60bduWxIkT07hxYypXrmxaLSIiEiRIwMyZMxk0aBANGzbEycmJwYMH8+2330b4HFHx/PlzRowYwZ07d7CzsyNHjhxMnDiRmjVrmvZZvXo1YJxc801eXl7Ur18fgD179jBgwADTtp49ewLQpUuXSC29Gt9ZGSIzCOl/IjLZpJWVFefPn49SUpa2bds2+vTpw8SJE80qX59aUFAQJ06cwMXFRauKSIzQMxY/nD8PderA33+HxmrXhuXLIXlyy+UFesYkZun5kpimZyz2iy0/t4tIxESpA2LZsmXRnYeIiETB1q3g4QFv/oJh6FAYNkxTO4iIiIhI7BKlAkSJEiWiOw8REYmE4GAYPdpYaAhhb29cVfN/nYAiIiIiIrFKlAoQbwoICODRo0ekTJkSW1vbDx8gIiIf5b//wNMTtmwJjeXIYeyGeGtZbhERERGRWCPKDbpnz57F09MTNzc3vvjiC44dOwbAgwcPaNmyJQcOHIi2JEVExOjSJShZ0rz4ULUqHDmi4oOIiIiIxG5RKkCcP3+e5s2bc+PGDdNaryFSp07Nq1ev2Lx5c7QkKCIiRr/+CsWLw7lzobE+fWD7dkiVynJ5iYiIyKdVsWJFvL29LZ1GjJsxY8Y7nzc/Vx4eHowZM8bSaXy0KBUgpk2bRtq0afnpp5/o1asXby+kUbJkSU6dOhUtCYqIfO4MBpgwAWrWNC63CZA4MaxaZYxrYnYREZHY58GDBwwdOpQvvviCggULUqZMGdq2bcuff/5p2sfZ2Zndu3dbMMuw+fn54ezsHGdXNYyMgwcP4uHhQYkSJShSpAhVq1alX79+BAYGArBp0yaKFStm4SzjjyjNAXHs2DHat2+Pvb09AQEB72zPmDEjd+/e/ejkREQ+d8+eQdu2sHZtaCxLFuMQDFdXi6UlIiIiH9C1a1cCAwMZN24cmTNn5sGDB/j6+vLkyRNLpxZtAgIC4vQ8gH///Tft2rXDw8ODIUOGYGdnx7Vr1/j1118JDg6O1Lni+nvxqUSpAPHq1SuSJUv23u3+/v5RTkhERIyuXYO6deHkydDYF1/AunXg6GihpEREROSDnj59yrFjx1i+fLlpBUEnJycKFy5s2qdixYoAdO7c2bR9z549XL9+HS8vL06ePMmLFy/IkSMHvXr1onTp0mbXePbsGb169WLPnj3Y29vToUMHPDw8IpzjkydPGDlyJPv37+f58+ekT5+eDh060KBBAypVqgRA3bp1AeMqiMuXL6d///48ffqUIkWKsHz5cmxtbdmzZw9bt25l6dKlXL16lSRJklCyZEkGDhxI6tSpATh06BCenp54e3szceJELl++TL58+Rg7diw5cuQw5TR//ny8vb158eIFNWrUIFUMjzHdv38/jo6O9O3b1xTLkiUL5cuXN+U9YMAAwNitAtClSxe6du1KxYoVadiwIdevX2fXrl1UrlyZ8ePHM3HiRHbv3s2dO3dIkyYNtWrVonPnztjY2ADGYSW7d++mdevWTJ8+nSdPnlC+fHlGjRpF0qRJAXj+/DnDhw9n165d2Nvb06ZNmxh9Hz6lKBUgsmTJwtmzZ9+7/eDBg+TKlSvKSYmIfO5++w2aNIEHD0Jj3brBpEnwv3+/REREJJZKkiQJSZIkYffu3bi4uIT5m/ENGzZQqlQpvLy8KFeuHNb/G1P5/PlzKlSoQI8ePUiUKBGbN2+mY8eO7Nixg4wZM5qOX7RoER07dqRLly74+Pjg5eVFjhw5KFOmTIRynDZtGpcvX2bBggWkTJmS69ev8/LlSwDWr19Po0aN8Pb2JleuXKYPzwC+vr4kTZqUJUuWmIbiv379mu7du5MjRw4ePHiAl5cX/fv3Z8GCBWbXnDp1Kv379ydVqlQMGzaMgQMHsmbNGgC2b9/O9OnTGTZsGEWLFmXr1q0sX76czJkzh3sfrh9oCS1atCgLFy4Mc5ujoyP37t3jyJEjFC9ePMxzDxw4kOnTp7Njxw7A+L0NsWjRIjp16sR3331nitnb2+Pl5UXatGm5ePEiQ4YMwd7ennbt2pn2uX79Or/99htz587l6dOn9OjRgwULFtCzZ08AJkyYwKFDh5g5cyZp0qRh6tSpnDlzhrx584Z7r3FBlAoQX3/9NbNnz6ZGjRrky5cPACsrKwAWL17Mvn37GDRoUPRlKSLymTAYYNo06N0bgoKMsUSJYO5caNXKoqmJiIhIBCVMmJBx48YxZMgQ1qxZQ/78+SlRogQ1a9Y0fYgM+e1+8uTJcXyjtTFv3rxmHzR79uzJ7t272bNnDy1atDDF3dzcaN++PQDZs2fn+PHjeHt7R7gAcevWLfLly0ehQoUAyJQpk2lbSG4ODg5muYHxA/jo0aPNiioNGzY0fZ05c2YGDRpEo0aNePbsGfb29mb3EtIR0r59e9q3b8+rV69IlCgRy5Yto0GDBjRq1Mi0r6+vL69evQr3Pra8uTRYGOzs7N67rXr16vj4+NCiRQscHR0pUqQIpUqVom7duiRNmhRbW1uSJUuGlZXVO+8DGOc+bNu2rVmsU6dOpq8zZcrElStX2L59u1kBwmAw4OXlZep4qF27Nr6+vvTs2ZNnz56xYcMGJkyYYPpejhs3jgoVKoR7n3FFlAoQbdq0Yf/+/bRt25YcOXJgZWWFl5cXDx8+5P79+5QuXZpvvvkmunMVEYnXXryADh1g+fLQmJMTbNoE//u3WkREROKIatWq8cUXX3D06FH+/PNPfHx8WLhwIaNHj6Z+/frvPe758+fMnDmTvXv3cvfuXYKCgnj58iW3bt0y28/FxeWd10uXLo1wfs2aNaNbt26cO3eOMmXKULlyZdzc3D54XJ48ed7p6Dh37hwzZszgr7/+4vHjx6bOiNu3b5t1xocMYwBMH+gfPHhAxowZuXz5Mk2bNn3nng4dOhRuPlmzZv1gzu9jbW2Nl5cXPXr0wNfXl5MnTzJ37lwWLFjA+vXrSZs2bbjHFwxjDfQdO3awdOlSrl+/zvPnzwkMDDQVGkI4OTmZxdKmTcuD/7W93rhxg9evX5t9fx0cHMiePXuU7zM2idIqGLa2tixZsoR+/fqRKFEiEiVKxLVr10iZMiV9+vRh3rx5JEgQpVOLiHyWrl+HsmXNiw9lysDRoyo+iIiIxFWJEiWiTJkydOnShTVr1lCvXj1mzJgR7jETJkxg586d9OzZk5UrV7Jlyxby5MnD69evP3i9kK70iKhQoQK///47LVu25O7du7Rq1Yrx48d/8LjEiRObvX7+/Dlt2rQhSZIkTJw4kQ0bNjBz5kyAd3JOmDD0998huUZ2sse3ubq6hvvn22+//eA50qVLR926dRk2bBg///wzr169Mg0NCc/b78WJEyf4/vvvKV++PHPnzjUNnwnvfQgRUrR5e4XJ+CZKHRBgfNNatWpFK/UEi4h8lL17oXFjuHcvNNaxo3EohiZTFhERiT9y5cpltuymjY0NQSFjLv/n2LFj1KtXjypVqgDGySZv3rz5zrlOvjlL9f9evzmhY0SkSpWK+vXrU79+fdasWcOECRPo16+fac6Ht3MLy5UrV3j06BG9e/cmQ4YMAJw5cyZSeQDkzJmTEydOmCa+hHfvMSwfMwQjLClSpMDR0ZEXL14AYX+P3uf48eNkzJjRbE6ItztXPiRLlizY2Nhw4sQJ05wfT5484dq1a2HOUxHXRLkAISIiH8dggBkz4PvvQ+d7sLGBWbPgjWGCIiIiEsc8evSI7t2706BBA5ydnbG3t+fMmTMsXLjQtMIEGFvxfX19cXNzw9bWlhQpUpAlSxZ27dpFxYoVsbKy4ocffgizS+D48eMsWLCAypUrc+DAAXbs2MG8efNM2/v27Uu6dOno1atXmDlOmzaNAgUKkDt3bgICAti7dy85c+YEIHXq1NjZ2bFv3z7Sp09PokSJ3rsKYsaMGbGxsWH58uU0a9aMixcvMnv27Ei/Z56envTr14+CBQtStGhRfvzxR/7+++8PTkL5MUMw1qxZw/nz56lSpQpZsmTh1atXbNmyhUuXLjFkyBDA+D16/vw5vr6+ODs7kzhx4nc6H0JkyZKF27dv8/PPP1OoUCH27t1rVnCKCHt7exo0aMDEiRNJmTIlqVOnZurUqZHqbonNolyA8Pf3Z+HChfz222/cuHEDME44UqlSJdq2bRvuMp0iIp+7Fy+MXQ7LloXGMmSAjRuhVCnL5SUiIiIfz97eniJFipjmAggMDCR9+vQ0atSIjh07mvbr168f48aNY/369aRLl449e/YwYMAABg4cSNOmTUmZMiXt2rXj2bNn71yjdevWnD17llmzZmFvb0+/fv0oV66cafvt27fDHRZvY2PDlClTuHnzJnZ2dhQtWpQpU6YAxm73wYMHM2vWLKZPn06xYsVY/uY40TekSpWKcePGMWXKFJYvX06BAgXo16+fWRdARNSsWZPr168zadIkXr16RbVq1WjWrBk+Pj6ROk9kFC5cmGPHjjFs2DDu3r1LkiRJyJ07N7NmzTJNlunm5kbTpk3p0aMHjx8/Ni3DGZbKlSvTsmVLRo4cSUBAAF988QXfffedaUhKRPXt25fnz5/z3XffYW9vT+vWrfH39//o+40NrAxRGGTy77//0rx5c/z8/HB0dCR79uwYDAauXbvGvXv3cHJyYuXKlaRPnz4mco5x27Zto0+fPkycOJHatWtbLI+goCBOnDiBi4uLaVkekeikZ8wybtyA+vWN8zuEKFUKNmyAN1bXihf0jElM0vMlMU3PWOwXW35uF5GIiVIHhJeXF7dv38bLy4u6deua2kEMBgObN29m6NChjBs3jh9++CE6cxURifP+7/+gUSPz+R7at4fp043LbYqIiIiIxFdRKkD4+PjQrFkz6tWrZxa3srKifv36nD17lq1bt0ZLgiIi8YHBADNnGud7CAw0xmxsjHNAdOhg2dxERERERD6FKBUgDAaDaYKSsOTKlSveLx8iIhJRYc33kD69cchFmTKWy0tERERE5FN6/6wk4ShWrBhHjhx57/YjR45QrFixKCclIhJf3LgB5cqZFx9KloRjx1R8EBEREZHPS5QKEIMGDeL48eNMmDCBhw8fmuIPHz5k/Pjx/PnnnwwaNCjakhQRiYv+7/+gaFFjsSFEu3awd2/8m2xSRERE4g5nZ+dILw8ZF/Xv359OnTpZOg15Q5SGYLRu3ZqAgACWLFnCkiVLSJ48OQBPnz4FIGXKlLRu3drsGCsrq8/iIRcRMRiMczt8/z0EBRljNjbGOSDat7dsbiIiIhI/+Pn5UalSJbZs2UK+fPksnY6ZQ4cO4enpyZEjR0yfFeMrZ2fnd2LDhw+nWbNmALx69Yphw4Zx9uxZLl++zBdffMHs2bPfe75jx47h4eFB7ty54+W8ilEqQGTUr+5ERML04oVxUsk3l8pOnx42boTSpS2Xl4iIiEhsExAQgK2traXT+GheXl6UK1fO9DpZsmSmr4OCgkiUKBEeHh78+uuv4Z7nv//+o1+/fpQqVYr79+/HWL6WFKUCxPI3f7IWEREArl2D+vXhzz9DY6VKGSebVN1WRETk8+Lh4YGzszO2trZs2LABGxsbmjZtSteuXSN8jidPnjBy5Ej279/P8+fPSZ8+PR06dKBBgwZUqlQJgLp16wJQokQJli9fzqlTp5g6dSrnzp0jMDCQfPnyMWDAAAoUKGB27rt37/Ltt99y+PBh0qRJQ58+fahRo0aEc7t58yajRo3i2LFjvH79GicnJ/r27UvOnDnx9PQEoHjx4gDUq1ePcePGmX6zb2Njw5YtW8idOzcrVqxgyZIlbNq0iRs3bpAiRQq+/PJL+vTpg729PQCbNm1i7NixTJ06lbFjx3Lnzh3c3Nzw8vIibdq0gPGD/oQJE9i4cSPW1tY0aNDgky2MkDx5chwdHcPcliRJEkaMGAHA8ePHTaMGwjJ06FC+/vprrK2t4+3ogSjNASEiIuZ++w2KFTMvPnTsqPkeREREPmebN28mSZIkrFu3jj59+jBr1iz2798f4eOnTZvG5cuXWbBgAdu3b2f48OGkTJkSgPXr1wPg7e2Nj48PM2bMAODZs2fUrVuXVatWsW7dOrJmzUr79u3x9/d/59zVqlVj69at1K5dm169enH58uUI5zZy5EgCAgJYsWIFP/74I7179yZJkiRkyJDBlMuOHTvw8fExmx9w8+bNWFtbs3r1atMHcysrKwYNGsSPP/7IuHHjOHjwIBMnTjS73suXL1m8eDETJkxgxYoV3L59m/Hjx5u2L168mI0bNzJmzBhWrVrFkydP2LVrV7j3cOvWLVxdXcP9M3To0Ai9F+7u7jRo0IDVq1cTHBwc4fcxxMaNG7l+/TpdunSJ9LFxSZQ6IN704sULHj9+HGZ1SUM1RCS+MxhgyhTo2xdC/q2xtYVZs+Dbby2bm4iIiFiWs7Oz6QNltmzZWLFiBb6+vpSJ4FJYt27dIl++fBQqVAiATJkymbalSpUKAAcHB7PfvpcqVcrsHCNHjqR48eIcOXKEL7/80hSvXr06jRo1AqBHjx4cOHCA5cuXM3z48AjnVq1aNdMcCJkzZzZtS5EiBQCpU6d+Zw6IrFmz0rdvX7NYq1atTF9nzpyZ7t27M3z4cLNcXr9+zYgRI8iSJQsAzZs3N5tLYenSpbRv355q1aoBMGLECHx8fMK9h7Rp07Jly5Zw90maNGm427t3706pUqWws7PD19eX8ePH8+jRo0hNfnnt2jUmT57MypUrSZjwoz+ix2pRurugoCAWLFjAypUrwx2bcv78+SgnJiIS2z17ZiwyrFkTGnNyMs734O5uubxEREQkdnh7gkJHR0cePHgQ4eObNWtGt27dOHfuHGXKlKFy5cq4ubmFe8yDBw+YNm0ahw4d4v79+wQHB/PixQtu3bpltp+rq6vZaxcXl0h9fvP09GT48OH4+PhQunRpqlatSt68eT94XMGCBd+JHTx4kHnz5nHp0iX8/f0JCgri1atXPH/+nCRJkgCQOHFiU/EBjMWDkPfyv//+4969e2b3lDBhQgoWLBjuMIyECROSNWvWCN9zWN4sNIRMBjpr1qwIFyCCgoLo1asXXbt2JXv27B+VS1wQpQKEl5cXK1asIH/+/FSvXt1U4RIR+Vxcvgz16sHp06GxcuVg/XpIl85yeYmIiEjs8fZvs62srCI1L0GFChX4/fff2bt3LwcOHKBVq1Y0b96cfv36vfeY/v378/DhQwYOHEjGjBmxtbWlSZMmvH79+oPXs7KyinBujRo1omzZsuzdu5f9+/czf/58+vXrh4eHR7jHJU6c2Oz1zZs3ad++PU2bNqV79+6kSJGCY8eOMWjQIAIDA037fex7GZZbt27x1VdfhbtPrVq1GDlyZITPWaRIEfz9/bl//z5p0qT54P7Pnj3jzJkznD9/nlGjRgEQHByMwWAgf/78LFq06J2ulrgsSgWIH3/8kapVqzJ9+vTozkdEJNb75Rf45ht4/Dg01qWLcSiGjY3F0hIREZF4KFWqVNSvX5/69f+/vXuP07nO/z/+uGYUcpjBOpRDqmE2hyLkUKEkW9+sQ0Y6kK1v2M4HHWwn1Xai7LfZtlZtW9EWhQ5UtpV02LBRCiGTFEKRY/mRua7fH9e6LhMyM+byua6Zx/12c5v5vD6fubym3rfLeHofejNu3DhGjBjBTTfdxCH//aEjf9eZ3/81Z84c7rjjDjp16gTA6tWr2bBhwx6vO2/evNgGlgCffPJJkY/zPPzwwznvvPM477zzeOihh3jhhRfo37//PnvbmwULFpCfn8/NN99MWlp0i8I33nijSH1UqVKFmjVrMm/evNjGlzt37mThwoU0adJkn19XEkswfm7RokWUL1++0MePVq5cmcmTJxeoPffcc8yaNYvc3NwCy25Kg2IFEDt37iz0uiVJKi3CYbjnHrjjjujeDwAVKsDo0fDfzZ4lSZIK7cYbb6R27dpcf/31e73/8MMP07RpUxo1asSOHTuYMWMGxxxzDBDdX6FChQq899571KlTh/Lly1OlShWOPPJIXn31VZo3b87WrVsZMWIEFSpU2OO1p06dSrNmzWjVqhWTJ0/m008/5Z577ondv+iii+jatSsXXnjhXnu755576NixIw0bNmTz5s3MmjUr1lvdunUJhULMmDGDTp06Ub58+diJFj/XoEEDdu7cydixYznttNOYO3cu43Zf31pIAwYM4IknnqBhw4YcffTRPP3007944gQc+BKM6dOns27dOlq0aEGFChWYPXs2f/rTn+jbt2+B40Xz8vL46aef2LhxIz/88ENsqcuxxx5LWloajRs3LvC6NWrUoHz58nvUS4NiBRAtW7YkLy+vpHuRpKS1aVM0ZHj11XjtyCNh0iTYz1JMSZKkvVq9enXsX/335pBDDmHUqFGsWrWKChUq0KpVK0aNGgVE//J866238pe//IXc3Fxat27N2LFjuffee7ntttvo2bMnRxxxBNdeey0jRozY47WvvPJKXn/9de68805q1qzJgw8+SFZWVuz+ihUr9jpzYpdwOMxdd93FmjVrqFy5MqeccgrDhg0DoHbt2lx55ZU89NBDDBs2jJ49e3L//ffv9XV2HRP6xBNPMGrUKFq3bs111133i8tM9ubiiy/mu+++i82kOOecc+jatStbtmwp0usURbly5Xjuuee47777iEQi1K9fn6uuuooLLrigwHODBg1i1apVsetdM0+WLFmSsN6SVShSjIUzS5YsYeDAgdx9992cfvrpiegrUK+++io33HADI0eO5Le//W1gfeTn5zNv3jxatGhBenp6YH2o9HKMFc5nn0X3e/j883jt9NPh+eehEEv7yjTHmBLJ8aVEc4wlv2T5uV1S4RRrBkR2djZ33303V111FbVq1aJevXp7JHehUIhnnnmmRJqUpKBMnAgDB8LuR2ffeGN0KUYpPyVJkiRJKlHF+vF5xowZXHPNNYTDYbZu3brHkS6SlOp27oRbboHdZyxWqgRPPQX/PTJbkiRJUhEUK4B46KGHOPzww3nkkUf2ONv2QKxZs4a//e1vfPbZZyxatIgff/yRMWPG0LZt2z2efffdd8nNzeXzzz+nSpUqnHHGGVx//fV77FK6detWHnroId588022bNlC48aNueqqq+jYsWOJ9S2pdFm3Dvr1g7feitcaNYru97CXo6slSZIkFcK+dzz5BV999RX9+/cv0fBh1+u+9tprVKxYkQ4dOuzzuZkzZzJkyBDq1avH6NGjufbaa3n99de57LLLCpwFG4lEuOyyy3j99de59tprGT16NPXq1WPIkCHMnDmzRHuXVDrMnQutWhUMH377W/jwQ8MHSZIk6UAUawbEEUccwfbt20u6F9q0aRMLBt5++22mTZu21+dGjhxJ48aNGTVqVGzviYyMDK644greeecdOnfuDESXisyePZtHHnmErl27AtC2bVt69+7Ngw8+yMSJE0v8e5CUup56Cn7/e9j19hYKwV13wR/+AL+wQbUkSZKkQijWj9QXXXQREyZM4IcffijZZgrxE/6aNWtYuHAhPXr0KPB8ly5dyMjIKBBaTJs2jSpVqtClS5cCv0fPnj1ZsGABa9asKdH+JaWm7dujwcPFF8fDh8xMeO01uPVWwwdJkiSpJBRrBkTFihWpWrUqZ511Fr1796ZevXp7PZpo1/mmJWnp0qUANGrUqEA9LS2NrKys2H2AvLw8srKy9gg2di0dWbp0KXXq1Nnn77VixQrmz59foFazZk1q1qx5QN9DYeXn5xf4KJU0xxisXAl9+6bxn/+EYrXjjovw4othjjkGyvB/mhLhGFMiOb6UaI6x5Lf78mtJya9YAcTNN98c+/yxxx7b6zOhUCghAcTGjRuB6JKLn8vIyGDZsmUFnm3YsOFenwPYtGnTL/5eubm55ObmFqj17t2bPn36FLHrA/PzEEQqaWV1jM2ZU5lhw45mw4Z4gHrmmeu55Zav2LIlwrx5wfVW2pTVMaaDw/GlRHOMJa/ly5cH3YKkIihWADFmzJiS7qPIQqHQ/h8qwnN7s7fTMg72DIj58+fTvHnzvc4wkQ5UWR1jkQiMGhXiD38IkZ8ffY8oVy7Cgw9GuPzyTEKhzGAbLEXK6hjTweH4UqI5xpLfypUrg25BUhEUK4A48cQTS7qPQsvMzATiMyF2t2nTptj9Xc/u6znY+yyK3dWvX5/mzZsXt9USk56e7h96SqiyNMa2bIFLLoEXX4zXDj8cXnwxxEknFT+w1C8rS2NMB5/jS4nmGEteB/KPjZIOvgPeWm3Hjh2sXbuWHTt2lEQ/+7Vr74fd93oACIfD5OXlFdgbIisriy+++IJwOFzg2SVLlgDQuHHjBHcrKZksWQJt2xYMH045JXr05kknBdeXJEmSVBYUO4BYuHAhAwYM4IQTTqBz587MnTsXgPXr13PRRRfxwQcflFiTu6tTpw7NmjVj8uTJBYKF6dOns2nTJk4//fRYrWvXrmzevJnp06fHauFwmFdffZVmzZpRu3bthPQoKflMmgRt2sCiRfHa1VfDW29FZ0BIkiRJSqxiLcFYtGgRF1xwAdWqVaNHjx5MmjQpdq9GjRps376dl156iQ4dOhT5tadOnQpEAw6ADz/8kA0bNlCxYkU6deoEwNChQ7nkkksYOnQoOTk5rF69mhEjRtC2bdvYMwCdOnWibdu23HrrrWzevJnDDz+cF198kcWLF/Pkk08W51uXlGJ27oRbboERI+K1ww6Dv/0NzjsvuL4kSZKksqZYAcTDDz9MrVq1eOmll9i+fTsTJ04scL9du3a88cYbxWro6quvLnD95z//GYC6devGZjK0b9+exx57jNzcXAYNGkTlypX5zW9+w9ChQwusAwuFQjz66KM8+OCDjBw5kq1bt9K4cWMee+wx2rdvX6z+JKWOb7+Ffv3g7bfjtays6GyIJNjeRZIkSSpTihVAzJ07l0GDBlGpUqW97v1wxBFH8O233xaroV37M+xPp06dCsx22JfKlSszfPhwhg8fXqx+JKWmWbOgTx9YtSpe69EDnnkG9rP/rCRJkqQEKNYeENu3b6dKlSr7vL9169ZiNyRJByISgUcfhY4d4+FDWhrce2905oPhgyRJkhSMYs2AaNCgQWyPhr2ZNWsWWVlZxW5Kkorjxx9hyBAYOzZeq1EDxo2D3fanlSRJkhSAYs2AOPvss3nllVcKnHSxa++Fv//977z33nv06NGjZDqUpEJYuhTatSsYPrRuDR99ZPggSZIkJYNCz4A49thjGTFiBN27d+fiiy/m3//+N5dccglHH300oVCI++67j++//55169bRoUMHzj///ET2LUkxr7wCAwbA5s3x2qBB8PDDUKFCcH1JkiRJiiv0DIhIJBL7/NBDD+Wpp57ipptuonz58pQvX57ly5dTrVo1brjhBkaPHk1aWrEmV0hSoe3cCcOGQc+e8fChQgV46ikYPdrwQZIkSUomxdoDAqBcuXIMHDiQgQMHlmA7klQ4334L550H/z2dF4Cjj4aJE6FFi8DakiRJkrQPTlOQlHJmzoQTTigYPpx9NsyZY/ggSZIkJasizYCYM2cO+fn5hX6+Z8+eRe1HkvYpEoFHHoHrrosuvwAIheDuu6NLMVz5JUmSJCWvIgUQL7zwAi+88MJ+n4tEIoRCIQMISSVm61a49NLokZq71KgBzz8PXbsG15ckSZKkwilSANG3b19aOL9Z0kG2aBGcc0704y4nnggvvggNGgTXlyRJkqTCK1IA0bp1a7p3756oXiRpDy+8ABdfDD/8EK9dfjk89BCULx9cX5IkSZKKxhXTkpLSTz/BNdfAuefGw4fDDoN//CO6D4ThgyRJkpRain0MpyQlysqV0eDhgw/itezs6BGbTZsG15ckSZKk4nMGhKSkMm0atGxZMHzIyYEPPzR8kCRJklJZoWdALF68OJF9SCrjwmG45x64447ocZsA5crByJFw9dXR4zYlSZIkpS6XYEgK3Pr1cOGFMHVqvHbEEdENKE86Kbi+JEmSJJUcAwhJgfrPf6JLLL7+Ol7r0gWeew5q1QquL0mSJEklyz0gJAUiEoG//AVOPrlg+HDrrfDPfxo+SJIkSaWNMyAkHXRbtsCgQTBuXLxWvTo8+yyceWZwfUmSJElKHAMISQfVwoVwzjmwZEm81ro1vPgiNGwYWFuSJEmSEswlGJIOmrFj4cQTC4YPl18O779v+CBJkiSVds6AkJRw/+//RY/SfPzxeK1SJfjb36Bfv+D6kiRJknTwGEBISqhly6KnXHz0UbzWpAlMmADHHhtcX5IkSZIOLpdgSEqYl16CE04oGD5ceGH06E3DB0mSJKlsMYCQVOJ++gmuvx5694ZNm6K1Qw+F0aNhzJjo8gtJkiRJZYtLMCSVqBUr4NxzYebMeO3oo6OnXJxwQnB9SZIkSQqWMyAklZh//hNatiwYPvTqBXPnGj5IkiRJZZ0BhKQDlp8Pt98OZ54J69dHa+XKwUMPwcSJkJkZaHuSJEmSkoBLMCQdkDVr4Pzz4e2347V69WD8eOjQIbi+JEmSJCUXZ0BIKra3344uudg9fOjWDT7+2PBBkiRJUkEGEJKKLByGe+6B00+PzoAASEuDu++G11+HX/0q2P4kSZIkJR+XYEgqknXr4MILoxtO7lK7Njz/PJx6anB9SZIkSUpuBhCSCu3f/44esblqVbx26qnw3HNQp05wfUmSJElKfi7BkLRf4TCMGAGdOsXDh1AIbrsN/vUvwwdJkiRJ++cMCEm/aP16uOgieO21eO1Xv4Jnn41uOClJkiRJhWEAIWmfZs6MLrlYsSJeO/nk6H4P9eoF15ckSZKk1OMSDEl7iETgoYegY8eC4cPNN0eP3DR8kCRJklRUzoCQVMD338PAgTB5crxWowaMGQNnnRVYW5IkSZJSnAGEpJjZs6NLLr76Kl7r0AHGjYP69YPrS5IkSVLqcwmGJCIRGDUqur/D7uHDDTfAjBmGD5IkSZIOnDMgpDJu06Z0evdOK7Dkonp1eOYZOPvs4PqSJEmSVLoYQEhl2KxZcMEFx7JmTShWa98+uuSiQYMAG5MkSZJU6rgEQyqDdp1y0blzGmvWlI/Vb7gB3nnH8EGSJElSyXMGhFTGFDzlIjrzoXr1CM88E3LJhSRJkqSEcQaEVJotWAA9e0Y/Ah98AC1aFDxi87jjtjJnTtjwQZIkSVJCGUBIpdmUKfDKK4Qnv8aIEdCxI6xYEb993XVhHn98iUsuJEmSJCWcSzCk0mz6dNZRgwGjTuWNdfFyjRrRUy5+85sI8+YF1p0kSZKkMsQAQipNVq2CtWujn0civPdOmPOYx6p19WKPnNxiK88/voV6bQ4nPz+gPiVJkiSVOS7BkEqTAQOgVSvCrVpzb+uJnLpjKquIhw/DuJe352VSb1j/AJuUJEmSVBY5A0IqTYYMYe2cFfTf/Aj/4oxYuSbfMpb+dONNyMyEwYOD61GSJElSmWQAIZUib1XP4YLyvVlLeqzWiRk8x/kcwWro1g3GjIFatQLsUpIkSVJZ5BIMqRTYuRNuuw26doW130XDhxBh7uBO3qILR4TWQCgE2dmGD5IkSZICYQAhpbiVK+G00+CPf4RIJFo7PG0Nb9GF4Zn/R/rtt0JGRvTmuHEQDgfbsCRJkqQyyQBCSmGvvQYtWsB778Vr3U7fybxm/Tm1VzVYsgTuvDP6sVcvaNAAtm0LrF9JkiRJZZd7QEgpaMcOGDYMRo2K19LT4Z574IYbypEWmRot7FKrFkyaBPn5BeuSJEmSdJAYQEgpZtky6NcPPvwwXqtfP7q6okOHXZV9hAyGD5IkSZIC4hIMKYW8+CK0bFkwfOjRA+bN2z18kCRJkqTkYwAhpYBt2+D3v4e+fWHz5mjt0EMhNxdeegmqVw+2P0mSJEnaH5dgSElu8WI491z49NN47ZhjYPx4aNUquL4kSZIkqSicASElsTFjoiHD7uFDv37w0UeGD5IkSZJSiwGElIS2boWLLor++vHHaK1CBXjiCXjuOahaNdj+JEmSJKmoXIIhJZlPP43u9bBkSbzWpEl0yUWzZsH1JUmSJEkHwhkQUpKIRGD0aDjxxILhwyWXRE+9MHyQJEmSlMqcASElgU2bYNAgeOGFeK1y5Wggcf75wfUlSZIkSSXFAEIK2Jw50VMuli2L11q2jC65aNQouL4kSZIkqSS5BEMKSCQCubnQoUPB8OHKK2HmTMMHSZIkSaWLMyCkAGzYABdfDC+/HK9lZMDf/w69ewfWliRJkiQljAGEdJDNmgX9+sFXX8VrJ54I48bBUUcF15ckSZIkJZJLMKSDJByGBx+EU04pGD5cfz28957hgyRJkqTSzRkQ0kGwfj0MHAhTpsRr1avD009D9+5BdSVJkiRJB48BhJRgM2dGT7lYsSJe69AhuuSifv3g+pIkSZKkg8klGFKChMMwciR07FgwfLjxRpgxw/BBkiRJUtniDAgpAdavh4sugtdei9dq1IAxY+Css4LrS5IkSZKCYgAhlbAPPoguuVi5Ml476aTokot69YLrS5IkSZKC5BIMqYREItFTLjp2LBg+3HwzvP224YMkSZKkss0ZEFIJ+P776CkXkyfHazVqwNixcOaZgbUlSZIkSUnDAEI6QP/5D/TtC199Fa+55EKSJEmSCnIJhlRMkQjk5sLJJxcMH2680SUXkiRJkvRzzoCQimHTJrjkEpg4MV6rVi16ysXZZwfXlyRJkiQlKwMIqYg+/hhycuCLL+K1tm1h/Hg48sjg+pIkSZKkZOYSDKmQIhF4/HFo375g+HDNNfDuu4YPkiRJkvRLnAEhFcIPP8Dvfx891WKXqlXhqaegd+/g+pIkSZKkVGEAIe3HokXRJRcLF8ZrLVvCiy/CMccE15ckSZIkpRKXYEi/4PnnoU2bguHDoEHwwQeGD5IkSZJUFAYQ0l5s3w6XXQbnnx9dfgFw2GHRJRijR0OFCsH2J0mSJEmpxiUY0s8sXx5dcjFnTrz261/DhAnQtGlgbUmSJElSSnMGhLSb116DE04oGD6cdx58+KHhgyRJkiQdCAMICcjPh1tugbPPhg0borVDD4VHH4V//AMqVw62P0mSJElKdS7BUJm3dm10r4fp0+O1Bg2iSy7atAmuL0mSJEkqTZwBoTLt/fejR2ruHj6ceSZ89JHhgyRJkiSVJAMIlUmRCIwaBZ07w+rV0VpaGvzxjzBlCtSoEWh7kiRJklTquARDZc7mzXDxxTBxYrxWsyY8/zx06RJcX5IkSZJUmhlAqEyZPx/OOQeWLo3XOnSAF16AunWD60uSJEmSSjuXYKjMGDsW2rYtGD5ccw3MmGH4IEmSJEmJ5gwIlXrbt0eDhr/+NV6rXBmefBL69g2sLUmSJEkqUwwgVKp99RXk5MCHH8ZrTZpE93/49a+D60uSJEmSyhqXYKjU+uc/4YQTCoYP558Ps2cbPkiSJEnSwWYAoVInHIY774Qzz4Tvv4/WDjkEHnkEnn02uvxCkiRJknRwuQRDpcr69XDhhTB1arxWrx5MmBDdgFKSJEmSFAwDCJUaH34IffrA11/Ha127wj/+ATVrBteXJEmSJMklGCoFIhEYPRpOPrlg+HDbbfDGG4YPkiRJkpQMDCCUWhYsgJ49ox+BH3+EgQNhyBDYsSP6SLVqMGUK3HUXpKcH1qkkSZIkaTcuwVBqmTIFXnkF2rdnaflm9OkDn34av92yZfSIzaOOCq5FSZIkSdKenAGh1DJ9OgAvj9tG69YFw4dLL4UPPjB8kCRJkqRk5AwIJbdVq2Dt2ujnkQg73/2AW7ifEfNuij1SoXyYR+/dxO+uqxZQk5IkSZKk/TGAUHIbMCA262EttTiXKbxD59jtY8hjwvY+tHj9V3DdtICalCRJkiTtj0swlNyGDIHMTP5NB1rycYHw4be8whxa0yLzKxg8OLgeJUmSJEn7ZQChpBbpk8PD139N59A7rOYIANLI535u4iV6kdmtHSxZAjk5AXcqSZIkSfolKbkEY/bs2QwYMGCv92bOnEn16tVj1++++y65ubl8/vnnVKlShTPOOIPrr7+eypUrH6x2VUxbt0Y3lhw3rkqsVpNvGUc/TgvNiBays6FWrWAalCRJkiQVWkoGELsMHTqUNm3aFKhVrVo19vnMmTMZMmRILHRYtWoVI0eO5IsvvuCZZ54hFAod7JZVSEuWQO/e8Nln8Vo7ZvJi1f+l3jV9IPdj2LgRxo2DP/0J0pzMI0mSJEnJLKUDiKOOOooWLVrs8/7IkSNp3Lgxo0aNIu2/f0HNyMjgiiuu4J133qFz584Hp1EVycSJ8LvfwZYt8doVNcfzUIeJHPr429EZD5dfHt0fYsUK2LYNKlUKrmFJkiRJ0n6V2n82XrNmDQsXLqRHjx6x8AGgS5cuZGRkMG2aJyYkm5074aaboE+fePhw2GHw7LPw59V9OPTlF+LLLWrVgkmTYNYswwdJkiRJSgEpHUDcfvvtNGnShNatW3PZZZfx+eefx+4tXboUgEaNGhX4mrS0NLKysmL3lRzWrYMzz4QRI+K1rKxovnDBBUB6+t6/cF91SZIkSVJSScklGFWqVGHgwIG0adOGjIwM8vLyePzxxzn33HMZP348jRs3ZuPGjUB0ycXPZWRksGzZsv3+PitWrGD+/PkFajVr1qRmzZol8n3sT35+foGPpdXHH0OfPml89VV8T47u3SM8/XSYjAwo5d9+oMrKGFNwHGNKJMeXEs0xlvwikUjQLUgqgpQMIJo0aUKTJk1i123atKFTp06cffbZPPLII+Tm5sbuHchGk7m5uQVeC6B379706dOn2K9ZHD8PQUqT116rzr33Hsn27dH/T6FQhMGDv+Hii9fw5ZcBN1eGlOYxpuTgGFMiOb6UaI6x5LV8+fKgW5BUBCkZQOzNEUccQatWrfj4448ByMzMBIjNhNjdpk2bYvd/yVVXXUXHjh0L1A72DIj58+fTvHlz0kvZUoOffoIbbgjxyCPxVUCZmRHGjAlz1ll1gDrBNVeGlOYxpuTgGFMiOb6UaI6x5Ldy5cqgW5BUBKUmgIDoFKxdG07u2vth6dKlnHzyybFnwuEweXl5nHHGGft9vfr169O8efPENFsE6enppeoPvbVrIScH3nsvXmveHCZNCpGVVXq+z1RS2saYko9jTInk+FKiOcaS14HMdpZ08KX0JpS7W7lyJXPnzuX4448HoE6dOjRr1ozJkycTDodjz02fPp1NmzZx+umnB9VqmfbRR9CmTcHw4dxzYebM6KaTkiRJkqTSKSVnQFx//fXUr1+fpk2bUqVKFfLy8njiiSdIS0vj6quvjj03dOhQLrnkEoYOHUpOTg6rV69mxIgRtG3blk6dOgX4HZRN48bBxRfDtm3R67Q0eOABuP56MLyWJEmSpNItJQOI7OxsXnvtNZ599lm2bdtGtWrVaNeuHVdccQUNGzaMPde+fXsee+wxcnNzGTRoEJUrV+Y3v/kNQ4cOdbrWQZSfD7feCvffH69Vqwbjx0PXrsH1JUmSJEk6eFIygBg0aBCDBg0q1LOdOnVytkOANm2CCy6A116L1449Fl591SUXkiRJklSWlJo9IJR8li6Fdu0Khg/du8OsWYYPkiRJklTWGEAoIaZPhxNPhMWL47VbboGXX4aqVQNrS5IkSZIUkJRcgqHk9uSTMGQI7NwZva5YEZ5+Gvr2DbQtSZIkSVKADCBUYsJhGDYMRoyI1+rVi+730LJlcH1JkiRJkoJnAKES8eOP0L8/TJoUr7VqFQ0fjjgiuL4kSZIkScnBAEIHbPVq+O1vYc6ceK1nT3j2WahUKbC2JEmSJElJxE0odUA++QTati0YPtx4I0ycaPggSZIkSYpzBoSKbfr06EyHLVui1+XKwaOPwqWXBtqWJEmSJCkJGUCoWF54Ibrnw44d0euMjOishy5dgu1LkiRJkpScXIKhIvvLX6Bfv3j4UL8+fPCB4YMkSZIkad8MIFRokQjcdhtccUX0c4CmTaPhQ5MmwfYmSZIkSUpuLsFQoezcCb//Pfztb/HaSSfB5MlQrVpwfUmSJEmSUoMzILRvCxZAz55sm7OQPn0Khg/du8Obbxo+SJIkSZIKxwBC+zZlClteeYvf9K3CK6/EyxdfDJMmwWGHBdeaJEmSJCm1GEBonza/OYszeYN3v2wQq/3hD9GZEOVcvCNJkiRJKgL/Gqm4Vatg7VoANm0JceaMm5lJu9jtUdet5NpzvoVvakPdukF1KUmSJElKQc6AUNyAAdCqFRtbncYZnXcwMxIPHx7hcq4dVR9atYKLLgqwSUmSJElSKjKAUNyQIWyoeiRd+Rf/oW2s/FcGczmPRi8yM2Hw4GD6kyRJkiSlLAMIxXzfJYfTGy5lDm0ACBHmCf6XwTwefaBbN1iyBHJyAuxSkiRJkpSK3ANCAKxfD6efDvM+PQSIhg9P8r/8jqcgFIo+lJ0NtWoF2KUkSZIkKVU5A0Js2QJnnAHz5kWvQ4R5it/xu8yX4PbbISMDIhEYNw7C4UB7lSRJkiSlJgOIMm7HDjjnHPjoo+h1WlqEMQ3v4KJeW6LLLe68M/qxVy9o0AC2bQu2YUmSJElSSnIJRhkWDsMll8C//hWvPflkiAv7D4f09HixVi2YNAny8wvWJUmSJEkqJAOIMmzYMHj22fj1vffCwIEA+wgZDB8kSZIkScXkEowyKjcXRoyIX19+Odx8c3D9SJIkSZJKNwOIMuiFF+Caa+LX55wDDz8cP+xCkiRJkqSSZgBRxsyYAf37Rw+1ADjllOgyDFdXSJIkSZISyQCiDJk/H3r2jJ58AdC0KbzyClSoEGhbkiRJkqQywACijNi8ORo+bNoUva5bF954A6pVC7QtSZIkSVIZYQBRBkQiMHgwLFsWvc7IgKlToX79YPuSJEmSJJUdBhBlwFNPwbhx8eu//x2aNQuuH0mSJElS2WMAUcotWgRXXhm/HjIEevcOrh9JkiRJUtlkAFFaLVjA/+ueQ7+e2/jxx2ipWTMYNSrYtiRJkiRJZVO5oBtQgkyZwtApnfiUigBUrAjjx0c/SpIkSZJ0sDkDopR6adx2/sIVsevcXGjSJMCGJEmSJEllmjMgSotVq2DtWgC+Xl2Oiz+5Knarb9fvuaTlcvgIqF07eganJEmSJEkHkQFEaTFgAEyfzk7SOZ8ZbKQaAA35ksf/1YLQvzZHn+vSBaZNC7BRSZIkSVJZ5BKM0mLIEMjM5AFu4t+cDEA5fmIc/cjgv+FDZiYMHhxcj5IkSZKkMssZEKVFTg7fNunMfcdXhvxo6Y/cSlv+E73o1g3GjIFatYLrUZIkSZJUZhlAlCL3/a0mP/w3fDiOT7iBkRAKRQvZ2YYPkiRJkqTAuASjlPj6a3j00Ujs+p5K95F2+22QkQGRCIwbB+FwgB1KkiRJksoyA4hS4u67YceO6GyHDtUX8z9f5MKdd8KSJdCrFzRoANu2BdylJEmSJKmscglGKfD55/DUU/Hreyf9mlDt/17UqgWTJkF+PqSnB9KfJEmSJEnOgCgFbr89mi9AdK/JTp328pDhgyRJkiQpQAYQKe7jj2H8+Pj1PfcE14skSZIkSftiAJHibr01/nmfPtCqVXC9SJIkSZK0LwYQKez99+H116Ofp6XBXXcF248kSZIkSftiAJGiIhEYNix+fdFFcOyxwfUjSZIkSdIvMYBIUVOnRmdAABx6KNxxR7D9SJIkSZL0SwwgUlA4DLfcEr8eMgSOPDK4fiRJkiRJ2h8DiBT00sNf8/HH0c8POwz+8Idg+5EkSZIkaX8MIFLQy2M2xz6/+mqoXTvAZiRJkiRJKgQDiBSU92X8f9uZZwbYiCRJkiRJhVQu6AZUCKtWwbp10c8jEfI2NYjdyvrxE/goP3pRuzbUrRtAg5IkSZIk/TIDiBSQNnAgvP02ABvJYB0bATiMH6jzmxbxB7t0gWnTDnp/kiRJkiTtj0swUkB48GDIzATgC46J1bPII7TrIjMTBg8+2K1JkiRJklQoBhCpoE8fWLIEunUjj6xYOYu86CfdukXv5+QE1KAkSZIkSb/MACJV1KoFjRuTR6NYKYsvIBSC7OzofUmSJEmSkpQBRKoIh2H8ePJ2X4JRcRVEIjBuXPS+JEmSJElJyk0oU8W2bVC/Pnn5bWF9tJT17HB4dgWsWBG9X6lSoC1KkiRJkrQvBhCp4ssvoW5d8r7abQlGm2rQexLk50N6eoDNSZIkSZL0ywwgUsWUKWx99S3WcAgA5ctD3br/vWf4IEmSJElKcu4BkSqmTy9wBOcxx0Ca//ckSZIkSSnCGRDJatUq+OYbKn7+eXSJxXvvkcdZsdtZNTfCR8ugdu3dpkJIkiRJkpScDCCS1YABpE+fTpNd16EQeWTFbme98yS0GgpdusC0aYG0KEmSJElSYTmJP1kNGUIkI4PIrutIpEAAcQxfQGYmDB4cRHeSJEmSJBWJAUSyyskhvGgRm9u1i4UQBWZAtMqEJUsgJyeQ9iRJkiRJKgoDiGRWqxbbjzwSQqE9l2A0qwC1agXYnCRJkiRJhWcAkczCYaq9+SahSIRtVWuzkvoAlOMnGrz+VwiHA25QkiRJkqTCMYBIZtu2saN2bSI9e7Ls1QWx8lGVvqPckXVh27YAm5MkSZIkqfAMIJJZpUosfuYZwhMmkLehRqyc1fEImDULKlUKsDlJkiRJkgrPACLJVfjyS9J69ybv/dWxWlYWkJ4eXFOSJEmSJBWRAUSSy3j/fUKvvkree2titaysX/gCSZIkSZKSkAFEkqvy4YcA5C2NxGoGEJIkSZKkVFMu6Ab0M6tWwdq10c937qTKvHkA5G2oHnska/tCWJUJdese/P4kSZIkSSoGA4hkM2AATJ8OQDoQCYXYzqF8HYkewZlGPg17t4QuHWHatAAblSRJkiSp8FyCkWyGDIHMzNhlKBJhOQ0JE9108ki+4tDMSjB4cEANSpIkSZJUdAYQySYnB5YsgW7d2LXrQx7xTR+yamyI3s/JCaY/SZIkSZKKwQAiGdWqBY0bQyhEhJ8FEEdsi96XJEmSJCmFGEAko3AYxo8nFIkQSU8vGEB8+Wb0viRJkiRJKcQAItmsWgUzZ0KNGkQ6d4afBxDVvo/eX7UquB4lSZIkSSoiA4hkM2AAnHwyLFpEaMYMQj/9VDCAWDE9ev+iiwJsUpIkSZKkojGASDY/OwVjZySd5TQEIESYo1kWve8pGJIkSZKkFGIAkWx+dgrG1zRgJ4cAUI+VVOjW2VMwJEmSJEkpxwAiGe12CsZSGsXKWeRBdranYEiSJEmSUo4BRDLa7RSMvLTGsXIWeTBunKdgSJIkSZJSTrmgG9BebNsG9esT+dWv+OKzo2LlrKYVoGKD6P1KlQJsUJIkSZKkonEGRDKqVAlmz4Yjjih4AsZdA2DWLMMHSZIkSVLKcQZEMlm1CtaujX4eicD775NHbux21k+L4JNtULs21K0bUJOSJEmSJBWdAUQyGTAApk+PXeaTzjKOjl0f06818CN06QLTpgXQoCRJkiRJxeMSjGQyZAhkZsYuV1GXHZQH4HC+oRI/Ru8PHhxMf5IkSZIkFZMBRDLJyYElS6BbN4CC+z+QF60vWRJ9TpIkSZKkFGIAkUxWrYKVK6OzHEIhlhYIIL6AatWi91etCq5HSZIkSZKKwQAimQwYAK1awfjxEIn8bAbEUhg3Lnr/oosCbFKSJEmSpKIzgEgmQ4ZARkbs8oufL8GA6H33gJAkSZIkpRgDiGSSkwOffw5nnAHsZQ+IM86I3ncPCEmSJElSijGASDa1akF2NmHS+IJjYuVjWAa//nX0viRJkiRJKcYAItmEwzB+PKupwzYOA6Am35LBpugeEOFwwA1KkiRJklR0BhDJZNUqmDkTatQgr0V8mUVW6As49VT41a+i9z0FQ5IkSZKUYsoF3YB2M2AATJ8OQB7tY+WsyFJ4++3oxcknQ5cuMG1aEB1KkiRJklQszoBIJkOGQGYmACEisfJxfBp/JjPTUzAkSZIkSSnHGRDJJCcHOnWCAQPo+c+XmcbpRAgxhL9G73frBmPGuBGlJEmSJCnlGEAkm1q1oHFjqr/5Jv/gQkKRCJFQCAhBdrbhgyRJkiQpJbkEI9n89xQMIhHIyOCbSy+FjIzotadgSJIkSZJSlAFEstm2DerXh169CH/2GasHDyb82WfQqxc0aBC9L0mSJElSinEJRrKpVAlmz4b0dMjPh2++iS67mDQpep2eHnSHkiRJkiQVmTMgktG+QgbDB0mSJElSijKAkCRJkiRJCWcAIUmSJEmSEq5MBBBbt27lzjvv5KSTTuK4446jT58+vPvuu0G3JUmSJElSmVHqA4hIJMJll13G66+/zrXXXsvo0aOpV68eQ4YMYebMmUG3J0mSJElSmVDqT8GYMWMGs2fP5pFHHqFr164AtG3blt69e/Pggw8yceLEgDuUJEmSJKn0K/UzIKZNm0aVKlXo0qVLrJaWlkbPnj1ZsGABa9asCbA7SZIkSZLKhlI/AyIvL4+srCzS0gpmLdnZ2QAsXbqUOnXq7PVrV6xYwfz58wvUatasSc2aNRPT7M/k5+cX+CiVNMeYEs0xpkRyfCnRHGPJLxKJBN2CpCIo9QHExo0badiw4R71jIwMADZt2rTPr83NzSU3N7dArXfv3vTp06dEe9yfn4cgUklzjCnRHGNKJMeXEs0xlryWL18edAuSiqDUBxAAoVCoWF931VVX0bFjxwK1gz0DYv78+TRv3pz09PSD8nuqbHGMKdEcY0okx5cSzTGW/FauXBl0C5KKoNQHEJmZmWzcuHGP+q6ZD7tmQuxN/fr1ad68eaJaK7T09HT/0FNCOcaUaI4xJZLjS4nmGEtexf2HRknBKPWbUGZlZfHFF18QDocL1JcsWQJA48aNg2hLkiRJkqQypdQHEF27dmXz5s1Mnz49VguHw7z66qs0a9aM2rVrB9idJEmSJEllQ6lfgtGpUyfatm3LrbfeyubNmzn88MN58cUXWbx4MU8++WTQ7UmSJEmSVCaU+gAiFArx6KOP8uCDDzJy5Ei2bt1K48aNeeyxx2jfvn3Q7UmSJEmSVCaU+gACoHLlygwfPpzhw4cH3YokSZIkSWVSqd8DQpIkSZIkBc8AIomtXr2a0aNHs3r16qBbUSnlGFOiOcaUSI4vJZpjTJJKlgFEElu9ejVPPPGEf+gpYRxjSjTHmBLJ8aVEc4xJUskygJAkSZIkSQlnACFJkiRJkhLOAEKSJEmSJCVcmTiGs6h++OEHAD766KNA+1i2bBlVqlTh3//+t2sPlRCOMSWaY0yJ5PhSojnGkt+un9d3/fwuKbmFIpFIJOgmks3w4cN5/vnng25DkiRJUiGcd955DB8+POg2JO2HMyD2YsCAAQBkZ2dTqVKlgLuRJEmStDc//PADS5Ysif38Lim5OQNCkiRJkiQlnJtQSpIkSZKkhDOAkCRJkiRJCWcAkYQ2b97M5ZdfTp06dahYsSInnngiU6dODbotlRIzZswgFArt9de6deuCbk8pZuXKlVx99dWccsopVK5cmVAoxIwZM/b67NSpUznxxBOpWLEiderU4fLLL2fz5s0Ht2GlnMKOsc6dO+/1fe2KK644+E0rZbz11lsMHDiQxo0bc9hhh1G/fn369u3LokWL9njW9zBJOnAGEEkmEonQs2dPxo8fz7333suUKVM46qijOPvss5k+fXrQ7akUeeCBB5g5c2aBX5mZmUG3pRSTl5fH888/z2GHHUbXrl33+dxbb73F2WefzVFHHcWUKVO49957GT9+PD179sStiPRLCjvGABo1arTH+9rQoUMPUqdKRX/9619ZuXIlQ4cO5Y033mDEiBEsXryY1q1bM3/+/NhzvodJUslwE8okM2XKFLp3786kSZPo1asXAOFwmFatWlGuXDk+/PDDgDtUqpsxYwannnoqL730Ej179gy6HaW4cDhMWlo0y971/vX222/TuXPnAs+1bt2a/Px85s6dG3v+pZdeonfv3kyZMoX/+Z//OditK0UUdox17tyZjRs3Mm/evIPfpFLWt99+S61atQrUvvvuO4488kjOO+88nnzyScD3MEkqKc6ASDIvv/wyGRkZ9OjRI1ZLS0tjwIABzJkzh5UrVwbYnSQVtOsH8V+ycuVK5s6dS//+/Qs836NHD6pVq8bLL7+cwA6V6gozxqTi+nn4AFCzZk0aNmwY+5nL9zBJKjn+qZ5kFi5cSJMmTfb4geu4446L3ZdKwuDBgylXrhyZmZn07NmTBQsWBN2SSqld71vNmjUrUE9LS6Np06aOPZWYJUuWUK1aNcqVK0d2djYjR44kPz8/6LaUYlatWkVeXh5NmzYFfA+TpJJULugGVND69etp3LjxHvXq1asD8P333x/sllTKZGRkcO2119KpUyeqVavGZ599xn333Uf79u2ZOXPmHj9gSQdq/fr1QPx9bHfVq1dn8eLFB7sllUKnnHIK5513HtnZ2WzdupWJEydy00038dlnn/HUU08F3Z5SRDgc5tJLL6V8+fJcc801gO9hklSSDCCSUCgUCroFlWItW7akZcuWseuOHTty1lln0axZM4YPH86ECRMC7E6lme9tSqS77767wPXZZ59N1apVyc3N5brrrqN58+YBdaZUcuWVV/LPf/6TcePG0aBBgwL3fA+TpAPnEowkU6NGjVjSvrtdMx/2lr5LB6pBgwacfPLJzJw5M+hWVArVqFEDYJ/vbb6vKVEuuOACAGbNmhVwJ0oF1113HY8++ih//etfycnJidV9D5OkkmMAkWSaNm3KokWLCIfDBeq7joJyerwSJRKJuNmbEmLXOuqfr5MOh8MsXLjQ9zUlzK6Dvnxv0/7cdNNN/OlPf+L//u//uPTSSwvc8z1MkkqOfyInmV69erFx40YmT54cq4XDYcaOHUvr1q2pW7dugN2ptFq+fDnvv/8+7dq1C7oVlUL16tWjdevW/OMf/ygQrk6ePJkNGzZ4HKwSZuzYsQC+t+kX3XbbbYwYMYIRI0Zw9dVX73Hf9zBJKjnuAZFkzjrrLE499VT+93//lw0bNtCgQQOeeOIJPvnkE958882g21MpcMEFF3D00UdzwgknkJmZycKFC3nggQdIS0vjrrvuCro9paBd+4bMnTsXgHfeeYd169ZRqVIlzjzzTADuv/9+unXrxgUXXMCll17K119/zQ033MCpp57KWWedFVjvSg37G2PvvfceDzzwAL179+bII49k69atTJgwgWeffZZLL7009i/Y0s89+OCD/PGPf6R3796ccsopBZbrlC9fPrZnku9hklQyQpFd8xOVNDZv3szNN9/MhAkT2Lx5M82bN+euu+6K/SAvHYj777+fcePGsXz5cn744Qdq1qzJaaedxh133EGjRo2Cbk8paF8bsx155JEsX748dv3GG29w++23M3/+fKpWrUqfPn24//77qVq16kHqVKlqf2MsLy+Pq666ik8++YR169ZRrlw5jj32WC655BKGDBni5oHap86dO/POO+/s9Z7vYZJU8gwgJEmSJElSwrkHhCRJkiRJSjgDCEmSJEmSlHAGEJIkSZIkKeEMICRJkiRJUsIZQEiSJEmSpIQzgJAkSZIkSQlnACFJkiRJkhLOAEKSJEmSJCWcAYQkKSmcdtpp9O/fP+g29qp///5kZ2eTnZ1N8+bNi/z13333Xezrs7OzufnmmxPQpSRJUnIrF3QDkqTSKTs7u9DPLlmyJIGdlIxq1aoxbNgw0tPTi/y1VatWZcSIEQDceOONJd2aJElSSjCAkCQlxK6/cO8yd+5cxo8fz7nnnkurVq32eH7q1KkHq7ViOeyww+jRo0exvrZ8+fKxrzWAkCRJZZUBhCQpIX7+l/X8/HzGjx9PixYt9voX+UMPPfRgtSZJkqQAuAeEJCkp7G0PiF37JcyePZt+/fpx/PHH07FjRx5//HEANm3axB/+8Afat2/P8ccfz+DBg1m7du0er71lyxZGjhxJ165dadasGe3ateO6665jxYoVB9z36tWrGTZsGKeeeirNmjWjffv29OvXj5dffvmAX1uSJKk0cQaEJCmpffbZZ7z99tv07duX3/72t7zxxhs89NBDHHroobzyyivUrVuXK664gq+//pqxY8dy00038fTTT8e+fsuWLfTr149vvvmGc845h0aNGvHdd9/x3HPPkZOTw8SJE6lbt26xetu5cye/+93v2LRpE+eeey716tVj8+bNLF68mI8++oiePXuWzH8ESZKkUsAAQpKU1D7//HPGjx/P8ccfD0CfPn047bTTuP/++7nwwgu59dZbCzz/9NNPs2zZMo4++mgAHn74YVasWMELL7zAr3/969hzvXr1onv37vz5z3/m/vvvL1ZveXl5fPnll/zpT3/irLPOKuZ3KEmSVDa4BEOSlNRatGgRCx8guldE8+bNiUQieyzZaN26NQBfffUVAJFIhMmTJ9OmTRtq1arF999/H/tVsWJFWrRowfvvv1/s3qpUqQLAu+++y5YtW4r9OpIkSWWBMyAkSUmtfv36e9QyMjIAqFevXoF61apVAdi4cSMA33//PRs3buT999+nffv2e339tLTiZ/F169ZlyJAhPP7440yZMoVmzZpx4okn0q1bN5o2bVrs15UkSSqNDCAkSUktPT29yPcikUiBjx06dODSSy8t+eaAa6+9lj59+jBjxgzmzJnDuHHjePzxxxk0aBDXXXddQn5PSZKkVGQAIUkqtapXr07VqlXZunUrHTp0SNjvU79+ffr370///v3Zvn07l1xyCaNHj2bgwIFUr149Yb+vJElSKnEPCElSqZWWlkb37t359NNPmTp16l6fWb9+fbFff8uWLfz0008FauXLl49tgLlrKYgkSZKcASFJKuWuvfZaPvroI6655hrOPPNMjj/+eA455BC++eYb3n33XZo2bVrsUzBmz57NbbfdxhlnnMFRRx1FpUqVWLBgARMmTOD444+PBRGSJEkygJAklXJVqlTh+eef5+9//ztTp07lrbfeIj09nTp16tCqVStycnKK/drZ2dl07dqV//znP0yePJlwOMzhhx/O4MGDufjii0vwu5AkSUp9ociuHbokSdJe9e/fnxUrVjBp0iRCoRDVqlUr0tdHIhE2bNgAQPv27enVq1exZ11IkiSlKmdASJJUCKtXr6Z9+/YceuihzJ8/v0hfu27dOk4++eQEdSZJkpQanAEhSdJ+LFiwgM2bNwPRjS3btWtXpK/fsWMHc+bMiV3XqlWLrKysEu1RkiQp2RlASJIkSZKkhPMYTkmSJEmSlHAGEJIkSZIkKeEMICRJkiRJUsIZQEiSJEmSpIQzgJAkSZIkSQlnACFJkiRJkhLOAEKSJEmSJCWcAYQkSZIkSUo4AwhJkiRJkpRw/x8EarB6DCx8HgAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| after optimization: dx_cable = 0.036662848004065086, aspect ratio = |\n", - "| 1.2870417543471038 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Adjust aspect ratio: dx_cable = 0.036846950948268814, aspect ratio = |\n", - "| 1.3000000000000003 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 12 + "outputs": [], + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:27.917456Z", - "start_time": "2024-10-18T17:30:27.914488Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "###########################################################\n", @@ -589,15 +380,10 @@ ], "id": "256e6bf3855c8dff", "outputs": [], - "execution_count": 13 + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:28.517221Z", - "start_time": "2024-10-18T17:30:27.961063Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# case parameters\n", @@ -629,41 +415,8 @@ "bluemira_print(f\"New number of conductors: {case.n_conductors}\")" ], "id": "45b647fb60e42429", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 2.0/170 have been added to complete the last winding pack |\n", - "| (nx=17, ny=10). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Previous number of conductors: 168.0 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| New number of conductors: 170 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 14 + "outputs": [], + "execution_count": null }, { "metadata": {}, @@ -672,12 +425,7 @@ "id": "dfe3620b106d67ed" }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:29.124236Z", - "start_time": "2024-10-18T17:30:28.540278Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# Optimization parameters\n", @@ -725,335 +473,19 @@ " plt.show()" ], "id": "5cd70786e1c5e361", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;208m+-------------------------------------------------------------------------+\n", - "| WARNING: 8.0/176 have been added to complete the last winding pack |\n", - "| (nx=22, ny=8). |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| pm: 48097255.74283442, fz: 163276267.89337236, T: 5.7, B: |\n", - "| 10.994616332819724, allowable_sigma: 512820512.8205128 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n", - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| bounds: [0.1, 2] |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Internal optimazion - iteration 1\n", - "before optimization: conductor jacket area = 0.001703815187400281\n", - "Optimal dx_jacket: 0.005085979267589973\n", - "Averaged sigma in the x-direction: 514.3521612867105 MPa\n", - "226070.8532465582\n", - "after optimization: conductor jacket area = 0.0007665864416300967\n", - "before optimization: case dy_vault = 0.6\n", - "after optimization: case dy_vault = 0.3972599551567517\n", - "err_dy_jacket = 0.5500765298378568\n", - "err_dy_vault = 0.5103460396939596\n", - "tot_err = 1.0604225695318164\n", - "Internal optimazion - iteration 2\n", - "before optimization: conductor jacket area = 0.0007665864416300967\n", - "Optimal dx_jacket: 0.0041096168242123286\n", - "Averaged sigma in the x-direction: 511.4847569151833 MPa\n", - "152009.78620165147\n", - "after optimization: conductor jacket area = 0.0006033738847477704\n", - "before optimization: case dy_vault = 0.3972599551567517\n", - "after optimization: case dy_vault = 0.4142671608339444\n", - "err_dy_jacket = 0.21290822276384816\n", - "err_dy_vault = 0.041053714330038\n", - "tot_err = 0.25396193709388615\n", - "Internal optimazion - iteration 3\n", - "before optimization: conductor jacket area = 0.0006033738847477704\n", - "Optimal dx_jacket: 0.0036265972891163915\n", - "Averaged sigma in the x-direction: 514.1360542202177 MPa\n", - "119221.90068381246\n", - "after optimization: conductor jacket area = 0.0005254500940031694\n", - "before optimization: case dy_vault = 0.4142671608339444\n", - "after optimization: case dy_vault = 0.4276533619789009\n", - "err_dy_jacket = 0.12914677402250455\n", - "err_dy_vault = 0.03130152206219992\n", - "tot_err = 0.16044829608470446\n", - "Internal optimazion - iteration 4\n", - "before optimization: conductor jacket area = 0.0005254500940031694\n", - "Optimal dx_jacket: 0.0034064935432520835\n", - "Averaged sigma in the x-direction: 514.6851043912599 MPa\n", - "104130.72752879365\n", - "after optimization: conductor jacket area = 0.0004905605947881787\n", - "before optimization: case dy_vault = 0.4276533619789009\n", - "after optimization: case dy_vault = 0.4316397811250321\n", - "err_dy_jacket = 0.06639926343752879\n", - "err_dy_vault = 0.009235523045954944\n", - "tot_err = 0.07563478648348373\n", - "Internal optimazion - iteration 5\n", - "before optimization: conductor jacket area = 0.0004905605947881787\n", - "Optimal dx_jacket: 0.003324123643457865\n", - "Averaged sigma in the x-direction: 511.9433091704933 MPa\n", - "97116.29964924548\n", - "after optimization: conductor jacket area = 0.00047760348110165445\n", - "before optimization: case dy_vault = 0.4316397811250321\n", - "after optimization: case dy_vault = 0.43310532352337405\n", - "err_dy_jacket = 0.026412870956581933\n", - "err_dy_vault = 0.003383801395973522\n", - "tot_err = 0.029796672352555453\n", - "Internal optimazion - iteration 6\n", - "before optimization: conductor jacket area = 0.00047760348110165445\n", - "Optimal dx_jacket: 0.0032663223970235632\n", - "Averaged sigma in the x-direction: 514.8539942746442 MPa\n", - "94518.22015277074\n", - "after optimization: conductor jacket area = 0.000468543522823692\n", - "before optimization: case dy_vault = 0.43310532352337405\n", - "after optimization: case dy_vault = 0.434163077739628\n", - "err_dy_jacket = 0.018969623623899267\n", - "err_dy_vault = 0.0024363062417948667\n", - "tot_err = 0.021405929865694135\n", - "Internal optimazion - iteration 7\n", - "before optimization: conductor jacket area = 0.000468543522823692\n", - "Optimal dx_jacket: 0.0032487505774551266\n", - "Averaged sigma in the x-direction: 513.5448966863272 MPa\n", - "92698.73794725668\n", - "after optimization: conductor jacket area = 0.00046579455555420275\n", - "before optimization: case dy_vault = 0.434163077739628\n", - "after optimization: case dy_vault = 0.4344977584191138\n", - "err_dy_jacket = 0.005867047852721333\n", - "err_dy_vault = 0.0007702702096865635\n", - "tot_err = 0.006637318062407896\n", - "Internal optimazion - iteration 8\n", - "before optimization: conductor jacket area = 0.00046579455555420275\n", - "Optimal dx_jacket: 0.0032432671784032707\n", - "Averaged sigma in the x-direction: 513.1632034826006 MPa\n", - "92145.15956182206\n", - "after optimization: conductor jacket area = 0.0004649372283619356\n", - "before optimization: case dy_vault = 0.4344977584191138\n", - "after optimization: case dy_vault = 0.4345971836353921\n", - "err_dy_jacket = 0.0018405693712909461\n", - "err_dy_vault = 0.00022877556510289833\n", - "tot_err = 0.0020693449363938443\n", - "Internal optimazion - iteration 9\n", - "before optimization: conductor jacket area = 0.0004649372283619356\n", - "Optimal dx_jacket: 0.0032415557150168935\n", - "Averaged sigma in the x-direction: 513.0454643960619 MPa\n", - "91973.1852881221\n", - "after optimization: conductor jacket area = 0.0004646696910185549\n", - "before optimization: case dy_vault = 0.4345971836353921\n", - "after optimization: case dy_vault = 0.4346282242978704\n", - "err_dy_jacket = 0.0005754268040081244\n", - "err_dy_vault = 7.141888341103928e-05\n", - "tot_err = 0.0006468456874191636\n", - "Internal optimazion - iteration 10\n", - "before optimization: conductor jacket area = 0.0004646696910185549\n", - "Optimal dx_jacket: 0.0032410210653212226\n", - "Averaged sigma in the x-direction: 513.0087751285605 MPa\n", - "91919.5196358774\n", - "after optimization: conductor jacket area = 0.0004645861189484285\n", - "before optimization: case dy_vault = 0.4346282242978704\n", - "after optimization: case dy_vault = 0.4346379219849359\n", - "err_dy_jacket = 0.0001798526388566911\n", - "err_dy_vault = 2.231210526040317e-05\n", - "tot_err = 0.0002021647441170943\n", - "Internal optimazion - iteration 11\n", - "before optimization: conductor jacket area = 0.0004645861189484285\n", - "Optimal dx_jacket: 0.0032408540049125483\n", - "Averaged sigma in the x-direction: 512.9973184411222 MPa\n", - "91902.75587103948\n", - "after optimization: conductor jacket area = 0.00046456000589983133\n", - "before optimization: case dy_vault = 0.4346379219849359\n", - "after optimization: case dy_vault = 0.4346409522692004\n", - "err_dy_jacket = 5.620712184913668e-05\n", - "err_dy_vault = 6.971925329850067e-06\n", - "tot_err = 6.317904717898675e-05\n", - "Internal optimazion - iteration 12\n", - "before optimization: conductor jacket area = 0.00046456000589983133\n", - "Optimal dx_jacket: 0.00324080180040045\n", - "Averaged sigma in the x-direction: 512.9937390413941 MPa\n", - "91897.51784722139\n", - "after optimization: conductor jacket area = 0.00046455184590954305\n", - "before optimization: case dy_vault = 0.4346409522692004\n", - "after optimization: case dy_vault = 0.4346418992066607\n", - "err_dy_jacket = 1.756498662101196e-05\n", - "err_dy_vault = 2.1786612427521574e-06\n", - "tot_err = 1.974364786376412e-05\n", - "Internal optimazion - iteration 13\n", - "before optimization: conductor jacket area = 0.00046455184590954305\n", - "Optimal dx_jacket: 0.00324078548672434\n", - "Averaged sigma in the x-direction: 512.992620560733 MPa\n", - "91895.881033058\n", - "after optimization: conductor jacket area = 0.0004645492959536128\n", - "before optimization: case dy_vault = 0.4346418992066607\n", - "after optimization: case dy_vault = 0.43464219512110225\n", - "err_dy_jacket = 5.489066403830805e-06\n", - "err_dy_vault = 6.808230882394393e-07\n", - "tot_err = 6.169889492070245e-06\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 15 + "outputs": [], + "execution_count": null }, { - "metadata": { - "ExecuteTime": { - "end_time": "2024-10-18T17:30:29.143743Z", - "start_time": "2024-10-18T17:30:29.140121Z" - } - }, + "metadata": {}, "cell_type": "code", "source": [ "# new operational current\n", "bluemira_print(f\"Operational current after optimization: {I_TF/case.n_conductors}\")" ], "id": "583be483879db7ad", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001B[38;5;27m+-------------------------------------------------------------------------+\n", - "| Operational current after optimization: 67034.80113636363 |\n", - "+-------------------------------------------------------------------------+\u001B[0m\n" - ] - } - ], - "execution_count": 16 + "outputs": [], + "execution_count": null } ], "metadata": { diff --git a/examples/magnets/example_tf_wp_optimization.py b/examples/magnets/example_tf_wp_optimization.py index fba5c1d2ed..a84c799bc1 100644 --- a/examples/magnets/example_tf_wp_optimization.py +++ b/examples/magnets/example_tf_wp_optimization.py @@ -203,7 +203,7 @@ #%% md # ***Change cable aspect ratio*** #%% -aspect_ratio = 1 +aspect_ratio = 1.3 cable.set_aspect_ratio(aspect_ratio) # This adjusts the cable dimensions while maintaining the total cross-sectional area. cable.plot(0, 0, show=True) bluemira_print(f"cable area: {cable.area}") @@ -258,11 +258,12 @@ # ## Optimize cable jacket and case vault thickness #%% # Optimization parameters -bounds = [1e-5, 2] -max_niter = 10 +bounds_cond_jacket = [1e-5, 0.1] +bounds_dy_vault = [0.1, 2] +max_niter = 100 err = 1e-5 -case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds, layout, wp_reduction_factor, +case.optimize_jacket_and_vault( pm, t_z, T0, B_TF_i, S_Y, bounds_cond_jacket, bounds_dy_vault, layout, wp_reduction_factor, min_gap_x, n_layers_reduction, max_niter, err, n_cond) if show: