From d32851fd48ffb0da25711b4ee237259cd83ee180 Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Mon, 10 Mar 2025 20:36:03 +0000 Subject: [PATCH 01/10] Modifications to PbLi material & geometry dimensions. --- analysis/neutron/.gitignore | 2 + analysis/neutron/.gitignore:Zone.Identifier | 0 analysis/neutron/cross_sections/.gitignore | 2 + .../cross_sections/.gitignore:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Ag107.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Ag109.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Al27.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Ar36.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Ar38.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Ar40.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_As75.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_B10.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_B11.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Be9.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_Bi209.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_C12.h5:Zone.Identifier | 0 .../ENDFB-8.0-NNDC_C13.h5:Zone.Identifier | 0 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a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn66.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn66.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn67.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn67.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn68.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn68.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn70.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zn70.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr90.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr90.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr91.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr91.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr92.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr92.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr94.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr94.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr96.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr96.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/cross_sections/cross_sections.xml:Zone.Identifier b/analysis/neutron/cross_sections/cross_sections.xml:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/helpers.py b/analysis/neutron/helpers.py new file mode 100644 index 0000000..b8844a0 --- /dev/null +++ b/analysis/neutron/helpers.py @@ -0,0 +1,59 @@ +import math +import openmc + + +def get_exp_cllif_density(temp, LiCl_frac=0.695): + """Calculates density of ClLiF [g/cc] from temperature in Celsius + and molar concentration of LiCl. Valid for 660 C - 1000 C. + Source: + G. J. Janz, R. P. T. Tomkins, C. B. Allen; + Molten Salts: Volume 4, Part 4 + Mixed Halide Melts Electrical Conductance, Density, Viscosity, and Surface Tension Data. + J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302. + https://doi.org/10.1063/1.555590 + """ + temp = temp + 273.15 # Convert temperature from Celsius to Kelvin + C = LiCl_frac * 100 # Convert molar concentration to molar percent + + a = 2.25621 + b = -8.20475e-3 + c = -4.09235e-4 + d = 6.37250e-5 + e = -2.52846e-7 + f = 8.73570e-9 + g = -5.11184e-10 + + rho = a + b * C + c * temp + d * C**2 + e * C**3 + f * temp * C**2 + g * C * temp**2 + + return rho + + +def calculate_cylinder_volume(radius, height): + volume = math.pi * radius**2 * height + return volume + + +def translate_surface( + surface: ( + openmc.XPlane | openmc.YPlane | openmc.ZPlane | openmc.Plane | openmc.Sphere + ), + dx: float, + dy: float, + dz: float, +) -> openmc.Surface: + """Translate an OpenMC surface by dx, dy, dz.""" + if isinstance(surface, openmc.XPlane): + surface.x0 += dx + elif isinstance(surface, openmc.YPlane): + surface.y0 += dy + elif isinstance(surface, openmc.ZPlane): + surface.z0 += dz + elif isinstance(surface, openmc.Plane): + surface.d += surface.a * dx + surface.b * dy + surface.c * dz + elif isinstance(surface, openmc.Sphere): + surface.x0 += dx + surface.y0 += dy + surface.z0 += dz + else: + raise TypeError(f"Unsupported surface type: {type(surface)}") + return surface diff --git a/analysis/neutron/helpers.py:Zone.Identifier b/analysis/neutron/helpers.py:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/model.xml:Zone.Identifier b/analysis/neutron/model.xml:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/openmc_model_PbLi.py b/analysis/neutron/openmc_model_PbLi.py new file mode 100644 index 0000000..2dabbd3 --- /dev/null +++ b/analysis/neutron/openmc_model_PbLi.py @@ -0,0 +1,528 @@ +import openmc +import vault +from libra_toolbox.neutronics.neutron_source import A325_generator_diamond +import helpers + + +def baby_geometry(x_c: float, y_c: float, z_c: float): + """Returns the geometry for the BABY experiment. + + Args: + x_c: x-coordinate of the center of the BABY experiment (cm) + y_c: y-coordinate of the center of the BABY experiment (cm) + z_c: z-coordinate of the center of the BABY experiment (cm) + + Returns: + the sphere, cllif cell, and cells + """ + + epoxy_thickness = 2.54 # 1 inch + alumina_compressed_thickness = 2.54 # 1 inch + base_thickness = 0.786 + alumina_thickness = 0.635 + he_thickness = 0.6 + inconel_thickness = 0.3 + heater_gap = 0.878 + PbLi_thickness = 6.388 + 0.13022 # without heater: 0.1081 + gap_thickness = 4.605 + cap = 1.422 + firebrick_thickness = 15.24 + high = 21.093 + cover = 2.392 + z_tab = 28.00 + lead_height = 4.00 + lead_width = 8.00 + lead_length = 16.00 + heater_r = 0.439 + heater_h = 25.40 + heater_z = ( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + he_thickness + + inconel_thickness + + heater_gap + + z_c + ) + + PbLi_radius = 7.00 + inconel_radius = 7.3 + he_radius = 9.144 + firebrick_radius = 14.224 + vessel_radius = 15.593 + external_radius = 15.812 + + source_h = 50.00 + source_x = x_c - 13.50 + source_z = z_c - 5.635 + source_external_r = 5.00 + source_internal_r = 4.75 + + ######## Surfaces ################# + z_plane_1 = openmc.ZPlane(0.0 + z_c) + z_plane_2 = openmc.ZPlane(epoxy_thickness + z_c) + z_plane_3 = openmc.ZPlane(epoxy_thickness + alumina_compressed_thickness + z_c) + z_plane_4 = openmc.ZPlane( + epoxy_thickness + alumina_compressed_thickness + base_thickness + z_c + ) + z_plane_5 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + z_c + ) + z_plane_6 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + he_thickness + + z_c + ) + z_plane_7 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + he_thickness + + inconel_thickness + + z_c + ) + z_plane_8 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + he_thickness + + inconel_thickness + + PbLi_thickness + + z_c + ) + z_plane_9 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + he_thickness + + inconel_thickness + + PbLi_thickness + + gap_thickness + + z_c + ) + z_plane_10 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + he_thickness + + inconel_thickness + + PbLi_thickness + + gap_thickness + + cap + + z_c + ) + z_plane_11 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + alumina_thickness + + firebrick_thickness + + z_c + ) + z_plane_12 = openmc.ZPlane( + epoxy_thickness + alumina_compressed_thickness + base_thickness + high + z_c + ) + z_plane_13 = openmc.ZPlane( + epoxy_thickness + + alumina_compressed_thickness + + base_thickness + + high + + cover + + z_c + ) + z_plane_14 = openmc.ZPlane(z_c - z_tab) + z_plane_15 = openmc.ZPlane(z_c - z_tab - epoxy_thickness) + + ######## Cylinder ################# + z_cyl_1 = openmc.ZCylinder(x0=x_c, y0=y_c, r=PbLi_radius) + z_cyl_2 = openmc.ZCylinder(x0=x_c, y0=y_c, r=inconel_radius) + z_cyl_3 = openmc.ZCylinder(x0=x_c, y0=y_c, r=he_radius) + z_cyl_4 = openmc.ZCylinder(x0=x_c, y0=y_c, r=firebrick_radius) + z_cyl_5 = openmc.ZCylinder(x0=x_c, y0=y_c, r=vessel_radius) + z_cyl_6 = openmc.ZCylinder(x0=x_c, y0=y_c, r=external_radius) + + right_cyl = openmc.model.RightCircularCylinder( + (x_c, y_c, heater_z), heater_h, heater_r, axis="z" + ) + ext_cyl_source = openmc.model.RightCircularCylinder( + (source_x, y_c, source_z), source_h, source_external_r, axis="x" + ) + source_region = openmc.model.RightCircularCylinder( + (source_x + 0.25, y_c, source_z), source_h - 0.50, source_internal_r, axis="x" + ) + + ######## Sphere ################# + sphere = openmc.Sphere(x0=x_c, y0=y_c, z0=z_c, r=50.00) # before r=50.00 + + ######## Lead bricks positioned under the source ################# + positions = [ + (x_c - 13.50, y_c, z_c - z_tab), + (x_c - 4.50, y_c, z_c - z_tab), + (x_c + 36.50, y_c, z_c - z_tab), + (x_c + 27.50, y_c, z_c - z_tab), + ] + + lead_blocks = [] + for position in positions: + lead_block_region = openmc.model.RectangularParallelepiped( + position[0] - lead_width / 2, + position[0] + lead_width / 2, + position[1] - lead_length / 2, + position[1] + lead_length / 2, + position[2], + position[2] + lead_height, + ) + lead_blocks.append(lead_block_region) + + # regions + source_wall_region = -ext_cyl_source & +source_region + source_region = -source_region + epoxy_region = +z_plane_1 & -z_plane_2 & -sphere + alumina_compressed_region = +z_plane_2 & -z_plane_3 & -sphere + bottom_vessel = +z_plane_3 & -z_plane_4 & -z_cyl_6 + top_vessel = +z_plane_12 & -z_plane_13 & -z_cyl_6 & +right_cyl + cylinder_vessel = +z_plane_4 & -z_plane_12 & +z_cyl_5 & -z_cyl_6 + vessel_region = bottom_vessel | cylinder_vessel | top_vessel + alumina_region = +z_plane_4 & -z_plane_5 & -z_cyl_5 + bottom_cap = +z_plane_6 & -z_plane_7 & -z_cyl_2 & +right_cyl + cylinder_cap = +z_plane_7 & -z_plane_9 & +z_cyl_1 & -z_cyl_2 & +right_cyl + top_cap = +z_plane_9 & -z_plane_10 & -z_cyl_2 & +right_cyl + cap_region = bottom_cap | cylinder_cap | top_cap + PbLi_region = +z_plane_7 & -z_plane_8 & -z_cyl_1 & +right_cyl + gap_region = +z_plane_8 & -z_plane_9 & -z_cyl_1 & +right_cyl + firebrick_region = +z_plane_5 & -z_plane_11 & +z_cyl_3 & -z_cyl_4 + heater_region = -right_cyl + table_under_source_region = +z_plane_15 & -z_plane_14 & -sphere + lead_block_1_region = -lead_blocks[0] + lead_block_2_region = -lead_blocks[1] + lead_block_3_region = -lead_blocks[2] + lead_block_4_region = -lead_blocks[3] + he_region = ( + +z_plane_5 + & -z_plane_12 + & -z_cyl_5 + & ~source_region + & ~epoxy_region + & ~alumina_compressed_region + & ~alumina_region + & ~PbLi_region + & ~gap_region + & ~firebrick_region + & ~vessel_region + & ~cap_region + & ~heater_region + & ~table_under_source_region + & ~lead_block_1_region + & ~lead_block_2_region + & ~lead_block_3_region + & ~lead_block_4_region + ) + sphere_region = ( + -sphere + & ~source_wall_region + & ~source_region + & ~epoxy_region + & ~alumina_compressed_region + & ~alumina_region + & ~PbLi_region + & ~gap_region + & ~firebrick_region + & ~he_region + & ~vessel_region + & ~cap_region + & ~heater_region + & ~table_under_source_region + & ~lead_block_1_region + & ~lead_block_2_region + & ~lead_block_3_region + & ~lead_block_4_region + ) + + # cells + source_wall_cell_1 = openmc.Cell(region=source_wall_region) + source_wall_cell_1.fill = SS304 + source_region = openmc.Cell(region=source_region) + source_region.fill = None + epoxy_cell = openmc.Cell(region=epoxy_region) + epoxy_cell.fill = epoxy + alumina_compressed_cell = openmc.Cell(region=alumina_compressed_region) + alumina_compressed_cell.fill = alumina + vessel_cell = openmc.Cell(region=vessel_region) + vessel_cell.fill = inconel625 + alumina_cell = openmc.Cell(region=alumina_region) + alumina_cell.fill = alumina + PbLi_cell = openmc.Cell(region=PbLi_region) + PbLi_cell.fill = lithium_lead # cllif_nat or lithium_lead + gap_cell = openmc.Cell(region=gap_region) + gap_cell.fill = he + cap_cell = openmc.Cell(region=cap_region) + cap_cell.fill = inconel625 + firebrick_cell = openmc.Cell(region=firebrick_region) + firebrick_cell.fill = firebrick + heater_cell = openmc.Cell(region=heater_region) + heater_cell.fill = heater_mat + table_cell = openmc.Cell(region=table_under_source_region) + table_cell.fill = epoxy + sphere_cell = openmc.Cell(region=sphere_region) + sphere_cell.fill = air + he_cell = openmc.Cell(region=he_region) + he_cell.fill = he + lead_block_1_cell = openmc.Cell(region=lead_block_1_region) + lead_block_1_cell.fill = lead + lead_block_2_cell = openmc.Cell(region=lead_block_2_region) + lead_block_2_cell.fill = lead + lead_block_3_cell = openmc.Cell(region=lead_block_3_region) + lead_block_3_cell.fill = lead + lead_block_4_cell = openmc.Cell(region=lead_block_4_region) + lead_block_4_cell.fill = lead + + cells = [ + source_wall_cell_1, + source_region, + epoxy_cell, + alumina_compressed_cell, + vessel_cell, + alumina_cell, + cap_cell, + PbLi_cell, + gap_cell, + firebrick_cell, + heater_cell, + he_cell, + sphere_cell, + table_cell, + lead_block_1_cell, + lead_block_2_cell, + lead_block_3_cell, + lead_block_4_cell, + ] + + return sphere, PbLi_cell, cells + + +def baby_model(): + """Returns an openmc model of the BABY experiment. + + Returns: + the openmc model + """ + + materials = [ + inconel625, + lithium_lead, + SS304, + heater_mat, + firebrick, + alumina, + lead, + air, + epoxy, + he, + ] + + # BABY coordinates + x_c = 587 # cm + y_c = 60 # cm + z_c = 100 # cm + sphere, PbLi_cell, cells = baby_geometry(x_c, y_c, z_c) + + ############################################################################ + # Define Settings + + settings = openmc.Settings() + + src = A325_generator_diamond((x_c, y_c, z_c - 5.635), (1, 0, 0)) + settings.source = src + settings.batches = 100 + settings.inactive = 0 + settings.run_mode = "fixed source" + settings.particles = int(1e4) + settings.output = {"tallies": False} + settings.photon_transport = False + + ############################################################################ + overall_exclusion_region = -sphere + + ############################################################################ + # Specify Tallies + tallies = openmc.Tallies() + + tbr_tally = openmc.Tally(name="TBR") + tbr_tally.scores = ["(n,Xt)"] + tbr_tally.filters = [openmc.CellFilter(PbLi_cell)] + tallies.append(tbr_tally) + + model = vault.build_vault_model( + settings=settings, + tallies=tallies, + added_cells=cells, + added_materials=materials, + overall_exclusion_region=overall_exclusion_region, + ) + + return model + + +############################################################################ +# Define Materials +# Source: PNNL Materials Compendium April 2021 +# PNNL-15870, Rev. 2 +inconel625 = openmc.Material(name="Inconel 625") +inconel625.add_element("C", 0.000990, "wo") +inconel625.add_element("Al", 0.003960, "wo") +inconel625.add_element("Si", 0.004950, "wo") +inconel625.add_element("P", 0.000148, "wo") +inconel625.add_element("S", 0.000148, "wo") +inconel625.add_element("Ti", 0.003960, "wo") +inconel625.add_element("Cr", 0.215000, "wo") +inconel625.add_element("Mn", 0.004950, "wo") +inconel625.add_element("Fe", 0.049495, "wo") +inconel625.add_element("Co", 0.009899, "wo") +inconel625.add_element("Ni", 0.580000, "wo") +inconel625.add_element("Nb", 0.036500, "wo") +inconel625.add_element("Mo", 0.090000, "wo") +inconel625.set_density("g/cm3", 8.44) + +# Lithium-Lead +lithium_lead = openmc.Material(name="Lithium Lead") +lithium_lead.add_element("Pb", 0.993479 , "wo") +lithium_lead.add_element("Li", 0.0064 , "wo") +lithium_lead.add_element("Tl", 0.00002 , "wo") +lithium_lead.add_element("Zn", 0.000002 , "wo") +lithium_lead.add_element("Sn", 0.000002, "wo") +lithium_lead.add_element("Sb", 0.000002, "wo") +lithium_lead.add_element("Ni", 0.000001, "wo") +lithium_lead.add_element("Cu", 0.000002, "wo") +lithium_lead.add_element("Cd", 0.000002, "wo") +lithium_lead.add_element("Bi", 0.00008, "wo") +lithium_lead.add_element("As", 0.000002, "wo") +lithium_lead.add_element("Ag", 0.000008, "wo") +lithium_lead.set_density("g/cm3", 10.45431283) # Density at 550C + +# Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2) +SS304 = openmc.Material(name="Stainless Steel 304") +# SS304.temperature = 700 + 273 +SS304.add_element("C", 0.000800, "wo") +SS304.add_element("Mn", 0.020000, "wo") +SS304.add_element("P", 0.000450, "wo") +SS304.add_element("S", 0.000300, "wo") +SS304.add_element("Si", 0.010000, "wo") +SS304.add_element("Cr", 0.190000, "wo") +SS304.add_element("Ni", 0.095000, "wo") +SS304.add_element("Fe", 0.683450, "wo") +SS304.set_density("g/cm3", 8.00) + +heater_mat = openmc.Material(name="heater") +heater_mat.add_element("C", 0.000990, "wo") +heater_mat.add_element("Al", 0.003960, "wo") +heater_mat.add_element("Si", 0.004950, "wo") +heater_mat.add_element("P", 0.000148, "wo") +heater_mat.add_element("S", 0.000148, "wo") +heater_mat.add_element("Ti", 0.003960, "wo") +heater_mat.add_element("Cr", 0.215000, "wo") +heater_mat.add_element("Mn", 0.004950, "wo") +heater_mat.add_element("Fe", 0.049495, "wo") +heater_mat.add_element("Co", 0.009899, "wo") +heater_mat.add_element("Ni", 0.580000, "wo") +heater_mat.add_element("Nb", 0.036500, "wo") +heater_mat.add_element("Mo", 0.090000, "wo") +heater_mat.set_density("g/cm3", 2.44) + +# Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2 +# https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/ +firebrick = openmc.Material(name="Firebrick") +# Estimate average temperature of Firebrick to be around 300 C +# Firebrick.temperature = 273 + 300 +firebrick.add_element("Al", 0.004, "ao") +firebrick.add_element("O", 0.666, "ao") +firebrick.add_element("Si", 0.240, "ao") +firebrick.add_element("Zr", 0.090, "ao") +firebrick.set_density("g/cm3", 0.30) + +# alumina insulation +# data from https://precision-ceramics.com/materials/alumina/ +alumina = openmc.Material(name="Alumina insulation") +alumina.add_element("O", 0.6, "ao") +alumina.add_element("Al", 0.4, "ao") +alumina.set_density("g/cm3", 3.98) + +# air +air = openmc.Material(name="Air") +air.add_element("C", 0.00012399, "wo") +air.add_element("N", 0.75527, "wo") +air.add_element("O", 0.23178, "wo") +air.add_element("Ar", 0.012827, "wo") +air.set_density("g/cm3", 0.0012) + +# epoxy +epoxy = openmc.Material(name="Epoxy") +epoxy.add_element("C", 0.70, "wo") +epoxy.add_element("H", 0.08, "wo") +epoxy.add_element("O", 0.15, "wo") +epoxy.add_element("N", 0.07, "wo") +epoxy.set_density("g/cm3", 1.2) + +# helium @5psig +pressure = 34473.8 # Pa ~ 5 psig +temperature = 300 # K +R_he = 2077 # J/(kg*K) +density = pressure / (R_he * temperature) / 1000 # in g/cm^3 +he = openmc.Material(name="Helium") +he.add_element("He", 1.0, "ao") +he.set_density("g/cm3", density) + +# lead +# data from https://wwwrcamnl.wr.usgs.gov/isoig/period/pb_iig.html +lead = openmc.Material() +lead.set_density("g/cm3", 11.34) +lead.add_nuclide("Pb204", 0.014, "ao") +lead.add_nuclide("Pb206", 0.241, "ao") +lead.add_nuclide("Pb207", 0.221, "ao") +lead.add_nuclide("Pb208", 0.524, "ao") + + +if __name__ == "__main__": + model = baby_model() + model.run() + sp = openmc.StatePoint(f"statepoint.{model.settings.batches}.h5") + tbr_tally = sp.get_tally(name="TBR").get_pandas_dataframe() + + print(f"TBR: {tbr_tally['mean'].iloc[0] :.6e}\n") + print(f"TBR std. dev.: {tbr_tally['std. dev.'].iloc[0] :.6e}\n") + + processed_data = { + "modelled_TBR": { + "mean": tbr_tally["mean"].iloc[0], + "std_dev": tbr_tally["std. dev."].iloc[0], + } + } + + import json + + processed_data_file = "../../data/processed_data.json" + + try: + with open(processed_data_file, "r") as f: + existing_data = json.load(f) + except FileNotFoundError: + print(f"Processed data file not found, creating it in {processed_data_file}") + existing_data = {} + + existing_data.update(processed_data) + + with open(processed_data_file, "w") as f: + json.dump(existing_data, f, indent=4) + + print(f"Processed data stored in {processed_data_file}") \ No newline at end of file diff --git a/analysis/neutron/openmc_model_PbLi.py:Zone.Identifier b/analysis/neutron/openmc_model_PbLi.py:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/postprocessing.ipynb b/analysis/neutron/postprocessing.ipynb new file mode 100644 index 0000000..7f33748 --- /dev/null +++ b/analysis/neutron/postprocessing.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LIBRA and the Lithium-Lead Liquid Immersion Blanket\n", + "## 1L BABY" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "import openmc\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import LogNorm, ListedColormap\n", + "from openmc_model_PbLi import baby_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "isotopes ['Ag107', 'Ag109', 'Al27', 'Ar36', 'Ar38', 'Ar40', 'As75', 'B10', 'B11', 'Be9', 'Bi209', 'C12', 'C13', 'Ca40', 'Ca42', 'Ca43', 'Ca44', 'Ca46', 'Ca48', 'Cd106', 'Cd108', 'Cd110', 'Cd111', 'Cd112', 'Cd113', 'Cd114', 'Cd116', 'Co59', 'Cr50', 'Cr52', 'Cr53', 'Cr54', 'Cu63', 'Cu65', 'F19', 'Fe54', 'Fe56', 'Fe57', 'Fe58', 'H1', 'H2', 'He3', 'He4', 'K39', 'K40', 'K41', 'Li6', 'Li7', 'Mg24', 'Mg25', 'Mg26', 'Mn55', 'Mo100', 'Mo92', 'Mo94', 'Mo95', 'Mo96', 'Mo97', 'Mo98', 'N14', 'N15', 'Na23', 'Nb93', 'Ni58', 'Ni60', 'Ni61', 'Ni62', 'Ni64', 'O16', 'O17', 'O18', 'P31', 'Pb204', 'Pb206', 'Pb207', 'Pb208', 'S32', 'S33', 'S34', 'S36', 'Sb121', 'Sb123', 'Si28', 'Si29', 'Si30', 'Sn112', 'Sn114', 'Sn115', 'Sn116', 'Sn117', 'Sn118', 'Sn119', 'Sn120', 'Sn122', 'Sn124', 'Ti46', 'Ti47', 'Ti48', 'Ti49', 'Ti50', 'Tl203', 'Tl205', 'W182', 'W183', 'W184', 'W186', 'Zn64', 'Zn66', 'Zn67', 'Zn68', 'Zn70', 'Zr90', 'Zr91', 'Zr92', 'Zr94', 'Zr96']\n", + "Searching libraries with the following priority {'ENDFB-8.0-NNDC': 1}\n", + "Isotopes found matching library requirements 556\n", + "Isotopes found matching particle requirements 1789\n", + "Isotopes found matching isotope requirements 458\n", + "Isotopes found matching all requirements 116\n", + " library remote_file \\\n", + "1233 ENDFB-8.0-NNDC H1.h5 \n", + "1234 ENDFB-8.0-NNDC H2.h5 \n", + "1236 ENDFB-8.0-NNDC He3.h5 \n", + "1237 ENDFB-8.0-NNDC He4.h5 \n", + "1238 ENDFB-8.0-NNDC Li6.h5 \n", + "... ... ... \n", + "1689 ENDFB-8.0-NNDC Pb204.h5 \n", + "1691 ENDFB-8.0-NNDC Pb206.h5 \n", + "1692 ENDFB-8.0-NNDC Pb207.h5 \n", + "1693 ENDFB-8.0-NNDC Pb208.h5 \n", + "1694 ENDFB-8.0-NNDC Bi209.h5 \n", + "\n", + " url \\\n", + "1233 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1234 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1236 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1237 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1238 https://github.com/openmc-data-storage/ENDF-B-... \n", + "... ... \n", + "1689 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1691 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1692 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1693 https://github.com/openmc-data-storage/ENDF-B-... \n", + "1694 https://github.com/openmc-data-storage/ENDF-B-... \n", + "\n", + " local_file particle isotope element priority \n", + "1233 ENDFB-8.0-NNDC_H1.h5 neutron H1 H 1 \n", + "1234 ENDFB-8.0-NNDC_H2.h5 neutron H2 H 1 \n", + "1236 ENDFB-8.0-NNDC_He3.h5 neutron He3 He 1 \n", + "1237 ENDFB-8.0-NNDC_He4.h5 neutron He4 He 1 \n", + "1238 ENDFB-8.0-NNDC_Li6.h5 neutron Li6 Li 1 \n", + "... ... ... ... ... ... \n", + "1689 ENDFB-8.0-NNDC_Pb204.h5 neutron Pb204 Pb 1 \n", + "1691 ENDFB-8.0-NNDC_Pb206.h5 neutron Pb206 Pb 1 \n", + "1692 ENDFB-8.0-NNDC_Pb207.h5 neutron Pb207 Pb 1 \n", + "1693 ENDFB-8.0-NNDC_Pb208.h5 neutron Pb208 Pb 1 \n", + "1694 ENDFB-8.0-NNDC_Bi209.h5 neutron Bi209 Bi 1 \n", + "\n", + "[116 rows x 8 columns]\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_H1.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_H2.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_He3.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_He4.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Li6.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Li7.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Be9.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_B10.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_B11.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_C12.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_C13.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_N14.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_N15.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_O16.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_O17.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_O18.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_F19.h5, already downloaded\n", + "Skipping 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cross_sections/ENDFB-8.0-NNDC_Tl205.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Pb204.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Pb206.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Pb207.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Pb208.h5, already downloaded\n", + "Skipping cross_sections/ENDFB-8.0-NNDC_Bi209.h5, already downloaded\n", + "written cross sections xml file to /home/rossmac/Repositories/BABY-1L-run-1/analysis/neutron/cross_sections/cross_sections.xml\n", + "setting OPENMC_CROSS_SECTIONS to /home/rossmac/Repositories/BABY-1L-run-1/analysis/neutron/cross_sections/cross_sections.xml\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rossmac/anaconda3/envs/baby_1l_run_1/lib/python3.12/site-packages/openmc/source.py:656: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n", + " warnings.warn(\"This class is deprecated in favor of 'IndependentSource'\", FutureWarning)\n" + ] + } + ], + "source": [ + "model = baby_model()\n", + "geometry = model.geometry" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vault Geometry" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualization\n", + "\n", + "from vault import Air\n", + "from openmc_model import air\n", + "\n", + "x_c = 587\n", + "y_c = 60\n", + "z_c = 100\n", + "ax = geometry.plot(\n", + " width=(2500, 1500),\n", + " origin=(x_c, y_c, z_c + 10),\n", + " pixels=(1000, 1000),\n", + " basis=\"xy\",\n", + " color_by=\"material\",\n", + " colors={Air: \"white\", air: \"white\"},\n", + ")\n", + "ax.tick_params(axis=\"both\", labelsize=20)\n", + "ax.set_xlabel(ax.get_xlabel(), fontsize=20)\n", + "ax.set_ylabel(ax.get_ylabel(), fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BABY 1L Geometry XZ View" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2AABQaVv+XSq6UA2q+h4uw4YN67Jzz5o1K4899liHjlmxYkUmT56chx9+eJtfX758eZYvX57LL78811xzTSZNmlTkUKFLbPlLTGgBAKCn+eNle2fIqU8liXu70GNV9QyXrY0cOTLvfOc7CznX0qVLc9FFF6Vv374ZOHBgu4554YUXMmXKlHJsOfnkk3Pbbbfl3nvvzfnnn58BAwZk9erVOeGEE/LQQw8VMk7oKma1AADQ05ntQk9X1cFl1qxZ+cEPfpDf//73efzxx3PZZZd1+pwvv/xyTj755Lz88sv5h3/4hwwZMqRdx82ZMyfLly9Pklx44YWZO3du3va2t2X8+PH5h3/4h/zkJz9JY2Nj1q9fn9NPP73T44Su4l4tAABUE9GFnqqqg8t5552XKVOmFLq06OKLL86DDz6Ygw46KJ/73OfadcxLL72Uiy++OEkyZsyYfPazn91un/Hjx+ekk05KkixcuDAPPvhgYWOGIrgxLgAA1coNdemJqjq4FO23v/1tZs2alST5+te/nt69e7fruEWLFuX5559PkkybNi29erX8bd36Rr433HBDp8YKRbKECACAameJET2N4LKVT3ziE1m3bl0+/OEP5+ijj273cXfddVd5e+LEia3uN3bs2PTv3z9Jcvfdd+/8QKEgZrUAAFBrzHahp6jqTykq0ne+85388Ic/zB577JE5c+Z06Nhly5aVt0ePHt3qfo2NjRk1alQeeuihbY6BShBaAACoVVv+nTvk1Kd8ihEVY4ZLkj/96U/lG9n+8z//c/baa68OHf/EE08kSfr375/dd9+9zX1HjhyZJFm1alU2btzY4bFCEcQWAADqgSVGVJLgkmTGjBn5wx/+kPHjx+fkk0/u8PFr165NkgwYMGCH+25ZUpS88lHSLdm4cWPWrFmzzQOKYAkRAAD1xhIjKqXug8udd96Zb37zm2lsbMw3vvGNnZputmHDhiRp1012+/TpU95+8cUXW9xn9uzZGTx4cPmxZVYMdIYb4wIAUK/cUJdKqOvgsnHjxpxyyikplUr5zGc+kze96U07dZ6+ffsmSTZt2tSu19yiX79+Le5z9tlnZ/Xq1eXHliVLsLPMagEAAEuM6F51HVzOP//8PPzwwxk5cmTOPffcnT7PwIEDk7S+RGhr69atK2+3tgSpT58+GTRo0DYP2FliCwAA/B/Rhe5S159SdMEFFyRJ3v72t+fmm29ucZ8tgWTdunX5zne+kyTZa6+98ra3va28z4gRI/Lzn/8869aty/PPP9/mjXO3zFYZOnToNsuLoGhb/hIRWwAAYFt/vGzvDDn1qSTxKUZ0mboOLluWAF1xxRW54oor2tz32WefzQc/+MEkycSJE7cJLgcffHCuv/76JMny5ctz+OGHt3iO5ubmrFixIkkyZsyYTo8fWmNWCwAAtM1HR9PV6npJUVGOOOKI8vYdd9zR6n5Lliwpz5iZMGFCl4+L+iS2AABA+1liRFep6+Cy5aPB2nrss88+SZJ99tmn/GuLFi3a5jxHHXVUBg8enCS58sorW/2Pdf78+eXt4447rkveE/XLRz4DAMDO8dHRdIW6Di5F6d27dz796U8nSZYtW5Y5c+Zst8/ixYszb968JK8sSRo3bly3jpHa5iOfAQCgc3x0NEWr6nu43H333Wlqaip//eyzz5a3m5qatplRkiTTp0/vsrHMmDEjCxYsyCOPPJKZM2emqakpJ554Yvr165eFCxfmy1/+cpqbm9OvX79cdNFFXTYO6o9ZLQAAUJwtN9R1Xxc6q6FUxflu+vTpufLKK9u9/8681X333Te//e1vs88++2TlypVt7tvU1JRJkybl0UcfbfH5QYMG5eqrr86UKVM6NIY1a9Zk8ODBWb16tY+IZhtiCwAAdA3RhZZ05OdzS4oKtP/++2fp0qW54IILMnbs2Oy+++7ZbbfdctBBB+WMM87IQw891OHYAi1xvxYAAOha7utCZ1X1DJd6YYYLWxNaAACge5ntwhZmuECNElsAAKD7uZkuO0NwgSohtgAAQOWILnSU4AI9nPu1AABAz+C+LnREVX8sNNQ6oQUAAHqWLf8+d18XdsQMF+ihxBYAAOi5LDFiRwQX6IHEFgAA6PlEF9oiuEAPI7YAAED1EF1ojeACPYSb4wIAQHVyM11a4qa50AMILQAAUN3cTJdXM8MFKkxsAQCA2mGJEVsILlBBYgsAANQe0YVEcIGKEVsAAKB2iS4ILtDN3BwXAADqg5vp1jc3zYVuJLQAAEB9cTPd+mWGC3QTsQUAAOqXJUb1R3CBbiC2AAAAokt9EVygi4ktAADAFqJL/RBcoAuJLQAAwKuJLvVBcIEuIrYAAACtEV1qn+ACXUBsAQAAdkR0qW2CCxSoVCqJLQAAQLttiS7CS+1prPQAoFYILQAAwM7Y8nPEkFOfSkNDQ4VHQ1HMcIECiC0AAEBnWWJUWwQX6CSxBQAAKIroUjsEF+gEsQUAACia6FIbBBfYSWILAADQVUSX6ie4wE4QWwAAgK4mulQ3wQU6SGwBAAC6i+hSvQQX6ACxBQAA6G6iS3USXKCdxBYAAKBSRJfqI7hAO4gtAABApYku1UVwgR0QWwAAgJ5CdKkeggu0olQqiS0AAECPsyW6CC89W2OlBwA9kdACAAD0ZFt+Xhly6lNpaGio8GhoiRku8CpiCwAAUC0sMeq5BBfYitgCAABUG9GlZxJc4H+JLQAAQLUSXXoewQUitgAAANVPdOlZBBfqntgCAADUCtGl5xBcqGtiCwAAUGtEl55BcKFuiS0AAECtEl0qT3ChLoktAABArRNdKktwoe6ILQAAQL0QXSpHcKGuiC0AAEC9EV0qQ3ChbogtAABAvRJdup/gQl0QWwAAgHonunQvwYWaJ7YAAAC8QnTpPoILNU1sAQAA2Jbo0j0EF2qW2AIAANAy0aXrCS7UJLEFAACgbaJL1xJcqDliCwAAQPuILl1HcKGmiC0AAAAdI7p0DcGFmiG2AAAA7Bw/SxVPcAEAAADMcimY4EJNMLsFAACgcywtKpbgQtUTWwAAAIohuhRHcKGqiS0AAADFEl2KIbhQtcQWAACAriG6dJ7gQlUSWwAAALqW6NI5ggtVR2wBAADoHqLLzhNcqCpiCwAAQPcSXXaO4ELVEFsAqCalUvseAFANRJeOa6z0AKA9xBYAqkmplHz06d+0Z8/M33tUl48HAIrwx8v2zpBTn0pDQ0Olh1IVzHChxxNbAAAAegYzXdpPcKFHE1sAAAB6FtGlfQQXeiyxBQAAoGfys9qOCS4AAABAh5nl0jbBhR7J7BYAAICezdKitgku9DhiCwAAQHUQXVonuNCjiC0AAADVRXRpmeBCjyG2AAAAVCfRZXuCCz2C2AIAAFDdRJdtCS5UnNgCAABQG0SX/yO4UFFiCwAAQG3xM94rBBcAAACgUGa5CC5UkNktAAAAtcnSIsGFChFbAAAAalu9RxfBhW4ntgAAANSHeo4uggvdSmwBAACoL/UaXQQXuo3YAgAAUJ/qMboILnQLsQUAAKC+1dvPhIILAAAA0C3qaZaL4EKXM7sFAACApL6WFgkudCmxBQAAgK3VS3QRXOgyYgsAAAAtqYfoIrjQJcQWAAAA2lLr0UVwoXBiCwAAAO1Ryz87Ci4AAABAxdTqLBfBhUKZ3QIAAEBH1OrSIsGFwogtAAAA7IxajC6CC4UQWwAAAOiMWosuggudJrYAAABQhFr62VJwAQAAAHqMWpnlIrjQKWa3AAAAUKRaWVokuLDTxBYAAAC6Qi1EF8GFnSK2AAAA0JWqPbpUdXB55plncvPNN2fWrFk59thjs+eee6ahoSENDQ2ZPn16u86xbNmy/Nu//VumTZuWv/iLv8iIESPSt2/f9O/fP69//evzgQ98IDfddFO7L/L69evzla98JYcddliGDBmSAQMGZMyYMTnrrLPy+OOPd+Ld9hxiCwAAAN2hmn/2bKz0ADpj2LBhnT7H+eefn6uvvrrF5x577LE89thj+e53v5uJEyfmhhtuyJAhQ1o914oVKzJ58uQ8/PDD2/z68uXLs3z58lx++eW55pprMmnSpE6PGwAAAOpBqVRKQ0NDpYfRYVU9w2VrI0eOzDvf+c4OH9fY2Ji3vOUtOfPMM3PFFVfkRz/6UZYsWZKf/vSnueSSS/LGN74xSXLHHXfk3e9+dzZv3tzieV544YVMmTKlHFtOPvnk3Hbbbbn33ntz/vnnZ8CAAVm9enVOOOGEPPTQQzv/RivM7BYAAAC6U7UuLarqGS6zZs3KuHHjMm7cuAwbNiwrV67Mfvvt16FzXH755WlsbPnb8Pa3vz0f//jH8/73vz833HBD7r333txyyy1597vfvd2+c+bMyfLly5MkF154YWbMmFF+bvz48Tn66KNz5JFHZv369Tn99NNz++23d2icPYHYAgAAQCX88bK9M+TUp6pqpktVz3A577zzMmXKlE4tLWottmyxyy67ZObMmeWv77zzzu32eemll3LxxRcnScaMGZPPfvaz2+0zfvz4nHTSSUmShQsX5sEHH9zpMVeC2AIAAEAlVdvPpFUdXLpL//79y9sbNmzY7vlFixbl+eefT5JMmzYtvXq1/G3d+ka+N9xwQ6FjBAAAgFpXTUuLBJd2uPbaa8vbo0eP3u75u+66q7w9ceLEVs8zduzYcry5++67Cxxh1zK7BQAAgJ6gmu7nIri04tlnn83ixYtz0kknZfbs2UmS17zmNfnrv/7r7fZdtmxZebulILNFY2NjRo0atd0xPZnYAgAAQE9SLdGlqm+aW7Sjjjoqd9xxR4vPDRkyJDfccEN233337Z574oknkryy9Kil57c2cuTIPPTQQ1m1alU2btyYPn36bLfPxo0bs3HjxvLXa9asaf+bKJDYAgAAQE/0x8v2zms+9nSlh9EmM1za4bTTTsuyZcty5JFHtvj82rVrkyQDBgzY4bm2vh/MCy+80OI+s2fPzuDBg8uPkSNH7sSoAQAAoHb19FkugstWrrjiivzqV7/KQw89lDvvvDNf/epXc8ABB+TSSy/NSSedlD/84Q8tHrflRrq9e/fe4WtsPaPlxRdfbHGfs88+O6tXry4/tsyg6U5mtwAAANCT9fSlRZYUbWW//fbb5uu//Mu/zMc//vGccMIJufnmmzNu3Ljce++9GTFixDb79e3bN0myadOmHb7G1kuF+vXr1+I+ffr0aXGpUXcRWwAAAKgGf7xs7ww59ak0NDRUeijbMcNlB/r27Zsrrrgiu+22W5544onMnDlzu30GDhyYpPUlQltbt25debs9S5C6m9gCAABANempP8MKLu2w5557ZsKECUmSm266Kc3Nzds8v2XGy7p16/L888+3ea4ty4OGDh1a0VksAAAAUCt64tIiwaWdhg4dmiRZv359Vq1atc1zBx98cHl7+fLlrZ6jubk5K1asSJKMGTOmC0bZOWa3AAAAUI164v1cBJd2euqpp8rbr14KdMQRR5S3W/tY6SRZsmRJeUnRlhkzPYXYAgAAQDXradHFTXPb4amnnsrixYuTJPvss0/5ni1bHHXUURk8eHBWr16dK6+8MjNnzmzxhj3z588vbx933HFdOuaOEFuAnqAH/d0I3crvfWpND7xvJVBH/njZ3nnNx56u9DCS1HlweeSRR/Lkk0/mbW97W6v7rF69Oh/84AfLn0D04Q9/eLt9evfunU9/+tP54he/mGXLlmXOnDmZMWPGNvssXrw48+bNS5JMnDgx48aNK/CdAFS3UimZ+eS3Kz0MKEwpad88Yr/3qTmlfGXk9v9eBuhOpVKpR3xqUVUHl7vvvjtNTU3lr5999tnydlNT0zYzSpJk+vTp23z99NNP55hjjsmf//mf5//9v/+XQw89NK997WvT2NiY3//+97nnnnsyb968/P73v0+SvPGNb8zf//3ftziWGTNmZMGCBXnkkUcyc+bMNDU15cQTT0y/fv2ycOHCfPnLX05zc3P69euXiy66qJD3XwSzWwAAAKglPeWjohtKPWmBUwdNnz49V155Zbv3f/VbXbRoUY4++uh2HTt58uRcccUV5ZvntqSpqSmTJk3Ko48+2uLzgwYNytVXX50pU6a0e8xJsmbNmvKSpUGDBnXo2LaILUBPYYYLtaaU5Nleb23HjqUMLS3u8vFA9zHDBeg5umJpUUd+Pq/qGS6dNWHChNxxxx25/fbbc/fdd+fxxx/PH/7wh6xfvz6DBg3Kfvvtl7e85S350Ic+1K6b3O6///5ZunRpLr300nzve99LU1NTNm3alJEjR2bSpEn5zGc+k3322acb3hkAAADUt0ovLarqGS71oitmuJjdAvQkZrhQa8xwoX6Z4QL0LEUvLerIz+c+FroOiS0AAADUg0p+VLTgUmfEFgAAAOpJpX4GFlwAAACAmlaJWS6CSx0xuwUAAIB6VImlRYJLnRBbAAAAqGfd/TOx4AIAAADUhe6c5SK41AGzWwAAAKB7lxYJLjVObAEAAID/010/IwsuAAAAQF3pjlkugksNM7sFAAAAttcdS4sElxoltgAAAEDrujq6CC41SGwBAACAHevKn50FFwAAAKBuddUsF8GlxpjdAgAAAO3XVUuLBJcaIrYAAABAx3XFz9KCCwAAAFD3ip7lIrjUCLNbAAAAYOcVvbRIcKkBYgsAAAB0XpE/WwsuAAAAAP+rqFkugkuVM7sFAAAAilPUz9iCCwAAAMBWipjlIrhUMbNbAAAAoHhF3EC3saCx0M3EFqCmFfyRfNDdGjqys9/v1IKGDv2uB6gKf7xs77zmY0/v9PGCCwA9S6mUE+6fUelRQKeUknx9/L3t2tfvd6pdKcl1b5lT6WEAdIlSqZSGnYzKlhRVIbNbAAAAoOt1ZmmR4FJlxBYAAADoPjv7M7jgAgAAANCGnZnlIrhUEbNbAAAAoPvtzNIiwaWK/OmbB1V6CAAAAFCXOjoBQnABAAAAaIeOzHIRXAAAAADaoSMrTwQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAoWGOlBwAARSulVOkhdJuGNFR6CFCz/FkCQGcILgDUlFJK+czo/6n0MLpHKfnXh99Y6VFATfJnCQCdZUkRAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMGqOrg888wzufnmmzNr1qwce+yx2XPPPdPQ0JCGhoZMnz69XefYsGFDbrrpppx22ml5y1vekiFDhmTXXXfNkCFDMn78+Jx77rn53e9+1+4xrV+/Pl/5yldy2GGHZciQIRkwYEDGjBmTs846K48//vhOvlMAAACgmjRWegCdMWzYsE4d/9BDD+WII47I2rVrt3vuT3/6U+67777cd999+epXv5rLL78873//+9s834oVKzJ58uQ8/PDD2/z68uXLs3z58lx++eW55pprMmnSpE6NGwAAAOjZqnqGy9ZGjhyZd77znR06Zs2aNeXYMmHChMyePTs//elP84tf/CI//vGPc+qpp2aXXXbJ2rVr86EPfSg/+tGPWj3XCy+8kClTppRjy8knn5zbbrst9957b84///wMGDAgq1evzgknnJCHHnpo598oAAAA0ONV9QyXWbNmZdy4cRk3blyGDRuWlStXZr/99mv38b169cr73//+nHPOOTn44IO3e/6d73xnjj322Bx33HF5+eWXc9ppp+XRRx9NQ0PDdvvOmTMny5cvT5JceOGFmTFjRvm58ePH5+ijj86RRx6Z9evX5/TTT8/tt9++E+8YAAAAqAZVPcPlvPPOy5QpU3Z6adFb3/rWLFiwoMXYssXUqVNz/PHHJ3llydB//dd/bbfPSy+9lIsvvjhJMmbMmHz2s5/dbp/x48fnpJNOSpIsXLgwDz744E6NGQAAAOj5qjq4dJejjz66vL1ixYrtnl+0aFGef/75JMm0adPSq1fL39atb+R7ww03FDpGAAAAoOcQXNph48aN5e2WYspdd91V3p44cWKr5xk7dmz69++fJLn77rsLHCEAAADQkwgu7XDHHXeUt0ePHr3d88uWLWvz+S0aGxszatSo7Y4BAAAAaovgsgO//OUvc8sttyRJ3vCGN7R4v5cnnngiSdK/f//svvvubZ5v5MiRSZJVq1ZtM3MGAAAAqB1V/SlFXW3jxo35u7/7u7z88stJki9/+cst7rflo6UHDBiww3NuWVKUvPJR0n369GnxdbeOMWvWrOnQuAEAAIDKMsOlDZ/61KeyZMmSJK/cDPc973lPi/tt2LAhSdK7d+8dnnPrwPLiiy+2uM/s2bMzePDg8mPLrBgA6C4lj049fK977vcbALqLGS6tmD17di6//PIkyaGHHppLL7201X379u2bJNm0adMOz7v1zJV+/fq1uM/ZZ5+dM888s/z1mjVrRBcAuk0pyXVvmVPpYVS1jkQA3+tOKpVywv0zKj0KANiO4NKCyy67LP/wD/+QJDnooIPyox/9aJulQK82cODAJK8sEdqRdevWlbdbW4LUp0+fFpcaAQAAANXBkqJXufbaa/OJT3wiSbLPPvvkZz/7WYYOHdrmMSNGjEjySkx5/vnn29x3yw12hw4dKqoAAABAjRJctvKf//mf+chHPpLNmzfnz/7sz3LbbbeVY0pbtv7kouXLl7e6X3Nzc1asWJEkGTNmTOcHDAAAAPRIgsv/uu222/L+978/zc3Nec1rXpOf/vSnGTVqVLuOPeKII8rbd9xxR6v7LVmypLykaMKECZ0bMAAAANBjCS5J7r333kydOjUbN27MoEGD8uMf/zhveMMb2n38UUcdlcGDBydJrrzyypRKLd8qb/78+eXt4447rlNjBgAAAHquug8u//Vf/5XJkydn3bp16d+/f374wx/m0EMP7dA5evfunU9/+tNJkmXLlmXOnO0/bWDx4sWZN29ekmTixIkZN25c5wcPAAAA9EhV/SlFd999d5qamspfP/vss+XtpqambWaUJMn06dO3+XrFihX5q7/6q/KNbr/0pS9l8ODB+e///u9WX3OvvfbKXnvttd2vz5gxIwsWLMgjjzySmTNnpqmpKSeeeGL69euXhQsX5stf/nKam5vTr1+/XHTRRR1+rwAAAED1qOrgcvnll+fKK69s8bl77rkn99xzzza/9urgctddd+WZZ54pf33GGWfs8DXPOeecnHvuudv9+sCBA3PLLbdk0qRJefTRRzN37tzMnTt3m30GDRqUq6++Om9+85t3+DoAAABA9ar7JUVF2n///bN06dJccMEFGTt2bHbffffstttuOeigg3LGGWfkoYceypQpUyo9TAAAAKCLVfUMl/nz52+3bKgjpk+fvt2sl87q379/Zs6cmZkzZxZ6XgAAAKB6mOECAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFq+pPKQIAukmpVOkRVJWGjuzse9txDR36DgNARQguAEDbSqWccP+MSo+iqpSSfH38ve3a1/e2Y0pJrnvLnEoPAwB2yJIiAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABSssdIDAACqQanSA6gyDR3Y1/e2YzryvQWAyhFcAIC2NSS9vvVflR5FVSmVkny9HTv63nZYqZTk0kqPAgB2zJIiAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABSsXTfNveqqq7p6HEmSj3zkI93yOgAAAABdqV3BZfr06Wlo6PqP4BNcAAAAgFrQoY+FLpVKXTWObgk6AAAAAN2hQ8HlJz/5SQ444IBCB/Dwww/nXe96V6HnBAAAAKikDgWX4cOHZ5999il0AC+88EKh5wMAAACoNJ9SBAAAAFCwds1wueKKK5IkI0aMKHwAI0aMKJ8fAAAAoBa0K7hMmzatywYwePDgLj0/AAAAQHezpAgAAACgYIILAAAAQMEEFwAAAICCdehjodvy3HPPZfHixfnNb36TtWvX5uWXX97hMbNmzSrq5QEAAAB6jE4Hl9///vc588wzc/3116e5ublDxwouAAAAQC3qVHBZtWpV3vrWt+a3v/1tSqVSUWMCAAAAqGqduofLOeeck5UrV6ZUKuWEE07I7bffnueeey4vv/xyNm/evMMHAAAAQC3q1AyXm2++OQ0NDfnwhz+c+fPnFzQkAAAAgOrWqRkuq1atSpL87d/+bSGDAQAAAKgFnQouw4cPT5L079+/kMEAAAAA1IJOBZcjjzwySfKrX/2qkMEAAAAA1IJOBZezzjorvXv3zr/8y79kw4YNRY0JAAAAoKp1Kri84Q1vyDe/+c08/PDD+au/+qs88sgjRY0LAAAAoGp16lOKkuSDH/xgDjjggEyePDkHH3xw3vSmN+XAAw/Mbrvt1uZxDQ0NmTdvXmdfHgAAAKDH6XRweeSRR3LmmWfm2WefTZL88pe/zC9/+cs2jymVSoILAAAAULM6FVwef/zxHHnkkVm1alVKpVKSZNCgQRk8eHB69erUaiUAAACAqtWp4PJP//RPeeaZZ9KrV6+cddZZ+cQnPpF99tmnqLEBAAAAVKVOBZfbbrstDQ0N+cxnPpMLLrigqDEBAAAAVLVOrfv5wx/+kCR573vfW8hgAAAAAGpBp4LLn/3ZnyVJevfuXchgAAAAAGpBp4LLO97xjiTJAw88UMhgAAAAAGpBp4LLWWedlf79++eCCy7IH//4x6LGBAAAAFDVOhVc9t9//9x4441Zu3ZtJkyYkJ/+9KdFjQsAAACganXqU4re9ra3JUn23HPPPPzww3nXu96V3XffPQcccEB22223No9taGjIbbfd1pmXzzPPPJP7778/999/fx544IE88MADee6555Ik06ZNy/z583d4js2bN2f58uXbnOehhx7Kpk2bkiQLFy7MUUcd1e4xrV+/Ppdeemm+973vpampKZs2bcrIkSMzefLkfPrTn87rXve6nXmrAAAAQBXpVHBZtGhRGhoayl+XSqX86U9/yv3339/qMQ0NDSmVStsct7OGDRvW6XN861vfyvTp0zt9niRZsWJFJk+enIcffnibX1++fHmWL1+eyy+/PNdcc00mTZpUyOsBAAAAPVOngsuRRx5ZSDgpwsiRIzNmzJj85Cc/6dBxpVKpvL3rrrvmjW98Y5qbm/OrX/2qQ+d54YUXMmXKlHJsOfnkk3PiiSemX79+WbhwYWbPnp3Vq1fnhBNOyOLFi/OmN72pQ+cHAAAAqkenZ7hU0qxZszJu3LiMGzcuw4YNy8qVK7Pffvt16BwHH3xwLr744hx22GF585vfnL59++bcc8/tcHCZM2dOli9fniS58MILM2PGjPJz48ePz9FHH50jjzwy69evz+mnn57bb7+9Q+cHAAAAqkengkulnXfeeZ0+x2GHHZbDDjusU+d46aWXcvHFFydJxowZk89+9rPb7TN+/PicdNJJueyyy7Jw4cI8+OCDOfTQQzv1ugAAAEDP1KlPKeIVixYtyvPPP5/klZv19urV8rd163vF3HDDDd0wMgAAAKASBJcC3HXXXeXtiRMntrrf2LFj079//yTJ3Xff3eXjAgAAACqjU8Fl6dKl2WWXXdKvX7889dRTO9z/qaeeSt++fdPY2Jhf//rXnXnpHmXZsmXl7dGjR7e6X2NjY0aNGrXdMQAAAEBt6VRwWbBgQUqlUqZMmZK99957h/vvvffeec973pPNmzfnO9/5Tmdeukd54oknkiT9+/fP7rvv3ua+I0eOTJKsWrUqGzdu7OqhAQAAABXQqeCyaNGiNDQ05Nhjj233MZMnT06S/OxnP+vMS/coa9euTZIMGDBgh/tuWVKUvPJR0i3ZuHFj1qxZs80DAAAAqB6dCi5bZnYcfPDB7T7moIMOSpI8+eSTnXnpHmXDhg1Jkt69e+9w3z59+pS3X3zxxRb3mT17dgYPHlx+bJkVAwAAAFSHTgWX5557LknSt2/fdh+zJTg888wznXnpHmXL+9+0adMO9916GVG/fv1a3Ofss8/O6tWry48tYQsAAACoDp0KLnvssUeS5PHHH2/3MVtmtgwaNKgzL92jDBw4MEnrS4S2tm7duvJ2a0uQ+vTpk0GDBm3zAAAAAKpHp4LLlqVE//mf/9nuY2688cYk/7e0qBaMGDEiySsx5fnnn29z3y2zVYYOHbrN8iIAAACgdnQquEyaNCmlUilXXXVV7rrrrh3uf+edd+Zb3/pWGhoaMmXKlM68dI+y9T1sli9f3up+zc3NWbFiRZJkzJgxXT4uAAAAoDI6FVxOPfXU7Lnnnnn55ZczadKkXHLJJeUbyG5tw4YN+dd//ddMnjw5L7/8cvbYY498/OMf78xL9yhHHHFEefuOO+5odb8lS5aUlxRNmDChy8cFAAAAVEangsuAAQNyzTXXZJdddsn69etz+umnZ+jQoTn66KPzoQ99KH/913+do48+OkOHDs0ZZ5yRdevWZdddd821115bU/clOeqoozJ48OAkyZVXXplSqdTifvPnzy9vH3fccd0xNAAAAKACOhVckuTtb397fvzjH+e1r31tSqVS1q1blzvvvDMLFizId77zndx5551Zt25dSqVS9t577/z4xz/OO97xjiLG3mP07t07n/70p5Mky5Yty5w5c7bbZ/HixZk3b16SZOLEiRk3bly3jhEAAADoPo1FnOToo4/OihUrctVVV+WWW27J0qVL8+yzzyZJ9txzz/zFX/xF3v3ud+dv/uZvCr1R7N13352mpqby11teM0mampq2mVGSJNOnT2/xPK/e77/+67/K27feemtWrlxZ/nr//fffZgnRFjNmzMiCBQvyyCOPZObMmWlqasqJJ56Yfv36ZeHChfnyl7+c5ubm9OvXLxdddFF73yIAAABQhQoJLknSt2/fnHLKKTnllFOKOuUOXX755bnyyitbfO6ee+7JPffcs82vtRZcPvrRj7b6GhdccME2X0+bNq3F4DJw4MDccsstmTRpUh599NHMnTs3c+fO3WafQYMG5eqrr86b3/zmVl8PAAAAqH6dXlLE/9l///2zdOnSXHDBBRk7dmx233337LbbbjnooINyxhln5KGHHqqpT2cCAAAAWlbYDJdKmD9//nbLgXZGaze53Rn9+/fPzJkzM3PmzMLOCQAAAFQXM1wAAAAACtau4PK2t70txxxzTH77298WPoCVK1eWzw8AAABQC9q1pGjRokVpaGjIunXrCh/AunXryucHAAAAqAWWFAEAAAAUrEM3zf33f//37LXXXoUO4Jlnnin0fAAAAACV1qHg8vWvf72rxgEAAABQM9odXIr86GQAAACAWtau4LJ58+auHgcAQI3xP6sAoJ51aEkRAAA71tCQfOoTu1V6GABABfmUIgAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCNVZ6AABAz1cqVXoEAADVpVPB5Z/+6Z+SJPvss0+mTZvWrmNWrVqVr3/960mSWbNmdeblAYBu0ZDvXnpbpQcBAFBVOhVczj333DQ0NCRJbr/99vzHf/xHevfu3eYxzzzzTPk4wQUAAACoRYUsKSqVSvn2t7+dRx99NDfeeGOGDRtWxGkBgEqxhggAoFMKCS7vete7cuutt+bnP/95DjvssNx0001585vfXMSpAYBu1pDkhPtnVHoYAABVrZBPKZozZ04uueSS7LLLLnniiSdyxBFH5Prrry/i1AAAAABVp7CPhf7kJz+ZH/3oR9ljjz2yfv36vP/97y/fVBcAAACgnhQWXJLkmGOOyX333ZcDDzwwpVIp5513Xj7wgQ9kw4YNRb4MAAAAQI9WaHBJkgMOOCA///nP8453vCOlUinXXXdd/vIv/zJPP/100S8FAAAA0CMVHlySZPDgwfnRj36UT33qUymVSvnFL36RcePG5YEHHuiKlwMAAADoUbokuCRJr1698q//+q/5xje+kcbGxvzud7/LxIkTc/XVV3fVSwJAkoaklPp4AF3InyUAdE4hHwvdllNOOSUHHnhgTjjhhDz33HO54IILuvolAah2Q87Y6UMbkvzrquKG0uMNqfQAoDb5swSAzuqyGS5bO+qoo3Lfffdl9OjRKZVkdAAAAKC2dWqGyxVXXJEkGTFixA73HTVqVH7+85/nk5/8ZJ544onOvCwAAABAj9ap4DJt2rQO7T9w4MBcddVVnXlJAAAAgB6vW5YUAQAAANQTwQUAAACgYIILAAAAQMG6/GOhAaCjfJ4dAADVTnABoGdpaMh1B+xd6VEAAECnWFIEAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABSssdIDoP1KpVceADXJH3AANcEf50At68ifcYJLFfnFXf+U3XbtV+lhAHSJE/J0pYcAQAEeaLqg0kMA6DLrX3oxyant2teSIgAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUrLHSA6D9SqVSSqVSpYcBAAAAdakjP5MLLlXkhJ/+S9KwS6WHAQAAAPWp9HK7d7WkCAAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIJVdXB55plncvPNN2fWrFk59thjs+eee6ahoSENDQ2ZPn16h89366235vjjj8+IESPSp0+fjBgxIscff3xuvfXWdp9j/fr1+cpXvpLDDjssQ4YMyYABAzJmzJicddZZefzxxzs8JgAAAKD6NJRKpVKlB7GzGhoaWn1u2rRpmT9/frvOUyqV8rGPfSxz585tdZ9TTjkl3/jGN9p8zRUrVmTy5Ml5+OGHW3x+8ODBueaaazJp0qR2jWuLNWvWZPDgwcluByQNu3ToWAAAAKAgpZeT9Y9m9erVGTRoUJu7VvUMl62NHDky73znO3fq2M9//vPl2HLIIYfk2muvzf33359rr702hxxySJJk7ty5+cIXvtDqOV544YVMmTKlHFtOPvnk3Hbbbbn33ntz/vnnZ8CAAVm9enVOOOGEPPTQQzs1TgAAAKA6VPUMl3POOSfjxo3LuHHjMmzYsKxcuTL77bdfkvbPcGlqasqYMWPS3NycsWPH5s4770y/fv3Kz69fvz4TJ07MkiVL0tjYmOXLl2fUqFHbnefcc8/NeeedlyS58MILM2PGjG2eX7x4cY488sg0Nzfn6KOPzu23397u92mGCwAAAPQA9TLD5bzzzsuUKVMybNiwnT7H1772tTQ3NydJLrnkkm1iS5LstttuueSSS5Ikzc3Nueiii7Y7x0svvZSLL744STJmzJh89rOf3W6f8ePH56STTkqSLFy4MA8++OBOjxkAAADo2ao6uHRWqVTKTTfdlCQZPXp0Dj/88Bb3O/zww3PQQQclSb7//e/n1ZOCFi1alOeffz7JKzNrevVq+du69Y18b7jhhk6OHgAAAOip6jq4PPbYY3nqqaeSJBMnTmxz3y3PP/nkk1m5cuU2z911113b7deSsWPHpn///kmSu+++e2eGDAAAAFSBug4uy5YtK2+PHj26zX23fn7r4zpynsbGxvL9X159DgAAAKB21HVweeKJJ8rbI0aMaHPfkSNHtnjc1l/3798/u+++e7vOs2rVqmzcuLHFfTZu3Jg1a9Zs8wAAAACqR2OlB1BJa9euLW8PGDCgzX23LAVKXvkI6JbOs6NztHSePn36bLfP7Nmzy594tK3S/z4AAACA7tf+n8nrOrhs2LChvN27d+829906jLz44ostnmdH59jRebY4++yzc+aZZ5a/XrNmTUaOHJnHvrQ2A/vV9aQkAAAAqJi1L27OfmfueL+kzoNL3759y9ubNm1qc9+tl/+8+qOjt5xnR+fY0Xm26NOnT4szXwAAAIDqUNfTJQYOHFjefvUyoVdbt25defvVS4e2nGdH59jReQAAAIDaUNfBZesb5T755JNt7rv1jXK3voHu1udZt25dnn/++XadZ+jQoWaxAAAAQI2q6+By8MEHl7eXL1/e5r5bPz9mzJidOk9zc3NWrFjR4jkAAACA2lHXwWW//fbL8OHDkyR33HFHm/veeeedSZK99947++677zbPHXHEEeXtts6zZMmS8pKiCRMm7MyQAQAAgCpQ18GloaEhU6dOTfLKzJT77ruvxf3uu+++8syVqVOnpqGhYZvnjzrqqAwePDhJcuWVV6ZUavljoubPn1/ePu644zo7fAB2UsnDw8PDw8PDw6PNB51X159SlCSnn356/uM//iPNzc057bTTcuedd27z6UEvvvhiTjvttCRJY2NjTj/99O3O0bt373z605/OF7/4xSxbtixz5szJjBkzttln8eLFmTdvXpJk4sSJGTduXNe9KQBaVUpyzut+VulhAAD0XKVS/umJd1R6FFWvqoPL3XffnaampvLXzz77bHm7qalpmxklSTJ9+vTtznHggQfmrLPOyj//8z9nyZIlmTBhQj73uc9l1KhRWbFiRS644IIsXbo0STJjxowccMABLY5lxowZWbBgQR555JHMnDkzTU1NOfHEE9OvX78sXLgwX/7yl9Pc3Jx+/frloosu6vR7BwAAAHquhlJr61+qwPTp03PllVe2e//W3urmzZtz8skn55vf/Garx5500kmZO3duevVqfRVWU1NTJk2alEcffbTF5wcNGpSrr746U6ZMafeYk2TNmjUZPHhwHvvqsAzsV9erwAA6zQwXAIAdMMOlVWtf3Jz9zvxDVq9enUGDBrW5b1XPcClKr169Mm/evLz3ve/N3Llz88ADD+TZZ5/NnnvumXHjxuXUU0/Nscceu8Pz7L///lm6dGkuvfTSfO9730tTU1M2bdqUkSNHZtKkSfnMZz6TffbZpxveEQDtdemkg3e8EwBAjSuVSvnUj5ZVehg1papnuNQLM1wAivPqGS6CCwDAq4KLGS6t6sgMFz+9AwAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1z+14YNG/Lv//7vOeaYYzJ06ND07t07e++9dyZPnpwFCxa0+zy33nprjj/++IwYMSJ9+vTJiBEjcvzxx+fWW2/twtEDAAAAPUljpQfQEzz88MOZOnVqHn744W1+/emnn87TTz+dH/7wh5k/f36uu+669O/fv8VzlEqlfOxjH8vcuXO3+fWnnnoqN954Y2688caccsop+cY3vpGGhoYuey8AAABA5dX9DJdVq1blHe94Rzm2nHDCCbn55pvzi1/8IjfffHNOOOGEJK/MXPngBz/Y6nk+//nPl2PLIYcckmuvvTb3339/rr322hxyyCFJkrlz5+YLX/hCF78jAAAAoNLqPricd955eeKJJ5Ik55xzTr773e9m8uTJOeSQQzJ58uR897vfzaxZs5IkP/jBD3LDDTdsd46mpqZceOGFSZKxY8fmnnvuyYknnphx48blxBNPzN13352xY8cmSS644IKsWLGim94dAAAAUAl1HVxefvnlXH311UmSffbZp9XZJ7NmzcrrXve6JMns2bO3e/5rX/tampubkySXXHJJ+vXrt83zu+22Wy655JIkSXNzcy666KKi3gIAAADQA9V1cHn00Ufz/PPPJ0ne8Y53ZJdddmlxv1122SXveMc7kiRLlizJypUry8+VSqXcdNNNSZLRo0fn8MMPb/Echx9+eA466KAkyfe///2USqWC3gUAAADQ09R1cPnjH/9Y3h42bFib+279/J133lnefuyxx/LUU08lSSZOnNjmObY8/+STT24TbQAAAIDaUtfBZetPHFq9enWb+279/K9//evy9rJly8rbo0ePbvMcWz+/9XEAAABAbanr4LL//vtn1113TbLtrJWWbP38448/Xt7ecsPdJBkxYkSb5xg5cmSLxwEAAAC1pa6DS//+/XPMMcckSR566KFce+21Le537bXX5le/+lX567Vr17a4PWDAgB2+3hYvvPBCq/tt3Lgxa9as2eYBAAAAVI+6Di7JKx8L3djYmCSZNm1avvSlL+Xxxx/PSy+9lMcffzxf+tKXMm3atPTu3bt8zIsvvlje3rBhQ3l7631a0qdPnxbP8WqzZ8/O4MGDy4+tZ8YAAAAAPV/dB5fDDjss8+bNS+/evfPSSy/lC1/4QvbZZ5/07t27/FHRmzdvzr/8y7+Ujxk4cGB5u2/fvuXtTZs2tflaGzduLG+/+qOjt3b22Wdn9erV5YflRwAAAFBd6j64JMlHPvKR3H///TnhhBO2iSm9evXKMccck3vuuSdHHXVU+df32GOP8vbW+7e1TChJ1q1bV95ua/lRnz59MmjQoG0eAAAAQPVorPQAeoo///M/z3e/+928/PLL+d3vfpcNGzZk+PDh2W233ZIk11xzTXnfgw8+uLy99Y1yn3zyyTZfY+uZKpYJAQAAQO0SXF5ll112afHThu6+++7y9lve8pby9tbxZfny5W2ee+vnx4wZ05lhAlCQUqlU6SEAAFCDBJd22LRpU6677rokyd577523vvWt5ef222+/DB8+PE8//XTuuOOONs+z5aOl99577+y7775dNl4A2u9TP1pW6SEAAFCD3MOlHS6++OKsWrUqSfKxj30su+yyS/m5hoaGTJ06NckrM1juu+++Fs9x3333lWe4TJ06NQ0NDV08agAAAKBSzHBJ8vjjj+d1r3tdi8/94Ac/yD/+4z8mSQ444ICcddZZ2+1z+umn5z/+4z/S3Nyc0047LXfeeec2n0L04osv5rTTTkuSNDY25vTTTy/+TQDQfpYRAQDQxQSXJG984xszfvz4nHDCCXnDG96Q3r17Z+XKlfne976XBQsWJHnlk4kWLFiwzcdAb3HggQfmrLPOyj//8z9nyZIlmTBhQj73uc9l1KhRWbFiRS644IIsXbo0STJjxowccMAB3fr+APg/DUn+6Yl3VHoYAADUuIaSuwVmwIAB23xk86sdfPDB+fa3v51DDjmk1X02b96ck08+Od/85jdb3eekk07K3Llz06tXx1ZyrVmzJoMHD85jXx2Wgf2sAgMAAIBKWPvi5ux35h+yevXqDBo0qM19zXBJcvnll+cnP/lJ7r///vzud7/LCy+8kKFDh+ZNb3pT3ve+9+XDH/5wdt111zbP0atXr8ybNy/vfe97M3fu3DzwwAN59tlns+eee2bcuHE59dRTc+yxx3bTOwIAAAAqyQyXKmCGCwAAAFReR2a4+OkdAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMEFAAAAoGCCCwAAAEDBBBcAAACAggkuAAAAAAUTXAAAAAAKJrgAAAAAFExwAQAAACiY4AIAAABQMMHlf23atCnz5s3Lu971rvzZn/1Z+vTpkwEDBuSggw7K3/7t3+a+++5r13luvfXWHH/88RkxYkT69OmTESNG5Pjjj8+tt97axe8AAAAA6CkaSqVSqdKDqLQnnngikydPzq9+9as29zvjjDPyL//yL2loaNjuuVKplI997GOZO3duq8efcsop+cY3vtHi8W1Zs2ZNBg8enMe+OiwD+2lkAAAAUAlrX9yc/c78Q1avXp1Bgwa1uW/d//Te3Ny8TWx505velPnz52fx4sX5yU9+klmzZqV///5Jkq997WuZM2dOi+f5/Oc/X44thxxySK699trcf//9ufbaa3PIIYckSebOnZsvfOEL3fCuAAAAgEqq+xku119/fd73vvclScaPH5+77roru+yyyzb7PPjggxk/fnxeeuml7LHHHnnmmWfS2NhYfr6pqSljxoxJc3Nzxo4dmzvvvDP9+vUrP79+/fpMnDgxS5YsSWNjY5YvX55Ro0a1e4xmuAAAAEDlmeHSAffcc095++yzz94utiTJoYcemilTpiRJ/vSnP2X58uXbPP+1r30tzc3NSZJLLrlkm9iSJLvttlsuueSSJK/MqLnooouKfAsAAABAD1P3wWXTpk3l7de//vWt7rf1jJSNGzeWt0ulUm666aYkyejRo3P44Ye3ePzhhx+egw46KEny/e9/P3U+sQgAAABqWt0HlwMPPLC8/Zvf/KbV/VasWJEkaWhoyAEHHFD+9cceeyxPPfVUkmTixIltvtaW55988smsXLlyZ4cMAAAA9HB1H1w++MEPltddXXDBBXn55Ze322fp0qW55ZZbkiQnnnjiNuu0li1bVt4ePXp0m6+19fNbHwcAAADUlroPLkOHDs38+fPTr1+/3HPPPRk3blyuuuqq3HffffnZz36W8847LxMnTsymTZvy5je/OV/96le3Of6JJ54ob48YMaLN1xo5cmSLxwEAAAC1pXHHu9S+4447LkuWLMlXv/rVfPOb38y0adO2eX7YsGE577zzcsopp5Q/InqLtWvXlrcHDBjQ5utsfewLL7zQ6n4bN27c5j4xa9asadf7AAAAAHqGup/hkiQvvfRSrrnmmvzgBz9o8Wa2f/jDH3Lttddm0aJF2z23YcOG8nbv3r3bfJ0+ffqUt1988cVW95s9e3YGDx5cfmw9MwYAAADo+eo+uKxbty5vf/vbc/755+e5557LzJkzs2zZsmzcuDGrV6/OT37ykxxxxBF54IEH8u53vzsXX3zxNsf37du3vL31Jx61ZOtZK6/+6OitnX322Vm9enX5YfkRAAAAVJe6Dy7nnHNO7rzzziTJvHnzcsEFF2T06NHp3bt3Bg0alHe84x1ZuHBhjj766JRKpZx55pl56KGHyscPHDiwvN3WMqHklbizRVvLj/r06ZNBgwZt8wAAAACqR10Hl1KplCuuuCLJKx8P/ep7t2zR2NiYL37xi0mSzZs3l49Jtr1R7pNPPtnm6209U8UyIQAAAKhddR1c/vCHP+SPf/xjkuSQQw5pc99DDz20vL18+fLy9sEHH9zir7dk6+fHjBnTobECAAAA1aOug0tj4/99SFNzc3Ob+7700kstHrfffvtl+PDhSZI77rijzXNsWbq09957Z9999+3ocAEAAIAqUdfBZciQIeX7oyxevLjN6LJ1TNlvv/3K2w0NDZk6dWqSV2aw3HfffS0ef99995VnuEydOjUNDQ2dHj8AAADQM9V1cOnVq1cmT56cJHn66adz/vnnt7jfn/70p3zuc58rfz1lypRtnj/99NPLs15OO+207T7y+cUXX8xpp52W5JXZMaeffnpRbwEAAADogeo6uCTJrFmzsttuuyVJzj333LznPe/J9ddfn6VLl2bx4sX52te+lje/+c359a9/nSQ55phj8s53vnObcxx44IE566yzkiRLlizJhAkTsmDBgixZsiQLFizIhAkTsmTJkiTJjBkzcsABB3TjOwQAAAC6W0OpVCpVehCV9rOf/Swf/OAH8+yzz7a539ve9rZcd9112WOPPbZ7bvPmzTn55JPzzW9+s9XjTzrppMydOze9enWsc61ZsyaDBw/OY18dloH96r6RAQAAQEWsfXFz9jvzD1m9enX5FiWt8dN7kre//e1Zvnx5Lrjgghx11FEZOnRodt111/Tr1y/77bdf3v/+9+f73/9+fvazn7UYW5JXlifNmzcvt9xyS6ZOnZrhw4end+/eGT58eKZOnZof/vCHufzyyzscWwAAAIDqY4ZLFTDDBQAAACrPDBcAAACAChJcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAAomuAAAAAAUTHABAAAAKJjgAgAAAFAwwQUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcAAAAAArWWOkBAFCdSqVKj6A+NTR0bH/XqTI6ep0AOsqf75XTkT/jXafK6Cl/DwsuAHRYqZRs2jit0sOoQ6X06XtV+/d2nSqkY9cJoKP8+V5J7f8z3nWqlJ7z97AlRQAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAAAomOACAAAAUDDBBQAAAKBgggsAAABAwQQXAAAAgIIJLgAAAAAFE1wAAAAACia4AAAAABRMcAEAAABohyEnPdLufQUXAAAAgIIJLlVkj799uNJDAAAAgLr0mo893aH9BRcAAACAggkuVaShoSFDTn2q0sMAAACAutLR2S2J4FJ1RBcAAADoPjsTWxLBBQAAAKBwgksVMssFAAAAut7Ozm5JBJeqJboAAABA1+lMbEkEFwAAAIDCCS5VzCwXAAAAKF5nZ7ckgkvVE10AAACgOEXElkRwAQAAACic4FIDzHIBAACAzitqdksiuNQM0QUAAAB2XpGxJRFcAAAAAAonuNQQs1wAAACg44qe3ZIILjVHdAEAAID264rYkiSNXXJWeoRSqdIjAGpZyR8yFdHRb7vrVBm+7UBX8+d75XTkW+86VUZHvu0NDV03joaS3wE93po1azJ48OCsXr06gwYNatcxpVIpvQYe3MUjAwAAgOpVemFZh/bvyM/nlhTVqIaGhmxe++tKDwMAAAB6pI7Glo4SXAAAAAAKJrjUMLNcAAAAYHtdPbslEVxqnugCAAAA/6c7YksiuAAAAAAUTnCpA2a5AAAAQPfNbkkEl7ohugAAAFDPujO2JIILAAAAQOEElzpilgsAAAD1qLtntySCS90RXQAAAKgnlYgtieBSl0QXAAAA6kGlYksiuAAAAAAUTnCpU2a5AAAAUMsqObslqfPgctRRR6WhoaFDj0WLFrV6vltvvTXHH398RowYkT59+mTEiBE5/vjjc+utt3bfm+oA0QUAAIBaVOnYktR5cOmoXr165YADDtju10ulUk499dQce+yxufHGG/PUU09l06ZNeeqpp3LjjTfm2GOPzamnnppSqVSBUQMAAADdrbHSA6ikK664IuvWrWtzn1//+tf5wAc+kCQ55phjsvfee2+3z+c///nMnTs3SXLIIYdk5syZGTVqVFasWJELL7wwS5cuzdy5czN06NB86UtfKv6NdMKWWS69Bh5c6aEAAABAp/WE2S1J0lAy7aJNn/vc53LhhRcmSb71rW/lb/7mb7Z5vqmpKWPGjElzc3PGjh2bO++8M/369Ss/v379+kycODFLlixJY2Njli9fnlGjRnVoDGvWrMngwYOzevXqDBo0qPNvqgWlUkl0AQAAoKp1dWzpyM/nlhS1YfPmzbn66quTJAMGDMjxxx+/3T5f+9rX0tzcnCS55JJLtoktSbLbbrvlkksuSZI0Nzfnoosu6tpB7yT3cwEAAKCa9ZSZLVsILm247bbb8tRTTyVJ3ve+92W33Xbb5vlSqZSbbropSTJ69OgcfvjhLZ7n8MMPz0EHHZQk+f73v+9eLgAAAFDjBJc2XHXVVeXtj3zkI9s9/9hjj5WDzMSJE9s815bnn3zyyaxcubK4QRbILBcAAACqUU+b3ZIILq164YUXcuONNyZJXve61+Woo47abp9ly/7vgo4ePbrN8239/NbH9TSiCwAAANWkJ8aWpM4/pagt119/ffkTjD784Q+noaFhu32eeOKJ8vaIESPaPN/IkSNbPK4lGzduzMaNG8tfr1mzpl1jLopPLgIAAKAa9NTYkpjh0qodLSdKkrVr15a3BwwY0Ob5+vfvX95+4YUX2tx39uzZGTx4cPmxdawBAAAAej7BpQVPPvlkFi1alOSVG94eeOCBLe63YcOG8nbv3r3bPGefPn3K2y+++GKb+5599tlZvXp1+bGjGTFdwdIiAAAAerKePLslsaSoRd/+9rezefPmJMm0adNa3a9v377l7U2bNrV5zq2XCL36o6NfrU+fPtsEmkqxtAgAAICeqKfHlsQMlxZ961vfSvJK+PjABz7Q6n4DBw4sb+9omdCW+8EkO15+1JOY6QIAAEBPUg2xJRFctrNkyZL8+tevBIYpU6Zkjz32aHXfrW+U++STT7Z53q2XBbknCwAAANQ2weVVtr5ZblvLiZLk4IP/b6nN8uXL29x36+fHjBmzk6OrDLNcAAAA6AmqZXZL4h4u23jppZfyne98J0kydOjQHHvssW3uv99++2X48OF5+umnc8cdd7S575133pkk2XvvvbPvvvsWMt7utO39XEqVHg4AAAB1paGqYksiuGzjRz/6UVatWpUk+dCHPpTGxra/PQ0NDZk6dWq+/vWvZ/ny5bnvvvty+OGHb7fffffdV57hMnXq1DQ0NBQ/+G6wJbr88bK9Kz0UAAAA6shrPvZ0pYfQYZYUbWXr5UQf+chH2nXM6aefXg4zp5122nYf+fziiy/mtNNOS5I0Njbm9NNPL2awAAAAQI8luPyvP/3pT7n55puTJG984xvzF3/xF+067sADD8xZZ52V5JUb7k6YMCELFizIkiVLsmDBgkyYMCFLlixJksyYMSMHHHBA17yBbtLQ0JAhpz5V6WEAAABQJ6pxdktiSVHZggULsnHjxiTtn92yxfnnn59nnnkm3/zmN7N06dKceOKJ2+1z0kkn5Utf+lIhY620LdHF0iIAAAC6UrXGlsQMl7JvfetbSZJddtklf/3Xf92hY3v16pV58+bllltuydSpUzN8+PD07t07w4cPz9SpU/PDH/4wl19+eXr1qp1vt5kuAAAAdKVqji1J0lAqlXzkTA+3Zs2aDB48OKtXr86gQYMqPZxtlEolM10AAAAoVE+NLR35+bx2plwAAAAA9BCCC51iaREAAABF6qmzWzpKcKHTRBcAAACKUCuxJRFcKIjoAgAAQGfUUmxJBBcKJLoAAACwM2ottiSCCwAAAEDhBBcKZZYLAAAAHVGLs1sSwYUuILoAAADQHrUaWxLBhS4iugAAANCWWo4tieBCFxJdAAAAaEmtx5ZEcKGLiS4AAABsrR5iSyK4AAAAABROcKHLmeUCAABAUj+zWxLBhW4iugAAANS3eootieBCNxJdAAAA6lO9xZZEcKGbiS4AAAD1pR5jS5I0VnoA7FipVEqSrFmzpsIjKU7jB5flT988qNLDAAAAoAsNOemRmvpZdst72fJzelsElyqwdu3aJMnIkSMrPBIAAADogDMHV3oEXWLt2rUZPLjt99ZQak+WoaI2b96cp59+OgMHDkxDQ0Olh1MRa9asyciRI/PEE09k0KBBlR4OBXFda49rWptc19rjmtYm17U2ua61xzWtbqVSKWvXrs3w4cPTq1fbd2kxw6UK9OrVKyNGjKj0MHqEQYMG+UOpBrmutcc1rU2ua+1xTWuT61qbXNfa45pWrx3NbNnCTXMBAAAACia4AAAAABRMcKEq9OnTJ+ecc0769OlT6aFQINe19rimtcl1rT2uaW1yXWuT61p7XNP64aa5AAAAAAUzwwUAAACgYIILAAAAQMEEFwAAAICCCS4AAAAABRNcKFRDQ0O7HkcddVSLx8+fP7/d55g/f/4Ox7N+/fp85StfyWGHHZYhQ4ZkwIABGTNmTM4666w8/vjjxb75GtXZa/pq999/fz7xiU9kzJgxGTRoUAYMGJBRo0Zl8uTJ+epXv5pVq1a1ebxrWozOXNeVK1e2+/gtj3333bfN8biunVfUf6u//e1v8/d///c59NBDs/vuu2fXXXfNkCFD8ta3vjVf/OIXd/jf6BauaTGKuq6PPfZYzjjjjLzxjW/MwIED079//xx44IH55Cc/mf/5n/9p93hc1+I9++yzufDCCzNhwoS89rWvTZ8+fTJ8+PC85S1vyYwZM7J48eIdnuPWW2/N8ccfnxEjRqRPnz4ZMWJEjj/++Nx6663tHodrW5ydvaabN2/Or3/968yfPz+f+MQnMm7cuPTp06f83/miRYs6NA7XtFg7e103bNiQm266Kaeddlre8pa3ZMiQIeW/W8ePH59zzz03v/vd79o9Dte1ypSgQEna9Zg4cWKLx19xxRXtPscVV1zR5liamppKBx10UKvHDx48uHTLLbcU/02oMZ29plts2LCh9Hd/93elhoaGNs9z4403tnoO17Q4nbmujz32WLuP3/J45zvf2epYXNdiFPHf6tVXX13abbfd2jz+Na95Tem2225rcyyuaXGKuK6XXXZZqXfv3q0e27t379K///u/73Asrmvxvvvd75Ze85rXtHltp06d2urxmzdvLp1yyiltHn/KKaeUNm/e3OY4XNvidOaazp8/v83jFi5c2O5xuKbF2tnr+stf/rI0cODAHf4ZPnDgwNKCBQt2OA7Xtfo0BrrAxz/+8XziE59o9fn+/fvv8Bw//vGPM3z48FafHzFiRKvPvfDCC5kyZUoefvjhJMnJJ5+cE088Mf369cvChQsze/bsrF69OieccEIWL16cN73pTTscT73rzDXdtGlTjjvuuPzoRz9KkvzlX/5lPvKRj2TMmDFpbGzMb3/72/zyl7/M9773vVbP4Zp2jZ25rnvvvXd+9atf7fDcs2fPzjXXXJMkmTZtWov7uK7F29n/VhcvXpyPfOQjefnll9OrV69MmzYtU6dOzfDhw/P444/nyiuvzA9+8IM899xzec973pP//u//bnHmkmvaNXb2un7nO9/JqaeemiQZPHhwPvvZz+Ztb3tb+vTpk6VLl+bCCy9MU1NTPvnJT2bo0KF53/ve1+J5XNfiXXXVVfnoRz+azZs3Z6+99srHP/7xHHHEERkyZEh+//vfZ8WKFfnBD36QXXfdtdVzfP7zn8/cuXOTJIccckhmzpyZUaNGZcWKFbnwwguzdOnSzJ07N0OHDs2XvvSlFs/h2hans9e0VCqVt3fddde88Y1vTHNzc7v+zt2aa1qszlzXNWvWZO3atUmSCRMmZMqUKRk7dmxe85rXZNWqVbnhhhty+eWXZ+3atfnQhz6UgQMH5thjj21xHK5rlap08aG25H/r6jnnnLNTx289w+Wxxx7b6XGcc8455fNceOGF2z1/7733lhobG0tJSkcfffROv0496Ow1LZVKpS984Qvl88yZM6fNfTdt2tTir7umxSriuralubm5NHz48PL/tVm3bl2L+7muxensNZ0yZUr5HJdeemmL+5x55pnlfU477bQW93FNi9WZ67pu3brSXnvtVUpSGjBgQOlXv/rVdvusXr269P/9f/9fKUnpta99bemFF15o8Vyua7F+/etfl/r06VNKUvrLv/zL0vPPP9/qvhs3bmzx1x999NHy93zs2LGl9evXb/P8unXrSmPHji0lKTU2NpaamppaPI9rW4wirunPf/7z0sUXX1xavHhx6cUXXyyVStten/bOcHFNi9PZ63rPPfeU3v/+95f+53/+p9Xjvv/975dngI8aNarVGWmua3USXChUTwgumzZtKu2+++6lJKUxY8aUXn755Rb3O/XUU8uvtWTJkp16rXrQ2Wu6YsWK0q677lpKUpo+ffpOncM1LV5XB5dbb721/Bof/ehHW9zHdS1WZ6/pHnvsUUpeWTLUmueff778Ooceeuh2z7umxevMdb3uuuvKx//jP/5jq/v99Kc/Le/3b//2b9s977oW75hjjiklKe25556lVatW7dQ5PvGJT5S/34sXL25xn8WLF5f3+dSnPrXd865tcYq4pi3paHBxTYvVVdf11d773veWr8cvfvGL7Z53XauXm+ZScxYtWpTnn38+ySvLGHr1avm3+fTp08vbN9xwQzeMrD7NnTs3L730UhoaGjJr1qydOodrWn2uuuqq8nZry4lc155l06ZNSZL99tuv1X0GDx6cPffcM0mycePG7Z53TXuWBx54oLzd2hT1JDnqqKPSt2/fJMl111233fOua7GWL1+e2267LUnyqU99qvzfVEeUSqXcdNNNSZLRo0fn8MMPb3G/ww8/PAcddFCS5Pvf//42S1YS17YoRVzTorimxenO63r00UeXt1esWLHd865r9RJcqDl33XVXeXvixImt7jd27Njymve77767y8dVr7bcl2Xs2LHlH+Q2b96cJ5/8/9u796CozvsN4A8XkasSRYgoUVEwImoo3jCOl7Q2aNtg6pDYeIEaG+8Z7RgNSIuX0SSiRY0k2qQ1LRIx6gS1EqwBMaTocE1MRhNvGEVDjIqioojw/f3BcH67sGdh2RcI5PnM7MzB9z3vvsszh7N+9+x5i1FUVIT79+83OAYzbVvu3LmDlJQUAECvXr0wZswYk/2Y60+Lv78/gJrVbPSUlZXh+vXrRv0NMdOflps3b2rbXl5euv3s7e3RpUsXAEB2djYePXpk1M5c1TK8X1l4eLi2XVpairNnz+LGjRsNjlFUVIQrV64AMJ+JYXtxcTEuXrxo1MZs1VCRqSrMVJ2WzNXwQwxTxRTm2nax4ELNYs+ePejfvz+cnJzg5uYGPz8/RERE4OjRo40eIzIyEl5eXnBwcICHhwdGjhyJmJgY7Q2GntOnT2vbTz75pG4/e3t79O3bt94+ZFpTMv3xxx9x4cIFAEBISAjKysqwePFieHh4wMfHB76+vujUqRPGjh2LQ4cO6Y7DTJuPimO1rr1796K8vBwAMHPmTNjY2Jjsx1ybR1Mzrb2x6o0bN7Bt2zaTfdasWVOvvyFm2nyakqvhjXRv376t209EUFZWBqDmSqdz584ZtTNXtU6cOAGg5oqxAQMGICkpCUOGDEGXLl3g7+8PDw8P+Pr6YtWqVbh7967JMRqbSd32urkwWzVUZKoKM1WnJXM9duyYtm0qN+bahrXyV5qonYHOEmWGj8mTJ+vecKoxy0I7OjrKtm3bdOcwYsQIASAuLi4Nzvc3v/mNNu6DBw+a/LrbM2syzczM1PosX75c+vbta3acJUuWmJwDM1XP2mPVnHHjxmljnD17Vrcfc1XL2kwfPXok06ZNEwBia2srs2fPlgMHDkhubq7s27dPnn/+eaPj2RRmqp41uW7fvl3rs3HjRt3nyM/PNxrv8OHDRu3MVa3evXsLABkyZIgsWLDAbLaBgYFy5cqVemO8++67Wp89e/aYfb49e/Zofeu+f2K2aqjIVI+l93Bhpuo0Z66GvvjiC7GzsxMAMnDgQJN9mGvbxStcSClnZ2dMnToV7733HrKyslBYWIj//ve/WLFiBbp27Qqg5jvEYWFhqKysNDmGr68vli5din379iEnJwc5OTlITk5GeHg4bGxs8ODBA8ydO1dbBrGu2qXXXF1dG5yv4ad/zf2JQ1tlTaaGl7Nv2rQJ58+fx6hRo3Ds2DGUl5fj5s2bSEpKQvfu3QEA8fHxJj9ZZ6bqqThWTbl06ZL2Kc2oUaPQr18/3b7MVS1rM7Wzs8POnTuxe/duDBkyBO+//z6ee+45DBs2DFOmTMHHH3+M8ePH4/Dhw3jzzTdNzoGZqmdNrpMmTdKWKf3b3/6mfR3MUHV1NVasWGH0b7U51v2ZuapRe2785ptvkJCQAHd3d2zbtg3Xrl3DgwcPkJubq91z5+uvv0Z4eDiqq6uNxjDMqKFczGXCbNVQkakqzFSdlsi1oqICs2fPRlVVFQBg3bp1Jvsx1zastSs+1L6UlpbqtpWUlEhQUJBWcd28eXO9Prdu3dJdCk1E5ODBg9qKN87OzvL999/X6+Pr6ysAxMfHp8H5zpgxQ5vP5cuXG+z/c2RNpomJiUbV/+DgYG2ZQ0NnzpwRFxcXASDdunWrt7QlM1XP2mNVz9q1a3U/Sa2LuaqlItPTp09LWFiYtqxk3Yejo6NMmzZNrl69anJ/Zqqetbm++uqrWru/v7+kpKTI7du35f79+3L8+HF59tlnBYA4ODho/RITE43GYK5q1X6SDUDs7OxMrjBUVVUlEydO1L2KZfXq1Vpbenq62edLT0/X+q5Zs8aojdmqoSJTPZZe4cJM1WnOXGvNnj1b2zciIkK3H3Ntu3iFCynl7u6u2+bl5YW9e/fCwcEBAPD222/X69O5c2fd+z0AwG9/+1vExsYCAMrLy/GPf/yjXp/alRZqV9wwx/AGVU5OTg32/zmyJtPaLGqtXbu23r8BgJ+fH+bNmweg5r4vn376qclxmKk61h6rehITEwEAHTt2xIsvvmi2L3NVy9pMs7KyEBISgv3796NHjx5ITExESUkJHj58iMuXLyMhIQFOTk5ISkrC8OHDTX43nJmqZ22ucXFx+N3vfgcAOHPmDCZPnozOnTvDyckJISEhOHz4MHx9ffHqq69q+7i5uRmNwVzVMjwPhoeHm1xhyNbWFnFxcdrPu3bt0h2joVzMZcJs1VCRqeq5MFPrNXeub7zxBt5//30AQHBwMBISEhqcC3Nte1hwoRbl6+uLCRMmAADOnTuHq1evWjzGn/70J60oY3iDqVq1bxQbcwndvXv3tO3GXKJH9ZnL1PBNu4ODg9GSd3U9++yz2rbhUqaG4zDTltOUYzUnJwfffPMNAOC5554z+x9FgLm2NHOZVlRU4A9/+ANu3bqFxx9/HCdOnMD06dPh5eWFDh06oGfPnpg/fz6ysrLg6OiI4uJizJw5s95zMNOW19Cx6uDggP3792PHjh0IDg42Wv3C3d0dixYtQkFBgdFywY899pjRGMxVLcNzo7nlugcOHIgePXoA0D8vAg3nYi4TZquGikxVz4WZWq85c92+fTuio6MBAP3798cnn3xi9FUgvbkw17aHBRdqcQEBAdp2QysOmeLp6QkPDw/d/Xv27Amg5o9N7Xr1ei5fvgwA6NatGzp27GjxXKiGXqY+Pj7adu2KU3oM+167ds2ojZm2DkuP1X//+9/atqn/jNfFXFueXqZpaWnaz4sWLcLjjz9ucv+BAwdi+vTpAIC8vDx8+eWXRu3MtHU0dKza2NggMjISeXl5uH37Ns6dO4dLly7h+vXr2LJlCzp37oyTJ0+aHA9grqoZnu9qf7cN9dU7LwI1yz2bU5tJ3ec2HIfZWkdFpqowU3WaK9ddu3Zh/vz5AIBevXrh008/Rbdu3czuw1zbLhZcqMUZforWHGMYvlGs/bTdlEePHuH8+fMAgAEDBlg9p58zvTz8/Py0GzbW3gxMj2G7vb29URszbR2WHKuVlZXYvXs3gJqiaGhoaIP7MNeWp5ep4deDfvGLX5gdIzg4WNuumxszbR2WHKuurq7o27cvfHx8YGdnB6DmEvWcnBwANVfM1H6oUYu5qjVw4EBtu7HnxqaeF+u2182F2aqhIlNVmKk6zZHrgQMHMHPmTFRXV6N79+5IT09vsJgDMNe2jAUXanGnTp3Str29vS3e/9q1a7hx44bu/qNHj9a2TX3lqFZeXp52yd3TTz9t8Tzo/+ll2qFDB4SEhAAAfvjhB6NLHOuqPTkA0C7LrMVMW4clx+qhQ4e0FVBeeumlRr2RZK4tTy9Tw7wePXpkdgzDlXDq5sxMW4e159XU1FTcvn0bAPDCCy/Ua2euao0ZM0bbNjz3mXLhwgUA9c+Lffr00bI2lwkAfPbZZ9oYvXv3NmpjtmqoyFQVZqqO6lzT09Pxwgsv4NGjR+jatSuOHDmCvn37NmouzLUNa8Ub9tLP0Pnz57VVhnx9fZs0xpo1a3Tvti8iUlFRIZ07dxYAMmDAAN1Vj+bMmaONk5OT06S5UMOZbt68Wfs9JyUl6Y4TGRmp9cvKyjJqY6Ytz9Jj9fnnn9d+94WFhY16DubassxlunfvXu13vGzZMrPjTJkyReubn59v1MZMW56159XKykoZPHiwAJAOHTrI+fPn6/Vhrmpdv35dy2zChAm6/TIzM7Xf58svv1yvfd68eVq7qdVTRESOHz+u9Zk/f369dmarhqpMTbF0lSJmqo7KXP/3v/9pK3J26tRJ8vLyLJoLc227WHAhZQ4cOCCVlZW67XWXr9y4caNRe1FRkRQUFJh9joMHD2pLVzo6OkpxcbHJfn/5y1+051m/fn299uzsbG3Z07Fjxzb84n6mrM1UROTOnTvi6ekpAKRXr15SUlJSr8/Ro0e1pfcCAwNNnkSYqToqcjV048YN7bgcNGiQRXNhrmpYm2lpaak4OzsLAHFzc5OTJ0+aHCc1NVVsbW0FgPTo0UOqqqrq9WGm6qg4Vn/88Ue5d++eyf0rKiqMlg+NiYnRfS7mqpZhsWTXrl312svKyuSpp54y+x+nb7/9VvudDx06VMrLy43ay8vLZejQoQJA7O3t5cyZMybnwmzVUJGpKZYWXESYqUoqci0sLBR3d3cBIC4uLvL55583aS7MtW1iwYWU6dWrl3h7e8uiRYvkww8/lOzsbCksLJQjR47IihUrpGvXrtofidGjR8uDBw+M9j969KgAkJCQEFm3bp2kpqZKXl6e5Obmyu7duyU8PFxsbGy0MbZu3ao7l7KyMvH399f6vvLKK5KRkSHHjx+XdevWiaurqwAQJyenRn8a/3Nkbaa1kpOTtex8fHwkISFBcnNzJSsrS6Kjo8XJyUl7Q6h3EmKm6qjKtVZCQoLWf8OGDRbNhbmqoSLT1atXa31cXV0lKipKMjIypLCwUNLS0mTevHnaGzkAkpiYaHIuzFQdFbnu2bNH3N3dZcGCBfLxxx9Lfn6+ZGVlyZYtWyQgIEDb/9e//rVUVFTozoW5qnXt2jV54okntHPfwoULJSMjQ/Ly8mTHjh3y5JNPar/refPm6Y7z+uuva/2CgoIkOTlZcnNzJTk52agYFxUVpTsGs1VDVaY7duwweoSFhWn7LV++3Kit7hXBtZipOtbmeu7cOe2DRwASHx8vX331ldnHDz/8YHIuzLVtYsGFlOnVq5f2B8DcY8qUKVJaWlpv/9qCS0MPZ2dn2b59e4PzOXv2rPj5+emO06lTJzl48GAz/CbaD2szNbR161btKghTD1dXV0lJSTE7BjNVQ2WuIiIjRowQAGJnZyfff/+9xfNhrtZTkWl1dbUsXrzYqLBt6tGhQweJi4szOx9mqoaKXPfs2dPg/pGRkXL//v0G58Nc1Tp16pT069fPbDazZs2Shw8f6o5RVVUls2bNMjvGyy+/bPJqNEPMVg0VmTbmmK99RERE6I7DTNWxJtcdO3ZYlCkAiY2N1Z0Lc217WHAhZTIzM2XVqlUSGhoq/v7+0qVLF7G3txd3d3cZNGiQzJkzR7Kzs3X3Lysrk507d8qCBQtkxIgR8sQTT4izs7M4ODiIl5eXPPPMM7J27Vrdqq8pd+/elbfeekuGDh0q7u7u4uzsLP3795clS5bIxYsXVbzsds3aTOv6+uuvZd68edKvXz9xcnISV1dXGTx4sCxbtkyuXr3aqDGYqfVU5nrmzBntJB8aGtrkOTFX66jMNC8vT+bOnSuBgYHi5uYmdnZ20rlzZwkODpY///nP8u233zZqHGZqPRW5lpSUSFxcnEycOFH69Okjzs7O4urqKv7+/jJnzhzde3/oYa5q3b17V+Li4mTEiBHSpUsXcXBwkJ49e8qLL74oGRkZjR7n0KFDEhYWJt7e3uLg4CDe3t4SFhYmqampFs2F2VrP2kxVFVxq58JM1WhqrqoLLrVzYa5th42IgjV6iYiIiIiIiIhIw2WhiYiIiIiIiIgUY8GFiIiIiIiIiEgxFlyIiIiIiIiIiBRjwYWIiIiIiIiISDEWXIiIiIiIiIiIFGPBhYiIiIiIiIhIMRZciIiIiIiIiIgUY8GFiIiIiIiIiEgxFlyIiIiIiIiIiBRjwYWIiIiIiIiISDEWXIiIiIgakJmZCRsbm3qPlStXtvbUrBIZGWnydV28eLG1p0ZERNTmseBCRERERERERKSYfWtPgIiIiKgt+ec//4lhw4YBADw9PVt5NtZZu3Ytli5dCgDYv38/YmJiWnlGRERE7QcLLkREREQW6NOnDwIDA1t7Gkr06NEDPXr0AADk5eW18myIiIjaF36liIiIiIiIiIhIMRZciIiIiIiIiIgUY8GFiIiI2qxdu3ZpK+vMmTNHt9+lS5fg7u4OGxsb+Pv74969ey0yv4sXL2L58uUIDg5G165d4ejoiD59+mD8+PHYuHEjLl26VG+fuisgHT16FJMnT4a3tzecnJwwYMAArFmzpt5rSE1NxaRJk7R+AQEBeOONN/Dw4cOWeKlERERUh42ISGtPgoiIiKippk+fjqSkJABASkoKwsLCjNqrq6vxzDPP4NixY7C3t0d2drZ209vGyszMxPjx4wHUFEDGjRvX4D4bNmxAdHQ0KisrdfuMHTsWmZmZRv9mY2MDAIiNjYWjoyOio6Nh6u3aqFGjcPjwYbi4uGDJkiXYvHmzyecIDQ3Ff/7zH9jZ2Zmd7wcffIA//vGPAICioiL07t3bbH8iIiIyj1e4EBERUZv2zjvvaMWB2bNno6SkxKg9Li4Ox44dAwCsXLnS4mJLU6xZswavvfYaKisr4e7ujujoaBw5cgQFBQXIyMjAhg0b8PTTT2vFFVM++eQTREVFYeTIkfjwww+Rl5eHtLQ0TJw4EQCQnZ2NN998E/Hx8di8eTMmTpyIffv2IT8/H/v378fIkSMBAGlpaXjvvfea/TUTERGRMV7hQkRERG3e559/jnHjxqGqqgqhoaFITU2FjY0NCgsLMXLkSDx8+BCjR49GZmZmg1d6mGLJFS4FBQUYNmwYqqur4e/vj/T0dPTs2dNk3+Li4npthkWYKVOmYPfu3UZzrqqqwujRo3HixAm4ubmhsrISc+fORXx8vNE45eXlCAgIwHfffYfBgwfjyy+/NPsaeYULERGRWrzChYiIiNq80aNHIyoqCkDNFR1bt27F/fv3MW3aNDx8+BCdOnVCYmJik4otloqLi0N1dTVsbGyQnJysW2wBYLbN2dkZf//73+vN2c7OTrtfzZ07d9CtWzesX7/e5P4REREAgJMnT+L27dtNeTlERETURCy4EBERUbsQGxuL4cOHAwCWLVuGl156CadPnwYAJCQktMgVG9XV1UhLSwNQc3+WoKCgJo81YcIEdOnSxWTb4MGDte3f//736NChg8l+Q4YM0baLioqaPBciIiKyHAsuRERE1C7Y29sjKSkJLi4uePDgAVJSUgAAU6dOxfTp01tkDkVFRbh16xYAYMyYMVaN5e/vr9vm7u5ucb87d+5YNR8iIiKyDAsuRERE1G7069cPr7/+uvazh4cH3n333RZ7/uvXr2vb3bt3t2osZ2dn3TZbW1uL+1VVVVk1HyIiIrIMCy5ERETUbty9exc7duzQfr5x4wYKCgpaZS7mViAiIiKi9o8FFyIiImo3Fi1ahAsXLgAA3NzcICKIiIhAaWlpizy/h4eHtn316tUWeU4iIiL6aWLBhYiIiNqFffv24YMPPgAAREZG4qOPPgJQs/Ty3LlzW2QOffr0wWOPPQYA+Oyzz1rkOYmIiOiniQUXIiIiavOuXr2KV155BQDg6+uLLVu2IDQ0FAsXLgQAfPTRR0hMTGz2edja2mLSpEkAgGPHjqGwsLDZn5OIiIh+mlhwISIiojZNRBAZGYmbN2/Czs4OO3fuhJubGwBg/fr1CAgIAAAsXLgQ3333XbPPZ+nSpbC1tYWIYOrUqSguLtbta66NiIiI2jYWXIiIiKhN27RpE44cOQIAiI6ORkhIiNbm5OSEnTt3wsHBAWVlZZgxYwaqq6ubdT5PPfUUVq1aBQA4c+YMBg0ahJiYGKSnp+OLL75AZmYmNm3ahDFjxmDGjBnNOhciIiJqPSy4EBERUZv11VdfISoqCgAwfPhw/PWvf63XJygoCKtXrwYAZGVl4a233mr2ecXExGDdunWwt7fHrVu3sHbtWvzqV79CUFAQxo8fjyVLliArKwsi0uxzISIiotbBggsRERG1SRUVFZg2bRoqKirg4uKCnTt3wt7e3mTf1157DWPHjgUAxMbGIj8/v9nnFxUVhVOnTmHx4sUIDAxEp06d4OjoCF9fX/zyl7/Epk2bkJyc3OzzICIiotZh+l0JERER0U9cx44dcfLkyUb1tbW1RWZmZvNOyAQ/Pz/Ex8dbtE9jrnrp3bt3o/qNGzeOV9EQERG1EhZciIiIiCxQVFQEDw8PAICnpyc8PT1beUZNd+XKFZSWlmrbREREpA4LLkREREQWmDVrlrYdGxuLlStXtt5krLRixQr861//au1pEBERtUu8hwsRERERERERkWI2wi/2EhEREREREREpxStciIiIiIiIiIgUY8GFiIiIiIiIiEgxFlyIiIiIiIiIiBRjwYWIiIiIiIiISDEWXIiIiIiIiIiIFGPBhYiIiIiIiIhIMRZciIiIiIiIiIgUY8GFiIiIiIiIiEgxFlyIiIiIiIiIiBRjwYWIiIiIiIiISDEWXIiIiIiIiIiIFPs/FCkY2kq2a14AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = geometry.plot(\n", + " width=(85, 85),\n", + " origin=(x_c, y_c, z_c + 10),\n", + " pixels=(1000, 1000),\n", + " basis=\"xz\",\n", + " color_by=\"material\",\n", + " colors={Air: \"white\", air: \"white\"},\n", + ")\n", + "ax.tick_params(axis=\"both\", labelsize=20)\n", + "ax.set_xlabel(ax.get_xlabel(), fontsize=20)\n", + "ax.set_ylabel(ax.get_ylabel(), fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BABY 1L Geometry YZ View" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = geometry.plot(\n", + " width=(85, 85),\n", + " origin=(x_c, y_c, z_c + 10),\n", + " pixels=(1000, 1000),\n", + " basis=\"yz\",\n", + " color_by=\"material\",\n", + " colors={Air: \"white\", air: \"white\"},\n", + ")\n", + "ax.tick_params(axis=\"both\", labelsize=20)\n", + "ax.set_xlabel(ax.get_xlabel(), fontsize=20)\n", + "ax.set_ylabel(ax.get_ylabel(), fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BABY 1L Geometry XY View" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = geometry.plot(\n", + " width=(85, 85),\n", + " origin=(x_c, y_c, z_c + 10),\n", + " pixels=(1000, 1000),\n", + " basis=\"xy\",\n", + " color_by=\"material\",\n", + " colors={Air: \"white\", air: \"white\"},\n", + ")\n", + "ax.tick_params(axis=\"both\", labelsize=20)\n", + "ax.set_xlabel(ax.get_xlabel(), fontsize=20)\n", + "ax.set_ylabel(ax.get_ylabel(), fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Result" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TBR: 8.337474e-04\n", + "\n", + "TBR std. dev.: 2.544478e-05\n", + "\n" + ] + } + ], + "source": [ + "sp = openmc.StatePoint(\"statepoint.100.h5\")\n", + "tbr_withvault = sp.get_tally(name=\"TBR\").get_pandas_dataframe()\n", + "print(f\"TBR: {tbr_withvault['mean'].iloc[0] :.6e}\\n\")\n", + "print(f\"TBR std. dev.: {tbr_withvault['std. dev.'].iloc[0] :.6e}\\n\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/neutron/postprocessing.ipynb:Zone.Identifier b/analysis/neutron/postprocessing.ipynb:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/statepoint.100.h5:Zone.Identifier b/analysis/neutron/statepoint.100.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/summary.h5:Zone.Identifier b/analysis/neutron/summary.h5:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/analysis/neutron/vault.py b/analysis/neutron/vault.py new file mode 100644 index 0000000..6ca9ecc --- /dev/null +++ b/analysis/neutron/vault.py @@ -0,0 +1,805 @@ +import openmc +import openmc.model +import numpy as np +from helpers import translate_surface + +# needed to download cross sections on the fly +import openmc_data_downloader as odd + + +def build_vault_model( + settings=openmc.Settings(), + tallies=openmc.Tallies(), + added_cells=[], + added_materials=[], + overall_exclusion_region=None, +) -> openmc.model.Model: + + materials = openmc.Materials( + [ + Aluminum, + Material_2, + Air, + Concrete, + IronConcrete, + Material_6, + Material_7, + Material_8, + Material_10, + Lead, + BPE, + Polyethylene, + HDPE, + Material_22, + Material_23, + Material_30, + Material_40, + Soil, + Brick, + RicoRad, + Steel, + SS304, + Firebrick, + Flibe_nat, + Copper, + Be, + ] + ) + + # Add materials from imported model + materials += added_materials + + materials.download_cross_section_data( + libraries=["ENDFB-8.0-NNDC"], + set_OPENMC_CROSS_SECTIONS=True, + particles=["neutron"], + destination="cross_sections", + ) + # + # Definition of the spherical void/blackhole boundary + Surface_95 = openmc.Sphere(x0=0.0, y0=0.0, z0=0.0, r=2500.0, boundary_type="vacuum") + + # 24 + Surface_24 = openmc.model.RectangularParallelepiped( + 1023.62 - 1104.9, 2247.9 - 1104.9, 0.0 - 99.38, 749.62 - 99.38, 0.0, 424.18 + ) + + # with an angle of 2.8 degrees. The positive vector points towards the + # lower-right (Southeast) corner of the geometry + Surface_49 = openmc.Plane(a=0.99881, b=-0.04885, c=0.0, d=2144.83) + + # + # Outer surface definition of the foundation underneath all basement labs + Surface_94 = openmc.model.RectangularParallelepiped( + 0.0 - 1104.9, 2247.9 - 1104.9, 0.0 - 99.38, 1998.37 - 99.38, -81.28, 0.0 + ) + + # Define Soil cell 3 meters wide + East_outer_plane = Surface_49.clone() + East_outer_plane = East_outer_plane.translate([500 * np.cos(np.deg2rad(2.8)), 0, 0]) + + # + # The cuboid defining the outermost boundary of the Vault door in Room III + Surface_13 = openmc.model.RectangularParallelepiped( + 1105.41 - 1104.9, + 1166.3700000000001 - 1104.9, + 368.0 - 99.38, + 611.84 - 99.38, + 0.0, + 223.52, + ) + + # The plane used to create the 30 degree north-most cut on the Vault door. + # The positive vector points towards the lower-left + Surface_14 = openmc.Plane(a=0.5, b=0.86603, c=0.0, d=1084.9) + + # The plane used to create the 30 degree south-most cut on the Vault door + # the positive vector points towards the upper-left + Surface_15 = openmc.Plane(a=0.5, b=-0.86603, c=0.0, d=238.93) + + # The main Vault shield door in Room III + Vault_door_reg = -Surface_13 & -Surface_14 & -Surface_15 + + # + # North B-HDPE shield in entrance to Vault in Room III + Surface_17 = openmc.model.RectangularParallelepiped( + 1066.8 - 1104.9, + 1104.8999999999999 - 1104.9, + 565.78 - 99.38, + 598.8 - 99.38, + 10.16, + 213.35999999999999, + ) + + # The northern Ricorad extra Vault door shielding in Room III + Vault_door_shield_n_pillar_reg = -Surface_17 + + # + # South B-HDPE shield in entrance to Vault in Room III + Surface_18 = openmc.model.RectangularParallelepiped( + 1066.8 - 1104.9, + 1104.8999999999999 - 1104.9, + 380.71 - 99.38, + 413.72999999999996 - 99.38, + 10.16, + 213.35999999999999, + ) + + # The southern Ricorad extra Vault door shielding in Room III + Vault_door_shield_s_pillar_reg = -Surface_18 + + # + # Surface definition for west iron-brick pile around DANTE selection magnet + Surface_10 = openmc.model.RectangularParallelepiped( + 1741.65 - 1104.9, 1808.49 - 1104.9, 512.12 - 99.38, 637.85 - 99.38, 10.16, 152.4 + ) + + # The western DANTE beamline (Fe or Pb fill?) concrete block shield + DANTE_vault_w_shield_reg = -Surface_10 + + # + # Surface definition for east iron-brick pile around DANTE selection magnet + Surface_9 = openmc.model.RectangularParallelepiped( + 1935.48 - 1104.9, + 1983.74 - 1104.9, + 512.12 - 99.38, + 637.85 - 99.38, + 10.16, + 135.89000000000001, + ) + + # The eastern DANTE beamline (Fe or Pb fill?) concrete block shield + DANTE_vault_e_shield_reg = -Surface_9 + + # + # 11 + Surface_11 = openmc.model.RightCircularCylinder( + (1858.01 - 1104.9, 637.86 - 99.38, 111.76), 111.76, 15.24, axis="y" + ) + + # + # 2 + Surface_22 = openmc.model.RectangularParallelepiped( + 1699.2 - 1104.9, 2119.2 - 1104.9, 637.85 - 99.38, 668.33 - 99.38, 10.16, 363.22 + ) + + # with an angle of 2.8 degrees. The positive vector points towards the + # lower-right (Southeast) corner of the geometry + Surface_48 = openmc.Plane(a=0.99881, b=-0.04885, c=0.0, d=2063.64) + + # The CMU wall partially covering the north shield wall in Room III + Vault_north_wall_ext_reg = -Surface_22 & -Surface_48 & +Surface_11 + + # The foundation underneath all basement lab rooms + Region_21 = -Surface_94 & -Surface_49 + + # + # 36 + Surface_36 = openmc.model.RectangularParallelepiped( + 1104.9 - 1104.9, 2254.9 - 1104.9, 0.0 - 99.38, 99.38 - 99.38, 0.0, 363.22 + ) + + # The south Vault shield wall in Room III + South_vault_wall_reg = -Surface_36 & -Surface_49 + + # + # 16 + Surface_16 = openmc.model.RectangularParallelepiped( + 2050.0 - 1104.9, 2200.0 - 1104.9, 99.38 - 99.38, 668.34 - 99.38, 0.0, 363.22 + ) + + # The east Vault shield wall in Room III with Room II entrance cutout + East_vault_wall_reg = -Surface_16 & +Surface_48 & -Surface_49 + + # + # 38 + Surface_38 = openmc.model.RectangularParallelepiped( + 1000.0 - 1104.9, + 1150.0 - 1104.9, + 380.71 - 99.38, + 598.8 - 99.38, + 10.16, + 213.35999999999999, + ) + + # + # 39 + Surface_39 = openmc.model.RectangularParallelepiped( + 1023.62 - 1104.9, 1104.9 - 1104.9, 0.0 - 99.38, 749.62 - 99.38, 0.0, 363.22 + ) + + # The west Vault shield wall in Room III with Vault entrance cutout + West_vault_wall_reg = -Surface_39 & +Surface_38 + + # + # 37 + Surface_37 = openmc.model.RectangularParallelepiped( + 1023.62 - 1104.9, 2253.62 - 1104.9, 0.0 - 99.38, 749.62 - 99.38, 363.22, 424.18 + ) + + # The top (roof) Vault shield wall in Room III + Vault_ceiling_reg = -Surface_37 & -Surface_49 + + # + # 12 + Surface_12 = openmc.model.RectangularParallelepiped( + 1169.67 - 1104.9, 2169.67 - 1104.9, 99.38 - 99.38, 668.34 - 99.38, 0.0, 10.16 + ) + + # The bottom Vault floor in Room III + Vault_floor_reg = -Surface_12 & -Surface_48 + + # 23 + Surface_23 = openmc.model.RectangularParallelepiped( + 1104.9 - 1104.9, 2254.9 - 1104.9, 668.34 - 99.38, 749.62 - 99.38, 0.0, 363.22 + ) + + # + # The cyclotron beamline cutout in the north Vault shield wall + Surface_102 = openmc.model.RightCircularCylinder( + (1422.0 - 1104.9, 668.34 - 99.38, 50.0), 81.28, 5.0, axis="y" + ) + + # The north Vault shield wall in Room III with beamline cutouts + North_vault_wall_reg = -Surface_23 & -Surface_49 & +Surface_11 & +Surface_102 + + # + # 82 + Surface_82 = openmc.model.RectangularParallelepiped( + 1135.9 - 1104.9, + 1147.3300000000002 - 1104.9, + 138.75 - 99.38, + 628.97 - 99.38, + 276.86, + 279.40000000000003, + ) + + # + # 83 + Surface_83 = openmc.model.RectangularParallelepiped( + 1135.9 - 1104.9, + 1147.3300000000002 - 1104.9, + 138.75 - 99.38, + 628.97 - 99.38, + 297.18, + 299.72, + ) + + # + # 84 + Surface_84 = openmc.model.RectangularParallelepiped( + 1140.35 - 1104.9, + 1142.8899999999999 - 1104.9, + 138.75 - 99.38, + 628.97 - 99.38, + 276.86, + 299.72, + ) + + # The I-beam support the main Vault shield door in Room III + I_beam_reg = -Surface_82 | -Surface_83 | -Surface_84 + + # + # Inner surface defining the top/bottom DANTE selection magnets + Surface_28 = openmc.model.RightCircularCylinder( + (1858.01 - 1104.9, 597.22 - 99.38, 99.7), 75.0, 20.95, axis="z" + ) + + # + # Outer surface defining the bottom DANTE selection magnet + Surface_30 = openmc.model.RightCircularCylinder( + (1858.01 - 1104.9, 597.22 - 99.38, 99.7), 8.0, 32.0, axis="z" + ) + + # The bottom DANTE beamline selection magnet in Room III + DANTE_vault_bot_magnet_reg = -Surface_30 & +Surface_28 + + # + # Outer surface defining the top DANTE selection magnet + Surface_35 = openmc.model.RightCircularCylinder( + (1858.01 - 1104.9, 597.22 - 99.38, 115.83), 8.0, 32.0, axis="z" + ) + + # The top DANTE beamline selection magnet in Room III + DANTE_vault_top_magnet_reg = -Surface_35 & +Surface_28 + + # + # Surface definition for selection magnet cutout of surface #27 + Surface_21 = openmc.model.RectangularParallelepiped( + 1825.87 - 1104.9, + 1890.1399999999999 - 1104.9, + 571.9 - 99.38, + 621.9 - 99.38, + 99.7, + 123.83, + ) + + # + # Surface definition of square box that contains DANTE selection magnets + Surface_27 = openmc.model.RectangularParallelepiped( + 1816.1 - 1104.9, + 1899.9199999999998 - 1104.9, + 576.9 - 99.38, + 617.54 - 99.38, + 89.54, + 133.99, + ) + + # + # Selection magnet SE support leg + Surface_29 = openmc.model.RectangularParallelepiped( + 1892.3 - 1104.9, + 1899.9199999999998 - 1104.9, + 576.9 - 99.38, + 584.52 - 99.38, + 10.16, + 88.89999999999999, + ) + + # + # Selection magnet NE support leg + Surface_31 = openmc.model.RectangularParallelepiped( + 1892.3 - 1104.9, + 1899.9199999999998 - 1104.9, + 609.92 - 99.38, + 617.54 - 99.38, + 10.16, + 88.89999999999999, + ) + + # + # Selection magnet SW support leg + Surface_33 = openmc.model.RectangularParallelepiped( + 1816.1 - 1104.9, + 1823.7199999999998 - 1104.9, + 576.9 - 99.38, + 584.52 - 99.38, + 10.16, + 88.89999999999999, + ) + + # + # Thin selection magnet table top plate + Surface_34 = openmc.model.RectangularParallelepiped( + 1816.1 - 1104.9, + 1899.9199999999998 - 1104.9, + 576.9 - 99.38, + 617.54 - 99.38, + 88.9, + 89.54, + ) + + # The DANTE beamline selection magnet stand in Room III + DANTE_vault_mag_stand_reg = ( + (-Surface_27 & +Surface_21) + | -Surface_29 + | -Surface_31 + | -Surface_33 + | -Surface_34 + ) + + Region_28 = ( + -Surface_24 + & -Surface_49 + & ~North_vault_wall_reg + & ~Vault_north_wall_ext_reg + & ~South_vault_wall_reg + & ~West_vault_wall_reg + & ~East_vault_wall_reg + & ~Vault_ceiling_reg + & ~Vault_floor_reg + & ~Vault_door_reg + & ~Vault_door_shield_n_pillar_reg + & ~Vault_door_shield_s_pillar_reg + & ~I_beam_reg + & ~DANTE_vault_w_shield_reg + & ~DANTE_vault_e_shield_reg + & ~DANTE_vault_bot_magnet_reg + & ~DANTE_vault_top_magnet_reg + & ~DANTE_vault_mag_stand_reg + ) + if overall_exclusion_region: + Region_28 = Region_28 & ~overall_exclusion_region + + translation_vector = [-1104.9, -99.38, 0.0] + + for surface in [Surface_49, East_outer_plane, Surface_14, Surface_15, Surface_48]: + translate_surface(surface, *translation_vector) + + Vault_air_cell = openmc.Cell(fill=Air, region=Region_28) + DANTE_vault_mag_stand_cell = openmc.Cell( + fill=Aluminum, region=DANTE_vault_mag_stand_reg + ) + DANTE_vault_top_magnet_cell = openmc.Cell( + fill=Material_2, region=DANTE_vault_top_magnet_reg + ) + DANTE_vault_bot_magnet_cell = openmc.Cell( + fill=Material_2, region=DANTE_vault_bot_magnet_reg + ) + I_beam_cell = openmc.Cell(fill=Material_6, region=I_beam_reg) + North_vault_wall_cell = openmc.Cell(fill=Concrete, region=North_vault_wall_reg) + Vault_floor_cell = openmc.Cell(fill=Concrete, region=Vault_floor_reg) + Vault_ceiling_cell = openmc.Cell(fill=Concrete, region=Vault_ceiling_reg) + West_vault_wall_cell = openmc.Cell(fill=Concrete, region=West_vault_wall_reg) + East_vault_wall_cell = openmc.Cell(fill=Concrete, region=East_vault_wall_reg) + South_vault_wall_cell = openmc.Cell(fill=Concrete, region=South_vault_wall_reg) + foundation = openmc.Cell(fill=Concrete, region=Region_21) + Vault_north_wall_ext_cell = openmc.Cell( + fill=Concrete, region=Vault_north_wall_ext_reg + ) + DANTE_vault_e_shield_cell = openmc.Cell( + fill=IronConcrete, region=DANTE_vault_e_shield_reg + ) + DANTE_vault_w_shield_cell = openmc.Cell( + fill=IronConcrete, region=DANTE_vault_w_shield_reg + ) + Vault_door_shield_s_pillar_cell = openmc.Cell( + fill=RicoRad, region=Vault_door_shield_s_pillar_reg + ) + Vault_door_shield_n_pillar_cell = openmc.Cell( + fill=RicoRad, region=Vault_door_shield_n_pillar_reg + ) + Vault_door_cell = openmc.Cell(fill=Concrete, region=Vault_door_reg) + + # Explicit declaration of the outer void + Region_1000 = ( + -Surface_95 + & ~Vault_door_reg + & ~Vault_door_shield_n_pillar_reg + & ~Vault_door_shield_s_pillar_reg + & ~DANTE_vault_w_shield_reg + & ~DANTE_vault_e_shield_reg + & ~Vault_north_wall_ext_reg + & ~Region_21 + & ~South_vault_wall_reg + & ~East_vault_wall_reg + & ~West_vault_wall_reg + & ~Vault_ceiling_reg + & ~Vault_floor_reg + & ~North_vault_wall_reg + & ~I_beam_reg + & ~DANTE_vault_bot_magnet_reg + & ~DANTE_vault_top_magnet_reg + & ~DANTE_vault_mag_stand_reg + & ~Region_28 + ) + + if overall_exclusion_region: + Region_1000 = Region_1000 & ~overall_exclusion_region + + Cell_1000 = openmc.Cell(fill=Air, region=Region_1000) + + Cells = [ + Cell_1000, + Vault_door_cell, + Vault_door_shield_n_pillar_cell, + Vault_door_shield_s_pillar_cell, + DANTE_vault_w_shield_cell, + DANTE_vault_e_shield_cell, + Vault_north_wall_ext_cell, + foundation, + South_vault_wall_cell, + East_vault_wall_cell, + West_vault_wall_cell, + Vault_ceiling_cell, + Vault_floor_cell, + North_vault_wall_cell, + I_beam_cell, + DANTE_vault_bot_magnet_cell, + DANTE_vault_top_magnet_cell, + DANTE_vault_mag_stand_cell, + Vault_air_cell, + ] + + Cells += added_cells + + Universe_1 = openmc.Universe(cells=Cells) + geometry = openmc.Geometry(Universe_1) + geometry.remove_redundant_surfaces() + + vault_model = openmc.model.Model( + geometry=geometry, materials=materials, settings=settings, tallies=tallies + ) + + return vault_model + + +# +# **** Natural elements **** +# +# Aluminum : 2.6989 g/cm3 +Aluminum = openmc.Material() +Aluminum.set_density("g/cm3", 2.6989) +Aluminum.add_nuclide("Al27", 1.0, "ao") + +# Copper : 8.96 g/cm3 +Material_2 = openmc.Material() +Material_2.set_density("g/cm3", 8.96) +Material_2.add_nuclide("Cu63", 0.6917, "ao") +Material_2.add_nuclide("Cu65", 0.3083, "ao") + +# Name: Air +# Density : 0.001205 g/cm3 +# Reference: None +# Describes: All atmospheric, non-object chambers +Air = openmc.Material(name="Air") +Air.set_density("g/cm3", 0.001205) +Air.add_element("C", 0.00015, "ao") +Air.add_nuclide("N14", 0.784431, "ao") +Air.add_nuclide("O16", 0.210748, "ao") +Air.add_nuclide("Ar40", 0.004671, "ao") + +# Name: Portland concrete +# Density: 2.3 g/cm3 +# Reference: PNNL Report 15870 (Rev. 1) +# Describes: facility foundation, floors, walls +Concrete = openmc.Material() +Concrete.set_density("g/cm3", 2.3) +Concrete.add_nuclide("H1", 0.168759, "ao") +Concrete.add_element("C", 0.001416, "ao") +Concrete.add_nuclide("O16", 0.562524, "ao") +Concrete.add_nuclide("Na23", 0.011838, "ao") +Concrete.add_element("Mg", 0.0014, "ao") +Concrete.add_nuclide("Al27", 0.021354, "ao") +Concrete.add_element("Si", 0.204115, "ao") +Concrete.add_element("K", 0.005656, "ao") +Concrete.add_element("Ca", 0.018674, "ao") +Concrete.add_element("Fe", 0.004264, "ao") + +# Name: Portland iron concrete +# Density: 3.8 g/cm3 as roughly measured using scale and assuming rectangular prism +# Reference: PNNL Report 15870 (Rev. 1) +# Describes: Potential new walls, shielding doors +IronConcrete = openmc.Material() +IronConcrete.set_density("g/cm3", 3.8) +IronConcrete.add_nuclide("H1", 0.135585, "ao") +IronConcrete.add_nuclide("O16", 0.150644, "ao") +IronConcrete.add_element("Mg", 0.002215, "ao") +IronConcrete.add_nuclide("Al27", 0.005065, "ao") +IronConcrete.add_element("Si", 0.013418, "ao") +IronConcrete.add_element("S", 0.000646, "ao") +IronConcrete.add_element("Ca", 0.040919, "ao") +IronConcrete.add_nuclide("Mn55", 0.002638, "ao") +IronConcrete.add_element("Fe", 0.648869, "ao") + +# Name: Stainless steel 304 +# Density: 8.0 g/cm3 +# Reference: PNNL Report 15870 (Rev. 1) +# Describes: vacuum pipes, flanges, general steel objects +Material_6 = openmc.Material() +Material_6.set_density("g/cm3", 8.0) +Material_6.add_element("C", 0.00183, "ao") +Material_6.add_element("Si", 0.009781, "ao") +Material_6.add_nuclide("P31", 0.000408, "ao") +Material_6.add_element("S", 0.000257, "ao") +Material_6.add_element("Cr", 0.200762, "ao") +Material_6.add_nuclide("Mn55", 0.010001, "ao") +Material_6.add_element("Fe", 0.690375, "ao") +Material_6.add_element("Ni", 0.086587, "ao") + +# Name: Wood (Southern Pine) +# Density: 0.64 g/cm3 +# Reference: PNNL Report 15870 (Rev. 1) +# Describes: doors +Material_7 = openmc.Material() +Material_7.set_density("g/cm3", 0.64) +Material_7.add_nuclide("H1", 0.462423, "ao") +Material_7.add_element("C", 0.323389, "ao") +Material_7.add_nuclide("N14", 0.002773, "ao") +Material_7.add_nuclide("O16", 0.208779, "ao") +Material_7.add_element("Mg", 0.000639, "ao") +Material_7.add_element("S", 0.001211, "ao") +Material_7.add_element("K", 0.000397, "ao") +Material_7.add_element("Ca", 0.000388, "ao") + +# Name: Gypsum (wallboard) +# Density: 2.32 g/cm3 +# Reference: PNNL Report 15870 (Rev. 1) +# Describes: drywall walls (GWB) +Material_8 = openmc.Material() +Material_8.set_density("g/cm3", 2.32) +Material_8.add_nuclide("H1", 0.333321, "ao") +Material_8.add_nuclide("O16", 0.500014, "ao") +Material_8.add_element("S", 0.083324, "ao") +Material_8.add_element("Ca", 0.083341, "ao") + +# **** Gamma shielding materials **** +# +# Tungsten : 19.3 g/cm3 +Material_10 = openmc.Material() +Material_10.set_density("g/cm3", 19.3) +Material_10.add_nuclide("W182", 0.265, "ao") +Material_10.add_nuclide("W183", 0.1431, "ao") +Material_10.add_nuclide("W184", 0.3064, "ao") +Material_10.add_nuclide("W186", 0.2855, "ao") + +# +# Lead : 11.34 g/cm3 +Lead = openmc.Material() +Lead.set_density("g/cm3", 11.34) +Lead.add_nuclide("Pb204", 0.014, "ao") +Lead.add_nuclide("Pb206", 0.241, "ao") +Lead.add_nuclide("Pb207", 0.221, "ao") +Lead.add_nuclide("Pb208", 0.524, "ao") + +# Name: Borated Polyethylene (5% B in via B4C additive) +# Density: 0.95 g/cm3 +# Reference: PNNL Report 15870 (Rev. 1) but revised to make it 5 wt.% B +# Describes: General purpose neutron shielding +BPE = openmc.Material() +BPE.set_density("g/cm3", 0.95) +BPE.add_nuclide("H1", 0.1345, "wo") +BPE.add_element("B", 0.0500, "wo") +BPE.add_element("C", 0.8155, "wo") + +# Name: Non-borated polyethylene +# Density: 0.93 g/cm3 +# Reference: PNNL Report 15870 (Rev. 1) +# Describes: General purpose neutron shielding +Polyethylene = openmc.Material() +Polyethylene.set_density("g/cm3", 0.93) +Polyethylene.add_nuclide("H1", 0.666662, "ao") +Polyethylene.add_element("C", 0.333338, "ao") + +# High Density Polyethylene +# Reference: PNNL Report 15870 (Rev. 1) +HDPE = openmc.Material(name="HDPE") +HDPE.set_density("g/cm3", 0.95) +HDPE.add_element("H", 0.143724, "wo") +HDPE.add_element("C", 0.856276, "wo") + +# Name: Zirconium dihydride +# Density: 5.6 g/cm3 +# Reference: JNM 386-388 (2009) 119-121 +# Describes: General purpose neutron shielding +Material_22 = openmc.Material() +Material_22.set_density("g/cm3", 5.6) +Material_22.add_nuclide("H1", 0.0216, "wo") +Material_22.add_element("Zr", 0.9784, "wo") + +# Name: Zirconium borohydride +# Density: 1.18 g/cm3 +# Reference: JNM 386-388 (2009) 119-121 +# Describes: General purpose neutron shielding +Material_23 = openmc.Material() +Material_23.set_density("g/cm3", 1.18) +Material_23.add_nuclide("H1", 0.1073, "wo") +Material_23.add_nuclide("B10", 0.0571, "wo") +Material_23.add_nuclide("B11", 0.23, "wo") +Material_23.add_element("Zr", 0.6056, "wo") + +# Density: 1.848 g/cm3 +# Reference: None +# Describes: Highest intenstiy neutron production target +# Notes: Uses ENDF-derived proton nuclear data libray +Material_30 = openmc.Material() +Material_30.set_density("g/cm3", 1.848) +Material_30.add_nuclide("Be9", 1.0, "ao") + +# Name: Concrete (Regular) +# Density: 2.3 g/cm3 +# Reference: Provided by Matthey Carey, MIT EHS/RPP (mgcarey@mit.edu) +# Describes: Facility walls, foundation, floors for activation calculations +Material_40 = openmc.Material() +Material_40.set_density("g/cm3", 2.3) +Material_40.add_nuclide("Fe54", 2.0138e-05, "ao") +Material_40.add_nuclide("Fe56", 0.00031874, "ao") +Material_40.add_nuclide("Fe57", 7.2915e-06, "ao") +Material_40.add_nuclide("Fe58", 1.0416e-06, "ao") +Material_40.add_nuclide("H1", 0.01374, "ao") +Material_40.add_nuclide("H2", 2.0613e-06, "ao") +Material_40.add_nuclide("O16", 0.045685, "ao") +Material_40.add_nuclide("O17", 1.8318e-05, "ao") +Material_40.add_nuclide("Mg24", 9.0027e-05, "ao") +Material_40.add_nuclide("Mg25", 1.1397e-05, "ao") +Material_40.add_nuclide("Mg26", 1.2548e-05, "ao") +Material_40.add_nuclide("Ca40", 0.001474, "ao") +Material_40.add_nuclide("Ca42", 9.8378e-06, "ao") +Material_40.add_nuclide("Ca43", 2.0527e-06, "ao") +Material_40.add_nuclide("Ca44", 3.1718e-05, "ao") +Material_40.add_nuclide("Ca46", 6.0821e-08, "ao") +Material_40.add_nuclide("Ca48", 2.8434e-06, "ao") +Material_40.add_nuclide("Si28", 0.015328, "ao") +Material_40.add_nuclide("Si29", 0.00077613, "ao") +Material_40.add_nuclide("Si30", 0.0005152, "ao") +Material_40.add_nuclide("Na23", 0.00096395, "ao") +Material_40.add_nuclide("K39", 0.00042949, "ao") +Material_40.add_nuclide("K40", 4.6053e-08, "ao") +Material_40.add_nuclide("K41", 3.0993e-05, "ao") +Material_40.add_nuclide("Al27", 0.0017453, "ao") +Material_40.add_nuclide("C12", 0.00011404, "ao") +Material_40.add_nuclide("C13", 1.28e-06, "ao") + +# Soil material taken from PNNL Materials Compendium for Earth, U.S. Average +Soil = openmc.Material(name="Soil") +Soil.set_density("g/cm3", 1.52) +Soil.add_element("O", 0.670604, percent_type="ao") +Soil.add_element("Na", 0.005578, percent_type="ao") +Soil.add_element("Mg", 0.011432, percent_type="ao") +Soil.add_element("Al", 0.053073, percent_type="ao") +Soil.add_element("Si", 0.201665, percent_type="ao") +Soil.add_element("K", 0.007653, percent_type="ao") +Soil.add_element("Ca", 0.026664, percent_type="ao") +Soil.add_element("Ti", 0.002009, percent_type="ao") +Soil.add_element("Mn", 0.000272, percent_type="ao") +Soil.add_element("Fe", 0.021050, percent_type="ao") + +# Brick material taken from "Brick, Common Silica" from the PNNL Materials Compendium +# PNNL-15870, Rev. 2 +Brick = openmc.Material(name="Brick") +Brick.set_density("g/cm3", 1.8) +Brick.add_element("O", 0.663427, percent_type="ao") +Brick.add_element("Al", 0.003747, percent_type="ao") +Brick.add_element("Si", 0.323229, percent_type="ao") +Brick.add_element("Ca", 0.007063, percent_type="ao") +Brick.add_element("Fe", 0.002534, percent_type="ao") + +# Previous model uses 10% borated high density polyethylene, but +# according to Melhus, et. al., RicoRad consists of "2.00% mass boron +# in a polyethylene-based matrix having a mass density of 0.945 g/cm^3" +# Source: +# Melhus, Christopher, et al. ‘Storage Safe Shielding Assessment for a +# HDR Californium-252 Brachytherapy Source’. +# Monte Carlo 2005 Topical Meeting, 01 2005, pp. 219–229. + +RicoRad = openmc.Material(name="RicoRad") +RicoRad.set_density("g/cm3", 0.945) +RicoRad.add_element("H", 0.14, percent_type="wo") +RicoRad.add_element("C", 0.84, percent_type="wo") +RicoRad.add_element("B", 0.02, percent_type="wo") + +### LIBRA Materials +Steel = openmc.Material(name="Steel") +Steel.add_element("C", 0.005, "wo") +Steel.add_element("Fe", 0.995, "wo") +Steel.set_density("g/cm3", 7.82) + +# Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2) +SS304 = openmc.Material(name="Stainless Steel 304") +# SS304.temperature = 700 + 273 +SS304.add_element("C", 0.000800, "wo") +SS304.add_element("Mn", 0.020000, "wo") +SS304.add_element("P", 0.000450, "wo") +SS304.add_element("S", 0.000300, "wo") +SS304.add_element("Si", 0.010000, "wo") +SS304.add_element("Cr", 0.190000, "wo") +SS304.add_element("Ni", 0.095000, "wo") +SS304.add_element("Fe", 0.683450, "wo") +SS304.set_density("g/cm3", 8.00) + +# Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2 +# https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/ +Firebrick = openmc.Material(name="Firebrick") +# Estimate average temperature of Firebrick to be around 300 C +# Firebrick.temperature = 273 + 300 +Firebrick.add_element("Al", 0.004, "ao") +Firebrick.add_element("O", 0.666, "ao") +Firebrick.add_element("Si", 0.240, "ao") +Firebrick.add_element("Zr", 0.090, "ao") +Firebrick.set_density("g/cm3", 0.30) + +# Using 2:1 atom ratio of LiF to BeF2, similar to values in +# Seifried, Jeffrey E., et al. ‘A General Approach for Determination of +# Acceptable FLiBe Impurity Concentrations in Fluoride-Salt Cooled High +# Temperature Reactors (FHRs)’. Nuclear Engineering and Design, vol. 343, 2019, +# pp. 85–95, https://doi.org10.1016/j.nucengdes.2018.09.038. +# Also using natural lithium enrichment (~7.5 a% Li6) +Flibe_nat = openmc.Material(name="Flibe_nat") +# Flibe_nat.temperature = 700 + 273 +Flibe_nat.add_element("Be", 0.142857, "ao") +Flibe_nat.add_nuclide("Li6", 0.021685, "ao") +Flibe_nat.add_nuclide("Li7", 0.264029, "ao") +Flibe_nat.add_element("F", 0.571429, "ao") +Flibe_nat.set_density("g/cm3", 1.94) + +Copper = openmc.Material(name="Copper") +# Estimate copper temperature to be around 100 C +# Copper.temperature = 100 + 273 +Copper.add_element("Cu", 1.0, "ao") +Copper.set_density("g/cm3", 8.96) + +Be = openmc.Material(name="Be") +# Estimate Be temperature to be around 100 C +# Be.temperature = 100 + 273 +Be.add_element("Be", 1.0, "ao") +Be.set_density("g/cm3", 1.848) diff --git a/analysis/neutron/vault.py:Zone.Identifier b/analysis/neutron/vault.py:Zone.Identifier new file mode 100644 index 0000000..e69de29 diff --git a/data/processed_data.json b/data/processed_data.json new file mode 100644 index 0000000..5fbe61c --- /dev/null +++ b/data/processed_data.json @@ -0,0 +1,6 @@ +{ + "modelled_TBR": { + "mean": 0.0008337474496467843, + "std_dev": 2.5444777554062273e-05 + } +} \ No newline at end of file diff --git a/environment.yml b/environment.yml index 75faaa1..a32ae23 100644 --- a/environment.yml +++ b/environment.yml @@ -1,9 +1,9 @@ -name: libra-run-env +name: BABY_1l_LiPb channels: - conda-forge dependencies: - python - - openmc # add version tag + - openmc~=0.15.0 - openmc-plotter - numpy # add version tag - scipy # add version tag @@ -16,7 +16,7 @@ dependencies: - pint - pip - pip: - - git+https://github.com/libra-project/libra-toolbox # add version tag - - openmc_data_downloader + - git+https://github.com/libra-project/libra-toolbox@v0.3 + - openmc_data_downloader~=0.6.0 - matplotlib-label-lines - - h-transport-materials + - h-transport-materials~=0.17 From 1047d7d8f67a33871176cd6a31d74f2951ab969c Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Mon, 10 Mar 2025 21:40:23 +0000 Subject: [PATCH 02/10] Added automatic PbLi height calculation for given volume. --- analysis/neutron/.gitignore:Zone.Identifier | 0 analysis/neutron/helpers.py:Zone.Identifier | 0 analysis/neutron/model.xml:Zone.Identifier | 0 analysis/neutron/openmc_model_PbLi.py | 16 +++++++++++++- .../openmc_model_PbLi.py:Zone.Identifier | 0 analysis/neutron/postprocessing.ipynb | 22 +++++++++---------- .../postprocessing.ipynb:Zone.Identifier | 0 .../neutron/statepoint.100.h5:Zone.Identifier | 0 analysis/neutron/summary.h5:Zone.Identifier | 0 analysis/neutron/vault.py:Zone.Identifier | 0 data/processed_data.json | 4 ++-- environment.yml | 1 + 12 files changed, 29 insertions(+), 14 deletions(-) delete mode 100644 analysis/neutron/.gitignore:Zone.Identifier delete mode 100644 analysis/neutron/helpers.py:Zone.Identifier delete mode 100644 analysis/neutron/model.xml:Zone.Identifier delete mode 100644 analysis/neutron/openmc_model_PbLi.py:Zone.Identifier delete mode 100644 analysis/neutron/postprocessing.ipynb:Zone.Identifier delete mode 100644 analysis/neutron/statepoint.100.h5:Zone.Identifier delete mode 100644 analysis/neutron/summary.h5:Zone.Identifier delete mode 100644 analysis/neutron/vault.py:Zone.Identifier diff --git a/analysis/neutron/.gitignore:Zone.Identifier b/analysis/neutron/.gitignore:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/helpers.py:Zone.Identifier b/analysis/neutron/helpers.py:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/model.xml:Zone.Identifier b/analysis/neutron/model.xml:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/openmc_model_PbLi.py b/analysis/neutron/openmc_model_PbLi.py index 2dabbd3..5eade0a 100644 --- a/analysis/neutron/openmc_model_PbLi.py +++ b/analysis/neutron/openmc_model_PbLi.py @@ -2,6 +2,7 @@ import vault from libra_toolbox.neutronics.neutron_source import A325_generator_diamond import helpers +import math def baby_geometry(x_c: float, y_c: float, z_c: float): @@ -23,7 +24,6 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): he_thickness = 0.6 inconel_thickness = 0.3 heater_gap = 0.878 - PbLi_thickness = 6.388 + 0.13022 # without heater: 0.1081 gap_thickness = 4.605 cap = 1.422 firebrick_thickness = 15.24 @@ -53,6 +53,10 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): vessel_radius = 15.593 external_radius = 15.812 + PbLi_volume = 1200 # 1L = 1000 cm3 + + PbLi_thickness = calculate_z_height(PbLi_radius, heater_r, heater_gap, PbLi_volume) + source_h = 50.00 source_x = x_c - 13.50 source_z = z_c - 5.635 @@ -311,6 +315,16 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): return sphere, PbLi_cell, cells +def calculate_z_height(R, r, g, V): + """Calculates the height (H) of a cylindrical volume (radius R & volume V) with an inserted inner cylinder (radius r & gap from larger cylinder floor of g).""" + v1 = math.pi * R**2 * g # Volume of cylinder beneath heater + v2 = V - v1 # Volume of annulus around heater + + h1 = g # Height below heater + h2 = v2 / (math.pi * (R**2 - r**2)) # Height of annulus around heater + + H = h1 + h2 # Total height for given volume + return H def baby_model(): """Returns an openmc model of the BABY experiment. diff --git a/analysis/neutron/openmc_model_PbLi.py:Zone.Identifier b/analysis/neutron/openmc_model_PbLi.py:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/postprocessing.ipynb b/analysis/neutron/postprocessing.ipynb index 7f33748..0ae8f7d 100644 --- a/analysis/neutron/postprocessing.ipynb +++ b/analysis/neutron/postprocessing.ipynb @@ -200,15 +200,15 @@ "Skipping cross_sections/ENDFB-8.0-NNDC_Pb207.h5, already downloaded\n", "Skipping cross_sections/ENDFB-8.0-NNDC_Pb208.h5, already downloaded\n", "Skipping cross_sections/ENDFB-8.0-NNDC_Bi209.h5, already downloaded\n", - "written cross sections xml file to /home/rossmac/Repositories/BABY-1L-run-1/analysis/neutron/cross_sections/cross_sections.xml\n", - "setting OPENMC_CROSS_SECTIONS to /home/rossmac/Repositories/BABY-1L-run-1/analysis/neutron/cross_sections/cross_sections.xml\n" + "written cross sections xml file to /home/rossmac/Repositories/Ross-BABY-LiPb/analysis/neutron/cross_sections/cross_sections.xml\n", + "setting OPENMC_CROSS_SECTIONS to /home/rossmac/Repositories/Ross-BABY-LiPb/analysis/neutron/cross_sections/cross_sections.xml\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/rossmac/anaconda3/envs/baby_1l_run_1/lib/python3.12/site-packages/openmc/source.py:656: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n", + "/home/rossmac/anaconda3/envs/BABY_1L_PbLi/lib/python3.12/site-packages/openmc/source.py:656: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n", " warnings.warn(\"This class is deprecated in favor of 'IndependentSource'\", FutureWarning)\n" ] } @@ -232,7 +232,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -245,7 +245,7 @@ "# Visualization\n", "\n", "from vault import Air\n", - "from openmc_model import air\n", + "from openmc_model_PbLi import air\n", "\n", "x_c = 587\n", "y_c = 60\n", @@ -278,7 +278,7 @@ "outputs": [ { "data": { - "image/png": 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", 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ePbrd6wcAAAA6pz36GS6lHHPMMdXtu+66q9lxixYtql4xM378+N2+LgAAAKBj7NHBpVKp7PTrgAMOSJIccMAB1T9bsGDBdvMcd9xxqaurS5JcffXVqVQqTb7e3Llzq9snn3zybnlPAAAAQMfbo4NLKT179szHPvaxJMmSJUsye/bsHcYsXLgwV155ZZKXbkkaO3bsK7pGAAAA4JXTpZ/hcu+996ahoaH6/YoVK6rbDQ0N211RkiTTpk3bbWuZPn165s2bl8ceeywzZsxIQ0NDTj311PTp0yfz58/PF77whTQ2NqZPnz655JJLdts6AAAAgI5XU2nu/pcuYNq0abn66qtbPX5X3uqBBx6Y3/zmNznggAOybNmyFsc2NDRk4sSJefzxx5vcP3DgwFxzzTWZPHlym9awevXq1NXVZdWqVRk4cGCbjgUAAADKaMvv524pKujggw/O4sWLc9FFF2XMmDHZZ5990rdv3xx22GE555xz8sgjj7Q5tgAAAABdT5e+wmVP4QoXAAAA6HiucAEAAADoQIILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYbUdvQAAgO6oUqm0alxNTc1uXgkA0BEEFwCAwiqVSt45c2Orxn7/c71382oAgI7gliIAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCunRwee6553Lrrbdm5syZOfHEEzN48ODU1NSkpqYm06ZNa9UcS5Ysyb/+679m6tSp+dM//dMMHz48vXv3Tr9+/fKa17wm7373u3PLLbekUqm0ar7169fni1/8Yo466qgMGjQo/fv3z+jRo3PuuefmySefbMe7BQAAALqK2o5eQHsMHTq03XNceOGFueaaa5rc98QTT+SJJ57Id7/73UyYMCE33XRTBg0a1OxcS5cuzaRJk/Loo49u9+f19fWpr6/PFVdckWuvvTYTJ05s97oBAACAzqtLB5dtjRgxIqNHj84dd9zRpuNqa2vzpje9KePHj8+f/Mmf5NWvfnWGDBmSP/zhD6mvr8/Xv/71/Nd//VfuuuuuvOMd78g999yTHj12vDBo7dq1mTx5cjW2nH766Tn11FPTp0+fzJ8/P7NmzcqqVasyZcqULFy4MK9//euLvG8AAACg8+nSwWXmzJkZO3Zsxo4dm6FDh2bZsmU56KCD2jTHFVdckdrapn8Mb33rW/OhD30o73rXu3LTTTfl/vvvz2233ZZ3vOMdO4ydPXt26uvrkyQXX3xxpk+fXt03bty4HH/88Tn22GOzfv36nH322fnpT3/apnUCAAAAXUeXfobLBRdckMmTJ7fr1qLmYstWe+21V2bMmFH9/u67795hzObNm3PppZcmSUaPHp1PfvKTO4wZN25cTjvttCTJ/Pnz8/DDD+/ymgEAAIDOrUsHl1dKv379qtsbNmzYYf+CBQuycuXKJMnUqVObvOUoyXYP8r3pppuKrhEAAADoPASXVrjuuuuq26NGjdph/z333FPdnjBhQrPzjBkzphpv7r333oIrBAAAADoTwaUZK1asyMKFC3Paaadl1qxZSZJXvepVed/73rfD2CVLllS3mwoyW9XW1mbkyJE7HAMAAAB0L136obmlHXfccbnrrrua3Ddo0KDcdNNN2WeffXbY99RTTyV56dajpvZva8SIEXnkkUeyfPnybNy4Mb169dphzMaNG7Nx48bq96tXr279mwAAAAA6nCtcWuGss87KkiVLcuyxxza5f82aNUmS/v3773SubZ8Hs3bt2ibHzJo1K3V1ddWvESNG7MKqAQAAgI7iCpdtXHXVVVm3bl0qlUpWrlyZRYsW5atf/Wouv/zyPPHEE7niiiua/ESkrQ/S7dmz505fY9srWl544YUmx5x33nn5xCc+Uf1+9erVogsAAAB0IYLLNg466KDtvv+zP/uzfOhDH8qUKVNy6623ZuzYsbn//vszfPjw7cb17t07SbJp06advsa2twr16dOnyTG9evVq8lYjAAAAoGtwS9FO9O7dO1dddVX69u2bp556KjNmzNhhzIABA5I0f4vQttatW1fdbs0tSAAAAEDXI7i0wuDBgzN+/PgkyS233JLGxsbt9m+94mXdunVZuXJli3NtfcDukCFDXMUCAAAA3ZTg0kpDhgxJkqxfvz7Lly/fbt/hhx9e3a6vr292jsbGxixdujRJMnr06N2wSgAAAKAzEFxa6Zlnnqluv/xWoGOOOaa63dzHSifJokWLqrcUbb1iBgAAAOh+PDS3FZ555pksXLgwSXLAAQdUn9my1XHHHZe6urqsWrUqV199dWbMmJGampod5pk7d251++STT96tawboaiqVSkcvATqEf/bpbpr672CAPdEeHVwee+yxPP3003nLW97S7JhVq1blPe95T/UTiN7//vfvMKZnz5752Mc+ls997nNZsmRJZs+enenTp283ZuHChbnyyiuTJBMmTMjYsWMLvhOArq1SqeS0T8/v6GVAMZUk6fHmVgz0zz7dzzcubP6/rQH2JF06uNx7771paGiofr9ixYrqdkNDw3ZXlCTJtGnTtvv+2WefzQknnJA3vOEN+cu//MsceeSRefWrX53a2tr87ne/y3333Zcrr7wyv/vd75Ikr3vd6/L//t//a3It06dPz7x58/LYY49lxowZaWhoyKmnnpo+ffpk/vz5+cIXvpDGxsb06dMnl1xySZH3DwAAAHRONZUufB3rtGnTcvXVV7d6/Mvf6oIFC3L88ce36thJkyblqquuqj48tykNDQ2ZOHFiHn/88Sb3Dxw4MNdcc00mT57c6jUnyerVq6u3LA0cOLBNxwJ0Ba5wobupJFnRyitchlQW7vb1wCvJFS5Ad9aW38+79BUu7TV+/Pjcdddd+elPf5p77703Tz75ZH7/+99n/fr1GThwYA466KC86U1vynvf+95WPeT24IMPzuLFi3P55Zfn+uuvT0NDQzZt2pQRI0Zk4sSJ+fjHP54DDjjgFXhnAAAAQEfq0le47Clc4QJ0d65wobtxhQt7Mle4AN1ZW34/97HQAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIUJLgAAAACFCS4AAAAAhQkuAAAAAIXVdvQCAGAHlUpHrwDapaYtg/3zTndQ06Z/6gH2CIILAJ1LpZIpD07v6FVAu1SSfHXc/a0a6593urpKkhveNLujlwHQ6bilCAAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoLDajl4AAJRWSaWjl/CKqUlNRy8Bui1/lwDQHoILAN1KJZV8fNR/d/QyXhmV5F8efV1HrwK6JX+XANBebikCAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAorEsHl+eeey633nprZs6cmRNPPDGDBw9OTU1NampqMm3atFbNsWHDhtxyyy0566yz8qY3vSmDBg3K3nvvnUGDBmXcuHH57Gc/m9/+9retXtP69evzxS9+MUcddVQGDRqU/v37Z/To0Tn33HPz5JNP7uI7BQAAALqS2o5eQHsMHTq0Xcc/8sgjOeaYY7JmzZod9v3hD3/IAw88kAceeCBf/vKXc8UVV+Rd73pXi/MtXbo0kyZNyqOPPrrdn9fX16e+vj5XXHFFrr322kycOLFd6wYAAAA6ty59hcu2RowYkbe//e1tOmb16tXV2DJ+/PjMmjUrP/7xj/Of//mf+dGPfpQzzzwze+21V9asWZP3vve9+eEPf9jsXGvXrs3kyZOrseX000/PnXfemfvvvz8XXnhh+vfvn1WrVmXKlCl55JFHdv2NAgAAAJ1el77CZebMmRk7dmzGjh2boUOHZtmyZTnooINafXyPHj3yrne9K+eff34OP/zwHfa//e1vz4knnpiTTz45L774Ys4666w8/vjjqamp2WHs7NmzU19fnyS5+OKLM3369Oq+cePG5fjjj8+xxx6b9evX5+yzz85Pf/rTXXjHAAAAQFfQpa9wueCCCzJ58uRdvrXozW9+c+bNm9dkbNnqpJNOyimnnJLkpVuGfv7zn+8wZvPmzbn00kuTJKNHj84nP/nJHcaMGzcup512WpJk/vz5efjhh3dpzQAAAEDn16WDyyvl+OOPr24vXbp0h/0LFizIypUrkyRTp05Njx5N/1i3fZDvTTfdVHSNAAAAQOchuLTCxo0bq9tNxZR77rmnuj1hwoRm5xkzZkz69euXJLn33nsLrhAAAADoTASXVrjrrruq26NGjdph/5IlS1rcv1VtbW1Gjhy5wzEAAABA9yK47MQvfvGL3HbbbUmS1772tU0+7+Wpp55KkvTr1y/77LNPi/ONGDEiSbJ8+fLtrpwBAAAAuo8u/SlFu9vGjRvz93//93nxxReTJF/4wheaHLf1o6X79++/0zm33lKUvPRR0r169WrydbeNMatXr27TugEAAICO5QqXFnz0ox/NokWLkrz0MNx3vvOdTY7bsGFDkqRnz547nXPbwPLCCy80OWbWrFmpq6urfm29KgYAXikVX+368rPuvD9vAHiluMKlGbNmzcoVV1yRJDnyyCNz+eWXNzu2d+/eSZJNmzbtdN5tr1zp06dPk2POO++8fOITn6h+v3r1atEFgFdMJckNb5rd0cvo0toSAfys26lSyZQHp3f0KgBgB4JLE77+9a/nH/7hH5Ikhx12WH74wx9udyvQyw0YMCDJS7cI7cy6deuq283dgtSrV68mbzUCAAAAuga3FL3Mddddlw9/+MNJkgMOOCA/+clPMmTIkBaPGT58eJKXYsrKlStbHLv1AbtDhgwRVQAAAKCbEly28R//8R/5wAc+kC1btuSP/uiPcuedd1ZjSku2/eSi+vr6Zsc1NjZm6dKlSZLRo0e3f8EAAABApyS4/K8777wz73rXu9LY2JhXvepV+fGPf5yRI0e26thjjjmmun3XXXc1O27RokXVW4rGjx/fvgUDAAAAnZbgkuT+++/PSSedlI0bN2bgwIH50Y9+lNe+9rWtPv64445LXV1dkuTqq69OpdL0o/Lmzp1b3T755JPbtWYAAACg89rjg8vPf/7zTJo0KevWrUu/fv3ygx/8IEceeWSb5ujZs2c+9rGPJUmWLFmS2bN3/LSBhQsX5sorr0ySTJgwIWPHjm3/4gEAAIBOqUt/StG9996bhoaG6vcrVqyobjc0NGx3RUmSTJs2bbvvly5dmj//8z+vPuj285//fOrq6vJf//Vfzb7mfvvtl/3222+HP58+fXrmzZuXxx57LDNmzEhDQ0NOPfXU9OnTJ/Pnz88XvvCFNDY2pk+fPrnkkkva/F4BAACArqNLB5crrrgiV199dZP77rvvvtx3333b/dnLg8s999yT5557rvr9Oeecs9PXPP/88/PZz352hz8fMGBAbrvttkycODGPP/545syZkzlz5mw3ZuDAgbnmmmvyxje+caevAwAAAHRde/wtRSUdfPDBWbx4cS666KKMGTMm++yzT/r27ZvDDjss55xzTh555JFMnjy5o5cJAAAA7GZd+gqXuXPn7nDbUFtMmzZth6te2qtfv36ZMWNGZsyYUXReAAAAoOtwhQsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGFd+lOKAIBXSKXS0SvoUmraMtjPtu1q2vQTBoAOIbgAAC2rVDLlwekdvYoupZLkq+Pub9VYP9u2qSS54U2zO3oZALBTbikCAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKKy2oxcAAHQFlY5eQBdT04axfrZt05afLQB0HMEFAGhZTdLjWz/v6FV0KZVKkq+2YqCfbZtVKkku7+hVAMDOuaUIAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoLBWPTT3m9/85u5eR5LkAx/4wCvyOgAAAAC7U6uCy7Rp01JTs/s/gk9wAQAAALqDNn0sdKVS2V3reEWCDgAAAMAroU3B5Y477sghhxxSdAGPPvpo/uIv/qLonAAAAAAdqU3BZdiwYTnggAOKLmDt2rVF5wMAAADoaD6lCAAAAKCwVl3hctVVVyVJhg8fXnwBw4cPr84PAAAA0B20KrhMnTp1ty2grq5ut84PAAAA8EpzSxEAAABAYYILAAAAQGGCCwAAAEBhbfpY6JY8//zzWbhwYX79619nzZo1efHFF3d6zMyZM0u9PAAAAECn0e7g8rvf/S6f+MQncuONN6axsbFNxwouAAAAQHfUruCyfPnyvPnNb85vfvObVCqVUmsCAAAA6NLa9QyX888/P8uWLUulUsmUKVPy05/+NM8//3xefPHFbNmyZadfAAAAAN1Ru65wufXWW1NTU5P3v//9mTt3bqElAQAAAHRt7brCZfny5UmSv/u7vyuyGAAAAIDuoF3BZdiwYUmSfv36FVkMAAAAQHfQruBy7LHHJkl++ctfFlkMAAAAQHfQruBy7rnnpmfPnvnSl76UDRs2lFoTAAAAQJfWruDy2te+Nt/4xjfy6KOP5s///M/z2GOPlVoXAAAAQJfVrk8pSpL3vOc9OeSQQzJp0qQcfvjhef3rX59DDz00ffv2bfG4mpqaXHnlle19eQAAAIBOp93B5bHHHssnPvGJrFixIknyi1/8Ir/4xS9aPKZSqQguAAAAQLfVruDy5JNP5thjj83y5ctTqVSSJAMHDkxdXV169GjX3UoAAAAAXVa7gss//dM/5bnnnkuPHj1y7rnn5sMf/nAOOOCAUmsDAAAA6JLaFVzuvPPO1NTU5OMf/3guuuiiUmsCAAAA6NLadd/P73//+yTJX/3VXxVZDAAAAEB30K7g8kd/9EdJkp49exZZDAAAAEB30K7g8ra3vS1J8tBDDxVZDAAAAEB30K7gcu6556Zfv3656KKL8j//8z+l1gQAAADQpbUruBx88MG5+eabs2bNmowfPz4//vGPS60LAAAAoMtq16cUveUtb0mSDB48OI8++mj+4i/+Ivvss08OOeSQ9O3bt8Vja2pqcuedd7bn5fPcc8/lwQcfzIMPPpiHHnooDz30UJ5//vkkydSpUzN37tydzrFly5bU19dvN88jjzySTZs2JUnmz5+f4447rtVrWr9+fS6//PJcf/31aWhoyKZNmzJixIhMmjQpH/vYx/LHf/zHu/JWAQAAgC6kXcFlwYIFqampqX5fqVTyhz/8IQ8++GCzx9TU1KRSqWx33K4aOnRou+f41re+lWnTprV7niRZunRpJk2alEcffXS7P6+vr099fX2uuOKKXHvttZk4cWKR1wMAAAA6p3YFl2OPPbZIOClhxIgRGT16dO644442HVepVKrbe++9d173utelsbExv/zlL9s0z9q1azN58uRqbDn99NNz6qmnpk+fPpk/f35mzZqVVatWZcqUKVm4cGFe//rXt2l+AAAAoOto9xUuHWnmzJkZO3Zsxo4dm6FDh2bZsmU56KCD2jTH4YcfnksvvTRHHXVU3vjGN6Z379757Gc/2+bgMnv27NTX1ydJLr744kyfPr26b9y4cTn++ONz7LHHZv369Tn77LPz05/+tE3zAwAAAF1Hu4JLR7vgggvaPcdRRx2Vo446ql1zbN68OZdeemmSZPTo0fnkJz+5w5hx48bltNNOy9e//vXMnz8/Dz/8cI488sh2vS4AAADQObXrU4p4yYIFC7Jy5cokLz2st0ePpn+s2z4r5qabbnoFVgYAAAB0BMGlgHvuuae6PWHChGbHjRkzJv369UuS3Hvvvbt9XQAAAEDHaFdwWbx4cfbaa6/06dMnzzzzzE7HP/PMM+ndu3dqa2vzq1/9qj0v3aksWbKkuj1q1Khmx9XW1mbkyJE7HAMAAAB0L+0KLvPmzUulUsnkyZOz//7773T8/vvvn3e+853ZsmVLvvOd77TnpTuVp556KknSr1+/7LPPPi2OHTFiRJJk+fLl2bhx4+5eGgAAANAB2hVcFixYkJqampx44omtPmbSpElJkp/85CfteelOZc2aNUmS/v3773Ts1luKkpc+SropGzduzOrVq7f7AgAAALqOdgWXrVd2HH744a0+5rDDDkuSPP300+156U5lw4YNSZKePXvudGyvXr2q2y+88EKTY2bNmpW6urrq19arYgAAAICuoV3B5fnnn0+S9O7du9XHbA0Ozz33XHteulPZ+v43bdq007Hb3kbUp0+fJsecd955WbVqVfVra9gCAAAAuoZ2BZd99903SfLkk0+2+pitV7YMHDiwPS/dqQwYMCBJ87cIbWvdunXV7eZuQerVq1cGDhy43RcAAADQdbQruGy9leg//uM/Wn3MzTffnOT/bi3qDoYPH57kpZiycuXKFsduvVplyJAh291eBAAAAHQf7QouEydOTKVSyTe/+c3cc889Ox1/991351vf+lZqamoyefLk9rx0p7LtM2zq6+ubHdfY2JilS5cmSUaPHr3b1wUAAAB0jHYFlzPPPDODBw/Oiy++mIkTJ+ayyy6rPkB2Wxs2bMi//Mu/ZNKkSXnxxRez77775kMf+lB7XrpTOeaYY6rbd911V7PjFi1aVL2laPz48bt9XQAAAEDHaFdw6d+/f6699trstddeWb9+fc4+++wMGTIkxx9/fN773vfmfe97X44//vgMGTIk55xzTtatW5e999471113Xbd6Lslxxx2Xurq6JMnVV1+dSqXS5Li5c+dWt08++eRXYmkAAABAB2hXcEmSt771rfnRj36UV7/61alUKlm3bl3uvvvuzJs3L9/5zndy9913Z926dalUKtl///3zox/9KG9729tKrL3T6NmzZz72sY8lSZYsWZLZs2fvMGbhwoW58sorkyQTJkzI2LFjX9E1AgAAAK+c2hKTHH/88Vm6dGm++c1v5rbbbsvixYuzYsWKJMngwYPzp3/6p3nHO96Rv/mbvyn6oNh77703DQ0N1e+3vmaSNDQ0bHdFSZJMmzatyXlePu7nP/95dfv222/PsmXLqt8ffPDB291CtNX06dMzb968PPbYY5kxY0YaGhpy6qmnpk+fPpk/f36+8IUvpLGxMX369Mkll1zS2rcIAAAAdEFFgkuS9O7dO2eccUbOOOOMUlPu1BVXXJGrr766yX333Xdf7rvvvu3+rLng8rd/+7fNvsZFF1203fdTp05tMrgMGDAgt912WyZOnJjHH388c+bMyZw5c7YbM3DgwFxzzTV54xvf2OzrAQAAAF1fu28p4v8cfPDBWbx4cS666KKMGTMm++yzT/r27ZvDDjss55xzTh555JFu9elMAAAAQNOKXeHSEebOnbvD7UC7ormH3O6Kfv36ZcaMGZkxY0axOQEAAICuxRUuAAAAAIW1Kri85S1vyQknnJDf/OY3xRewbNmy6vwAAAAA3UGrbilasGBBampqsm7duuILWLduXXV+AAAAgO7ALUUAAAAAhbXpobn/9m//lv3226/oAp577rmi8wEAAAB0tDYFl69+9au7ax0AAAAA3Uarg0vJj04GAAAA6M5aFVy2bNmyu9cBANDN+D+rAGBP1qZbigAA2LmamuSjH+7b0csAADqQTykCAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAorLajFwAAdH6VSkevAACga2lXcPmnf/qnJMkBBxyQqVOntuqY5cuX56tf/WqSZObMme15eQDgFVGT715+Z0cvAgCgS2lXcPnsZz+bmpqaJMlPf/rT/Pu//3t69uzZ4jHPPfdc9TjBBQAAAOiOitxSVKlU8u1vfzuPP/54br755gwdOrTEtABAR3EPEQBAuxQJLn/xF3+R22+/PT/72c9y1FFH5ZZbbskb3/jGElMDAK+wmiRTHpze0csAAOjSinxK0ezZs3PZZZdlr732ylNPPZVjjjkmN954Y4mpAQAAALqcYh8L/ZGPfCQ//OEPs++++2b9+vV517veVX2oLgAAAMCepFhwSZITTjghDzzwQA499NBUKpVccMEFefe7350NGzaUfBkAAACATq1ocEmSQw45JD/72c/ytre9LZVKJTfccEP+7M/+LM8++2zplwIAAADolIoHlySpq6vLD3/4w3z0ox9NpVLJf/7nf2bs2LF56KGHdsfLAQAAAHQquyW4JEmPHj3yL//yL/na176W2tra/Pa3v82ECRNyzTXX7K6XBIAkNUkle8YXsBv5uwSA9inysdAtOeOMM3LooYdmypQpef7553PRRRft7pcEoKsbdM4uH1qT5F+Wl1tKpzeooxcA3ZO/SwBor912hcu2jjvuuDzwwAMZNWpUKhUZHQAAAOje2nWFy1VXXZUkGT58+E7Hjhw5Mj/72c/ykY98JE899VR7XhYAAACgU2tXcJk6dWqbxg8YMCDf/OY32/OSAAAAAJ3eK3JLEQAAAMCeRHABAAAAKExwAQAAAChst38sNAC0lc+zAwCgqxNcAOhcampywyH7d/QqAACgXdxSBAAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFBYbUcvgNarVCqpVCodvQyA3cPfbwBdX02N/14FurW2/B0nuHQhP5k2L3337tPRywDYLaZ09AIAKOL2d3+7o5cAsNus3/xCq8e6pQgAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgsNqOXgCtV6lUUqlUOnoZAAAAsEdqy+/kgksXMuXHX0pq9uroZQAAAMCeqfJiq4e6pQgAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgsC4dXJ577rnceuutmTlzZk488cQMHjw4NTU1qampybRp09o83+23355TTjklw4cPT69evTJ8+PCccsopuf3221s9x/r16/PFL34xRx11VAYNGpT+/ftn9OjROffcc/Pkk0+2eU0AAABA11NTqVQqHb2IXVVTU9PsvqlTp2bu3LmtmqdSqeSDH/xg5syZ0+yYM844I1/72tdafM2lS5dm0qRJefTRR5vcX1dXl2uvvTYTJ05s1bq2Wr16derq6pK+hyQ1e7XpWAAAAKCQyovJ+sezatWqDBw4sMWhXfoKl22NGDEib3/723fp2E9/+tPV2HLEEUfkuuuuy4MPPpjrrrsuRxxxRJJkzpw5+cxnPtPsHGvXrs3kyZOrseX000/PnXfemfvvvz8XXnhh+vfvn1WrVmXKlCl55JFHdmmdAAAAQNfQpa9wOf/88zN27NiMHTs2Q4cOzbJly3LQQQclaf0VLg0NDRk9enQaGxszZsyY3H333enTp091//r16zNhwoQsWrQotbW1qa+vz8iRI3eY57Of/WwuuOCCJMnFF1+c6dOnb7d/4cKFOfbYY9PY2Jjjjz8+P/3pT1v9Pl3hAgAAAJ3AnnKFywUXXJDJkydn6NChuzzHV77ylTQ2NiZJLrvssu1iS5L07ds3l112WZKksbExl1xyyQ5zbN68OZdeemmSZPTo0fnkJz+5w5hx48bltNNOS5LMnz8/Dz/88C6vGQAAAOjcunRwaa9KpZJbbrklSTJq1KgcffTRTY47+uijc9hhhyVJvve97+XlFwUtWLAgK1euTPLSlTU9ejT9Y932Qb433XRTO1cPAAAAdFZ7dHB54okn8swzzyRJJkyY0OLYrfuffvrpLFu2bLt999xzzw7jmjJmzJj069cvSXLvvffuypIBAACALmCPDi5Lliypbo8aNarFsdvu3/a4tsxTW1tbff7Ly+cAAAAAuo89Org89dRT1e3hw4e3OHbEiBFNHrft9/369cs+++zTqnmWL1+ejRs3Njlm48aNWb169XZfAAAAQNexRweXNWvWVLf79+/f4tittwIlL30EdFPz7GyOnc2z1axZs1JXV1f92jb2AAAAAJ1fbUcvoCNt2LChut2zZ88Wx/bq1au6/cILLzQ5z87m2Nk8W5133nn5xCc+Uf1+9erVGTFiRFb9dtFOP3YKAAAA2D1Wr16durq6Vo3do4NL7969q9ubNm1qcey2t/+8/KOjt86zszl2Ns9WvXr12i7MAAAAAF3LHn1L0YABA6rbzd3es9W6deuq2y+/dWjrPDubY2fzAAAAAN3DHh1ctn1Q7tNPP93i2G0flPvyZ6psnWfdunVZuXJlq+YZMmSIq1gAAACgm9qjg8vhhx9e3a6vr29x7Lb7R48evUvzNDY2ZunSpU3OAQAAAHQfe3RwOeiggzJs2LAkyV133dXi2LvvvjtJsv/+++fAAw/cbt8xxxxT3W5pnkWLFlVvKRo/fvyuLBkAAADoAvboh+bW1NTkpJNOyle/+tXU19fngQceyNFHH73DuAceeKB65cpJJ52Umpqa7fYfd9xxqaury6pVq3L11VdnxowZO4xJkrlz51a3Tz755LJvBoBWq1QqHb0EAIBOranfaWmbPTq4JMnZZ5+df//3f09jY2POOuus3H333dt9etALL7yQs846K0lSW1ubs88+e4c5evbsmY997GP53Oc+lyVLlmT27NmZPn36dmMWLlyYK6+8MkkyYcKEjB07dve9KQCaValU8tEfLunoZQAAdGqXTzx854NoUZcOLvfee28aGhqq369YsaK63dDQsN0VJUkybdq0HeY49NBDc+655+af//mfs2jRoowfPz6f+tSnMnLkyCxdujQXXXRRFi9enCSZPn16DjnkkCbXMn369MybNy+PPfZYZsyYkYaGhpx66qnp06dP5s+fny984QtpbGxMnz59cskll7T7vQMAAACdV02lC19XPW3atFx99dWtHt/cW92yZUtOP/30fOMb32j22NNOOy1z5sxJjx7NP/amoaEhEydOzOOPP97k/oEDB+aaa67J5MmTW73mJFm9enX1lqWBAwe26VgAtucKFwCAnXOFS9Pa8vt5l77CpZQePXrkyiuvzF/91V9lzpw5eeihh7JixYoMHjw4Y8eOzZlnnpkTTzxxp/McfPDBWbx4cS6//PJcf/31aWhoyKZNmzJixIhMnDgxH//4x3PAAQe8Au8IgNbyHxMAAP5Pqd2hS1/hsqdwhQtAOS//jwnBBQDAfyO1Vlt+P9+jPxYaAAAAYHcQXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1z+14YNG/Jv//ZvOeGEEzJkyJD07Nkz+++/fyZNmpR58+a1ep7bb789p5xySoYPH55evXpl+PDhOeWUU3L77bfvxtUDAAAAnUltRy+gM3j00Udz0kkn5dFHH93uz5999tk8++yz+cEPfpC5c+fmhhtuSL9+/Zqco1Kp5IMf/GDmzJmz3Z8/88wzufnmm3PzzTfnjDPOyNe+9rXU1NTstvcCAAAAdLw9/gqX5cuX521ve1s1tkyZMiW33npr/vM//zO33nprpkyZkuSlK1fe8573NDvPpz/96WpsOeKII3LdddflwQcfzHXXXZcjjjgiSTJnzpx85jOf2c3vCAAAAOhoe3xwueCCC/LUU08lSc4///x897vfzaRJk3LEEUdk0qRJ+e53v5uZM2cmSb7//e/npptu2mGOhoaGXHzxxUmSMWPG5L777supp56asWPH5tRTT829996bMWPGJEkuuuiiLF269BV6dwAAAEBH2KODy4svvphrrrkmSXLAAQc0e/XJzJkz88d//MdJklmzZu2w/ytf+UoaGxuTJJdddln69Omz3f6+ffvmsssuS5I0NjbmkksuKfUWAAAAgE5ojw4ujz/+eFauXJkkedvb3pa99tqryXF77bVX3va2tyVJFi1alGXLllX3VSqV3HLLLUmSUaNG5eijj25yjqOPPjqHHXZYkuR73/teKpVKoXcBAAAAdDZ7dHD5n//5n+r20KFDWxy77f677767uv3EE0/kmWeeSZJMmDChxTm27n/66ae3izYAAABA97JHB5dtP3Fo1apVLY7ddv+vfvWr6vaSJUuq26NGjWpxjm33b3scAAAA0L3s0cHl4IMPzt57751k+6tWmrLt/ieffLK6vfWBu0kyfPjwFucYMWJEk8cBAAAA3cseHVz69euXE044IUnyyCOP5Lrrrmty3HXXXZdf/vKX1e/XrFnT5Hb//v13+npbrV27ttlxGzduzOrVq7f7AgAAALqOPTq4JC99LHRtbW2SZOrUqfn85z+fJ598Mps3b86TTz6Zz3/+85k6dWp69uxZPeaFF16obm/YsKG6ve2YpvTq1avJOV5u1qxZqaurq35te2UMAAAA0Pnt8cHlqKOOypVXXpmePXtm8+bN+cxnPpMDDjggPXv2rH5U9JYtW/KlL32pesyAAQOq2717965ub9q0qcXX2rhxY3X75R8dva3zzjsvq1atqn65/QgAAAC6lj0+uCTJBz7wgTz44IOZMmXKdjGlR48eOeGEE3LffffluOOOq/75vvvuW93ednxLtwklybp166rbLd1+1KtXrwwcOHC7LwAAAKDrqO3oBXQWb3jDG/Ld7343L774Yn77299mw4YNGTZsWPr27Zskufbaa6tjDz/88Or2tg/Kffrpp1t8jW2vVHGbEAAAAHRfgsvL7LXXXk1+2tC9995b3X7Tm95U3d42vtTX17c497b7R48e3Z5lAlBIpVLp6CUAANANCS6tsGnTptxwww1Jkv333z9vfvObq/sOOuigDBs2LM8++2zuuuuuFufZ+tHS+++/fw488MDdtl4AWu+jP1zS0UsAAKAb8gyXVrj00kuzfPnyJMkHP/jB7LXXXtV9NTU1Oemkk5K8dAXLAw880OQcDzzwQPUKl5NOOik1NTW7edUAAABARxFckjz55JPN7vv+97+ff/zHf0ySHHLIITn33HN3GHP22WdXP1r6rLPO2uEjn1944YWcddZZSZLa2tqcffbZhVYOAAAAdEZuKUryute9LuPGjcuUKVPy2te+Nj179syyZcty/fXXZ968eUle+mSiefPmbfcx0FsdeuihOffcc/PP//zPWbRoUcaPH59PfepTGTlyZJYuXZqLLrooixcvTpJMnz49hxxyyCv6/gD4PzU1Nbl84uE7HwgAAO1QU/G0wPTv33+7j2x+ucMPPzzf/va3c8QRRzQ7ZsuWLTn99NPzjW98o9kxp512WubMmZMePdp2YdHq1atTV1eXVatW+YhoAAAA6CBt+f1ccEnyne98J3fccUcefPDB/Pa3v83atWszZMiQvP71r89f//Vf5/3vf3/23nvvVs31gx/8IHPmzMlDDz2UFStWZPDgwRk7dmzOPPPMnHjiibu0PsEFAAAAOp7g0s0ILgAAANDx2vL7uYfmAgAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4/K9NmzblyiuvzF/8xV/kj/7oj9KrV6/0798/hx12WP7u7/4uDzzwQKvmuf3223PKKadk+PDh6dWrV4YPH55TTjklt99++25+BwAAAEBnUVOpVCodvYiO9tRTT2XSpEn55S9/2eK4c845J1/60pdSU1Ozw75KpZIPfvCDmTNnTrPHn3HGGfna177W5PEtWb16derq6rJq1aoMHDiwTccCAAAAZbTl9/M9/gqXxsbG7WLL61//+sydOzcLFy7MHXfckZkzZ6Zfv35Jkq985SuZPXt2k/N8+tOfrsaWI444Itddd10efPDBXHfddTniiCOSJHPmzMlnPvOZV+BdAQAAAB1pj7/C5cYbb8xf//VfJ0nGjRuXe+65J3vttdd2Yx5++OGMGzcumzdvzr777pvnnnsutbW11f0NDQ0ZPXp0GhsbM2bMmNx9993p06dPdf/69eszYcKELFq0KLW1tamvr8/IkSNbvUZXuAAAAEDHc4VLG9x3333V7fPOO2+H2JIkRx55ZCZPnpwk+cMf/pD6+vrt9n/lK19JY2NjkuSyyy7bLrYkSd++fXPZZZcleemKmksuuaTkWwAAAAA6mT0+uGzatKm6/ZrXvKbZcdtekbJx48bqdqVSyS233JIkGTVqVI4++ugmjz/66KNz2GGHJUm+973vZQ+/sAgAAAC6tT0+uBx66KHV7V//+tfNjlu6dGmSpKamJoccckj1z5944ok888wzSZIJEya0+Fpb9z/99NNZtmzZri4ZAAAA6OT2+ODynve8p3rf1UUXXZQXX3xxhzGLFy/ObbfdliQ59dRTt7tPa8mSJdXtUaNGtfha2+7f9jgAAACge9njg8uQIUMyd+7c9OnTJ/fdd1/Gjh2bb37zm3nggQfyk5/8JBdccEEmTJiQTZs25Y1vfGO+/OUvb3f8U089Vd0ePnx4i681YsSIJo8DAAAAupfanQ/p/k4++eQsWrQoX/7yl/ONb3wjU6dO3W7/0KFDc8EFF+SMM86ofkT0VmvWrKlu9+/fv8XX2fbYtWvXNjtu48aN2z0nZvXq1a16HwAAAEDnsMdf4ZIkmzdvzrXXXpvvf//7TT7M9ve//32uu+66LFiwYId9GzZsqG737Nmzxdfp1atXdfuFF15odtysWbNSV1dX/dr2yhgAAACg89vjg8u6devy1re+NRdeeGGef/75zJgxI0uWLMnGjRuzatWq3HHHHTnmmGPy0EMP5R3veEcuvfTS7Y7v3bt3dXvbTzxqyrZXrbz8o6O3dd5552XVqlXVL7cfAQAAQNeyxweX888/P3fffXeS5Morr8xFF12UUaNGpWfPnhk4cGDe9ra3Zf78+Tn++ONTqVTyiU98Io888kj1+AEDBlS3W7pNKHkp7mzV0u1HvXr1ysCBA7f7AgAAALqOPTq4VCqVXHXVVUle+njolz+7Zava2tp87nOfS5Js2bKlekyy/YNyn3766RZfb9srVdwmBAAAAN3XHh1cfv/73+d//ud/kiRHHHFEi2OPPPLI6nZ9fX11+/DDD2/yz5uy7f7Ro0e3aa0AAABA17FHB5fa2v/7kKbGxsYWx27evLnJ4w466KAMGzYsSXLXXXe1OMfWW5f233//HHjggW1dLgAAANBF7NHBZdCgQdXnoyxcuLDF6LJtTDnooIOq2zU1NTnppJOSvHQFywMPPNDk8Q888ED1CpeTTjopNTU17V4/AAAA0Dnt0cGlR48emTRpUpLk2WefzYUXXtjkuD/84Q/51Kc+Vf1+8uTJ2+0/++yzq1e9nHXWWTt85PMLL7yQs846K8lLV8ecffbZpd4CAAAA0Ant0cElSWbOnJm+ffsmST772c/mne98Z2688cYsXrw4CxcuzFe+8pW88Y1vzK9+9askyQknnJC3v/3t281x6KGH5txzz02SLFq0KOPHj8+8efOyaNGizJs3L+PHj8+iRYuSJNOnT88hhxzyCr5DAAAA4JVWU6lUKh29iI72k5/8JO95z3uyYsWKFse95S1vyQ033JB99913h31btmzJ6aefnm984xvNHn/aaadlzpw56dGjbZ1r9erVqaury6pVq3xENAAAAHSQtvx+Lrj8r+effz5XXnllfvjDH+a///u/s3LlytTW1ubVr351xo4dm/e+97155zvfudNnr/zgBz/InDlz8tBDD2XFihUZPHhwxo4dmzPPPDMnnnjiLq1NcAEAAICOJ7h0M4ILAAAAdLy2/H6+xz/DBQAAAKA0wQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKCw2o5eAABdU6VS6egl7JFqamraNN556hhtPU8AbeXv947Tlr/jnaeO0Vn+d1hwAaDNKpVKfnvpP3T0MvZIw86e1eqxzlPHact5Amgrf793rNb+He88dZzO8r/DbikCAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAorLajF8DuU6lUOnoJQDfm75iOUNPmn7vz1BHafp4A2srfMx2lbX/HO08doW3nqKamZvetpOKfgE5v9erVqaury6pVqzJw4MBWHVOpVNJjwOG7eWUAAADQdVXWLmnT+Lb8fu6Wom6qpqYmW9b8qqOXAQAAAJ1SW2NLWwkuAAAAAIUJLt2Yq1wAAABgR7v76pZEcOn2RBcAAAD4P69EbEkEFwAAAIDiBJc9gKtcAAAA4JW7uiURXPYYogsAAAB7slcytiSCCwAAAEBxgssexFUuAAAA7Ile6atbEsFljyO6AAAAsCfpiNiSCC57JNEFAACAPUFHxZZEcAEAAAAoTnDZQ7nKBQAAgO6sI69uSfbw4HLcccelpqamTV8LFixodr7bb789p5xySoYPH55evXpl+PDhOeWUU3L77be/cm+qDUQXAAAAuqOOji3JHh5c2qpHjx455JBDdvjzSqWSM888MyeeeGJuvvnmPPPMM9m0aVOeeeaZ3HzzzTnxxBNz5plnplKpdMCqAQAAgFdabUcvoCNdddVVWbduXYtjfvWrX+Xd7353kuSEE07I/vvvv8OYT3/605kzZ06S5IgjjsiMGTMycuTILF26NBdffHEWL16cOXPmZMiQIfn85z9f/o20w9arXHoMOLyjlwIAAADt1hmubkmSmorLLlr0qU99KhdffHGS5Fvf+lb+5m/+Zrv9DQ0NGT16dBobGzNmzJjcfffd6dOnT3X/+vXrM2HChCxatCi1tbWpr6/PyJEj27SG1atXp66uLqtWrcrAgQPb/6aaUKlURBcAAAC6tN0dW9ry+7lbilqwZcuWXHPNNUmS/v3755RTTtlhzFe+8pU0NjYmSS677LLtYkuS9O3bN5dddlmSpLGxMZdccsnuXfQu8jwXAAAAurLOcmXLVoJLC+68884888wzSZK//uu/Tt++fbfbX6lUcssttyRJRo0alaOPPrrJeY4++ugcdthhSZLvfe97nuUCAAAA3Zzg0oJvfvOb1e0PfOADO+x/4oknqkFmwoQJLc61df/TTz+dZcuWlVtkQa5yAQAAoCvqbFe3JIJLs9auXZubb745SfLHf/zHOe6443YYs2TJ/53QUaNGtTjftvu3Pa6zEV0AAADoSjpjbEn28E8pasmNN95Y/QSj97///ampqdlhzFNPPVXdHj58eIvzjRgxosnjmrJx48Zs3Lix+v3q1atbteZSfHIRAAAAXUFnjS2JK1yatbPbiZJkzZo11e3+/fu3OF+/fv2q22vXrm1x7KxZs1JXV1f92jbWAAAAAJ2f4NKEp59+OgsWLEjy0gNvDz300CbHbdiwobrds2fPFufs1atXdfuFF15ocex5552XVatWVb92dkXM7uDWIgAAADqzznx1S+KWoiZ9+9vfzpYtW5IkU6dObXZc7969q9ubNm1qcc5tbxF6+UdHv1yvXr22CzQdxa1FAAAAdEadPbYkrnBp0re+9a0kL4WPd7/73c2OGzBgQHV7Z7cJbX0eTLLz2486E1e6AAAA0Jl0hdiSCC47WLRoUX71q5cCw+TJk7Pvvvs2O3bbB+U+/fTTLc677W1BnskCAAAA3Zvg8jLbPiy3pduJkuTww//vVpv6+voWx267f/To0bu4uo7hKhcAAAA6g65ydUsiuGxn8+bN+c53vpMkGTJkSE488cQWxx900EEZNmxYkuSuu+5qcezdd9+dJNl///1z4IEHtn+xrzDRBQAAgI7UlWJL4qG52/nhD3+Y5cuXJ0ne+973pra25R9PTU1NTjrppHz1q19NfX19HnjggRx99NE7jHvggQeqV7icdNJJqampKb/4V0BNTU2X+wccAAAAOoIrXLax7e1EH/jAB1p1zNlnn10NM2edddYOH/n8wgsv5KyzzkqS1NbW5uyzzy6zWAAAAKDTElz+1x/+8IfceuutSZLXve51+dM//dNWHXfooYfm3HPPTfLSA3fHjx+fefPmZdGiRZk3b17Gjx+fRYsWJUmmT5+eQw45ZPe8AQAAAKDTcEvR/5o3b142btyYpPVXt2x14YUX5rnnnss3vvGNLF68OKeeeuoOY0477bR8/vOfL7JWAAAAoHNzhcv/+ta3vpUk2WuvvfK+972vTcf26NEjV155ZW677bacdNJJGTZsWHr27Jlhw4blpJNOyg9+8INcccUV6dHDjxsAAAD2BDWVSqXS0YugZatXr05dXV1WrVqVgQMHdvRyAAAAYI/Ult/PXXIBAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUFhtRy+AnatUKkmS1atXd/BKAAAAYM+19ffyrb+nt0Rw6QLWrFmTJBkxYkQHrwQAAABYs2ZN6urqWhxTU2lNlqFDbdmyJc8++2wGDBiQmpqajl5Oh1i9enVGjBiRp556KgMHDuzo5VCI89r9OKfdk/Pa/Tin3ZPz2j05r92Pc9q1VSqVrFmzJsOGDUuPHi0/pcUVLl1Ajx49Mnz48I5eRqcwcOBAfyl1Q85r9+Ocdk/Oa/fjnHZPzmv35Lx2P85p17WzK1u28tBcAAAAgMIEFwAAAIDCBBe6hF69euX8889Pr169OnopFOS8dj/OaffkvHY/zmn35Lx2T85r9+Oc7jk8NBcAAACgMFe4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuFBUTU1Nq76OO+64Jo+fO3duq+eYO3fuTtezfv36fPGLX8xRRx2VQYMGpX///hk9enTOPffcPPnkk2XffDfV3nP6cg8++GA+/OEPZ/To0Rk4cGD69++fkSNHZtKkSfnyl7+c5cuXt3i8c1pGe87rsmXLWn381q8DDzywxfU4r+1X6t/V3/zmN/l//+//5cgjj8w+++yTvffeO4MGDcqb3/zmfO5zn9vpv6NbOadllDqvTzzxRM4555y87nWvy4ABA9KvX78ceuih+chHPpL//u//bvV6nNfyVqxYkYsvvjjjx4/Pq1/96vTq1SvDhg3Lm970pkyfPj0LFy7c6Ry33357TjnllAwfPjy9evXK8OHDc8opp+T2229v9Tqc23J29Zxu2bIlv/rVrzJ37tx8+MMfztixY9OrV6/qv+cLFixo0zqc07J29bxu2LAht9xyS84666y86U1vyqBBg6r/2zpu3Lh89rOfzW9/+9tWr8N57WIqUFCSVn1NmDChyeOvuuqqVs9x1VVXtbiWhoaGymGHHdbs8XV1dZXbbrut/A+hm2nvOd1qw4YNlb//+7+v1NTUtDjPzTff3Owczmk57TmvTzzxRKuP3/r19re/vdm1OK9llPh39Zprrqn07du3xeNf9apXVe68884W1+KcllPivH7961+v9OzZs9lje/bsWfm3f/u3na7FeS3vu9/9buVVr3pVi+f2pJNOavb4LVu2VM4444wWjz/jjDMqW7ZsaXEdzm057Tmnc+fObfG4+fPnt3odzmlZu3pef/GLX1QGDBiw07/DBwwYUJk3b95O1+G8dj21gd3gQx/6UD784Q83u79fv347neNHP/pRhg0b1uz+4cOHN7tv7dq1mTx5ch599NEkyemnn55TTz01ffr0yfz58zNr1qysWrUqU6ZMycKFC/P6179+p+vZ07XnnG7atCknn3xyfvjDHyZJ/uzP/iwf+MAHMnr06NTW1uY3v/lNfvGLX+T6669vdg7ndPfYlfO6//7755e//OVO5541a1auvfbaJMnUqVObHOO8lrer/64uXLgwH/jAB/Liiy+mR48emTp1ak466aQMGzYsTz75ZK6++up8//vfz/PPP593vvOd+a//+q8mr1xyTnePXT2v3/nOd3LmmWcmSerq6vLJT34yb3nLW9KrV68sXrw4F198cRoaGvKRj3wkQ4YMyV//9V83OY/zWt43v/nN/O3f/m22bNmS/fbbLx/60IdyzDHHZNCgQfnd736XpUuX5vvf/3723nvvZuf49Kc/nTlz5iRJjjjiiMyYMSMjR47M0qVLc/HFF2fx4sWZM2dOhgwZks9//vNNzuHcltPec1qpVKrbe++9d173utelsbGxVf+buy3ntKz2nNfVq1dnzZo1SZLx48dn8uTJGTNmTF71qldl+fLluemmm3LFFVdkzZo1ee9735sBAwbkxBNPbHIdzmsX1dHFh+4l/1tXzz///F06ftsrXJ544oldXsf5559fnefiiy/eYf/9999fqa2trSSpHH/88bv8OnuC9p7TSqVS+cxnPlOdZ/bs2S2O3bRpU5N/7pyWVeK8tqSxsbEybNiw6v9rs27duibHOa/ltPecTp48uTrH5Zdf3uSYT3ziE9UxZ511VpNjnNOy2nNe161bV9lvv/0qSSr9+/ev/PKXv9xhzKpVqyp/8id/UklSefWrX11Zu3Ztk3M5r2X96le/qvTq1auSpPJnf/ZnlZUrVzY7duPGjU3++eOPP179mY8ZM6ayfv367favW7euMmbMmEqSSm1tbaWhoaHJeZzbMkqc05/97GeVSy+9tLJw4cLKCy+8UKlUtj8/rb3CxTktp73n9b777qu8613vqvz3f/93s8d973vfq14BPnLkyGavSHNeuybBhaI6Q3DZtGlTZZ999qkkqYwePbry4osvNjnuzDPPrL7WokWLdum19gTtPadLly6t7L333pUklWnTpu3SHM5pebs7uNx+++3V1/jbv/3bJsc4r2W195zuu+++leSlW4aas3LlyurrHHnkkTvsd07La895veGGG6rH/+M//mOz43784x9Xx/3rv/7rDvud1/JOOOGESpLK4MGDK8uXL9+lOT784Q9Xf94LFy5scszChQurYz760Y/usN+5LafEOW1KW4OLc1rW7jqvL/dXf/VX1fPxn//5nzvsd167Lg/NpdtZsGBBVq5cmeSl2xh69Gj6H/Np06ZVt2+66aZXYGV7pjlz5mTz5s2pqanJzJkzd2kO57Tr+eY3v1ndbu52Iue1c9m0aVOS5KCDDmp2TF1dXQYPHpwk2bhx4w77ndPO5aGHHqpuN3eJepIcd9xx6d27d5Lkhhtu2GG/81pWfX197rzzziTJRz/60eq/U21RqVRyyy23JElGjRqVo48+uslxRx99dA477LAkyfe+973tbllJnNtSSpzTUpzTcl7J83r88cdXt5cuXbrDfue16xJc6Hbuueee6vaECROaHTdmzJjqPe/33nvvbl/Xnmrrc1nGjBlT/UVuy5Ytefrpp/PEE0/khRde2OkczmnXsmbNmnzve99LkhxwwAE59thjmxznvHYuhx56aJKXPs2mOatXr86KFSu2G78t57Rz+Z//+Z/q9tChQ5sdV1tbm0GDBiVJ7r///jQ2Nm6333kta9vnlU2ZMqW6/Yc//CGPP/54nn/++Z3O8cQTT+SZZ55J0vI52Xb/008/nWXLlm23z7kto8Q5LcU5LeeVPK/b/p8YTcUU57XrElzYLa6//vocdthh6dOnTwYMGJBDDjkkU6dOzfz581s9x7Rp0zJ06ND07NkzgwcPztFHH51Pf/rT1f/AaM6SJUuq26NGjWp2XG1tbUaOHLnDMTRtV87p8uXL8+tf/zpJMm7cuKxevTpnn312Bg8enBEjRuQ1r3lNBg4cmAkTJuS2225rdh7ndPcp8e/qy91www1Zv359kuQDH/hAampqmhznvO4eu3pOtz5Y9fnnn8/Xvva1Jsd87nOf22H8tpzT3WdXzuu2D9JdtWpVs+MqlUpWr16d5KUrnRoaGrbb77yW9cADDyR56Yqx0aNH55prrskb3vCGDBo0KIceemgGDx6c17zmNbnggguydu3aJudo7Tl5+f6XnxfntowS57QU57ScV/K83nXXXdXtps6b89qFdfAtTXQzaeYjyrb9+su//MtmHzjVmo+F7t27d+VrX/tas2t405veVElS6dev307XO2nSpOq8GzZs2OX33Z2155wuWLCgOuZTn/pUZeTIkS3Oc8455zS5Bue0vPb+u9qS4447rjrH448/3uw457Ws9p7TxsbGyvve975KkkqPHj0qf//3f1/5j//4j8pDDz1UufHGGysnn3zydv8+N8U5La895/XrX/96dcyXvvSlZl/j4Ycf3m6+H/3oR9vtd17LOvDAAytJKm94wxsqH/nIR1o8t6973esqzzzzzA5zfPWrX62Ouf7661t8veuvv7469uX//eTcllHinDanrc9wcU7L2Z3ndVs///nPK3vttVclSeW1r31tk2Oc167LFS4U1bdv35x66qn593//99xzzz1ZvHhx7rjjjvzjP/5jXvWqVyV56R7ik046KZs3b25yjte85jU599xzc+ONN+bBBx/Mgw8+mO985zuZMmVKamr+f3v3HhxVef9x/JMrSUhg5SoJCESI5appuMtwsaUGpjW2DGoFJEUqF8GRjqIJtEGYoBIsl5K2traxTSLgZQSpEQYTgrHgQCAVHVQKBoEiUjBcA0lInt8fTM4vm+xustmTpMH3a2ZnTjjPefa7+5nNWb57so+frl27pjlz5ljLINZWvfRaeHh4vfXW/PSvqT9xaK18ybTm5exr1qzR0aNHNWrUKO3atUulpaX69ttvlZ2drW7dukmSVq9e7fKTdTK1nx2vVVeOHz9ufUozatQo9enTx+1YcrWXr5kGBAQoKytLmzZt0p133qlXXnlF9913n4YOHarJkyfr7bff1vjx47V9+3a98MILLmsgU/v5kuukSZOsZUp/+9vfWn8OVlNVVZUWL17s9G/VOdb+mVztUX1u/Pzzz5Weni6Hw6E//vGPOnPmjK5du6Z9+/ZZ37nz6aefasqUKaqqqnKao2ZG9eXiKROytYcdmdqFTO3THLmWlZVp1qxZqqyslCStWLHC5ThybcVauuODm0tJSYnbfadPnzaxsbFWx3Xt2rV1xpw/f97tUmjGGLN161ZrxZuwsDDz9ddf1xkTHR1tJJkePXrUW+/06dOtek6cOFHv+O8iXzLNzMx06v7HxcVZyxzWdPjwYdO2bVsjyXTu3LnO0pZkaj9fX6vupKamuv0ktTZytZcdmX722WcmISHBWlay9i0kJMRMnTrVnDp1yuXxZGo/X3N94oknrP0xMTFm8+bN5sKFC+bq1atmz5495t577zWSTHBwsDUuMzPTaQ5ytVf1J9mSTEBAgMsVhiorK83EiRPdXsWybNkya19ubq7H+8vNzbXGLl++3Gkf2drDjkzd8fYKFzK1T1PmWm3WrFnWsTNmzHA7jlxbL65wga0cDofbfV27dtWbb76p4OBgSdLvfve7OmPat2/v9vseJOnHP/6xUlJSJEmlpaX6y1/+UmdM9UoL1StueFLzC6pCQ0PrHf9d5Eum1VlUS01NrfNvktS3b1/NnTtX0o3vfXn//fddzkOm9vH1tepOZmamJKlNmzZ68MEHPY4lV3v5mmlBQYFGjhypLVu2KCoqSpmZmTp9+rTKy8t14sQJpaenKzQ0VNnZ2Ro2bJjLvw0nU/v5mmtaWpp+8pOfSJIOHz6s+++/X+3bt1doaKhGjhyp7du3Kzo6Wk888YR1TEREhNMc5GqvmufBKVOmuFxhyN/fX2lpadbPGzZscDtHfbl4yoRs7WFHpnbXQqa+a+pcn3/+eb3yyiuSpLi4OKWnp9dbC7m2PjRc0Kyio6M1YcIESdKRI0d06tQpr+f45S9/aTVlan7BVLXqN4oNuYTuypUr1nZDLtFDXZ4yrfmmPTg42GnJu9ruvfdea7vmUqY15yHT5tOY1+revXv1+eefS5Luu+8+j/9RlMi1uXnKtKysTD//+c91/vx53Xrrrfroo480bdo0de3aVUFBQerevbvmzZungoIChYSE6OTJk3rkkUfq3AeZNr/6XqvBwcHasmWLMjIyFBcX57T6hcPh0IIFC3TgwAGn5YJvueUWpznI1V41z42eluseMGCAoqKiJLk/L0r15+IpE7K1hx2Z2l0LmfquKXN9+eWXlZycLEm644479N577zn9KZC7Wsi19aHhgmbXv39/a7u+FYdc6dKlizp16uT2+O7du0u68cumer16d06cOCFJ6ty5s9q0aeN1LbjBXaY9evSwtqtXnHKn5tgzZ8447SPTluHta/Xvf/+7te3qP+O1kWvzc5fptm3brJ8XLFigW2+91eXxAwYM0LRp0yRJhYWF+vjjj532k2nLqO+16ufnp8TERBUWFurChQs6cuSIjh8/rrNnz2rdunVq3769Dh486HI+iVztVvN8V/3c1jfW3XlRurHcsyfVmdS+75rzkK1v7MjULmRqn6bKdcOGDZo3b54kqWfPnnr//ffVuXNnj8eQa+tFwwXNruanaE0xR803itWftrty/fp1HT16VJLUr18/n2v6LnOXR9++fa0vbKz+MjB3au4PDAx02kemLcOb12pFRYU2bdok6UZTND4+vt5jyLX5ucu05p8Hff/73/c4R1xcnLVdOzcybRnevFbDw8N1++23q0ePHgoICJB04xL1vXv3SrpxxUz1hxrVyNVeAwYMsLYbem5s7Hmx9v7auZCtPezI1C5kap+myPWdd97RI488oqqqKnXr1k25ubn1NnMkcm3NaLig2R06dMjajoyM9Pr4M2fO6Ny5c26PHz16tLXt6k+OqhUWFlqX3N19991e14H/5y7ToKAgjRw5UpL0zTffOF3iWFv1yUGSdVlmNTJtGd68Vt99911rBZSHH364QW8kybX5ucu0Zl7Xr1/3OEfNlXBq50ymLcPX82pOTo4uXLggSXrggQfq7CdXe40ZM8barnnuc+XLL7+UVPe82Lt3bytrT5lI0gcffGDN0atXL6d9ZGsPOzK1C5nax+5cc3Nz9cADD+j69evq2LGjduzYodtvv71BtZBrK9aCX9iL76CjR49aqwxFR0c3ao7ly5e7/bZ9Y4wpKysz7du3N5JMv3793K56NHv2bGuevXv3NqoW1J/p2rVrrec5Ozvb7TyJiYnWuIKCAqd9ZNr8vH2t/vSnP7We+6KiogbdB7k2L0+Zvvnmm9ZzvGjRIo/zTJ482Rq7f/9+p31k2vx8Pa9WVFSYwYMHG0kmKCjIHD16tM4YcrXX2bNnrcwmTJjgdlx+fr71fD766KN19s+dO9fa72r1FGOM2bNnjzVm3rx5dfaTrT3sytQVb1cpIlP72JnrP//5T2tFznbt2pnCwkKvaiHX1ouGC2zzzjvvmIqKCrf7ay9f+dJLLzntLy4uNgcOHPB4H1u3brWWrgwJCTEnT550Oe7Xv/61dT8rV66ss3/37t3Wsqdjx46t/8F9R/maqTHGXLp0yXTp0sVIMj179jSnT5+uM2bnzp3W0nsDBw50eRIhU/vYkWtN586ds16XgwYN8qoWcrWHr5mWlJSYsLAwI8lERESYgwcPupwnJyfH+Pv7G0kmKirKVFZW1hlDpvax47X63//+11y5csXl8WVlZU7Lhy5ZssTtfZGrvWo2SzZs2FBn/8WLF81dd93l8T9OX3zxhfWcDxkyxJSWljrtLy0tNUOGDDGSTGBgoDl8+LDLWsjWHnZk6oq3DRdjyNROduRaVFRkHA6HkWTatm1rPvzww0bVQq6tEw0X2KZnz54mMjLSLFiwwLz22mtm9+7dpqioyOzYscMsXrzYdOzY0folMXr0aHPt2jWn43fu3GkkmZEjR5oVK1aYnJwcU1hYaPbt22c2bdpkpkyZYvz8/Kw51q9f77aWixcvmpiYGGvsY489ZvLy8syePXvMihUrTHh4uJFkQkNDG/xp/HeRr5lW27hxo5Vdjx49THp6utm3b58pKCgwycnJJjQ01HpD6O4kRKb2sSvXaunp6db4VatWeVULudrDjkyXLVtmjQkPDzdJSUkmLy/PFBUVmW3btpm5c+dab+QkmczMTJe1kKl97Mj1jTfeMA6Hwzz++OPm7bffNvv37zcFBQVm3bp1pn///tbxP/rRj0xZWZnbWsjVXmfOnDG33Xabde6bP3++ycvLM4WFhSYjI8N873vfs57ruXPnup3n2WeftcbFxsaajRs3mn379pmNGzc6NeOSkpLczkG29rAr04yMDKdbQkKCddwzzzzjtK/2FcHVyNQ+vuZ65MgR64NHSWb16tXmk08+8Xj75ptvXNZCrq0TDRfYpmfPntYvAE+3yZMnm5KSkjrHVzdc6ruFhYWZl19+ud56/v3vf5u+ffu6naddu3Zm69atTfBM3Dx8zbSm9evXW1dBuLqFh4ebzZs3e5yDTO1hZ67GGDN8+HAjyQQEBJivv/7a63rI1Xd2ZFpVVWWefPJJp8a2q1tQUJBJS0vzWA+Z2sOOXN944416j09MTDRXr16ttx5ytdehQ4dMnz59PGYzc+ZMU15e7naOyspKM3PmTI9zPProoy6vRquJbO1hR6YNec1X32bMmOF2HjK1jy+5ZmRkeJWpJJOSkuK2FnJtfWi4wDb5+fnmueeeM/Hx8SYmJsZ06NDBBAYGGofDYQYNGmRmz55tdu/e7fb4ixcvmqysLPP444+b4cOHm9tuu82EhYWZ4OBg07VrV3PPPfeY1NRUt11fVy5fvmxefPFFM2TIEONwOExYWJi54447zMKFC82xY8fseNg3NV8zre3TTz81c+fONX369DGhoaEmPDzcDB482CxatMicOnWqQXOQqe/szPXw4cPWST4+Pr7RNZGrb+zMtLCw0MyZM8cMHDjQREREmICAANO+fXsTFxdnfvWrX5kvvviiQfOQqe/syPX06dMmLS3NTJw40fTu3duEhYWZ8PBwExMTY2bPnu32uz/cIVd7Xb582aSlpZnhw4ebDh06mODgYNO9e3fz4IMPmry8vAbP8+6775qEhAQTGRlpgoODTWRkpElISDA5OTle1UK2vvM1U7saLtW1kKk9Gpur3Q2X6lrItfXwM8aGNXoBAAAAAABgYVloAAAAAAAAm9FwAQAAAAAAsBkNFwAAAAAAAJvRcAEAAAAAALAZDRcAAAAAAACb0XABAAAAAACwGQ0XAAAAAAAAm9FwAQAAAAAAsBkNFwAAAAAAAJvRcAEAAAAAALAZDRcAAIB65Ofny8/Pr85t6dKlLV2aTxITE10+rmPHjrV0aQAAtHo0XAAAAAAAAGwW2NIFAAAAtCZ//etfNXToUElSly5dWrga36Smpuqpp56SJG3ZskVLlixp4YoAALh50HABAADwQu/evTVw4MCWLsMWUVFRioqKkiQVFha2cDUAANxc+JMiAAAAAAAAm9FwAQAAAAAAsBkNFwAA0Gpt2LDBWlln9uzZbscdP35cDodDfn5+iomJ0ZUrV5qlvmPHjumZZ55RXFycOnbsqJCQEPXu3Vvjx4/XSy+9pOPHj9c5pvYKSDt37tT999+vyMhIhYaGql+/flq+fHmdx5CTk6NJkyZZ4/r376/nn39e5eXlzfFQAQBALX7GGNPSRQAAADTWtGnTlJ2dLUnavHmzEhISnPZXVVXpnnvu0a5duxQYGKjdu3dbX3rbUPn5+Ro/frykGw2QcePG1XvMqlWrlJycrIqKCrdjxo4dq/z8fKd/8/PzkySlpKQoJCREycnJcvV2bdSoUdq+fbvatm2rhQsXau3atS7vIz4+Xv/4xz8UEBDgsd5XX31Vv/jFLyRJxcXF6tWrl8fxAADAM65wAQAArdrvf/97qzkwa9YsnT592ml/Wlqadu3aJUlaunSp182Wxli+fLmefvppVVRUyOFwKDk5WTt27NCBAweUl5enVatW6e6777aaK6689957SkpK0ogRI/Taa6+psLBQ27Zt08SJEyVJu3fv1gsvvKDVq1dr7dq1mjhxot566y3t379fW7Zs0YgRIyRJ27Zt05///Ocmf8wAAMAZV7gAAIBW78MPP9S4ceNUWVmp+Ph45eTkyM/PT0VFRRoxYoTKy8s1evRo5efn13ulhyveXOFy4MABDR06VFVVVYqJiVFubq66d+/ucuzJkyfr7KvZhJk8ebI2bdrkVHNlZaVGjx6tjz76SBEREaqoqNCcOXO0evVqp3lKS0vVv39/ffXVVxo8eLA+/vhjj4+RK1wAALAXV7gAAIBWb/To0UpKSpJ044qO9evX6+rVq5o6darKy8vVrl07ZWZmNqrZ4q20tDRVVVXJz89PGzdudNtskeRxX1hYmP70pz/VqTkgIMD6vppLly6pc+fOWrlypcvjZ8yYIUk6ePCgLly40JiHAwAAGomGCwAAuCmkpKRo2LBhkqRFixbp4Ycf1meffSZJSk9Pb5YrNqqqqrRt2zZJN76fJTY2ttFzTZgwQR06dHC5b/Dgwdb2z372MwUFBbkcd+edd1rbxcXFja4FAAB4j4YLAAC4KQQGBio7O1tt27bVtWvXtHnzZknSQw89pGnTpjVLDcXFxTp//rwkacyYMT7NFRMT43afw+HwetylS5d8qgcAAHiHhgsAALhp9OnTR88++6z1c6dOnfSHP/yh2e7/7Nmz1na3bt18missLMztPn9/f6/HVVZW+lQPAADwDg0XAABw07h8+bIyMjKsn8+dO6cDBw60SC2eViACAAA3PxouAADgprFgwQJ9+eWXkqSIiAgZYzRjxgyVlJQ0y/136tT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" ] @@ -316,7 +316,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -354,7 +354,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -394,9 +394,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "TBR: 8.337474e-04\n", + "TBR: 8.337513e-04\n", "\n", - "TBR std. dev.: 2.544478e-05\n", + "TBR std. dev.: 2.544477e-05\n", "\n" ] } @@ -411,7 +411,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "BABY_1L_PbLi", "language": "python", "name": "python3" }, diff --git a/analysis/neutron/postprocessing.ipynb:Zone.Identifier b/analysis/neutron/postprocessing.ipynb:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/statepoint.100.h5:Zone.Identifier b/analysis/neutron/statepoint.100.h5:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/summary.h5:Zone.Identifier b/analysis/neutron/summary.h5:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/vault.py:Zone.Identifier b/analysis/neutron/vault.py:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/data/processed_data.json b/data/processed_data.json index 5fbe61c..f8e423e 100644 --- a/data/processed_data.json +++ b/data/processed_data.json @@ -1,6 +1,6 @@ { "modelled_TBR": { - "mean": 0.0008337474496467843, - "std_dev": 2.5444777554062273e-05 + "mean": 0.0008337512821171342, + "std_dev": 2.5444773403162198e-05 } } \ No newline at end of file diff --git a/environment.yml b/environment.yml index a32ae23..2ee71ff 100644 --- a/environment.yml +++ b/environment.yml @@ -8,6 +8,7 @@ dependencies: - numpy # add version tag - scipy # add version tag - matplotlib # add version tag + - math - sympy - pandas - pint-pandas From ba1deee45d05ce9080831792c2ed9bc9c65e9ac3 Mon Sep 17 00:00:00 2001 From: rossmacdonald98 <57036681+rossmacdonald98@users.noreply.github.com> Date: Tue, 11 Mar 2025 15:26:46 +0000 Subject: [PATCH 03/10] Updated ci.yml --- .github/workflows/ci.yml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e4a95aa..01ef60b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -17,8 +17,9 @@ jobs: environment-file: environment.yml activate-environment: libra-run-env - # example how to run a notebook - # - name: Run tritium model - # shell: bash -l {0} - # working-directory: analysis/tritium - # run: jupyter-nbconvert --to notebook tritium_model.ipynb --execute + - name: Run neutronics model + shell: bash -l {0} + working-directory: analysis/neutron + run: | + python openmc_model_PbLi.py + jupyter-nbconvert --to notebook postprocessing.ipynb --execute From e7cc565f2a5ff79be6383d9c71458910b5c5891a Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Tue, 11 Mar 2025 15:29:11 +0000 Subject: [PATCH 04/10] Checked some items in ToDo list & updated ci.yml --- .github/workflows/ci.yml | 2 +- README.md | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 01ef60b..bebf61d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,7 +15,7 @@ jobs: with: auto-update-conda: true environment-file: environment.yml - activate-environment: libra-run-env + activate-environment: BABY_1l_LiPb - name: Run neutronics model shell: bash -l {0} diff --git a/README.md b/README.md index 211ecd7..6e46a24 100644 --- a/README.md +++ b/README.md @@ -24,13 +24,13 @@ conda env create -f environment.yml ## Todo list: - [ ] [Link to Zenodo](https://zenodo.org/) -- [ ] Change environment name in [`environment.yml`](environment.yml) -- [ ] Change environment name in [CI workflows](.github/workflows) +- [x] Change environment name in [`environment.yml`](environment.yml) +- [x] Change environment name in [CI workflows](.github/workflows) - [ ] Modify [binder](https://mybinder.org/) badge by inserting the repo name - [ ] Add general run data to [`data/general.json`](data/general.json) - [ ] Add LSC data to [`data/tritium_detection`](data/tritium_detection) - [ ] Add neutron detection data to [`data/neutron_detection`](data/neutron_detection) -- [ ] Add OpenMC model to [`analysis/neutron`](analysis/neutron) +- [x] Add OpenMC model to [`analysis/neutron`](analysis/neutron) - [ ] Add Tritium model to [`analysis/tritium`](analysis/tritium) - [ ] Add the right version tags to [`environment.yml`](environment.yml) - [ ] Add and update information in the README From 18703abe3f535445408dc4bcdad8aaceafb149e9 Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Tue, 11 Mar 2025 16:11:28 +0000 Subject: [PATCH 05/10] Model run with 1000cm3 PbLi --- analysis/neutron/openmc_model_PbLi.py | 2 +- analysis/neutron/postprocessing.ipynb | 8 ++++---- data/processed_data.json | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/analysis/neutron/openmc_model_PbLi.py b/analysis/neutron/openmc_model_PbLi.py index 5eade0a..5ea6ac0 100644 --- a/analysis/neutron/openmc_model_PbLi.py +++ b/analysis/neutron/openmc_model_PbLi.py @@ -53,7 +53,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): vessel_radius = 15.593 external_radius = 15.812 - PbLi_volume = 1200 # 1L = 1000 cm3 + PbLi_volume = 1000 # 1L = 1000 cm3 PbLi_thickness = calculate_z_height(PbLi_radius, heater_r, heater_gap, PbLi_volume) diff --git a/analysis/neutron/postprocessing.ipynb b/analysis/neutron/postprocessing.ipynb index 0ae8f7d..64aa316 100644 --- a/analysis/neutron/postprocessing.ipynb +++ b/analysis/neutron/postprocessing.ipynb @@ -278,7 +278,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -316,7 +316,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -394,9 +394,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "TBR: 8.337513e-04\n", + "TBR: 9.215772e-04\n", "\n", - "TBR std. dev.: 2.544477e-05\n", + "TBR std. dev.: 2.335166e-05\n", "\n" ] } diff --git a/data/processed_data.json b/data/processed_data.json index f8e423e..c591610 100644 --- a/data/processed_data.json +++ b/data/processed_data.json @@ -1,6 +1,6 @@ { "modelled_TBR": { - "mean": 0.0008337512821171342, - "std_dev": 2.5444773403162198e-05 + "mean": 0.0009215771730202245, + "std_dev": 2.3351662077517377e-05 } } \ No newline at end of file From f2326acb7d66d488a50f5212ae253c1da471df68 Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Thu, 20 Mar 2025 19:50:42 +0000 Subject: [PATCH 06/10] Updated dimension variable names. --- analysis/neutron/openmc_model_PbLi.py | 145 +++++++++++++------------- analysis/neutron/postprocessing.ipynb | 8 +- data/processed_data.json | 4 +- 3 files changed, 80 insertions(+), 77 deletions(-) diff --git a/analysis/neutron/openmc_model_PbLi.py b/analysis/neutron/openmc_model_PbLi.py index 5ea6ac0..8efadf3 100644 --- a/analysis/neutron/openmc_model_PbLi.py +++ b/analysis/neutron/openmc_model_PbLi.py @@ -17,35 +17,37 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): the sphere, cllif cell, and cells """ + ####### Dimensions (all in cm) ###### epoxy_thickness = 2.54 # 1 inch alumina_compressed_thickness = 2.54 # 1 inch - base_thickness = 0.786 + ov_base_thickness = 0.786 alumina_thickness = 0.635 he_thickness = 0.6 - inconel_thickness = 0.3 + iv_base_thickness = 0.3 heater_gap = 0.878 - gap_thickness = 4.605 - cap = 1.422 - firebrick_thickness = 15.24 - high = 21.093 - cover = 2.392 - z_tab = 28.00 + iv_height = 10.8903 + iv_cap = 1.422 + furnace_thickness = 15.24 + ov_height = 21.093 + ov_cap = 2.392 + table_height = 28.00 lead_height = 4.00 lead_width = 8.00 lead_length = 16.00 - heater_r = 0.439 - heater_h = 25.40 + + heater_length = 25.40 heater_z = ( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + he_thickness - + inconel_thickness + + iv_base_thickness + heater_gap + z_c ) + heater_radius = 0.439 PbLi_radius = 7.00 inconel_radius = 7.3 he_radius = 9.144 @@ -55,7 +57,9 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): PbLi_volume = 1000 # 1L = 1000 cm3 - PbLi_thickness = calculate_z_height(PbLi_radius, heater_r, heater_gap, PbLi_volume) + PbLi_thickness = calculate_z_height(PbLi_radius, heater_radius, heater_gap, PbLi_volume) + + cover_he_thickness = iv_height - PbLi_thickness # Gap between surface of PbLi and top boundary of inner vessel. source_h = 50.00 source_x = x_c - 13.50 @@ -68,19 +72,19 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): z_plane_2 = openmc.ZPlane(epoxy_thickness + z_c) z_plane_3 = openmc.ZPlane(epoxy_thickness + alumina_compressed_thickness + z_c) z_plane_4 = openmc.ZPlane( - epoxy_thickness + alumina_compressed_thickness + base_thickness + z_c + epoxy_thickness + alumina_compressed_thickness + ov_base_thickness + z_c ) z_plane_5 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + z_c ) z_plane_6 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + he_thickness + z_c @@ -88,66 +92,66 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): z_plane_7 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + he_thickness - + inconel_thickness + + iv_base_thickness + z_c ) z_plane_8 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + he_thickness - + inconel_thickness + + iv_base_thickness + PbLi_thickness + z_c ) z_plane_9 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + he_thickness - + inconel_thickness + + iv_base_thickness + PbLi_thickness - + gap_thickness + + cover_he_thickness + z_c ) z_plane_10 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness + he_thickness - + inconel_thickness + + iv_base_thickness + PbLi_thickness - + gap_thickness - + cap + + cover_he_thickness + + iv_cap + z_c ) z_plane_11 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness + + ov_base_thickness + alumina_thickness - + firebrick_thickness + + furnace_thickness + z_c ) z_plane_12 = openmc.ZPlane( - epoxy_thickness + alumina_compressed_thickness + base_thickness + high + z_c + epoxy_thickness + alumina_compressed_thickness + ov_base_thickness + ov_height + z_c ) z_plane_13 = openmc.ZPlane( epoxy_thickness + alumina_compressed_thickness - + base_thickness - + high - + cover + + ov_base_thickness + + ov_height + + ov_cap + z_c ) - z_plane_14 = openmc.ZPlane(z_c - z_tab) - z_plane_15 = openmc.ZPlane(z_c - z_tab - epoxy_thickness) + z_plane_14 = openmc.ZPlane(z_c - table_height) + z_plane_15 = openmc.ZPlane(z_c - table_height - epoxy_thickness) ######## Cylinder ################# z_cyl_1 = openmc.ZCylinder(x0=x_c, y0=y_c, r=PbLi_radius) @@ -158,7 +162,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): z_cyl_6 = openmc.ZCylinder(x0=x_c, y0=y_c, r=external_radius) right_cyl = openmc.model.RightCircularCylinder( - (x_c, y_c, heater_z), heater_h, heater_r, axis="z" + (x_c, y_c, heater_z), heater_length, heater_radius, axis="z" ) ext_cyl_source = openmc.model.RightCircularCylinder( (source_x, y_c, source_z), source_h, source_external_r, axis="x" @@ -172,10 +176,10 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): ######## Lead bricks positioned under the source ################# positions = [ - (x_c - 13.50, y_c, z_c - z_tab), - (x_c - 4.50, y_c, z_c - z_tab), - (x_c + 36.50, y_c, z_c - z_tab), - (x_c + 27.50, y_c, z_c - z_tab), + (x_c - 13.50, y_c, z_c - table_height), + (x_c - 4.50, y_c, z_c - table_height), + (x_c + 36.50, y_c, z_c - table_height), + (x_c + 27.50, y_c, z_c - table_height), ] lead_blocks = [] @@ -206,7 +210,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): cap_region = bottom_cap | cylinder_cap | top_cap PbLi_region = +z_plane_7 & -z_plane_8 & -z_cyl_1 & +right_cyl gap_region = +z_plane_8 & -z_plane_9 & -z_cyl_1 & +right_cyl - firebrick_region = +z_plane_5 & -z_plane_11 & +z_cyl_3 & -z_cyl_4 + furnace_region = +z_plane_5 & -z_plane_11 & +z_cyl_3 & -z_cyl_4 heater_region = -right_cyl table_under_source_region = +z_plane_15 & -z_plane_14 & -sphere lead_block_1_region = -lead_blocks[0] @@ -223,7 +227,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): & ~alumina_region & ~PbLi_region & ~gap_region - & ~firebrick_region + & ~furnace_region & ~vessel_region & ~cap_region & ~heater_region @@ -242,7 +246,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): & ~alumina_region & ~PbLi_region & ~gap_region - & ~firebrick_region + & ~furnace_region & ~he_region & ~vessel_region & ~cap_region @@ -264,7 +268,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): alumina_compressed_cell = openmc.Cell(region=alumina_compressed_region) alumina_compressed_cell.fill = alumina vessel_cell = openmc.Cell(region=vessel_region) - vessel_cell.fill = inconel625 + vessel_cell.fill = SS316L alumina_cell = openmc.Cell(region=alumina_region) alumina_cell.fill = alumina PbLi_cell = openmc.Cell(region=PbLi_region) @@ -272,9 +276,9 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): gap_cell = openmc.Cell(region=gap_region) gap_cell.fill = he cap_cell = openmc.Cell(region=cap_region) - cap_cell.fill = inconel625 - firebrick_cell = openmc.Cell(region=firebrick_region) - firebrick_cell.fill = firebrick + cap_cell.fill = SS316L + furnace_cell = openmc.Cell(region=furnace_region) + furnace_cell.fill = furnace heater_cell = openmc.Cell(region=heater_region) heater_cell.fill = heater_mat table_cell = openmc.Cell(region=table_under_source_region) @@ -302,7 +306,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): cap_cell, PbLi_cell, gap_cell, - firebrick_cell, + furnace_cell, heater_cell, he_cell, sphere_cell, @@ -334,11 +338,11 @@ def baby_model(): """ materials = [ - inconel625, + SS316L, lithium_lead, SS304, heater_mat, - firebrick, + furnace, alumina, lead, air, @@ -393,21 +397,20 @@ def baby_model(): # Define Materials # Source: PNNL Materials Compendium April 2021 # PNNL-15870, Rev. 2 -inconel625 = openmc.Material(name="Inconel 625") -inconel625.add_element("C", 0.000990, "wo") -inconel625.add_element("Al", 0.003960, "wo") -inconel625.add_element("Si", 0.004950, "wo") -inconel625.add_element("P", 0.000148, "wo") -inconel625.add_element("S", 0.000148, "wo") -inconel625.add_element("Ti", 0.003960, "wo") -inconel625.add_element("Cr", 0.215000, "wo") -inconel625.add_element("Mn", 0.004950, "wo") -inconel625.add_element("Fe", 0.049495, "wo") -inconel625.add_element("Co", 0.009899, "wo") -inconel625.add_element("Ni", 0.580000, "wo") -inconel625.add_element("Nb", 0.036500, "wo") -inconel625.add_element("Mo", 0.090000, "wo") -inconel625.set_density("g/cm3", 8.44) + +# 316L Stainless Steel +# Data from https://www.thyssenkrupp-materials.co.uk/stainless-steel-316l-14404.html +SS316L = openmc.Material(name="316L Steel") +SS316L.add_element("C", 0.0003, "wo") +SS316L.add_element("Si", 0.01, "wo") +SS316L.add_element("Mn", 0.02, "wo") +SS316L.add_element("P", 0.00045, "wo") +SS316L.add_element("S", 0.000151, "wo") +SS316L.add_element("Cr", 0.175, "wo") +SS316L.add_element("Ni", 0.115, "wo") +SS316L.add_element("N", 0.001, "wo") +SS316L.add_element("Mo", 0.00225, "wo") +SS316L.add_element("Fe", 0.655599, "wo") # Lithium-Lead lithium_lead = openmc.Material(name="Lithium Lead") @@ -456,14 +459,14 @@ def baby_model(): # Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2 # https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/ -firebrick = openmc.Material(name="Firebrick") +furnace = openmc.Material(name="Furnace") # Estimate average temperature of Firebrick to be around 300 C # Firebrick.temperature = 273 + 300 -firebrick.add_element("Al", 0.004, "ao") -firebrick.add_element("O", 0.666, "ao") -firebrick.add_element("Si", 0.240, "ao") -firebrick.add_element("Zr", 0.090, "ao") -firebrick.set_density("g/cm3", 0.30) +furnace.add_element("Al", 0.004, "ao") +furnace.add_element("O", 0.666, "ao") +furnace.add_element("Si", 0.240, "ao") +furnace.add_element("Zr", 0.090, "ao") +furnace.set_density("g/cm3", 0.30) # alumina insulation # data from https://precision-ceramics.com/materials/alumina/ diff --git a/analysis/neutron/postprocessing.ipynb b/analysis/neutron/postprocessing.ipynb index 64aa316..0ae8f7d 100644 --- a/analysis/neutron/postprocessing.ipynb +++ b/analysis/neutron/postprocessing.ipynb @@ -278,7 +278,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -316,7 +316,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -394,9 +394,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "TBR: 9.215772e-04\n", + "TBR: 8.337513e-04\n", "\n", - "TBR std. dev.: 2.335166e-05\n", + "TBR std. dev.: 2.544477e-05\n", "\n" ] } diff --git a/data/processed_data.json b/data/processed_data.json index c591610..c4e54a0 100644 --- a/data/processed_data.json +++ b/data/processed_data.json @@ -1,6 +1,6 @@ { "modelled_TBR": { - "mean": 0.0009215771730202245, - "std_dev": 2.3351662077517377e-05 + "mean": 0.0008337512821171342, + "std_dev": 2.5444773403162218e-05 } } \ No newline at end of file From d0a8229c7d37d94961adcb4538e52467464bdaf8 Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Fri, 21 Mar 2025 17:21:07 +0000 Subject: [PATCH 07/10] Updated variable names --- analysis/neutron/openmc_model_PbLi.py | 32 +++++++++++++++------------ analysis/neutron/postprocessing.ipynb | 8 +++---- data/processed_data.json | 4 ++-- 3 files changed, 24 insertions(+), 20 deletions(-) diff --git a/analysis/neutron/openmc_model_PbLi.py b/analysis/neutron/openmc_model_PbLi.py index 8efadf3..55456c6 100644 --- a/analysis/neutron/openmc_model_PbLi.py +++ b/analysis/neutron/openmc_model_PbLi.py @@ -18,6 +18,8 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): """ ####### Dimensions (all in cm) ###### + + ## Vertical dimensions epoxy_thickness = 2.54 # 1 inch alumina_compressed_thickness = 2.54 # 1 inch ov_base_thickness = 0.786 @@ -47,27 +49,29 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): + z_c ) + ## Radial dimensions heater_radius = 0.439 PbLi_radius = 7.00 - inconel_radius = 7.3 + iv_external_radius = 7.3 he_radius = 9.144 - firebrick_radius = 14.224 - vessel_radius = 15.593 - external_radius = 15.812 + furnace_radius = 14.224 + ov_internal_radius = 15.593 + ov_external_radius = 15.812 + ## Calculated dimensions PbLi_volume = 1000 # 1L = 1000 cm3 PbLi_thickness = calculate_z_height(PbLi_radius, heater_radius, heater_gap, PbLi_volume) - cover_he_thickness = iv_height - PbLi_thickness # Gap between surface of PbLi and top boundary of inner vessel. + ## Source dimensions source_h = 50.00 source_x = x_c - 13.50 source_z = z_c - 5.635 source_external_r = 5.00 source_internal_r = 4.75 - ######## Surfaces ################# + ########## Surfaces ########## z_plane_1 = openmc.ZPlane(0.0 + z_c) z_plane_2 = openmc.ZPlane(epoxy_thickness + z_c) z_plane_3 = openmc.ZPlane(epoxy_thickness + alumina_compressed_thickness + z_c) @@ -153,13 +157,13 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): z_plane_14 = openmc.ZPlane(z_c - table_height) z_plane_15 = openmc.ZPlane(z_c - table_height - epoxy_thickness) - ######## Cylinder ################# + ########## Cylinder ########## z_cyl_1 = openmc.ZCylinder(x0=x_c, y0=y_c, r=PbLi_radius) - z_cyl_2 = openmc.ZCylinder(x0=x_c, y0=y_c, r=inconel_radius) + z_cyl_2 = openmc.ZCylinder(x0=x_c, y0=y_c, r=iv_external_radius) z_cyl_3 = openmc.ZCylinder(x0=x_c, y0=y_c, r=he_radius) - z_cyl_4 = openmc.ZCylinder(x0=x_c, y0=y_c, r=firebrick_radius) - z_cyl_5 = openmc.ZCylinder(x0=x_c, y0=y_c, r=vessel_radius) - z_cyl_6 = openmc.ZCylinder(x0=x_c, y0=y_c, r=external_radius) + z_cyl_4 = openmc.ZCylinder(x0=x_c, y0=y_c, r=furnace_radius) + z_cyl_5 = openmc.ZCylinder(x0=x_c, y0=y_c, r=ov_internal_radius) + z_cyl_6 = openmc.ZCylinder(x0=x_c, y0=y_c, r=ov_external_radius) right_cyl = openmc.model.RightCircularCylinder( (x_c, y_c, heater_z), heater_length, heater_radius, axis="z" @@ -171,10 +175,10 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): (source_x + 0.25, y_c, source_z), source_h - 0.50, source_internal_r, axis="x" ) - ######## Sphere ################# + ########## Sphere ########## sphere = openmc.Sphere(x0=x_c, y0=y_c, z0=z_c, r=50.00) # before r=50.00 - ######## Lead bricks positioned under the source ################# + ########## Lead bricks positioned under the source ########## positions = [ (x_c - 13.50, y_c, z_c - table_height), (x_c - 4.50, y_c, z_c - table_height), @@ -194,7 +198,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): ) lead_blocks.append(lead_block_region) - # regions + ########## Regions ########## source_wall_region = -ext_cyl_source & +source_region source_region = -source_region epoxy_region = +z_plane_1 & -z_plane_2 & -sphere diff --git a/analysis/neutron/postprocessing.ipynb b/analysis/neutron/postprocessing.ipynb index 0ae8f7d..a6bc62a 100644 --- a/analysis/neutron/postprocessing.ipynb +++ b/analysis/neutron/postprocessing.ipynb @@ -278,7 +278,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -316,7 +316,7 @@ "outputs": [ { "data": { - "image/png": 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SJMmIESPaPX8AAACgc+rRz3Ap5Zhjjqlu33333c2OW7hwYXXFzJgxY3b6vAAAAICO0aODS6VS2e7XAQcckCQ54IADqr921113NTrPcccdl0GDBiVJrrvuulQqlSZfb9asWdXtk046aae8JwAAAKDj9ejgUkrv3r3ziU98IkmyePHiXHbZZduMmT9/fq655pokL96SNGrUqF06RwAAAGDX6dLPcLn33nuzZMmS6vfPPPNMdXvJkiWNVpQkydSpU3faXKZNm5bZs2fnsccey/Tp07NkyZKccsop6du3b+bOnZsLL7ww9fX16du3by6//PKdNg8AAACg49VUmrv/pQuYOnVqrrvuulaP35G3+upXvzq///3vc8ABB2TZsmUtjl2yZEkmTJiQxx9/vMn9AwcOzA033JBJkya1aQ5r1qzJoEGDsnr16gwcOLBNxwIAAABltOXnc7cUFXTwwQdn0aJFufjiizNy5Mjsueee2WOPPXLYYYflrLPOysMPP9zm2AIAAAB0PV16hUtPYYULAAAAdDwrXAAAAAA6kOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQWG1HTwAAoDuqVCqtGldTU7OTZwIAdATBBQCgsEqlknfM2NSqsT/64u47eTYAQEdwSxEAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhXTq4rFixInPmzMmMGTMyfvz4DB48ODU1NampqcnUqVNbdY7FixfnP/7jPzJlypT87d/+bYYOHZrdd989/fr1y2te85q8+93vzg9/+MNUKpVWnW/Dhg259NJLc9RRR2XvvfdO//79M2LEiJx99tl58skn2/FuAQAAgK6itqMn0B777rtvu89xwQUX5IYbbmhy3xNPPJEnnngi3/3udzN27Njceuut2XvvvZs919KlSzNx4sQ8+uijjX69rq4udXV1ufrqq3PjjTdmwoQJ7Z43AAAA0Hl16eDS0LBhwzJixIjccccdbTqutrY2b3rTmzJmzJj8zd/8TV7xildkyJAh+etf/5q6urp84xvfyH//93/n7rvvztvf/vbcc8896dVr24VB69aty6RJk6qx5bTTTsspp5ySvn37Zu7cubnooouyevXqTJ48OfPnz8/rX//6Iu8bAAAA6Hy6dHCZMWNGRo0alVGjRmXffffNsmXLcuCBB7bpHFdffXVqa5v+bXjLW96Sj3zkI3nXu96VW2+9Nffff39uu+22vP3tb99m7GWXXZa6urokySWXXJJp06ZV940ePTrjxo3Lsccemw0bNuTMM8/ML37xizbNEwAAAOg6uvQzXM4///xMmjSpXbcWNRdbttptt90yffr06vfz5s3bZszzzz+fK664IkkyYsSIfPrTn95mzOjRo3PqqacmSebOnZuHHnpoh+cMAAAAdG5dOrjsKv369atub9y4cZv9d911V1atWpUkmTJlSpO3HCVp9CDfW2+9tegcAQAAgM5DcGmFm266qbo9fPjwbfbfc8891e2xY8c2e56RI0dW4829995bcIYAAABAZyK4NOOZZ57J/Pnzc+qpp+aiiy5Kkrz85S/P+973vm3GLl68uLrdVJDZqra2NgcddNA2xwAAAADdS5d+aG5pxx13XO6+++4m9+2999659dZbs+eee26zb/ny5UlevPWoqf0NDRs2LA8//HBWrlyZTZs2pU+fPtuM2bRpUzZt2lT9fs2aNa1/EwAAAECHs8KlFc4444wsXrw4xx57bJP7165dmyTp37//ds/V8Hkw69ata3LMRRddlEGDBlW/hg0btgOzBgAAADqKFS4NXHvttVm/fn0qlUpWrVqVhQsX5mtf+1quuuqqPPHEE7n66qub/ESkrQ/S7d2793Zfo+GKlueee67JMeecc04+9alPVb9fs2aN6AIAAABdiODSwIEHHtjo+7//+7/PRz7ykUyePDlz5szJqFGjcv/992fo0KGNxu2+++5Jks2bN2/3NRreKtS3b98mx/Tp06fJW40AAACArsEtRdux++6759prr80ee+yR5cuXZ/r06duMGTBgQJLmbxFqaP369dXt1tyCBAAAAHQ9gksrDB48OGPGjEmS/PCHP0x9fX2j/VtXvKxfvz6rVq1q8VxbH7A7ZMgQq1gAAACgmxJcWmnIkCFJkg0bNmTlypWN9h1++OHV7bq6umbPUV9fn6VLlyZJRowYsRNmCQAAAHQGgksrPf3009Xtl94KdMwxx1S3m/tY6SRZuHBh9ZairStmAAAAgO7HQ3Nb4emnn878+fOTJAcccED1mS1bHXfccRk0aFBWr16d6667LtOnT09NTc0255k1a1Z1+6STTtqpcwboaiqVSkdPATqEP/t0N039PRigJ+rRweWxxx7LU089lTe/+c3Njlm9enXe8573VD+B6P3vf/82Y3r37p1PfOIT+eIXv5jFixfnsssuy7Rp0xqNmT9/fq655pokydixYzNq1KiC7wSga6tUKjn1c3M7ehpQTCVJev1dKwb6s0/3880Lmv+7NUBP0qWDy7333pslS5ZUv3/mmWeq20uWLGm0oiRJpk6d2uj7P/zhDzn++OPzhje8If/n//yfvPGNb8wrXvGK1NbW5k9/+lPuu+++XHPNNfnTn/6UJHnd616X/+//+/+anMu0adMye/bsPPbYY5k+fXqWLFmSU045JX379s3cuXNz4YUXpr6+Pn379s3ll19e5P0DAAAAnVNNpQuvY506dWquu+66Vo9/6Vu96667Mm7cuFYdO3HixFx77bXVh+c2ZcmSJZkwYUIef/zxJvcPHDgwN9xwQyZNmtTqOSfJmjVrqrcsDRw4sE3HAnQFVrjQ3VSSPNPKFS5DKvN3+nxgV7LCBejO2vLzeZde4dJeY8aMyd13351f/OIXuffee/Pkk0/mz3/+czZs2JCBAwfmwAMPzJve9Ka8973vbdVDbg8++OAsWrQoV111Vb73ve9lyZIl2bx5c4YNG5YJEybkk5/8ZA444IBd8M4AAACAjtSlV7j0FFa4AN2dFS50N1a40JNZ4QJ0Z235+dzHQgMAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUVtvREwCAbVQqHT0DaJeatgz2553uoKZNf+oBegTBBYDOpVLJ5AemdfQsoF0qSb42+v5WjfXnna6ukuTmN13W0dMA6HTcUgQAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFBYbUdPAABKq6TS0VPYZWpS09FTgG7Lv0sAaA/BBYBupZJKPjn8/3X0NHaNSvLvj76uo2cB3ZJ/lwDQXm4pAgAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKKxLB5cVK1Zkzpw5mTFjRsaPH5/BgwenpqYmNTU1mTp1aqvOsXHjxvzwhz/MGWeckTe96U3Ze++987KXvSx77713Ro8enfPOOy9//OMfWz2nDRs25NJLL81RRx2VvffeO/3798+IESNy9tln58knn9zBdwoAAAB0JbUdPYH22Hfffdt1/MMPP5xjjjkma9eu3WbfX//61yxYsCALFizIl7/85Vx99dV517ve1eL5li5dmokTJ+bRRx9t9Ot1dXWpq6vL1VdfnRtvvDETJkxo17wBAACAzq1Lr3BpaNiwYXnrW9/apmPWrFlTjS1jxozJRRddlJ/97Gf51a9+lZ/+9Kf50Ic+lN122y1r167Ne9/73vzkJz9p9lzr1q3LpEmTqrHltNNOy5133pn7778/F1xwQfr375/Vq1dn8uTJefjhh3f8jQIAAACdXpde4TJjxoyMGjUqo0aNyr777ptly5blwAMPbPXxvXr1yrve9a6ce+65Ofzww7fZ/9a3vjXjx4/PSSedlBdeeCFnnHFGHn/88dTU1Gwz9rLLLktdXV2S5JJLLsm0adOq+0aPHp1x48bl2GOPzYYNG3LmmWfmF7/4xQ68YwAAAKAr6NIrXM4///xMmjRph28t+ru/+7vMnj27ydiy1YknnpiTTz45yYu3DP3617/eZszzzz+fK664IkkyYsSIfPrTn95mzOjRo3PqqacmSebOnZuHHnpoh+YMAAAAdH5dOrjsKuPGjatuL126dJv9d911V1atWpUkmTJlSnr1avq3teGDfG+99daicwQAAAA6D8GlFTZt2lTdbiqm3HPPPdXtsWPHNnuekSNHpl+/fkmSe++9t+AMAQAAgM5EcGmFu+++u7o9fPjwbfYvXry4xf1b1dbW5qCDDtrmGAAAAKB7EVy24ze/+U1uu+22JMlrX/vaJp/3snz58iRJv379sueee7Z4vmHDhiVJVq5c2WjlDAAAANB9dOlPKdrZNm3alA9+8IN54YUXkiQXXnhhk+O2frR0//79t3vOrbcUJS9+lHSfPn2afN2GMWbNmjVtmjcAAADQsaxwacHHP/7xLFy4MMmLD8N9xzve0eS4jRs3Jkl69+693XM2DCzPPfdck2MuuuiiDBo0qPq1dVUMAOwqFV/t+vJ73Xl/vwFgV7HCpRkXXXRRrr766iTJG9/4xlx11VXNjt19992TJJs3b97ueRuuXOnbt2+TY84555x86lOfqn6/Zs0a0QWAXaaS5OY3XdbR0+jS2hIB/F63U6WSyQ9M6+hZAMA2BJcmfOMb38hnP/vZJMlhhx2Wn/zkJ41uBXqpAQMGJHnxFqHtWb9+fXW7uVuQ+vTp0+StRgAAAEDX4Jail7jpppvy0Y9+NElywAEH5Oc//3mGDBnS4jFDhw5N8mJMWbVqVYtjtz5gd8iQIaIKAAAAdFOCSwP/9V//lQ984APZsmVLXvnKV+bOO++sxpSWNPzkorq6umbH1dfXZ+nSpUmSESNGtH/CAAAAQKckuPyPO++8M+9617tSX1+fl7/85fnZz36Wgw46qFXHHnPMMdXtu+++u9lxCxcurN5SNGbMmPZNGAAAAOi0BJck999/f0488cRs2rQpAwcOzE9/+tO89rWvbfXxxx13XAYNGpQkue6661KpNP2ovFmzZlW3TzrppHbNGQAAAOi8enxw+fWvf52JEydm/fr16devX3784x/njW98Y5vO0bt373ziE59IkixevDiXXbbtpw3Mnz8/11xzTZJk7NixGTVqVPsnDwAAAHRKXfpTiu69994sWbKk+v0zzzxT3V6yZEmjFSVJMnXq1EbfL126NP/wD/9QfdDtv/7rv2bQoEH57//+72Zfc5999sk+++yzza9PmzYts2fPzmOPPZbp06dnyZIlOeWUU9K3b9/MnTs3F154Yerr69O3b99cfvnlbX6vAAAAQNfRpYPL1Vdfneuuu67Jfffdd1/uu+++Rr/20uByzz33ZMWKFdXvzzrrrO2+5rnnnpvzzjtvm18fMGBAbrvttkyYMCGPP/54Zs6cmZkzZzYaM3DgwNxwww054ogjtvs6AAAAQNfV428pKunggw/OokWLcvHFF2fkyJHZc889s8cee+Swww7LWWedlYcffjiTJk3q6GkCAAAAO1mXXuEya9asbW4baoupU6dus+qlvfr165fp06dn+vTpRc8LAAAAdB1WuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFNalP6UIANhFKpWOnkGXUtOWwX5v266mTb/DANAhBBcAoGWVSiY/MK2jZ9GlVJJ8bfT9rRrr97ZtKkluftNlHT0NANgutxQBAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFFbb0RMAALqCSkdPoIupacNYv7dt05bfWwDoOIILANCymqTXt37d0bPoUiqVJF9rxUC/t21WqSS5qqNnAQDb55YiAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMJa9dDc66+/fmfPI0nygQ98YJe8DgAAAMDO1KrgMnXq1NTU7PyP4BNcAAAAgO6gTR8LXalUdtY8dknQAQAAANgV2hRc7rjjjhxyyCFFJ/Doo4/mbW97W9FzAgAAAHSkNgWX/fbbLwcccEDRCaxbt67o+QAAAAA6mk8pAgAAACisVStcrr322iTJ0KFDi09g6NCh1fMDAAAAdAetCi5TpkzZaRMYNGjQTj0/AAAAwK7mliIAAACAwgQXAAAAgMIEFwAAAIDC2vSx0C159tlnM3/+/Pzud7/L2rVr88ILL2z3mBkzZpR6eQAAAIBOo93B5U9/+lM+9alP5ZZbbkl9fX2bjhVcAAAAgO6oXcFl5cqV+bu/+7v8/ve/T6VSKTUnAAAAgC6tXc9wOffcc7Ns2bJUKpVMnjw5v/jFL/Lss8/mhRdeyJYtW7b7BQAAANAdtWuFy5w5c1JTU5P3v//9mTVrVqEpAQAAAHRt7VrhsnLlyiTJv/zLvxSZDAAAAEB30K7gst9++yVJ+vXrV2QyAAAAAN1Bu4LLsccemyT57W9/W2QyAAAAAN1Bu4LL2Wefnd69e+ff/u3fsnHjxlJzAgAAAOjS2hVcXvva1+ab3/xmHn300fzDP/xDHnvssVLzAgAAAOiy2vUpRUnynve8J4ccckgmTpyYww8/PK9//etz6KGHZo899mjxuJqamlxzzTXtfXkAAACATqfdweWxxx7Lpz71qTzzzDNJkt/85jf5zW9+0+IxlUpFcAEAAAC6rXYFlyeffDLHHntsVq5cmUqlkiQZOHBgBg0alF692nW3EgAAAECX1a7g8oUvfCErVqxIr169cvbZZ+ejH/1oDjjggFJzAwAAAOiS2hVc7rzzztTU1OSTn/xkLr744lJzAgAAAOjS2nXfz5///OckyT/+4z8WmQwAAABAd9Cu4PLKV74ySdK7d+8ikwEAAADoDtoVXE444YQkyYMPPlhkMgAAAADdQbuCy9lnn51+/frl4osvzl/+8pdScwIAAADo0toVXA4++OB8//vfz9q1azNmzJj87Gc/KzUvAAAAgC6rXZ9S9OY3vzlJMnjw4Dz66KN529velj333DOHHHJI9thjjxaPrampyZ133tmel8+KFSvywAMP5IEHHsiDDz6YBx98MM8++2ySZMqUKZk1a9Z2z7Fly5bU1dU1Os/DDz+czZs3J0nmzp2b4447rtVz2rBhQ6666qp873vfy5IlS7J58+YMGzYsEydOzCc+8Ym86lWv2pG3CgAAAHQh7Qoud911V2pqaqrfVyqV/PWvf80DDzzQ7DE1NTWpVCqNjttR++67b7vP8a1vfStTp05t93mSZOnSpZk4cWIeffTRRr9eV1eXurq6XH311bnxxhszYcKEIq8HAAAAdE7tCi7HHntskXBSwrBhwzJixIjccccdbTquUqlUt1/2spflda97Xerr6/Pb3/62TedZt25dJk2aVI0tp512Wk455ZT07ds3c+fOzUUXXZTVq1dn8uTJmT9/fl7/+te36fwAAABA19HuFS4dacaMGRk1alRGjRqVfffdN8uWLcuBBx7YpnMcfvjhueKKK3LUUUfliCOOyO67757zzjuvzcHlsssuS11dXZLkkksuybRp06r7Ro8enXHjxuXYY4/Nhg0bcuaZZ+YXv/hFm84PAAAAdB3tCi4d7fzzz2/3OY466qgcddRR7TrH888/nyuuuCJJMmLEiHz605/eZszo0aNz6qmn5hvf+Ebmzp2bhx56KG984xvb9boAAABA59SuTyniRXfddVdWrVqV5MWH9fbq1fRva8Nnxdx66627YGYAAABARxBcCrjnnnuq22PHjm123MiRI9OvX78kyb333rvT5wUAAAB0jHYFl0WLFmW33XZL37598/TTT293/NNPP53dd989tbW1eeSRR9rz0p3K4sWLq9vDhw9vdlxtbW0OOuigbY4BAAAAupd2BZfZs2enUqlk0qRJ2X///bc7fv/998873vGObNmyJd/5znfa89KdyvLly5Mk/fr1y5577tni2GHDhiVJVq5cmU2bNu3sqQEAAAAdoF3B5a677kpNTU3Gjx/f6mMmTpyYJPn5z3/enpfuVNauXZsk6d+//3bHbr2lKHnxo6SbsmnTpqxZs6bRFwAAANB1tCu4bF3Zcfjhh7f6mMMOOyxJ8tRTT7XnpTuVjRs3Jkl69+693bF9+vSpbj/33HNNjrnooosyaNCg6tfWVTEAAABA19Cu4PLss88mSXbfffdWH7M1OKxYsaI9L92pbH3/mzdv3u7YhrcR9e3bt8kx55xzTlavXl392hq2AAAAgK6hXcFlr732SpI8+eSTrT5m68qWgQMHtuelO5UBAwYkaf4WoYbWr19f3W7uFqQ+ffpk4MCBjb4AAACArqNdwWXrrUT/9V//1epjvv/97yf531uLuoOhQ4cmeTGmrFq1qsWxW1erDBkypNHtRQAAAED30a7gMmHChFQqlVx//fW55557tjt+3rx5+da3vpWamppMmjSpPS/dqTR8hk1dXV2z4+rr67N06dIkyYgRI3b6vAAAAICO0a7g8qEPfSiDBw/OCy+8kAkTJuTKK6+sPkC2oY0bN+bf//3fM3HixLzwwgvZa6+98pGPfKQ9L92pHHPMMdXtu+++u9lxCxcurN5SNGbMmJ0+LwAAAKBjtCu49O/fPzfeeGN22223bNiwIWeeeWaGDBmScePG5b3vfW/e9773Zdy4cRkyZEjOOuusrF+/Pi972cty0003davnkhx33HEZNGhQkuS6665LpVJpctysWbOq2yeddNKumBoAAADQAdoVXJLkLW95S37605/mFa94RSqVStavX5958+Zl9uzZ+c53vpN58+Zl/fr1qVQq2X///fPTn/40J5xwQom5dxq9e/fOJz7xiSTJ4sWLc9lll20zZv78+bnmmmuSJGPHjs2oUaN26RwBAACAXae2xEnGjRuXpUuX5vrrr89tt92WRYsW5ZlnnkmSDB48OH/7t3+bt7/97fmnf/qnog+Kvffee7NkyZLq91tfM0mWLFnSaEVJkkydOrXJ87x03K9//evq9u23355ly5ZVvz/44IMb3UK01bRp0zJ79uw89thjmT59epYsWZJTTjklffv2zdy5c3PhhRemvr4+ffv2zeWXX97atwgAAAB0QUWCS5LsvvvuOf3003P66aeXOuV2XX311bnuuuua3Hffffflvvvua/RrzQWXf/7nf272NS6++OJG30+ZMqXJ4DJgwIDcdtttmTBhQh5//PHMnDkzM2fObDRm4MCBueGGG3LEEUc0+3oAAABA19fuW4r4XwcffHAWLVqUiy++OCNHjsyee+6ZPfbYI4cddljOOuusPPzww93q05kAAACAphVb4dIRZs2atc3tQDuiuYfc7oh+/fpl+vTpmT59erFzAgAAAF2LFS4AAAAAhbUquLz5zW/O8ccfn9///vfFJ7Bs2bLq+QEAAAC6g1bdUnTXXXelpqYm69evLz6B9evXV88PAAAA0B24pQgAAACgsDY9NPerX/1q9tlnn6ITWLFiRdHzAQAAAHS0NgWXr33taztrHgAAAADdRquDS8mPTgYAAADozloVXLZs2bKz5wEA0M34n1UA0JO16ZYiAAC2r6Ym+fhH9+joaQAAHcinFAEAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABRW29ETAAA6v0qlo2cAANC1tCu4fOELX0iSHHDAAZkyZUqrjlm5cmW+9rWvJUlmzJjRnpcHAHaJmnz3qjs7ehIAAF1Ku4LLeeedl5qamiTJL37xi/znf/5nevfu3eIxK1asqB4nuAAAAADdUZFbiiqVSr797W/n8ccfz/e///3su+++JU4LAHQU9xABALRLkeDytre9Lbfffnt++ctf5qijjsoPf/jDHHHEESVODQDsYjVJJj8wraOnAQDQpRX5lKLLLrssV155ZXbbbbcsX748xxxzTG655ZYSpwYAAADocop9LPTHPvax/OQnP8lee+2VDRs25F3velf1oboAAAAAPUmx4JIkxx9/fBYsWJBDDz00lUol559/ft797ndn48aNJV8GAAAAoFMrGlyS5JBDDskvf/nLnHDCCalUKrn55pvz93//9/nDH/5Q+qUAAAAAOqXiwSVJBg0alJ/85Cf5+Mc/nkqlkl/96lcZNWpUHnzwwZ3xcgAAAACdyk4JLknSq1ev/Pu//3u+/vWvp7a2Nn/84x8zduzY3HDDDTvrJQEgSU1SSc/4AnYi/y4BoH2KfCx0S04//fQceuihmTx5cp599tlcfPHFO/slAejq9j5rhw+tSfLvK8tNpdPbu6MnAN2Tf5cA0F47bYVLQ8cdd1wWLFiQ4cOHp1KR0QEAAIDurV0rXK699tokydChQ7c79qCDDsovf/nLfOxjH8vy5cvb87IAAAAAnVq7gsuUKVPaNH7AgAG5/vrr2/OSAAAAAJ3eLrmlCAAAAKAnEVwAAAAAChNcAAAAAArb6R8LDQBt5fPsAADo6gQXADqXmprcfMj+HT0LAABoF7cUAQAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABQmuAAAAAAUJrgAAAAAFCa4AAAAABRW29EToPUqlUoqlUpHTwNg5/DvN4Cur6bG31eBbq0t/44TXLqQn0+dnT1e1rejpwGwU0zu6AkAUMTt7/52R08BYKfZ8PxzrR7rliIAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwAAAIDCBBcAAACAwmo7egK0XqVSSaVS6ehpAAAAQI/Ulp/JBZcuZPLP/i2p2a2jpwEAAAA9U+WFVg91SxEAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYYILAAAAQGGCCwAAAEBhggsAAABAYV06uKxYsSJz5szJjBkzMn78+AwePDg1NTWpqanJ1KlT23y+22+/PSeffHKGDh2aPn36ZOjQoTn55JNz++23t/ocGzZsyKWXXpqjjjoqe++9d/r3758RI0bk7LPPzpNPPtnmOQEAAABdT02lUql09CR2VE1NTbP7pkyZklmzZrXqPJVKJR/+8Iczc+bMZsecfvrp+frXv97iay5dujQTJ07Mo48+2uT+QYMG5cYbb8yECRNaNa+t1qxZk0GDBiV7HJLU7NamYwEAAIBCKi8kGx7P6tWrM3DgwBaHdukVLg0NGzYsb33rW3fo2M997nPV2HLkkUfmpptuygMPPJCbbropRx55ZJJk5syZ+fznP9/sOdatW5dJkyZVY8tpp52WO++8M/fff38uuOCC9O/fP6tXr87kyZPz8MMP79A8AQAAgK6hS69wOffcczNq1KiMGjUq++67b5YtW5YDDzwwSetXuCxZsiQjRoxIfX19Ro4cmXnz5qVv377V/Rs2bMjYsWOzcOHC1NbWpq6uLgcddNA25znvvPNy/vnnJ0kuueSSTJs2rdH++fPn59hjj019fX3GjRuXX/ziF61+n1a4AAAAQCfQU1a4nH/++Zk0aVL23XffHT7HV77yldTX1ydJrrzyykaxJUn22GOPXHnllUmS+vr6XH755duc4/nnn88VV1yRJBkxYkQ+/elPbzNm9OjROfXUU5Mkc+fOzUMPPbTDcwYAAAA6ty4dXNqrUqnkhz/8YZJk+PDhOfroo5scd/TRR+ewww5LkvzgBz/ISxcF3XXXXVm1alWSF1fW9OrV9G9rwwf53nrrre2cPQAAANBZ9ejg8sQTT+Tpp59OkowdO7bFsVv3P/XUU1m2bFmjfffcc88245oycuTI9OvXL0ly77337siUAQAAgC6gRweXxYsXV7eHDx/e4tiG+xse15bz1NbWVp//8tJzAAAAAN1Hjw4uy5cvr24PHTq0xbHDhg1r8riG3/fr1y977rlnq86zcuXKbNq0qckxmzZtypo1axp9AQAAAF1Hjw4ua9eurW7379+/xbFbbwVKXvwI6KbOs71zbO88W1100UUZNGhQ9ath7AEAAAA6v9qOnkBH2rhxY3W7d+/eLY7t06dPdfu5555r8jzbO8f2zrPVOeeck0996lPV79esWZNhw4Zl9R8XbvdjpwAAAICdY82aNRk0aFCrxvbo4LL77rtXtzdv3tzi2Ia3/7z0o6O3nmd759jeebbq06dPozADAAAAdC09+paiAQMGVLebu71nq/Xr11e3X3rr0NbzbO8c2zsPAAAA0D306ODS8EG5Tz31VItjGz4o96XPVNl6nvXr12fVqlWtOs+QIUOsYgEAAIBuqkcHl8MPP7y6XVdX1+LYhvtHjBixQ+epr6/P0qVLmzwHAAAA0H306OBy4IEHZr/99kuS3H333S2OnTdvXpJk//33z6tf/epG+4455pjqdkvnWbhwYfWWojFjxuzIlAEAAIAuoEcHl5qampx44olJXlyZsmDBgibHLViwoLpy5cQTT0xNTU2j/ccdd1z1KcXXXXddKpVKk+eZNWtWdfukk05q7/QBgE6oUqns0BcA0L306E8pSpIzzzwz//mf/5n6+vqcccYZmTdvXqNPD3ruuedyxhlnJElqa2tz5plnbnOO3r175xOf+ES++MUvZvHixbnssssybdq0RmPmz5+fa665JkkyduzYjBo1aue9KQBgp2suknz8J4t36Hz/Mb7p241f+j96AICuoabShf+Xyr333pslS5ZUv3/mmWeqoWPMmDH54Ac/2Gj81KlTmzzPOeecky996UtJkiOPPDKf+cxnctBBB2Xp0qW5+OKLs2jRouq4Cy+8sMlzrF27NiNHjsxjjz2WJDn99NNzyimnpG/fvpk7d24uvPDCrFu3Ln379s3999+fI444otXvc+vnfK9evToDBw5s9XEAQDkv/SvTjoaVtnppiBFgAKDjtOXn8y4dXKZOnZrrrruu1eObe6tbtmzJaaedlm9+85vNHnvqqadm5syZ6dWr+buwlixZkgkTJuTxxx9vcv/AgQNzww03ZNKkSa2ecyK4AEBHafh3h10VWLanYYARXwBg12rLz+c9/paiJOnVq1euueaa/OM//mNmzpyZBx98MM8880wGDx6cUaNG5UMf+lDGjx+/3fMcfPDBWbRoUa666qp873vfy5IlS7J58+YMGzYsEyZMyCc/+ckccMABu+AdAQDtsTW07EhkuWrC4dsf1MTrtfa1Go7bGl+EFwDofLr0CpeewgoXANj52rqaZUfCSlu1NsRY9QIAu0aPuaWopxBcAGDnactqll0RWZrT1vgivABAeYJLNyO4AEB5rQ0tHRlZmtOa+CK8AEB5gks3I7gAQFnbCxadMbI0Z3vv5T/GjxBdAKAQwaWbEVwAoIztrWrpSqHlpVoKL1a7AEAZgks3I7gAQPu1FCS6cmh5qe2FF9EFAHac4NLNCC4AsONaWtXSnULLSzUXXqx2AYAdJ7h0M4ILAOyY5qJDdw4tL9VSeBFdAKBt2vLzea9dNCcAgF1KbHlRTU1NdVVLQx//yeL4/24AsPNY4dIFWOECAK3X3C1EPS20NKWpCOUWIwBoPbcUdTOCCwC0jlUt2+cWIwDYcW4pAgB6HLGlddxiBAC7huACAHR5YkvbiC4AsPMJLgBAlya27BjRBQB2Ls9w6QI8wwUAmtZUbBFa2q65h+l6pgsANOYZLgBAtye2lNPUahcrXQCgfQQXAKDLEVvKay66AAA7RnABAKBZVrkAwI4RXACALsXqlp3HrUUAUI7gAgB0GWLLzie6AEAZggsA0CWILbuO57kAQPsJLgBAlyS27FxNRRerXACg9QQXAKDTe+nqFrFl13hpdHFrEQC0nuACAHRqTd1KRMdxLQCgdQQXAKBLsbpl13JrEQDsGMEFAOi0rG7pnFwTANg+wQUA6DKsbukYVrkAQNsJLgBAp+RBuZ2LB+gCQNsILgBAp+NWoq7BNQKA5gkuAECnZ3VL5+DWIgBoPcEFAOhUrG7pWlwrAGia4AIAdGpWt3QuTa1yAQC2JbgAANAubisCgG0JLgBAp+GTibqGpj6xCABoTHABAKDdrHIBgMYEFwCgU/Cw3K7NtQOAxgQXAKBTcjtR5+bhuQDQMsEFAAAAoDDBBQCAIjzHBQD+l+ACAHQ4n07UNfm0IgBonuACAAAAUJjgAgAAAFCY4AIAQDGe4wIALxJcAIAO5fktXZvnuABA0wQXAAAAgMIEFwAAAIDCBBcAAACAwgQXAAAAgMIEFwCg0/DA3K7ppQ/O9UlFACC4AAAd6KWfUET34JoCgOACAAAAUJzgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgAAAFCY4AIAAABQmOACAAAAUJjgAgB0GpVKpaOnAABQhOACAHSYmpqa/Mf4EdXvP/6TxR04G3ZUpVJpdO2umnB4B84GADoHwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAOhUfFIRANAdCC4AQIfySUVdm08oAoCmCS4AAAAAhQkuAAAAAIUJLgBAp+M5LgBAVye4AAAdznNcuibPbwGA5gkuAAAAAIUJLgBAp+S2IgCgKxNcAIBOwW1FXYvbiQCgZYILAAAAQGGCCwDQabmtCADoqgQXAKDTcFtR1+B2IgDYPsEFAOjUrHIBALoiwQUA6FSscuncrG4BgNYRXACATs8qFwCgqxFcAIBOxyqXzsnqFgBoPcEFAOgSrHIBALoSwQUA6JSaWuUiunQcq1sAoG0EFwCgy3BrUcd4aWwBALZPcAEAOq2XrnJJ3FrUGVjdAgDbJ7gAAF2KW4t2LatbAGDHCC4AQKfW1CoXAWDXaCq2WN0CAK0juAAAnZ5bizoHsQUAWk9wAQC6JLcW7VxuJQKA9hFcAIAuoblbi0SX8txKBADtJ7gAAF2G6LLziS0AUIbgAgB0KaLLziO2AEA5ggsA0OWILuWJLQBQluACAHQbosuO8YBcAChPcAEAuqSmVrkkoktbNRdbrG4BgPYRXP7Hxo0b89WvfjXHH398hgwZkt69e2f//ffPxIkTM3v27Faf5/bbb8/JJ5+coUOHpk+fPhk6dGhOPvnk3H777Ttx9gDQM4ku7SO2AMDOU1Pxt5E8+uijOfHEE/Poo482O+Ztb3tbbr755vTr16/J/ZVKJR/+8Iczc+bMZs9x+umn5+tf/3pqamraNL81a9Zk0KBBWb16dQYOHNimYwGgJ2guHPzH+BFt/u9uTyG2AEDbteXn8x6/wmXlypU54YQTqrFl8uTJmTNnTn71q19lzpw5mTx5cpIXV6685z3vafY8n/vc56qx5cgjj8xNN92UBx54IDfddFOOPPLIJMnMmTPz+c9/fie/IwDoeax0aRuxBQB2vh6/wuXjH/94rrrqqiTJueeem/POO2+bMeeee26+8IUvJEluueWWnHzyyY32L1myJCNGjEh9fX1GjhyZefPmpW/fvtX9GzZsyNixY7Nw4cLU1tamrq4uBx10UKvnaIULALReUzFha4zp6atdtv61z6cRAcCOacvP5z06uLzwwgsZPHhwVq1alQMOOCBLly7Nbrvt1uS417zmNXnyySczcuTIPPjgg432f+xjH8tXv/rVJMn8+fNz9NFHb3OOBQsWZPTo0UlejDxXXnllq+cpuABA27jFaFtWtQBA+7mlqJUef/zxrFq1KklywgknNBlbkmS33XbLCSeckCRZuHBhli1bVt1XqVTywx/+MEkyfPjwJmNLkhx99NE57LDDkiQ/+MEPLG8GgJ1oe7cY9aT/Dm99v2ILAOxaPTq4/OUvf6lu77vvvi2Obbh/3rx51e0nnngiTz/9dJJk7NixLZ5j6/6nnnqqUbQBAMprKbr0lGe7bA0tYgsA7Ho9Org0/MSh1atXtzi24f5HHnmkur148f/+BWb48OEtnqPh/obHAQA7R01NTa6acHiPW+2yvVUtYgsA7Hw9OrgcfPDBednLXpak8aqVpjTc/+STT1a3ly9fXt0eOnRoi+cYNmxYk8cBADtXa1a7dIfw0jC0WNUCAB2rtqMn0JH69euX448/Prfffnsefvjh3HTTTU1+9PNNN92U3/72t9Xv165d2+R2//79t/t6W61bt67ZcZs2bcqmTZuq369Zs6blNwIAbNfW1S5NrfzY+n1X/TSj5j59aCuhBQB2vR4dXJLk/PPPz89//vPU19dnypQpWbp0aT7wgQ/kla98Zf74xz/m+uuvzxe+8IX07t07mzdvTpI899xz1eM3btxY3e7du3eLr9WnT5/qdsNzvNRFF12U888/f0ffEgDQgraEl63jO6OGK3KEFgDofHp8cDnqqKNyzTXX5LTTTsvmzZvz+c9/Pp///Ocbjdltt91y+eWX54wzzkiSDBgwoLpv9913r25vDTLNabhqpW/fvs2OO+ecc/KpT32q+v2aNWsa3Y4EALRfa8JL0rniS2siSyK0AEBn0OODS5J84AMfyBve8IZccMEFuf3226u3CfXq1Svjxo3LBRdc0Oh2oL322qu63TC+tHSbUJKsX7++ut3S7Ud9+vRptBoGANh5WgovSfPxZeuxO9NLnyvTUmRJhBYA6EwEl//xhje8Id/97nfzwgsv5I9//GM2btyY/fbbL3vssUeS5MYbb6yOPfzw//3LTMMH5T711FMtvkbDB+VasQIAncv2wkuybfBo6kG8W8/VFs09sHd7gWUroQUAOh/B5SV22223Jj9t6N57761uv+lNb6puN4wvdXV1LZ674f4RI5r+CxoA0LG2hpckLcaXpPkg0lyIaet5WiKyAEDnVlPpDp+BuJNt3rw5Q4cOzcqVK7P//vvn97//fXbbbbckL/5FbOjQofnDH/6Q4cOHZ/Hi5v/CNGLEiNTV1WX//ffP8uXLW/1/v9asWZNBgwZl9erVGThwYJH3BAC0zfbiy64gsgBAx2rLz+dWuLTCFVdckZUrVyZJPvzhD1djS/Li/wU78cQT87WvfS11dXVZsGBBjj766G3OsWDBguoKlxNPPLHDH7oHALRNw5Uvya4JMAILAHRdVrgkefLJJ/OqV72qyX0/+tGP8o//+I95/vnnc8ghh+Thhx9u9MlESfLYY4/lta99berr6zNy5MjMmzev0acQPffcczn22GOzcOHC1NbW5pFHHskhhxzS6vlZ4QIAXceOhBhhBQC6Bitc2uh1r3tdRo8encmTJ+e1r31tevfunWXLluV73/teZs+eneTFTyaaPXv2NrElSQ499NCcffbZ+dKXvpSFCxdmzJgx+cxnPpODDjooS5cuzcUXX5xFixYlSaZNm9am2AIAdC0vXQkDAPRMVrjkxY9obviRzS91+OGH59vf/naOPPLIZsds2bIlp512Wr75zW82O+bUU0/NzJkz06tXrzbNzwoXAAAA6Hht+flccEnyne98J3fccUceeOCB/PGPf8y6desyZMiQvP71r8873/nOvP/978/LXvayVp3rxz/+cWbOnJkHH3wwzzzzTAYPHpxRo0blQx/6UMaPH79D8xNcAAAAoOMJLt2M4AIAAAAdry0/n7ft3hYAAAAAtktwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHABAAAAKExwAQAAAChMcAEAAAAoTHD5H5s3b84111yTt73tbXnlK1+ZPn36pH///jnssMPyL//yL1mwYEGrznP77bfn5JNPztChQ9OnT58MHTo0J598cm6//fad/A4AAACAzqKmUqlUOnoSHW358uWZOHFifvvb37Y47qyzzsq//du/paamZpt9lUolH/7whzNz5sxmjz/99NPz9a9/vcnjW7JmzZoMGjQoq1evzsCBA9t0LAAAAFBGW34+7/ErXOrr6xvFlte//vWZNWtW5s+fnzvuuCMzZsxIv379kiRf+cpXctlllzV5ns997nPV2HLkkUfmpptuygMPPJCbbropRx55ZJJk5syZ+fznP78L3hUAAADQkXr8Cpdbbrkl73znO5Mko0ePzj333JPddtut0ZiHHnooo0ePzvPPP5+99torK1asSG1tbXX/kiVLMmLEiNTX12fkyJGZN29e+vbtW92/YcOGjB07NgsXLkxtbW3q6upy0EEHtXqOVrgAAABAx7PCpQ3uu+++6vY555yzTWxJkje+8Y2ZNGlSkuSvf/1r6urqGu3/yle+kvr6+iTJlVde2Si2JMkee+yRK6+8MsmLK2ouv/zykm8BAAAA6GR6fHDZvHlzdfs1r3lNs+MarkjZtGlTdbtSqeSHP/xhkmT48OE5+uijmzz+6KOPzmGHHZYk+cEPfpAevrAIAAAAurUeH1wOPfTQ6vbvfve7ZsctXbo0SVJTU5NDDjmk+utPPPFEnn766STJ2LFjW3ytrfufeuqpLFu2bEenDAAAAHRyPT64vOc976ned3XxxRfnhRde2GbMokWLcttttyVJTjnllEb3aS1evLi6PXz48BZfq+H+hscBAAAA3UuPDy5DhgzJrFmz0rdv39x3330ZNWpUrr/++ixYsCA///nPc/7552fs2LHZvHlzjjjiiHz5y19udPzy5cur20OHDm3xtYYNG9bkcQAAAED3Urv9Id3fSSedlIULF+bLX/5yvvnNb2bKlCmN9u+77745//zzc/rpp1c/InqrtWvXVrf79+/f4us0PHbdunXNjtu0aVOj58SsWbOmVe8DAAAA6Bx6/AqXJHn++edz44035kc/+lGTD7P985//nJtuuil33XXXNvs2btxY3e7du3eLr9OnT5/q9nPPPdfsuIsuuiiDBg2qfjVcGQMAAAB0fj0+uKxfvz5vectbcsEFF+TZZ5/N9OnTs3jx4mzatCmrV6/OHXfckWOOOSYPPvhg3v72t+eKK65odPzuu+9e3W74iUdNabhq5aUfHd3QOeeck9WrV1e/3H4EAAAAXUuPDy7nnntu5s2blyS55pprcvHFF2f48OHp3bt3Bg4cmBNOOCFz587NuHHjUqlU8qlPfSoPP/xw9fgBAwZUt1u6TSh5Me5s1dLtR3369MnAgQMbfQEAAABdR48OLpVKJddee22SFz8e+qXPbtmqtrY2X/ziF5MkW7ZsqR6TNH5Q7lNPPdXi6zVcqeI2IQAAAOi+enRw+fOf/5y//OUvSZIjjzyyxbFvfOMbq9t1dXXV7cMPP7zJX29Kw/0jRoxo01wBAACArqNHB5fa2v/9kKb6+voWxz7//PNNHnfggQdmv/32S5LcfffdLZ5j661L+++/f1796le3dboAAABAF9Gjg8vee+9dfT7K/PnzW4wuDWPKgQceWN2uqanJiSeemOTFFSwLFixo8vgFCxZUV7iceOKJqampaff8AQAAgM6pRweXXr16ZeLEiUmSP/zhD7nggguaHPfXv/41n/nMZ6rfT5o0qdH+M888s7rq5YwzztjmI5+fe+65nHHGGUleXB1z5plnlnoLAAAAQCfUo4NLksyYMSN77LFHkuS8887LO97xjtxyyy1ZtGhR5s+fn6985Ss54ogj8sgjjyRJjj/++Lz1rW9tdI5DDz00Z599dpJk4cKFGTNmTGbPnp2FCxdm9uzZGTNmTBYuXJgkmTZtWg455JBd+A4BAACAXa2mUqlUOnoSHe3nP/953vOe9+SZZ55pcdyb3/zm3Hzzzdlrr7222bdly5acdtpp+eY3v9ns8aeeempmzpyZXr3a1rnWrFmTQYMGZfXq1T4iGgAAADpIW34+F1z+x7PPPptrrrkmP/nJT/L//t//y6pVq1JbW5tXvOIVGTVqVN773vfmHe94x3afvfLjH/84M2fOzIMPPphnnnkmgwcPzqhRo/KhD30o48eP36G5CS4AAADQ8QSXbkZwAQAAgI7Xlp/Pe/wzXAAAAABKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAoTXAAAAAAKE1wAAAAAChNcAAAAAAqr7egJsPNUKpWOngIAAAB0WjU1NTvt3IJLN1WpVNJrwOEdPQ0AAADotCrrFu+0c7ulqJuqqanJlrWPdPQ0AAAAoFPambElEVwAAAAAihNcujGrXAAAAGBbO3t1SyK4dHuiCwAAAPyvXRFbEsEFAAAAoDjBpQewygUAAAB23eqWRHDpMUQXAAAAerJdGVsSwQUAAACgOMGlB7HKBQAAgJ5oV69uSQSXHkd0AQAAoCfpiNiSCC49kugCAABAT9BRsSURXAAAAACKE1x6KKtcAAAA6M46cnVL0sODy3HHHZeampo2fd11113Nnu/222/PySefnKFDh6ZPnz4ZOnRoTj755Nx+++277k21gegCAABAd9TRsSXp4cGlrXr16pVDDjlkm1+vVCr50Ic+lPHjx+f73/9+nn766WzevDlPP/10vv/972f8+PH50Ic+lEql0gGzBgAAAHa12o6eQEe69tprs379+hbHPPLII3n3u9+dJDn++OOz//77bzPmc5/7XGbOnJkkOfLIIzN9+vQcdNBBWbp0aS655JIsWrQoM2fOzJAhQ/Kv//qv5d9IO2xd5dJrwOEdPRUAAABot86wuiVJaiqWXbToM5/5TC655JIkybe+9a380z/9U6P9S5YsyYgRI1JfX5+RI0dm3rx56du3b3X/hg0bMnbs2CxcuDC1tbWpq6vLQQcd1KY5rFmzJoMGDcrq1aszcODA9r+pJlQqFdEFAACALm1nx5a2/HzulqIWbNmyJTfccEOSpH///jn55JO3GfOVr3wl9fX1SZIrr7yyUWxJkj322CNXXnllkqS+vj6XX375zp30DvI8FwAAALqyzrKyZSvBpQV33nlnnn766STJO9/5zuyxxx6N9lcqlfzwhz9MkgwfPjxHH310k+c5+uijc9hhhyVJfvCDH3iWCwAAAHRzgksLrr/++ur2Bz7wgW32P/HEE9UgM3bs2BbPtXX/U089lWXLlpWbZEFWuQAAANAVdbbVLYng0qx169bl+9//fpLkVa96VY477rhtxixe/L8XdPjw4S2er+H+hsd1NqILAAAAXUlnjC1JD/+Uopbccsst1U8wev/735+ampptxixfvry6PXTo0BbPN2zYsCaPa8qmTZuyadOm6vdr1qxp1ZxL8clFAAAAdAWdNbYkVrg0a3u3EyXJ2rVrq9v9+/dv8Xz9+vWrbq9bt67FsRdddFEGDRpU/WoYawAAAIDOT3BpwlNPPZW77roryYsPvD300EObHLdx48bqdu/evVs8Z58+farbzz33XItjzznnnKxevbr6tb0VMTuDW4sAAADozDrz6pbELUVN+va3v50tW7YkSaZMmdLsuN133726vXnz5hbP2fAWoZd+dPRL9enTp1Gg6ShuLQIAAKAz6uyxJbHCpUnf+ta3krwYPt797nc3O27AgAHV7e3dJrT1eTDJ9m8/6kysdAEAAKAz6QqxJRFctrFw4cI88siLgWHSpEnZa6+9mh3b8EG5Tz31VIvnbXhbkGeyAAAAQPcmuLxEw4fltnQ7UZIcfvj/3mpTV1fX4tiG+0eMGLGDs+sYVrkAAADQGXSV1S2J4NLI888/n+985ztJkiFDhmT8+PEtjj/wwAOz3377JUnuvvvuFsfOmzcvSbL//vvn1a9+dfsnu4uJLgAAAHSkrhRbEg/NbeQnP/lJVq5cmSR573vfm9raln97ampqcuKJJ+ZrX/ta6urqsmDBghx99NHbjFuwYEF1hcuJJ56Ympqa8pPfBWpqarrcH3AAAADoCFa4NNDwdqIPfOADrTrmzDPPrIaZM844Y5uPfH7uuedyxhlnJElqa2tz5plnlpksAAAA0GkJLv/jr3/9a+bMmZMked3rXpe//du/bdVxhx56aM4+++wkLz5wd8yYMZk9e3YWLlyY2bNnZ8yYMVm4cGGSZNq0aTnkkEN2zhsAAAAAOg23FP2P2bNnZ9OmTUlav7plqwsuuCArVqzIN7/5zSxatCinnHLKNmNOPfXU/Ou//muRuQIAAACdmxUu/+Nb3/pWkmS33XbL+973vjYd26tXr1xzzTW57bbbcuKJJ2a//fZL7969s99+++XEE0/Mj3/841x99dXp1ctvNwAAAPQENZVKpdLRk6Bla9asyaBBg7J69eoMHDiwo6cDAAAAPVJbfj635AIAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwQUAAACgsNqOngDbV6lUkiRr1qzp4JkAAABAz7X15/KtP6e3RHDpAtauXZskGTZsWAfPBAAAAFi7dm0GDRrU4piaSmuyDB1qy5Yt+cMf/pABAwakpqZml73umjVrMmzYsCxfvjwDBw7cZa/LzuOadk+ua/fkunY/rmn35Lp2P65p9+S6dk8dcV0rlUrWrl2b/fbbL716tfyUFitcuoBevXpl6NChHfb6AwcO9C+lbsY17Z5c1+7Jde1+XNPuyXXtflzT7sl17Z529XXd3sqWrTw0FwAAAKAwwQUAAACgMMGFZvXp0yfnnntu+vTp09FToRDXtHtyXbsn17X7cU27J9e1+3FNuyfXtXvq7NfVQ3MBAAAACrPCBQAAAKAwwQUAAACgMMEFAAAAoDDBBQAAAKAwwaUH+NWvfpULL7ww48ePz7Bhw9KnT5/0798/hx56aKZOnZp77rmnTee7/fbbc/LJJ2fo0KHp06dPhg4dmpNPPjm33377TnoHvNSaNWvyne98J5/+9KczduzYHHzwwRk0aFB69+6dffbZJ8cdd1wuueSSPPvss606n2va+U2fPj01NTXVr7vuumu7x7iunUPD69bS13HHHbfdc7mmndMzzzyTSy65JGPGjMkrXvGK9OnTJ/vtt1/e9KY3Zdq0aZk/f/52z+Hadqzjjjuu1f+stubfw65n57N58+Zcc801edvb3pZXvvKV1b8PH3bYYfmXf/mXLFiwoFXncW07j40bN+arX/1qjj/++AwZMiS9e/fO/vvvn4kTJ2b27NmtPo9ruvOtWLEic+bMyYwZMzJ+/PgMHjy4+u/SqVOntvl8Ja7Zhg0bcumll+aoo47K3nvvnf79+2fEiBE5++yz8+STT7Z5Ts2q0K0de+yxlSTb/Xr/+99f2bRpU4vn2rJlS+X0009v8Tynn356ZcuWLbvo3fVcP/vZz1p1XQcPHly5/fbbmz2Pa9o1/PrXv67U1tY2ui5z585tdrzr2rm05p/VJJWxY8c2ew7XtPP67ne/W3n5y1/e4rU58cQTmz3ete0cxo4d2+p/VpNUevXqVXnqqae2OY/r2Tk9+eSTlb/5m7/Z7nU966yzmr02rm3nUldXVznssMNavB5ve9vbKuvWrWv2HK7prtPS7/GUKVNafZ5S12zJkiUt/vkZNGhQ5bbbbmvnu36R4NLNHXTQQZUklf3226/yyU9+snLzzTdXHnjggcr8+fMrX/7ylyv7779/9Q/We97znhbP9dnPfrY69sgjj6zcdNNNlQceeKBy0003VY488sjqvv/7f//vLnp3PdfPfvazyrBhwyof+MAHKldccUXl1ltvrcyfP79y3333VWbPnl2ZPHlyZbfddqskqfTu3bvym9/8psnzuKad3wsvvFAZNWpUJUlln332aVVwcV07l62/3x/5yEcqv/3tb5v9+t3vftfsOVzTzum6666r9OrVq/rP57nnnlv52c9+VnnooYcqt912W+Xf//3fKyeccELlne98Z7PncG07h9/97nct/vP529/+tjJ79uzq9TjhhBOaPI/r2fk8//zzjWLL61//+sqsWbMq8+fPr9xxxx2VGTNmVPr161fdf8kllzR5Hte281ixYkVl2LBh1d/zyZMnV+bMmVP51a9+VZkzZ05l8uTJ1X1vf/vbmz2Pa7rrNIwZw4YNq7z1rW/doeBS4pqtXbu2Mnz48OrY0047rXLnnXdW7r///soFF1xQ6d+/fyVJZY899mj2Z6g2vfd2n4FObeLEiZXZs2dX6uvrm9y/cuXKyqGHHlr9Azdv3rwmxz3++OPV/8M+cuTIyoYNGxrtX79+fWXkyJGVJJXa2trKkiVLir8X/ldz17Oh73//+9XrevLJJ2+z3zXtGr7yla9UklSGDx9eOeecc7YbXFzXzmfrNTv33HN36HjXtHN65JFHKn369Kkkqfz93/99ZdWqVc2ObW4FqWvbtUyfPr36z/O3vvWtbfa7np3TzTffXL1uo0ePbvLvUAsXLqy87GUvqySp7LXXXpXnn3++0X7XtnP52Mc+tt3/ts6YMaM65pZbbtlmv2u6a82YMaPyox/9qPKnP/2pUqlUKk888USbg0upa3buuee2GFjvv//+6uuMGzeubW+0CYILlR/96EfVP3Sf+MQnmhzz0Y9+tDpm/vz5TY6ZP39+dczHP/7xnTllWmlrvR08ePA2+1zTzu/JJ5+sVva5c+c2+g9Ec8HFde182htcXNPO6fjjj6/++3XlypU7dA7Xtut44YUXqquC+/fvX1m/fv02Y1zPzumss86q/p7/13/9V7PjTjrppOq43/72t432ubadR319fWXPPfesJKkccMABzf5PyPr6+sqrXvWq6g/nL+WadqwdCS4lrtnmzZurf35GjBhReeGFF5o8z4c+9KHqeRYuXNjq99UUwYXK2rVrq3+gJk6cuM3+LVu2VP+SMXz48BbPtfVeuKFDh7rfsRN44xvfWP3LYUOuadcwadKkRv8h2l5wcV07p/YEF9e0c1q8eHH1up533nk7dA7Xtmu54447qtd86tSp2+x3PTuvhqsh/vu//7vZcWeffXaTP2C5tp1Lw3//fvCDH2xx7Kmnnlod+8QTT1R/3TXteG0NLqWuWcN/l3/pS19q9hwNw81nP/vZVr2n5viUIrJ58+bqdq9e2/6ReOKJJ/L0008nScaOHdviubbuf+qpp7Js2bJyk6TNFi9enF//+tdJkuHDhzfa55p2ft/97nczZ86c7L333rn00ktbdYzr2v24pp3T9773ver25MmTq9t//etf8/jjj7fqE+Jc267l+uuvr25/4AMf2Ga/69l5HXroodXt3/3ud82OW7p0aZIXP1nukEMOqf66a9u5/OUvf6lu77vvvi2Obbh/3rx51W3XtOspdc0afjpvS+cZOXJk+vXrlyS59957d2TKVYILufvuu6vbL/3BPHnxB/eW9jfUcH/D49g1NmzYkMcffzxf/vKXM27cuLzwwgtJkk9+8pONxrmmnduqVauq1+ziiy/OkCFDWnWc69q5fe9738thhx2Wvn37ZsCAATnkkEMyZcqUzJ07t9ljXNPOaevHxw4aNCgjRozIDTfckDe84Q3Ze++9c+ihh2bw4MF5zWtek/PPPz/r1q1r8hyubdexbt26fP/730+SvOpVr2ryI9xdz87rPe95TwYOHJjkxf+mbv27UUOLFi3KbbfdliQ55ZRTquMT17az2fpDcJKsXr26xbEN9z/yyCPVbde06yl1zVp7ntra2hx00EFNnqOtBJcebsuWLfnSl75U/f5d73rXNmOWL19e3R46dGiL5xs2bFiTx7HzzJo1q/o59v369cuhhx6aT3/60/nzn/+cJDn77LPzvve9r9ExrmnnNn369PzpT3/K3/3d3+XUU09t9XGua+f2yCOP5LHHHsvGjRuzbt26LFmyJNdff33e/OY356STTmryL46uaee09S/ur371q3PGGWfkn/7pn/Lwww83GvPEE0/kvPPOy+jRo/OHP/xhm3O4tl3HLbfckvXr1ydJ3v/+96empmabMa5n5zVkyJDMmjUrffv2zX333ZdRo0bl+uuvz4IFC/Lzn/88559/fsaOHZvNmzfniCOOyJe//OVGx7u2ncvBBx+cl73sZUkar1ppSsP9Tz75ZHXbNe16Sl2zrd/369cve+65Z6vOs3LlymzatKkt021EcOnhvvKVr+SBBx5Ikpx00kkZOXLkNmPWrl1b3e7fv3+L52tYnZv7v3rsGkcccUQWLFiQSy+9dJu/HLqmnde9996bq6++OrW1tfn617/e5F/sm+O6dk577LFHTjnllPznf/5n7rnnnixatCh33HFH/u///b95+ctfniT5wQ/+//buPyrL+v7j+IvfgkmIP2rgQtxkiWh61OmCKUawWXOkzEy3IrWyztqZ7VhpaurxR/5IzbOz1VlHkeoIM2yzWjhZiGXklNCJKWkDdOLSmWgqKrfy+f7Bua/vfQs3P+wG7hufj3M45/K+Ptfn/ly85Ib7fX+uz/VXpaamymazOR1Lpp7JPqW9tLRUf/jDHxQWFqbXXntNp06d0uXLl7Vnzx6NGTNGknTgwAFNmDBBtbW1Tn2Qrfdo6nIiiTw93bhx41RUVKRp06Zp3759Sk9P149+9CMlJydrwYIFCgkJ0erVq7Vz507dfvvtTseSrWfp3LmzkpKSJEn79+9XVlZWg+2ysrJUUlJi/dsxRzL1Pu7KzN5PU3001U9LUHC5ie3YsUOzZs2SJPXs2VOvvvpqg+0uX75sbQcGBjbaZ1BQkLV96dIlN4wSTXnggQdUUlKikpIS7d69W1lZWRo3bpz27dunX/7yl3r//ffrHUOmnqmmpkZPPPGEjDF65plnNGDAgBYdT66eqbKyUllZWXrssceUkJCgQYMGKTk5WYsXL9bnn3+uwYMHS6p7Tb7+dZhMPZN9tsOVK1fk5+en3NxcTZ8+XT169FBQUJCGDh2q999/3yq6FBYW6p133nHqg2y9w/Hjx1VQUCBJGjFihNN6II7I07PZbDZt3LhR7733nowx9fafPHlSWVlZVtaOyNbzLFy4UP7+/pKk9PR0LV68WMeOHZPNZtOxY8e0ePFipaenO+XlmAeZeh93ZWbvp6k+muqnJSi43KQ+//xzjRs3TlevXlVQUJA2bdrkcuGpTp06WduOC+w2xHG6VXBwsHsGi0aFhYUpLi5OcXFxGjZsmB566CG98847euONN1RWVqbU1FRt2LDB6Rgy9UxLly7VoUOHdMcdd2j+/PktPp5cPVNjU1Zvu+025eTkWL/4f//73zvtJ1PP5JjLhAkTNGLEiHptfH19nRa8vv5TWLL1Dm+99ZY1Oyk9Pd1lO/L0XBcvXtS9996rJUuW6Ouvv9Zzzz2nQ4cO6cqVKzp37py2bdumhIQE7dmzR2PHjtXatWudjidbz/PDH/5Q69atU2BgoGw2m+bNm6eoqCgFBgYqKipK8+bNU21trVatWmUd06VLF2ubTL2PuzKz99NUH0310xIUXG5C5eXlSklJUVVVlfz8/JSVldXoKs2OL1BNTaeyf+onNW+qFlrPww8/bE1jf/rpp1VVVWXtI1PPU1paqpdeeklS3Ztux2mMzUWu3qlPnz5KTk6WJH355ZdO632QqWdyzMU+i6Uh/fv3V2RkpCRpz549LvsgW8/15ptvSqr7pHPixIku25Gn55o/f761lse6deu0fPly3XnnnQoMDFRoaKiSk5O1fft2jR49WsYY/e53v3Nak4lsPdMjjzyi3bt3a8KECU4Z+fr6KikpSZ988onTAtddu3a1tsnU+7grM3s/zblEyF3ZU3C5yZw4cUL33nuvTpw4IR8fH61fv17jxo1r9BjHhYmOHz/eaFvHhYkcFyxC+0hNTZVU94KRm5trPU6mnmfNmjWqqalRnz59VF1drezs7HpfBw4csNrn5+dbj9t/IZCr94qNjbW27bc9lMjUUzl+f5u7eN+pU6ecHidbz1dUVGQtkPyzn/3M6Q3b9cjTMxljlJGRIanu9tCuZin5+/tr0aJFkupuKGE/RiJbT3bXXXdp06ZNqqqq0n/+8x8dOXJE58+f1z/+8Q8NHz7cqXDm+HuWTL2PuzKz93Px4kWdPXu2Wf3YLxe+Uf43fCS8zunTp5WcnKyysjJJdZ+iu1r8zZHjC1RpaWmjbR339+vX7wZHCndxvJ3w0aNHrW0y9Tz2aYtlZWWaNGlSk+3tfxhKdbPWOnfuTK5erKE1BSR+Vj1V//79rRkrDd1i1pF9v329ATuy9XyOi+U2djmRRJ6e6uTJk9Yi1/b1slwZMmSIte2YEdl6Pj8/vwaL3zt37rS2hw8fbm2TqfdxV2axsbHavHmz1a6hS4Il6erVq/r3v//dYB8txQyXm8S5c+f0k5/8xPqkZtmyZfr1r3/drGOjo6MVEREhqW5Rx8bYp2xGRkaqd+/eNz5guIXjJ+WOU+HItGMiV+9lf22WZGUokamnGjlypLVt/4PMFfuHHPZLi+zI1rPZbDZlZ2dLqvvworFLxyTy9FSOhc6rV6822tbxLnGOx5Gtd6qpqVFOTo6kujzuvvtuax+Zeh93ZZaQkGBtN9ZPUVGRNYM8Pj7+RoZsoeByE6iurtb999+v4uJiSdKcOXP0/PPPN/t4Hx8f69KU0tJS7dq1q8F2u3btsiqKqampLbqdLVrH22+/bW073vGGTD3Phg0bZIxp9MtxId3t27dbj9t/mZCrdyorK1NeXp6kuvVcHN+Yk6ln+vnPf66AgABJqnf3IUc7duzQ119/LUn68Y9/7LSPbD1bbm6u/ve//0mSJk+eXG+G0vXI0zOFh4crNDRUkvTpp582WnRxfPMVHR1tbZOtd1q7dq31M/zkk0/Kz8/P2kem3sddmSUmJurWW2+VJGVmZrqcYex4w5Gmlt9okkGHduXKFZOSkmIkGUnmt7/97Q3188UXXxh/f38jyQwdOtRUV1c77a+urjZDhw41koy/v785fPiwG0YPVzIyMsylS5cabbN69Wor9969exubzea0n0y9z/z5861Mt2/f3mAbcvUs7777br2fPUdfffWVGTx4sJXrqlWr6rUhU8/01FNPWbllZWXV2//NN9+YQYMGWW12795drw3Zeq60tDQru88++6xZx5CnZ5o0aZKV5YIFCxpsc+bMGRMbG2u1+/vf/+60n2w9z9GjR13ue/fdd01AQICRZPr27dvg38xk2r7Ky8utn7f09PRmHeOuzObNm2c994oVK+rtLywstJ5n1KhRLT21eii4dHDjx4+3/kPdc889Zv/+/aakpMTl1xdffOGyr1mzZll9DR482GRnZ5s9e/aY7OxspzcMs2fPbsMzvDlFRUWZ8PBw8/jjj5vMzEyzc+dOs2/fPvPxxx+bP/7xjyY+Pt7KIzAw0OTl5TXYD5l6l+YUXIwhV08SFRVlIiIizG9+8xuzceNGU1hYaPbu3Wvy8vLMnDlzTLdu3aw8EhISzOXLlxvsh0w9z6lTp8wdd9xh/WH39NNPm/z8fFNUVGQyMjLMnXfeaeXy1FNPueyHbD3PmTNnTFBQkJFk4uLiWnQseXqeQ4cOmZCQEOt7P3bsWJOTk2OKi4tNYWGhWb16tfWzLMkkJSU12A/ZepYuXbqYlJQU8/rrr5vCwkJTVFRkcnJyzMSJE60sunbtaoqLi132QaZt5+OPPzYZGRnW18qVK63vb3x8vNO+jIwMl/24I7NvvvnGxMTEWG2feOIJk5+fbz799FOzdOlSc8sttxhJJjg42Ozdu/dbnzsFlw7O/h+puV9RUVEu+7p27ZqZOnVqo8dPmzbNXLt2re1O8CYVFRXVrDx79epltm3b5rIfMvUuzS24kKvnaO7PalpamqmqqnLZD5l6poMHD5rvf//7jeYydepUU1NT47IPsvU8r776qvW9b+jTz8aQp2fKy8sz3bt3b/K1+J577jFnzpxpsA+y9SydO3duNIvY2NhGiy3GkGlbSk9Pb9F7UlfcldmRI0dM3759XfYRGhpq3nvvPbecOwWXDq4l/7Glxgsudn/7299MamqqiYiIMIGBgSYiIsKkpqaaDz74oPVPCMYYY7788kvz2muvmYkTJ5qBAwea2267zfj7+5tbbrnFfO973zNpaWkmIyPDXLx4sVn9kal3aG7BxY5c219BQYFZuHCh+elPf2piYmJMeHi48ff3N2FhYWbAgAFm+vTpprCwsNn9kannuXDhglm5cqUZPny4CQ8PN4GBgaZXr15m4sSJJj8/v9n9kK3nuPvuu40k4+fnZyorK2+oD/L0PKdPnzbLly83iYmJpkePHiYgIMAEBweb6Oho8+CDD5q//vWvpra2tsl+yNYzZGVlmSlTppj+/ftbr72RkZFmzJgxZt26dY0Wuq9Hpq3PXQUXO3dkduHCBbN8+XIzdOhQExYWZkJCQswPfvAD88wzz5iKiopvc7pOfIxxsVIMAAAAAAAAbgh3KQIAAAAAAHAzCi4AAAAAAABuRsEFAAAAAADAzSi4AAAAAAAAuBkFFwAAAAAAADej4AIAAAAAAOBmFFwAAAAAAADcjIILAAAAAACAm1FwAQAAAAAAcDMKLgAAAAAAAG5GwQUAAKAJBQUF8vHxqfe1YMGC9h7at/Loo482eF4VFRXtPTQAALweBRcAAAAAAAA382/vAQAAAHiT9evXa9iwYZKknj17tvNovp0lS5Zo5syZkqQtW7Zo7ty57TwiAAA6DgouAAAALRAdHa24uLj2HoZbREZGKjIyUpJUVFTUzqMBAKBj4ZIiAAAAAAAAN6PgAgAAAAAA4GYUXAAAgFey2Wy6/fbb5ePjozFjxjTZ/sCBA9ZdeJYuXdoGI5QqKir0/PPPa8iQIerWrZs6deqk6OhojR49WqtWrdKxY8fqHXP9HZC2b9+uBx54QBEREQoODla/fv20aNEiXbx40em4Dz74QPfdd5/VLjY2Vi+99JJqamra4lQBAMB1KLgAAACvFBAQoEceeUSStG3bNlVWVjbafv369ZIkPz8/paent/r4Xn75ZcXExGjFihUqLi7WmTNndOXKFVVUVKigoEAzZ860xu/KsmXLlJSUpC1btui///2vLl++rNLSUr344otKSUnRhQsXZIzRjBkzdP/99ys3N9dqd+jQIb3wwgtKTU3VtWvXWv18AQCAMwouAADAaz322GOSpNraWr3xxhsu29lsNr311luSpJSUFGuh2NayaNEiPfvss7LZbAoLC9MLL7ygvLw8FRcXKz8/Xy+//LLi4+Pl4+Pjso/c3FzNnj1bI0aM0MaNG1VUVKStW7das3kKCwu1bNkyrVmzRmvXrtWYMWO0efNmffbZZ9qyZYtGjBghSdq6datef/31Vj1fAABQn48xxrT3IAAAAG7UqFGj9NFHH6lv3746fPhwg23+8pe/aPz48ZKknJwcpaWlteg5CgoKNHr0aEl1l/gkJia6bFtcXKxhw4aptrZWMTEx+vDDD9WrV68G2x4/frzePsciTFpamv785z/Lz8/PeuzatWtKSEjQrl271KVLF9lsNj355JNas2aNUz/V1dWKjY3V0aNHNXDgQP3rX/9q9Bw3bNigKVOmSJLKy8vVu3fvRtsDAIDGMcMFAAB4NfsslyNHjui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"text/plain": [ "
" ] @@ -394,9 +394,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "TBR: 8.337513e-04\n", + "TBR: 1.101485e-03\n", "\n", - "TBR std. dev.: 2.544477e-05\n", + "TBR std. dev.: 3.031488e-05\n", "\n" ] } diff --git a/data/processed_data.json b/data/processed_data.json index c4e54a0..4d63a44 100644 --- a/data/processed_data.json +++ b/data/processed_data.json @@ -1,6 +1,6 @@ { "modelled_TBR": { - "mean": 0.0008337512821171342, - "std_dev": 2.5444773403162218e-05 + "mean": 0.0011014846214768993, + "std_dev": 3.031488200303597e-05 } } \ No newline at end of file From 6e9b319d50902a265b0d7fbe3b3ff1eb259ec965 Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Thu, 27 Mar 2025 15:34:26 +0000 Subject: [PATCH 08/10] Tidied up code and improved formatting. --- analysis/neutron/openmc_model_PbLi.py | 74 ++++++++++++++++++--------- analysis/neutron/postprocessing.ipynb | 20 ++++---- data/processed_data.json | 4 +- 3 files changed, 63 insertions(+), 35 deletions(-) diff --git a/analysis/neutron/openmc_model_PbLi.py b/analysis/neutron/openmc_model_PbLi.py index 55456c6..37b1d80 100644 --- a/analysis/neutron/openmc_model_PbLi.py +++ b/analysis/neutron/openmc_model_PbLi.py @@ -4,6 +4,32 @@ import helpers import math +############################################################################ +# Functions + + +def calculate_breeder_depth(R, r, g, V): + """Calculates the height (H) of a cylindrical volume (radius R & volume V) with an + inserted inner cylinder (of radius r & gap from larger cylinder floor of g). + + Args: + R (float): Major radius of breeder volume cylinder (cm) + r (float): Radius of inner cylinder inserted into breeder volume (cm) + g (float): Gap between floor of breeder volume cylinder and inserted inner cylinder (cm) + V (float): Volume of breeder material(cm3) + + Returns: + (float): Depth of breeder material volume. + """ + v1 = math.pi * R**2 * g # Volume of cylinder beneath heater + v2 = V - v1 # Volume of annulus around heater + + h1 = g # Height below heater + h2 = v2 / (math.pi * (R**2 - r**2)) # Height of annulus around heater + + H = h1 + h2 # Total height for given volume + return H + def baby_geometry(x_c: float, y_c: float, z_c: float): """Returns the geometry for the BABY experiment. @@ -36,7 +62,7 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): lead_height = 4.00 lead_width = 8.00 lead_length = 16.00 - + heater_length = 25.40 heater_z = ( epoxy_thickness @@ -59,10 +85,14 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): ov_external_radius = 15.812 ## Calculated dimensions - PbLi_volume = 1000 # 1L = 1000 cm3 + PbLi_volume = 1000 # 1L = 1000 cm3 - PbLi_thickness = calculate_z_height(PbLi_radius, heater_radius, heater_gap, PbLi_volume) - cover_he_thickness = iv_height - PbLi_thickness # Gap between surface of PbLi and top boundary of inner vessel. + PbLi_thickness = calculate_breeder_depth( + PbLi_radius, heater_radius, heater_gap, PbLi_volume + ) + cover_he_thickness = ( + iv_height - PbLi_thickness + ) # Gap between surface of PbLi and top boundary of inner vessel. ## Source dimensions source_h = 50.00 @@ -144,7 +174,11 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): + z_c ) z_plane_12 = openmc.ZPlane( - epoxy_thickness + alumina_compressed_thickness + ov_base_thickness + ov_height + z_c + epoxy_thickness + + alumina_compressed_thickness + + ov_base_thickness + + ov_height + + z_c ) z_plane_13 = openmc.ZPlane( epoxy_thickness @@ -323,16 +357,6 @@ def baby_geometry(x_c: float, y_c: float, z_c: float): return sphere, PbLi_cell, cells -def calculate_z_height(R, r, g, V): - """Calculates the height (H) of a cylindrical volume (radius R & volume V) with an inserted inner cylinder (radius r & gap from larger cylinder floor of g).""" - v1 = math.pi * R**2 * g # Volume of cylinder beneath heater - v2 = V - v1 # Volume of annulus around heater - - h1 = g # Height below heater - h2 = v2 / (math.pi * (R**2 - r**2)) # Height of annulus around heater - - H = h1 + h2 # Total height for given volume - return H def baby_model(): """Returns an openmc model of the BABY experiment. @@ -417,11 +441,12 @@ def baby_model(): SS316L.add_element("Fe", 0.655599, "wo") # Lithium-Lead +# Composition from certificate of analysis provided with Lithium-Lead from Camex lithium_lead = openmc.Material(name="Lithium Lead") -lithium_lead.add_element("Pb", 0.993479 , "wo") -lithium_lead.add_element("Li", 0.0064 , "wo") -lithium_lead.add_element("Tl", 0.00002 , "wo") -lithium_lead.add_element("Zn", 0.000002 , "wo") +lithium_lead.add_element("Pb", 0.993479, "wo") +lithium_lead.add_element("Li", 0.0064, "wo") +lithium_lead.add_element("Tl", 0.00002, "wo") +lithium_lead.add_element("Zn", 0.000002, "wo") lithium_lead.add_element("Sn", 0.000002, "wo") lithium_lead.add_element("Sb", 0.000002, "wo") lithium_lead.add_element("Ni", 0.000001, "wo") @@ -430,7 +455,7 @@ def baby_model(): lithium_lead.add_element("Bi", 0.00008, "wo") lithium_lead.add_element("As", 0.000002, "wo") lithium_lead.add_element("Ag", 0.000008, "wo") -lithium_lead.set_density("g/cm3", 10.45431283) # Density at 550C +lithium_lead.set_density("g/cm3", 9.10411395) # Density at 600C # Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2) SS304 = openmc.Material(name="Stainless Steel 304") @@ -445,6 +470,8 @@ def baby_model(): SS304.add_element("Fe", 0.683450, "wo") SS304.set_density("g/cm3", 8.00) +# Central heater material +# Data from where??? heater_mat = openmc.Material(name="heater") heater_mat.add_element("C", 0.000990, "wo") heater_mat.add_element("Al", 0.003960, "wo") @@ -464,8 +491,8 @@ def baby_model(): # Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2 # https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/ furnace = openmc.Material(name="Furnace") -# Estimate average temperature of Firebrick to be around 300 C -# Firebrick.temperature = 273 + 300 +# Estimate average temperature of furnace insulation to be around 300 C +# furnace.temperature = 273 + 300 furnace.add_element("Al", 0.004, "ao") furnace.add_element("O", 0.666, "ao") furnace.add_element("Si", 0.240, "ao") @@ -488,6 +515,7 @@ def baby_model(): air.set_density("g/cm3", 0.0012) # epoxy +# Data from where?? epoxy = openmc.Material(name="Epoxy") epoxy.add_element("C", 0.70, "wo") epoxy.add_element("H", 0.08, "wo") @@ -546,4 +574,4 @@ def baby_model(): with open(processed_data_file, "w") as f: json.dump(existing_data, f, indent=4) - print(f"Processed data stored in {processed_data_file}") \ No newline at end of file + print(f"Processed data stored in {processed_data_file}") diff --git a/analysis/neutron/postprocessing.ipynb b/analysis/neutron/postprocessing.ipynb index a6bc62a..cdb71e1 100644 --- a/analysis/neutron/postprocessing.ipynb +++ b/analysis/neutron/postprocessing.ipynb @@ -34,6 +34,14 @@ "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rossmac/anaconda3/envs/BABY_1L_PbLi/lib/python3.12/site-packages/openmc/source.py:656: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n", + " warnings.warn(\"This class is deprecated in favor of 'IndependentSource'\", FutureWarning)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -203,14 +211,6 @@ "written cross sections xml file to /home/rossmac/Repositories/Ross-BABY-LiPb/analysis/neutron/cross_sections/cross_sections.xml\n", "setting OPENMC_CROSS_SECTIONS to /home/rossmac/Repositories/Ross-BABY-LiPb/analysis/neutron/cross_sections/cross_sections.xml\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rossmac/anaconda3/envs/BABY_1L_PbLi/lib/python3.12/site-packages/openmc/source.py:656: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n", - " warnings.warn(\"This class is deprecated in favor of 'IndependentSource'\", FutureWarning)\n" - ] } ], "source": [ @@ -394,9 +394,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "TBR: 1.101485e-03\n", + "TBR: 1.028456e-03\n", "\n", - "TBR std. dev.: 3.031488e-05\n", + "TBR std. dev.: 2.928848e-05\n", "\n" ] } diff --git a/data/processed_data.json b/data/processed_data.json index 4d63a44..de05710 100644 --- a/data/processed_data.json +++ b/data/processed_data.json @@ -1,6 +1,6 @@ { "modelled_TBR": { - "mean": 0.0011014846214768993, - "std_dev": 3.031488200303597e-05 + "mean": 0.0010284561041604611, + "std_dev": 2.92884836844823e-05 } } \ No newline at end of file From f48a37428c341656e10fd3df13e134762e11d951 Mon Sep 17 00:00:00 2001 From: Ross MacDonald Date: Mon, 31 Mar 2025 19:52:01 +0100 Subject: [PATCH 09/10] rm identifiers --- analysis/neutron/cross_sections/.gitignore:Zone.Identifier | 0 .../cross_sections/ENDFB-8.0-NNDC_Ag107.h5:Zone.Identifier | 0 .../cross_sections/ENDFB-8.0-NNDC_Ag109.h5:Zone.Identifier | 0 .../neutron/cross_sections/ENDFB-8.0-NNDC_Al27.h5:Zone.Identifier | 0 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a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr94.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr94.h5:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr96.h5:Zone.Identifier b/analysis/neutron/cross_sections/ENDFB-8.0-NNDC_Zr96.h5:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/analysis/neutron/cross_sections/cross_sections.xml:Zone.Identifier b/analysis/neutron/cross_sections/cross_sections.xml:Zone.Identifier deleted file mode 100644 index e69de29..0000000 From b04a2789ff6edbefcc097b410d539ce01f3932ed Mon Sep 17 00:00:00 2001 From: rossmacdonald98 <57036681+rossmacdonald98@users.noreply.github.com> Date: Mon, 31 Mar 2025 19:56:15 +0100 Subject: [PATCH 10/10] Update environment.yml MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Rémi Delaporte-Mathurin <40028739+RemDelaporteMathurin@users.noreply.github.com> --- environment.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/environment.yml b/environment.yml index 2ee71ff..a32ae23 100644 --- a/environment.yml +++ b/environment.yml @@ -8,7 +8,6 @@ dependencies: - numpy # add version tag - scipy # add version tag - matplotlib # add version tag - - math - sympy - pandas - pint-pandas