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Merge pull request #3 from suavecode/develop
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14 files changed

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‎BWB_CFD/BWB.py‎

Lines changed: 945 additions & 0 deletions
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‎BWB_CFD/base_data.txt‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
# AoA Mach CL CD
2+
0.00000000 0.30000000 0.00357426 0.00198925
3+
0.00000000 0.50000000 0.00385438 0.00262971
4+
0.00000000 0.70000000 0.00471753 0.00444076
5+
0.00000000 0.85000000 0.00856885 0.01859320
6+
0.05235988 0.30000000 0.20620500 0.00821743
7+
0.05235988 0.50000000 0.21761550 0.01013710
8+
0.05235988 0.70000000 0.24444750 0.01578280
9+
0.05235988 0.85000000 0.29654400 0.05575920
10+
0.10471976 0.30000000 0.40600150 0.02727500
11+
0.10471976 0.50000000 0.42532150 0.03379330
12+
0.10471976 0.70000000 0.46278650 0.05568500
13+
0.10471976 0.85000000 0.51038000 0.13394200

‎Regional_Jet_Optimization/Optimize.py‎

Lines changed: 42 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -17,30 +17,32 @@
1717
import matplotlib.pyplot as plt
1818
from SUAVE.Optimization import Nexus, carpet_plot
1919
import SUAVE.Optimization.Package_Setups.scipy_setup as scipy_setup
20+
import VyPy
21+
import pyOpt
2022
# ----------------------------------------------------------------------
2123
# Run the whole thing
2224
# ----------------------------------------------------------------------
2325
def main():
2426
problem = setup()
25-
26-
#output = problem.objective() #uncomment this line when using the default inputs
2727

28-
'''
2928
#uncomment these lines when you want to start an optimization problem from a different initial guess
29+
'''
3030
inputs = [1.28, 1.38]
3131
scaling = problem.optimization_problem.inputs[:,3] #have to rescale inputs to start problem from here
3232
scaled_inputs = np.multiply(inputs,scaling)
3333
problem.optimization_problem.inputs[:,1] = scaled_inputs
3434
'''
35-
#output = scipy_setup.SciPy_Solve(problem,solver='SLSQP')
3635

37-
36+
#optimize
37+
#output = scipy_setup.SciPy_Solve(problem,solver='SLSQP')
38+
#print output
39+
3840

3941
#variable_sweep(problem) #uncomment this to view some contours of the problem
4042
print 'fuel burn=', problem.summary.base_mission_fuelburn
4143
print 'fuel margin=', problem.all_constraints()
4244

43-
Plot_Mission.plot_mission(problem.results)
45+
#Plot_Mission.plot_mission(problem)
4446

4547
return
4648

@@ -101,10 +103,24 @@ def setup():
101103
[ 'fuel_burn' , 'summary.base_mission_fuelburn' ],
102104
[ 'design_range_fuel_margin' , 'summary.max_zero_fuel_margin' ],
103105
]
106+
'''
107+
#TASOPT imports
108+
from mission_B737_TASOPT import full_setup, mission_setup
109+
110+
configs, analyses = full_setup()
111+
nexus.vehicle_configurations = configs
112+
nexus.analyses = analyses
113+
nexus.mission = analyses.mission
114+
#nexus.mission = mission_setup(nexus.analyses)
115+
'''
116+
104117

105118
# -------------------------------------------------------------------
106119
# Vehicles
107120
# -------------------------------------------------------------------
121+
122+
#import Vehicle_TASOPT
123+
#nexus.vehicle_configurations = Vehicle_TASOPT.setup()
108124
nexus.vehicle_configurations = Vehicles.setup()
109125

110126

@@ -119,7 +135,7 @@ def setup():
119135
# -------------------------------------------------------------------
120136
nexus.missions = Missions.setup(nexus.analyses)
121137

122-
138+
123139
# -------------------------------------------------------------------
124140
# Procedure
125141
# -------------------------------------------------------------------
@@ -129,11 +145,11 @@ def setup():
129145
# Summary
130146
# -------------------------------------------------------------------
131147
nexus.summary = Data()
132-
148+
nexus.total_number_of_iterations = 0
133149
return nexus
134150

135151
def variable_sweep(problem):
136-
number_of_points=5
152+
number_of_points = 5
137153
outputs=carpet_plot(problem, number_of_points, 0, 0) #run carpet plot, suppressing default plots
138154
inputs =outputs.inputs
139155
objective=outputs.objective
@@ -153,14 +169,23 @@ def variable_sweep(problem):
153169
plt.xlabel('wing area (m^2)')
154170
plt.ylabel('cruise_altitude (km)')
155171

156-
'''
172+
173+
wing_1 = np.array([ 0.95, 0.9500000149011611, 0.95, 0.95, 0.9500000149011611, 0.95, 0.95, 0.9500000149011611, 0.95, 1.0830895945429801, 1.0830896094441413, 1.0830895945429801, 1.0830895945429801, 1.0830896094441413, 1.0830895945429801, 1.0830895945429801, 1.0830896094441413, 1.0830895945429801, 1.021757391325076, 1.0217574062262371, 1.021757391325076, 1.021757391325076, 1.0217574062262371, 1.021757391325076, 1.021757391325076, 1.0217574062262371, 1.021757391325076, 0.923430766276824, 0.9234307811779852, 0.923430766276824, 0.923430766276824, 0.9234307811779852, 0.923430766276824, 0.923430766276824, 0.9234307811779852, 0.923430766276824, 0.9458241453484385, 0.9458241602495997, 0.9458241453484385, 0.9458241453484385, 0.9458241602495997, 0.9458241453484385, 0.9458241453484385, 0.9458241602495997, 0.9458241453484385, 0.943053052966747, 0.9430530678679082, 0.943053052966747, 0.943053052966747, 0.9430530678679082, 0.943053052966747, 0.943053052966747, 0.9430530678679082, 0.943053052966747, 0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 1.0, 1.0, 1.0, 1.0, 1.0, 1.1000000000000001, 1.1000000000000001, 1.1000000000000001, 1.1000000000000001, 1.1000000000000001, 1.2, 1.2, 1.2, 1.2, 1.2, 1.3, 1.3, 1.3, 1.3, 1.3, 0.94305305 ])*100.
174+
alt_1 = [11. , 11. , 11.00000015, 11. , 11. , 11.00000015, 11. , 11. , 11.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 9. , 9.00000015, 9. , 10.25 , 11.5 , 12.75 , 14. , 9. , 10.25 , 11.5 , 12.75 , 14. , 9. , 10.25 , 11.5 , 12.75 , 14. , 9. , 10.25 , 11.5 , 12.75 , 14. , 9. , 10.25 , 11.5 , 12.75 , 14. , 9. ]
175+
176+
wing_2 = np.array([1.28, 1.2800000149011612, 1.28, 1.28, 1.2800000149011612, 1.28, 1.28, 1.2800000149011612, 1.28, 1.3, 1.3000000149011612, 1.3, 1.3, 1.3000000149011612, 1.3, 1.3, 1.3000000149011612, 1.3, 1.2512983213829585, 1.2512983362841197, 1.2512983213829585, 1.2512983213829585, 1.2512983362841197, 1.2512983213829585, 1.2512983213829585, 1.2512983362841197, 1.2512983213829585, 1.2027018036450188, 1.20270181854618, 1.2027018036450188, 1.2027018036450188, 1.20270181854618, 1.2027018036450188, 1.2027018036450188, 1.20270181854618, 1.2027018036450188, 0.9696195074510734, 0.9696195223522346, 0.9696195074510734, 0.9696195074510734, 0.9696195223522346, 0.9696195074510734, 0.9696195074510734, 0.9696195223522346, 0.9696195074510734, 0.9356100057984995, 0.9356100206996607, 0.9356100057984995, 0.9356100057984995, 0.9356100206996607, 0.9356100057984995, 0.9356100057984995, 0.9356100206996607, 0.9356100057984995, 0.9442290521247874, 0.9442290670259486, 0.9442290521247874, 0.9442290521247874, 0.9442290670259486, 0.9442290521247874, 0.9442290521247874, 0.9442290670259486, 0.9442290521247874, 0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 0.90000000000000002, 1.0, 1.0, 1.0, 1.0, 1.0, 1.1000000000000001, 1.1000000000000001, 0.94422905 ])* 100.
177+
178+
alt_2 = [ 13.8 , 13.8 , 13.80000015 , 13.8 , 13.8 , 13.80000015 , 13.8 , 13.8, 13.80000015 , 10.86578142 , 10.86578142, 10.86578157 , 10.86578142 , 10.86578142 , 10.86578157 , 10.86578142, 10.86578142 , 10.86578157 , 9. , 9. , 9.00000015 , 9., 9., 9.00000015 , 9. , 9. , 9.00000015 , 9.26299784 , 9.26299784 , 9.26299799 , 9.26299784 , 9.26299784 , 9.26299799 , 9.26299784 , 9.26299784 , 9.26299799 , 9.21358943 , 9.21358943 , 9.21358957 , 9.21358943 , 9.21358943 , 9.21358957 , 9.21358943 , 9.21358943 , 9.21358957 , 9. , 9., 9.00000015 , 9., 9. , 9.00000015 , 9., 9. , 9.00000015 , 9. , 9., 9.00000015 , 9. , 9. , 9.00000015 , 9. , 9., 9.00000015 , 9. , 10.25 , 11.5 , 12.75, 14., 9.,10.25 , 11.5 , 12.75 , 14. , 9. , 10.25 , 9. ]
179+
180+
181+
157182
#now plot optimization path (note that these data points were post-processed into a plottable format)
158-
wing_1 = [95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.2149828]
159-
alt_1 = [11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.52829047]
160-
wing_2 = [128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891]
161-
alt_2 = [13.8, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334]
183+
#wing_1 = [95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 95 , 95.00000149 , 95 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 106.674165 , 106.6741665 , 106.674165 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 105.6274294 , 105.6274309 , 105.6274294 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 106.9084316 , 106.9084331 , 106.9084316 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 110.520489 , 110.5204905 , 110.520489 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 113.2166831 , 113.2166845 , 113.2166831 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.1649262 , 114.1649277 , 114.1649262 , 114.2149828]
184+
#alt_1 = [11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 11.0 , 11.0 , 11.000000149011612, 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 9.540665954351425 , 9.540665954351425 , 9.540666103363037 , 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.023015652305284, 10.023015652305284, 10.023015801316896, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.190994033521863, 10.190994033521863, 10.190994182533474, 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.440582829327589, 10.440582829327589, 10.4405829783392 , 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.536514606250261, 10.536514606250261, 10.536514755261873, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.535957839878783, 10.535957839878783, 10.535957988890395, 10.52829047]
185+
#wing_2 = [128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 128 , 128.0000015, 128 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 130 , 130.0000015, 130 , 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 122.9564124, 122.9564139, 122.9564124, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.5744347, 116.5744362, 116.5744347, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891, 116.3530891, 116.3530906, 116.3530891]
186+
#alt_2 = [13.8, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 13.799999999999999, 13.799999999999999, 13.80000014901161, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.302562430674953, 11.302562430674953, 11.302562579686565, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.158808932491421, 11.158808932491421, 11.158809081503033, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.412913394878741, 11.412913394878741, 11.412913543890353, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334, 11.402627869388722, 11.402627869388722, 11.402628018400334]
162187

163-
188+
'''
164189
opt_1 = plt.plot(wing_1, alt_1, label='optimization path 1')
165190
init_1 = plt.plot(wing_1[0], alt_1[0], 'ko')
166191
final_1 = plt.plot(wing_1[-1], alt_1[-1], 'kx')
@@ -170,13 +195,13 @@ def variable_sweep(problem):
170195
final_2 = plt.plot(wing_2[-1], alt_2[-1], 'kx', label= 'final points')
171196
'''
172197
plt.legend(loc='upper left')
173-
plt.show()
198+
plt.show(block=True)
174199

175200

176201

177202
return
178203

179-
204+
180205
if __name__ == '__main__':
181206
main()
182207

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