regression updates

regression/scripts/Regional_Jet_Optimization/Optimize2.py

@@ -32,10 +32,10 @@ def main():
 
     actual = Data()
 
-    actual.obj  = 0.6597376929765844
-    actual.con  = 1.1472174
-    actual.obj2 = 0.6739732443282955
-    actual.con3 = 1.21059794
+    actual.obj  = 0.6472697676582065
+    actual.con  = 2.25820087
+    actual.obj2 = 0.6797630188805102
+    actual.con3 = 2.44649677
 
     print('Fuel Burn   =', obj)
     print('Fuel Margin =', con)    

regression/scripts/aerodynamics/aerodynamics.py

@@ -174,9 +174,9 @@ def main():
     #compute_aircraft_lift(conditions, configuration, geometry) 
 
     lift   = state.conditions.aerodynamics.lift_coefficient
-    lift_r =  np.array([-1.22715059, -0.48562864, -0.4312893 , -0.3442654 , -0.19773265,
-                         0.12732151,  0.37221063,  0.60102923,  0.90031948,  1.26978863,
-                         1.2846731 ])[:,None] 
+    lift_r =  np.array([-1.07644788, -0.42599003, -0.37832394, -0.30198719, -0.1734497 ,
+                        0.11168553,  0.32650055,  0.52721862,  0.78975393,  1.11384968,
+                        1.12690623])[:,None] 
 
     print('lift = ', lift)
 
@@ -244,9 +244,9 @@ def reg_values():
                         1.97066436e-04, 3.69256330e-05, 1.88894447e-09, 3.54759275e-11,
                         6.37419857e-05, 3.95877837e-03, 7.35755549e-14]]).T   
 
-    cd_i_r = np.array([[0.06105457, 0.00969473, 0.00833303, 0.00485191, 0.00161951,
-                        0.00064978, 0.00571189, 0.0154989 , 0.03335475, 0.06541178,
-                        0.06815139]]).T   
+    cd_i_r = np.array([[0.02050313, 0.00826837, 0.0073281 , 0.00489907, 0.00166702,
+                        0.00107478, 0.00470895, 0.01120806, 0.02173496, 0.03730588,
+                        0.04865803]]).T   
 
     cd_m_r = np.array([[ 0.0011752,0.0011752,0.0011752,0.0011752,0.0011752,
                          0.0011752,0.0011752,0.0011752,0.0011752,0.0011752,
@@ -275,9 +275,9 @@ def reg_values():
                                   0.00653004,0.00501665,0.00599058,0.00759737,
                                   0.00600963,0.00556283,0.00602578]]).T
 
-    cd_tot_r        = np.array([[0.08031175, 0.02817425, 0.03669165, 0.02277785, 0.02127734,
-                                 0.0162443 , 0.02443606, 0.03891821, 0.05248558, 0.08775736,
-                                 0.08829963]]).T 
+    cd_tot_r        = np.array([[0.03894927, 0.02671936, 0.03566663, 0.02282595, 0.0213258 ,
+                                 0.0166778 , 0.02341306, 0.03454155, 0.0406334 , 0.05908934,
+                                 0.0684164 ]]).T 
 
     return cd_c_r, cd_i_r, cd_m_r, cd_m_fuse_base_r, cd_m_fuse_up_r, \
            cd_m_nac_base_r, cd_m_ctrl_r, cd_p_fuse_r, cd_p_wing_r, cd_tot_r

regression/scripts/lifting_line/lifting_line.py

@@ -153,9 +153,9 @@ def main():
     # Test compute Lift
     # -------------------------------------------------------------------- 
     lift = state.conditions.aerodynamics.lift_coefficient
-    lift_r = np.array([-1.27291303,-0.9966004 ,-0.72028776,-0.44397513,
-                       -0.16766249, 0.10865015, 0.38496278, 0.66127542,
-                       0.93758805, 1.21390069, 1.49021333])[:,None]  
+    lift_r = np.array([-1.11659038, -0.87421087, -0.63183137, -0.38945186, -0.14707236,
+                        0.09530715,  0.33768665,  0.58006616,  0.82244566,  1.06482517,
+                        1.30720467])[:,None]  
 
     print('lift = ', lift)
 

regression/scripts/noise_optimization/Noise_Test.py

@@ -45,7 +45,7 @@ def main():
     noise_cumulative_margin = objectives[0]
 
     actual = Data()    
-    actual.noise_cumulative_margin = 20.98780244500979
+    actual.noise_cumulative_margin = 21.085590711765263
 
     error = Data()
     error.noise_cumulative_margin = abs(actual.noise_cumulative_margin - noise_cumulative_margin)/actual.noise_cumulative_margin

regression/scripts/noise_optimization/Procedure.py

@@ -233,8 +233,8 @@ def estimate_clmax(nexus):
     # Condition to CLmax calculation: 90KTAS @ 10000ft, ISA
     conditions = Data()
     conditions.freestream = Data()
-    conditions.freestream.density           = 0.90477283
-    conditions.freestream.dynamic_viscosity = 1.69220918e-05
+    conditions.freestream.density           = [0.90477283]
+    conditions.freestream.dynamic_viscosity = [1.69220918e-05]
     conditions.freestream.velocity          = 90. * Units.knots
 
     #Takeoff CL_max

regression/scripts/segments/segment_test.py

@@ -60,22 +60,22 @@ def main():
     descent_throttle_2 = results.segments.descent_2.conditions.propulsion.throttle[3][0]
 
     # Truth values 
-    climb_throttle_1_truth   = 0.9857468628944336 
-    climb_throttle_2_truth   = 0.7952188198483993 
-    climb_throttle_3_truth   = 0.5066828579416367 
-    climb_throttle_4_truth   = 0.878134600022338 
-    climb_throttle_5_truth   = 1.021806039249955 
-    climb_throttle_6_truth   = 0.7136864572517849 
-    climb_throttle_7_truth   = 0.8877493204092387 
-    climb_throttle_8_truth   = 1.0868679444818625 
-    cruise_CL_1_truth        = 0.634777526547744 
-    cruise_CL_2_truth        = 0.6250634424249245 
-    cruise_CL_3_truth        = 0.7081424772096907 
-    descent_throttle_1_truth = 0.17517658209416148 
-    single_pt_CL_1_truth     = 0.2588077147541633 
-    single_pt_CL_2_truth     = 0.25874688162066317
-    loiter_CL_truth          = 0.5273996120576298 
-    descent_throttle_2_truth = 0.15532873504026581 
+    climb_throttle_1_truth   = 1.2161368583680352 
+    climb_throttle_2_truth   = 1.0286455601603097
+    climb_throttle_3_truth   = 0.4914388256260948 
+    climb_throttle_4_truth   = 0.8571027568586671
+    climb_throttle_5_truth   = 0.9894122714719386 
+    climb_throttle_6_truth   = 0.6762603498943198 
+    climb_throttle_7_truth   = 0.8519667257766178 
+    climb_throttle_8_truth   = 1.0464547236050787 
+    cruise_CL_1_truth        = 0.6341101279210396 
+    cruise_CL_2_truth        = 0.6257545720064928 
+    cruise_CL_3_truth        = 0.705480454862047 
+    descent_throttle_1_truth = 0.0807508137286019 
+    single_pt_CL_1_truth     = 0.25937963745079545 
+    single_pt_CL_2_truth     = 1.4915004256240918
+    loiter_CL_truth          = 0.5284986930857494 
+    descent_throttle_2_truth = 0.13671073467701336
 
     # Store errors 
     error = Data()

regression/scripts/sizing_loop/sizing_loop.py

@@ -70,11 +70,11 @@ def main():
     results  = nexus.results
     err      = nexus.sizing_loop.norm_error
 
-    err_true = 0.0017121126327114442
+    err_true = 0.0035657111473885845
     error    = abs((err-err_true)/err_true)
 
     data_inputs, data_outputs, read_success = read_sizing_residuals(sizing_loop, problem.inputs)
-    check_read_res = -0.08366387990897325
+    check_read_res = -0.11199816214846839
 
     error_res      = abs((data_outputs[1][0]-check_read_res)/check_read_res)
 

regression/scripts/slipstream/slipstream_test.py

@@ -39,7 +39,7 @@ def main():
 
     # lift coefficient  
     lift_coefficient              = results.segments.cruise.conditions.aerodynamics.lift_coefficient[1][0]
-    lift_coefficient_true         = 0.47277854912172756
+    lift_coefficient_true         = 0.47195035931255075
     print(lift_coefficient)
     diff_CL                       = np.abs(lift_coefficient  - lift_coefficient_true) 
     print('CL difference')
@@ -48,21 +48,21 @@ def main():
 
     # sectional lift coefficient check
     sectional_lift_coeff            = results.segments.cruise.conditions.aerodynamics.lift_breakdown.inviscid_wings_sectional_lift[0]
-    sectional_lift_coeff_true       = np.array([1.30785100e-01, 1.29573061e-01, 1.25529913e-01, 1.19252449e-01,
-                                                1.11143124e-01, 1.01521133e-01, 9.06611205e-02, 7.88194883e-02,
-                                                6.62645393e-02, 5.33188318e-02, 4.04067325e-02, 2.80607103e-02,
-                                                1.68588961e-02, 7.56561908e-03, 1.51112918e-03, 1.30847294e-01,
-                                                1.29743713e-01, 1.25770313e-01, 1.19520253e-01, 1.11404695e-01,
-                                                1.01755340e-01, 9.08577590e-02, 7.89758953e-02, 6.63824787e-02,
-                                                5.34023668e-02, 4.04611083e-02, 2.80919505e-02, 1.68735644e-02,
-                                                7.57031938e-03, 1.51169375e-03, 2.28686698e-03, 2.39010336e-03,
-                                                2.48594024e-03, 2.56050765e-03, 2.59254004e-03, 2.56508092e-03,
-                                                2.46776333e-03, 2.29377774e-03, 2.04285167e-03, 1.72410497e-03,
-                                                1.35485739e-03, 9.60298290e-04, 5.77024161e-04, 2.55662292e-04,
-                                                5.08508154e-05, 2.28750242e-03, 2.39200290e-03, 2.48885107e-03,
-                                                2.56406472e-03, 2.59638239e-03, 2.56889806e-03, 2.47131117e-03,
-                                                2.29687440e-03, 2.04537787e-03, 1.72600969e-03, 1.35615751e-03,
-                                                9.61070620e-04, 5.77392626e-04, 2.55779663e-04, 5.08646276e-05,
+    sectional_lift_coeff_true       = np.array([1.46119243e-01, 1.44958967e-01, 1.40691164e-01, 1.33934602e-01,
+                                                1.25106762e-01, 1.14539760e-01, 1.02522103e-01, 8.93282976e-02,
+                                                7.52534595e-02, 6.06620465e-02, 4.60423586e-02, 3.20130513e-02,
+                                                1.92496660e-02, 8.64213099e-03, 1.72540808e-03, 1.46196644e-01,
+                                                1.45171946e-01, 1.40992335e-01, 1.34271561e-01, 1.25437402e-01,
+                                                1.14837212e-01, 1.02773015e-01, 8.95287753e-02, 7.54052650e-02,
+                                                6.07699716e-02, 4.61128354e-02, 3.20536469e-02, 1.92687646e-02,
+                                                8.64825925e-03, 1.72614457e-03, 4.38671022e-03, 4.55001036e-03,
+                                                4.66682694e-03, 4.72118604e-03, 4.68980769e-03, 4.55491812e-03,
+                                                4.30701096e-03, 3.94199437e-03, 3.46475306e-03, 2.89241422e-03,
+                                                2.25312811e-03, 1.58632336e-03, 9.48729145e-04, 4.19170945e-04,
+                                                8.32865503e-05, 4.38914132e-03, 4.55717251e-03, 4.67748090e-03,
+                                                4.73370729e-03, 4.70277821e-03, 4.56728781e-03, 4.31807938e-03,
+                                                3.95133440e-03, 3.47215673e-03, 2.89786562e-03, 2.25677825e-03,
+                                                1.58845898e-03, 9.49736436e-04, 4.19489381e-04, 8.33239105e-05,
                                                 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
                                                 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
                                                 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
@@ -146,7 +146,7 @@ def base_analysis(vehicle):
 
     # ------------------------------------------------------------------
     #  Weights
-    weights = SUAVE.Analyses.Weights.Weights_Tube_Wing()
+    weights = SUAVE.Analyses.Weights.Weights_Transport()
     weights.vehicle = vehicle
     analyses.append(weights)
 
@@ -216,10 +216,10 @@ def mission_setup(analyses,vehicle):
     base_segment.process.iterate.initials.initialize_battery = SUAVE.Methods.Missions.Segments.Common.Energy.initialize_battery
     base_segment.process.iterate.conditions.planet_position  = SUAVE.Methods.skip
     base_segment.state.numerics.number_control_points        = 2
-    base_segment.process.iterate.unknowns.network            = vehicle.propulsors.propulsor.unpack_unknowns
-    base_segment.process.iterate.residuals.network           = vehicle.propulsors.propulsor.residuals
+    base_segment.process.iterate.unknowns.network            = vehicle.propulsors.battery_propeller.unpack_unknowns
+    base_segment.process.iterate.residuals.network           = vehicle.propulsors.battery_propeller.residuals
     base_segment.state.unknowns.propeller_power_coefficient  = 0.2 * ones_row(1) 
-    bat                                                      = vehicle.propulsors.propulsor.battery 
+    bat                                                      = vehicle.propulsors.battery_propeller.battery 
     base_segment.state.unknowns.battery_voltage_under_load   = bat.max_voltage * ones_row(1)  
     base_segment.state.residuals.network                     = 0. * ones_row(2) 
     base_segment.max_energy                                  = bat.max_energy 

regression/scripts/solar_network/solar_network.py

@@ -73,10 +73,10 @@ def main():
     energy  = results.segments.cruise1.conditions.propulsion.battery_energy[8,0]  
 
     # Truth results
-    truth_F   = 106.17937888428949  
-    truth_rpm = 160.76100043739908 
-    truth_i   = 131.4126494489281  
-    truth_bat = 88521341.18491648
+    truth_F   = 362.85769809909533 
+    truth_rpm = 253.22473731901562 
+    truth_i   = 142.1902845645215
+    truth_bat = 100157292.65998347
 
     print('battery energy')
     print(energy)

regression/scripts/sweeps/test_sweeps.py

@@ -30,7 +30,7 @@ def main():
         [ 'cruise_altitude' ,  11, (   10   ,   13.   ) ,   10.  , Units.km]])
 
     outputs_sweep    = linear_sweep(problem)
-    truth_obj_sweeps = [[6739.73244319, 6595.26198099]]
+    truth_obj_sweeps = [[6797.67968084, 6522.77993438]]
 
     #print outputs_sweep
     max_err_sweeps = (np.max(np.abs(outputs_sweep['objective']-truth_obj_sweeps )/truth_obj_sweeps))
@@ -40,7 +40,7 @@ def main():
     outputs_carpet = variable_sweep(problem)
 
     #print outputs_carpet
-    truth_obj_carp  =  [[6641.92992406, 6631.87070262],[6923.62495863, 6476.54755072]]
+    truth_obj_carp  =  [[6551.06765041, 6451.17553565],[6997.00098209, 6500.79231882]]
     max_err_carp    = np.max(np.abs(outputs_carpet['objective']-truth_obj_carp)/truth_obj_carp) 
     print(' max_err_carp = ',  max_err_carp)
     assert(max_err_carp<1e-6)

regression/scripts/variable_cruise_distance/variable_cruise_distance.py

@@ -38,14 +38,14 @@ def main():
 
     plot_results(results)
 
-    distance_regression = 4178523.102867511
+    distance_regression = 4255957.25870598
     distance_calc       = results.conditions.frames.inertial.position_vector[-1,0]
     error_distance      = abs((distance_regression - distance_calc )/distance_regression)
     assert error_distance < 1e-6
 
     error_weight = abs(mission.target_landing_weight - results.conditions.weights.total_mass[-1,0])
     print('landing weight error' , error_weight)
-    assert error_weight < 1e-4
+    assert error_weight < 1e-3
 
     return
 

trunk/SUAVE/Methods/Missions/Segments/converge_root.py

@@ -3,7 +3,6 @@
 # 
 # Created:  Jul 2014, SUAVE Team
 # Modified: Jan 2016, E. Botero
-#           May 2019, E. Botero
 
 # ----------------------------------------------------------------------
 #  Imports
@@ -36,27 +35,13 @@ def converge_root(segment):
     Outputs:
     state.unknowns                     [Any]
     segment.state.numerics.converged   [Unitless]
-    segment.state.normalization_factor [Array]
 
     Properties Used:
     N/A
     """       
 
     unknowns = segment.state.unknowns.pack_array()
 
-    # Find the normalization factors
-    segment.state.unknowns_normalization_factor  = unknowns*1.
-    segment.state.unknowns_normalization_factor[segment.state.unknowns_normalization_factor==0] = 1e-16 
-
-    # Run one iteration to get the scaling
-    segment.process.iterate(segment)
-    segment.state.residual_normalization_factor = 1*segment.state.residuals.pack_array() 
-    segment.state.residual_normalization_factor[segment.state.residual_normalization_factor==0] = 1e-16
-
-    # Normalize the unknowns
-    unknowns = unknowns/segment.state.unknowns_normalization_factor
-
-
     try:
         root_finder = segment.settings.root_finder
     except AttributeError:
@@ -72,10 +57,8 @@ def converge_root(segment):
         print("Segment did not converge. Segment Tag: " + segment.tag)
         print("Error Message:\n" + msg)
         segment.state.numerics.converged = False
-        segment.converged = False
     else:
         segment.state.numerics.converged = True
-        segment.converged = True
 
 
     return
@@ -96,28 +79,22 @@ def iterate(unknowns, segment):
     N/A
 
     Inputs:
-    state.unknowns                     [Data]
-    segment.process.iterate            [Data]
-    segment.state.normalization_factor [array]
+    state.unknowns                [Data]
+    segment.process.iterate       [Data]
 
     Outputs:
     residuals                     [Unitless]
 
     Properties Used:
     N/A
     """       
-
-    unknowns_normal = segment.state.unknowns_normalization_factor * unknowns
-
-    if isinstance(unknowns_normal,array_type):
-        segment.state.unknowns.unpack_array(unknowns_normal)
+    if isinstance(unknowns,array_type):
+        segment.state.unknowns.unpack_array(unknowns)
     else:
-        segment.state.unknowns_normal = unknowns_normal
+        segment.state.unknowns = unknowns
 
     segment.process.iterate(segment)
 
     residuals = segment.state.residuals.pack_array()
-
-    residuals_normalized = residuals/segment.state.residual_normalization_factor
 
-    return residuals_normalized 
+    return residuals