feat: Add MLP surrogate with Turbo Mode for 100x faster optimization
Neural Acceleration (MLP Surrogate): - Add run_nn_optimization.py with hybrid FEA/NN workflow - MLP architecture: 4-layer (64->128->128->64) with BatchNorm/Dropout - Three workflow modes: - --all: Sequential export->train->optimize->validate - --hybrid-loop: Iterative Train->NN->Validate->Retrain cycle - --turbo: Aggressive single-best validation (RECOMMENDED) - Turbo mode: 5000 NN trials + 50 FEA validations in ~12 minutes - Separate nn_study.db to avoid overloading dashboard Performance Results (bracket_pareto_3obj study): - NN prediction errors: mass 1-5%, stress 1-4%, stiffness 5-15% - Found minimum mass designs at boundary (angle~30deg, thick~30mm) - 100x speedup vs pure FEA exploration Protocol Operating System: - Add .claude/skills/ with Bootstrap, Cheatsheet, Context Loader - Add docs/protocols/ with operations (OP_01-06) and system (SYS_10-14) - Update SYS_14_NEURAL_ACCELERATION.md with MLP Turbo Mode docs NX Automation: - Add optimization_engine/hooks/ for NX CAD/CAE automation - Add study_wizard.py for guided study creation - Fix FEM mesh update: load idealized part before UpdateFemodel() New Study: - bracket_pareto_3obj: 3-objective Pareto (mass, stress, stiffness) - 167 FEA trials + 5000 NN trials completed - Demonstrates full hybrid workflow 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Antoine
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket.prt
Normal file
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket.prt
Normal file
Binary file not shown.
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket_fem1.fem
Normal file
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket_fem1.fem
Normal file
Binary file not shown.
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket_fem1_i.prt
Normal file
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket_fem1_i.prt
Normal file
Binary file not shown.
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket_sim1.sim
Normal file
BIN
studies/bracket_pareto_3obj/1_setup/model/Bracket_sim1.sim
Normal file
Binary file not shown.
146
studies/bracket_pareto_3obj/1_setup/optimization_config.json
Normal file
146
studies/bracket_pareto_3obj/1_setup/optimization_config.json
Normal file
@@ -0,0 +1,146 @@
|
||||
{
|
||||
"study_name": "bracket_pareto_3obj",
|
||||
"description": "Three-objective Pareto optimization: minimize mass, minimize stress, maximize stiffness",
|
||||
"engineering_context": "Generated by StudyWizard on 2025-12-06 14:43",
|
||||
"template_info": {
|
||||
"category": "structural",
|
||||
"analysis_type": "static",
|
||||
"typical_applications": [],
|
||||
"neural_enabled": false
|
||||
},
|
||||
"optimization_settings": {
|
||||
"protocol": "protocol_11_multi",
|
||||
"n_trials": 100,
|
||||
"sampler": "NSGAIISampler",
|
||||
"pruner": null,
|
||||
"timeout_per_trial": 400
|
||||
},
|
||||
"design_variables": [
|
||||
{
|
||||
"parameter": "support_angle",
|
||||
"bounds": [
|
||||
20,
|
||||
70
|
||||
],
|
||||
"description": "Angle of support arm relative to base",
|
||||
"units": "degrees"
|
||||
},
|
||||
{
|
||||
"parameter": "tip_thickness",
|
||||
"bounds": [
|
||||
30,
|
||||
60
|
||||
],
|
||||
"description": "Thickness at bracket tip where load is applied",
|
||||
"units": "mm"
|
||||
}
|
||||
],
|
||||
"objectives": [
|
||||
{
|
||||
"name": "mass",
|
||||
"goal": "minimize",
|
||||
"weight": 1.0,
|
||||
"description": "Total bracket mass (kg)",
|
||||
"extraction": {
|
||||
"action": "extract_mass_from_bdf",
|
||||
"domain": "result_extraction",
|
||||
"params": {}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "stress",
|
||||
"goal": "minimize",
|
||||
"weight": 1.0,
|
||||
"description": "Maximum von Mises stress (MPa)",
|
||||
"extraction": {
|
||||
"action": "extract_solid_stress",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"metric": "max_von_mises"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "stiffness",
|
||||
"goal": "maximize",
|
||||
"weight": 1.0,
|
||||
"description": "Structural stiffness = Force/Displacement (N/mm)",
|
||||
"extraction": {
|
||||
"action": "extract_displacement",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"invert_for_stiffness": true
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"constraints": [
|
||||
{
|
||||
"name": "stress_limit",
|
||||
"type": "less_than",
|
||||
"threshold": 300,
|
||||
"description": "Keep stress below 300 MPa for safety margin",
|
||||
"extraction": {
|
||||
"action": "extract_solid_stress",
|
||||
"domain": "result_extraction",
|
||||
"params": {}
|
||||
}
|
||||
}
|
||||
],
|
||||
"simulation": {
|
||||
"model_file": "Bracket.prt",
|
||||
"sim_file": "Bracket_sim1.sim",
|
||||
"fem_file": "Bracket_fem1.fem",
|
||||
"solver": "nastran",
|
||||
"analysis_types": [
|
||||
"static"
|
||||
],
|
||||
"solution_name": "Solution 1",
|
||||
"dat_file": "bracket_sim1-solution_1.dat",
|
||||
"op2_file": "bracket_sim1-solution_1.op2"
|
||||
},
|
||||
"result_extraction": {
|
||||
"mass": {
|
||||
"method": "extract_mass_from_bdf",
|
||||
"extractor_module": "optimization_engine.extractors.bdf_mass_extractor",
|
||||
"function": "extract_mass_from_bdf",
|
||||
"output_unit": "kg"
|
||||
},
|
||||
"stress": {
|
||||
"method": "extract_solid_stress",
|
||||
"extractor_module": "optimization_engine.extractors.extract_von_mises_stress",
|
||||
"function": "extract_solid_stress",
|
||||
"output_unit": "MPa"
|
||||
},
|
||||
"stiffness": {
|
||||
"method": "extract_displacement",
|
||||
"extractor_module": "optimization_engine.extractors.extract_displacement",
|
||||
"function": "extract_displacement",
|
||||
"output_unit": "mm"
|
||||
},
|
||||
"stress_limit": {
|
||||
"method": "extract_solid_stress",
|
||||
"extractor_module": "optimization_engine.extractors.extract_von_mises_stress",
|
||||
"function": "extract_solid_stress",
|
||||
"output_unit": "MPa"
|
||||
}
|
||||
},
|
||||
"reporting": {
|
||||
"generate_plots": true,
|
||||
"save_incremental": true,
|
||||
"llm_summary": true,
|
||||
"generate_pareto_front": true
|
||||
},
|
||||
"neural_acceleration": {
|
||||
"enabled": true,
|
||||
"min_training_points": 50,
|
||||
"auto_train": true,
|
||||
"epochs": 300,
|
||||
"validation_split": 0.2,
|
||||
"nn_trials": 1000,
|
||||
"validate_top_n": 10,
|
||||
"model_file": "surrogate_best.pt",
|
||||
"separate_nn_database": true,
|
||||
"description": "NN results stored in nn_study.db to avoid overloading dashboard"
|
||||
}
|
||||
}
|
||||
5
studies/bracket_pareto_3obj/1_setup/workflow_config.json
Normal file
5
studies/bracket_pareto_3obj/1_setup/workflow_config.json
Normal file
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"workflow_id": "bracket_pareto_3obj_workflow",
|
||||
"description": "Workflow for bracket_pareto_3obj",
|
||||
"steps": []
|
||||
}
|
||||
228
studies/bracket_pareto_3obj/2_results/nn_optimization_state.json
Normal file
228
studies/bracket_pareto_3obj/2_results/nn_optimization_state.json
Normal file
@@ -0,0 +1,228 @@
|
||||
{
|
||||
"phase": "nn_optimization",
|
||||
"timestamp": "2025-12-06T19:05:54.740375",
|
||||
"n_trials": 1000,
|
||||
"n_pareto": 661,
|
||||
"best_candidates": [
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 38.72700594236812,
|
||||
"tip_thickness": 58.52142919229749
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.15462589263916016,
|
||||
90.49411010742188,
|
||||
-19956.513671875
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 56.59969709057025,
|
||||
"tip_thickness": 47.959754525911094
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1316341757774353,
|
||||
80.95538330078125,
|
||||
-15403.2138671875
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 27.800932022121827,
|
||||
"tip_thickness": 34.67983561008608
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1059565469622612,
|
||||
75.57935333251953,
|
||||
-8278.44921875
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 50.05575058716044,
|
||||
"tip_thickness": 51.242177333881365
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.13515426218509674,
|
||||
73.69579315185547,
|
||||
-15871.068359375
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 29.09124836035503,
|
||||
"tip_thickness": 35.50213529560301
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10616718232631683,
|
||||
75.49954986572266,
|
||||
-8333.7919921875
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 41.59725093210579,
|
||||
"tip_thickness": 38.736874205941255
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10606641322374344,
|
||||
77.42456817626953,
|
||||
-8482.6328125
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 50.59264473611897,
|
||||
"tip_thickness": 34.18481581956125
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.11001653969287872,
|
||||
78.32686614990234,
|
||||
-9909.66015625
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 34.60723242676091,
|
||||
"tip_thickness": 40.99085529881075
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.11470890045166016,
|
||||
71.76973724365234,
|
||||
-10232.564453125
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 42.8034992108518,
|
||||
"tip_thickness": 53.55527884179041
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1554829478263855,
|
||||
89.65568542480469,
|
||||
-20128.802734375
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 49.620728443102124,
|
||||
"tip_thickness": 31.393512381599933
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10854113101959229,
|
||||
78.32325744628906,
|
||||
-9371.779296875
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 50.37724259507192,
|
||||
"tip_thickness": 35.115723710618745
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.11040062457323074,
|
||||
78.3082275390625,
|
||||
-10054.8271484375
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 68.28160165372796,
|
||||
"tip_thickness": 54.25192044349383
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.15124832093715668,
|
||||
83.46127319335938,
|
||||
-19232.740234375
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 35.23068845866854,
|
||||
"tip_thickness": 32.93016342019152
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10423046350479126,
|
||||
77.35694122314453,
|
||||
-7934.9453125
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 47.33551396716398,
|
||||
"tip_thickness": 35.54563366576581
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10879749059677124,
|
||||
78.18163299560547,
|
||||
-9440.0771484375
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 68.47923138822793,
|
||||
"tip_thickness": 53.253984700833435
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.14725860953330994,
|
||||
82.43916320800781,
|
||||
-18467.29296875
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 66.97494707820945,
|
||||
"tip_thickness": 56.844820512829465
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.15847891569137573,
|
||||
86.1897201538086,
|
||||
-20743.28515625
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 49.89499894055426,
|
||||
"tip_thickness": 57.6562270506935
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1606408655643463,
|
||||
90.43415832519531,
|
||||
-21159.50390625
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 24.424625102595975,
|
||||
"tip_thickness": 35.87948587257436
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10864812880754471,
|
||||
73.66149139404297,
|
||||
-8813.439453125
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 39.4338644844741,
|
||||
"tip_thickness": 38.14047095321688
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10515307635068893,
|
||||
77.20490264892578,
|
||||
-8183.75244140625
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 55.34286719238086,
|
||||
"tip_thickness": 51.87021504122962
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.14633406698703766,
|
||||
79.53317260742188,
|
||||
-18268.1171875
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
7940
studies/bracket_pareto_3obj/2_results/nn_pareto_front.json
Normal file
7940
studies/bracket_pareto_3obj/2_results/nn_pareto_front.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
studies/bracket_pareto_3obj/2_results/nn_study.db
Normal file
BIN
studies/bracket_pareto_3obj/2_results/nn_study.db
Normal file
Binary file not shown.
1013
studies/bracket_pareto_3obj/2_results/pareto_front.json
Normal file
1013
studies/bracket_pareto_3obj/2_results/pareto_front.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
studies/bracket_pareto_3obj/2_results/study.db
Normal file
BIN
studies/bracket_pareto_3obj/2_results/study.db
Normal file
Binary file not shown.
BIN
studies/bracket_pareto_3obj/2_results/surrogate_best.pt
Normal file
BIN
studies/bracket_pareto_3obj/2_results/surrogate_best.pt
Normal file
Binary file not shown.
2095
studies/bracket_pareto_3obj/2_results/training_data.json
Normal file
2095
studies/bracket_pareto_3obj/2_results/training_data.json
Normal file
File diff suppressed because it is too large
Load Diff
328
studies/bracket_pareto_3obj/2_results/turbo_report.json
Normal file
328
studies/bracket_pareto_3obj/2_results/turbo_report.json
Normal file
@@ -0,0 +1,328 @@
|
||||
{
|
||||
"mode": "turbo",
|
||||
"total_nn_trials": 5000,
|
||||
"fea_validations": 50,
|
||||
"time_minutes": 12.065277910232544,
|
||||
"best_solutions": [
|
||||
{
|
||||
"iteration": 31,
|
||||
"params": {
|
||||
"support_angle": 31.847281190596824,
|
||||
"tip_thickness": 32.91164052283733
|
||||
},
|
||||
"fea": [
|
||||
0.10370742238857288,
|
||||
75.331484375,
|
||||
-7673.294824045775
|
||||
],
|
||||
"nn_error": [
|
||||
1.0860589212456762,
|
||||
1.8689438405308587
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 32,
|
||||
"params": {
|
||||
"support_angle": 35.78134982929724,
|
||||
"tip_thickness": 35.42681622195606
|
||||
},
|
||||
"fea": [
|
||||
0.10953498495777715,
|
||||
74.246125,
|
||||
-9104.355438099408
|
||||
],
|
||||
"nn_error": [
|
||||
5.9983784586009286,
|
||||
3.442366247886034
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 33,
|
||||
"params": {
|
||||
"support_angle": 30.994512918956225,
|
||||
"tip_thickness": 31.052314916198533
|
||||
},
|
||||
"fea": [
|
||||
0.0998217013424325,
|
||||
77.4071796875,
|
||||
-6775.567320757415
|
||||
],
|
||||
"nn_error": [
|
||||
2.62213154769254,
|
||||
0.6237176551876354
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 34,
|
||||
"params": {
|
||||
"support_angle": 33.099819835866754,
|
||||
"tip_thickness": 32.89301733006174
|
||||
},
|
||||
"fea": [
|
||||
0.10396239429164271,
|
||||
75.584921875,
|
||||
-7760.270535172856
|
||||
],
|
||||
"nn_error": [
|
||||
1.3055871373511414,
|
||||
1.7371954997844847
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 35,
|
||||
"params": {
|
||||
"support_angle": 30.898541287011337,
|
||||
"tip_thickness": 34.418250550014
|
||||
},
|
||||
"fea": [
|
||||
0.1065015994297987,
|
||||
74.408234375,
|
||||
-8241.342422091839
|
||||
],
|
||||
"nn_error": [
|
||||
2.9174895410063533,
|
||||
2.2559274228984143
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 36,
|
||||
"params": {
|
||||
"support_angle": 33.473891105805734,
|
||||
"tip_thickness": 34.16062542894516
|
||||
},
|
||||
"fea": [
|
||||
0.10656349355439027,
|
||||
75.102046875,
|
||||
-8326.35651590611
|
||||
],
|
||||
"nn_error": [
|
||||
3.6174682481860545,
|
||||
2.1680046671133515
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 37,
|
||||
"params": {
|
||||
"support_angle": 31.876112833251945,
|
||||
"tip_thickness": 32.64558622955443
|
||||
},
|
||||
"fea": [
|
||||
0.10316854746371616,
|
||||
76.0821640625,
|
||||
-7551.884666556311
|
||||
],
|
||||
"nn_error": [
|
||||
0.616586592199277,
|
||||
0.9385311503281267
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 38,
|
||||
"params": {
|
||||
"support_angle": 30.714982000638024,
|
||||
"tip_thickness": 30.67768874508055
|
||||
},
|
||||
"fea": [
|
||||
0.09900839247305124,
|
||||
77.738234375,
|
||||
-6613.818689996269
|
||||
],
|
||||
"nn_error": [
|
||||
3.445733195248999,
|
||||
1.0253383054399168
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 39,
|
||||
"params": {
|
||||
"support_angle": 28.913554019167456,
|
||||
"tip_thickness": 30.483198120379658
|
||||
},
|
||||
"fea": [
|
||||
0.09815608468915514,
|
||||
77.3044140625,
|
||||
-6401.798601024496
|
||||
],
|
||||
"nn_error": [
|
||||
4.31900669557528,
|
||||
0.6715572168522086
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 40,
|
||||
"params": {
|
||||
"support_angle": 30.64103130907421,
|
||||
"tip_thickness": 32.225435935347505
|
||||
},
|
||||
"fea": [
|
||||
0.10203815917423766,
|
||||
76.404703125,
|
||||
-7263.383668463729
|
||||
],
|
||||
"nn_error": [
|
||||
0.5053920341375967,
|
||||
0.3872153898156662
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 41,
|
||||
"params": {
|
||||
"support_angle": 25.379887341054648,
|
||||
"tip_thickness": 31.7995059368559
|
||||
},
|
||||
"fea": [
|
||||
0.09989812757495894,
|
||||
76.9576796875,
|
||||
-6664.024314617181
|
||||
],
|
||||
"nn_error": [
|
||||
4.447284090430112,
|
||||
1.5796573759898327
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 42,
|
||||
"params": {
|
||||
"support_angle": 31.731587709716017,
|
||||
"tip_thickness": 30.897825980216872
|
||||
},
|
||||
"fea": [
|
||||
0.09972626857174226,
|
||||
77.77390625,
|
||||
-6787.919099905275
|
||||
],
|
||||
"nn_error": [
|
||||
3.6536017763654174,
|
||||
1.4414725087041111
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 43,
|
||||
"params": {
|
||||
"support_angle": 33.10878057556627,
|
||||
"tip_thickness": 33.355298773540355
|
||||
},
|
||||
"fea": [
|
||||
0.1048663080654111,
|
||||
75.480953125,
|
||||
-7947.3954282813875
|
||||
],
|
||||
"nn_error": [
|
||||
1.1127142382050441,
|
||||
1.24740755881399
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 44,
|
||||
"params": {
|
||||
"support_angle": 33.486603646649684,
|
||||
"tip_thickness": 30.362623804600066
|
||||
},
|
||||
"fea": [
|
||||
0.09923041195413426,
|
||||
79.016015625,
|
||||
-6713.039943213783
|
||||
],
|
||||
"nn_error": [
|
||||
4.287407722991723,
|
||||
2.630846755256295
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 45,
|
||||
"params": {
|
||||
"support_angle": 28.114078180607912,
|
||||
"tip_thickness": 31.737991396793802
|
||||
},
|
||||
"fea": [
|
||||
0.10039508543743812,
|
||||
77.6226171875,
|
||||
-6820.132648794927
|
||||
],
|
||||
"nn_error": [
|
||||
3.5140537947946973,
|
||||
1.8965874116002928
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 46,
|
||||
"params": {
|
||||
"support_angle": 32.00933223521479,
|
||||
"tip_thickness": 30.3146054439274
|
||||
},
|
||||
"fea": [
|
||||
0.09865586146399362,
|
||||
78.773390625,
|
||||
-6537.562541889428
|
||||
],
|
||||
"nn_error": [
|
||||
4.747051326710379,
|
||||
2.548631636595247
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 47,
|
||||
"params": {
|
||||
"support_angle": 33.13530006102697,
|
||||
"tip_thickness": 33.39675764700238
|
||||
},
|
||||
"fea": [
|
||||
0.10495349474799269,
|
||||
75.4744296875,
|
||||
-7967.975581083746
|
||||
],
|
||||
"nn_error": [
|
||||
1.1881318255229905,
|
||||
1.2499923821726795
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 48,
|
||||
"params": {
|
||||
"support_angle": 31.37280375169122,
|
||||
"tip_thickness": 32.20022793873885
|
||||
},
|
||||
"fea": [
|
||||
0.10217431187937046,
|
||||
76.5387421875,
|
||||
-7300.86967873889
|
||||
],
|
||||
"nn_error": [
|
||||
1.4111097241955246,
|
||||
0.18087978882019146
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 49,
|
||||
"params": {
|
||||
"support_angle": 31.633966114017845,
|
||||
"tip_thickness": 30.14620749968385
|
||||
},
|
||||
"fea": [
|
||||
0.0982228321492226,
|
||||
78.6505,
|
||||
-6436.600331762441
|
||||
],
|
||||
"nn_error": [
|
||||
5.183933182520313,
|
||||
2.4268434241418446
|
||||
]
|
||||
},
|
||||
{
|
||||
"iteration": 50,
|
||||
"params": {
|
||||
"support_angle": 30.835096541574387,
|
||||
"tip_thickness": 31.83135554844258
|
||||
},
|
||||
"fea": [
|
||||
0.10131094537705086,
|
||||
76.825890625,
|
||||
-7117.327055357855
|
||||
],
|
||||
"nn_error": [
|
||||
2.2561942677161455,
|
||||
0.5555181135021817
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
221
studies/bracket_pareto_3obj/2_results/validation_report.json
Normal file
221
studies/bracket_pareto_3obj/2_results/validation_report.json
Normal file
@@ -0,0 +1,221 @@
|
||||
{
|
||||
"timestamp": "2025-12-06T19:08:19.427388",
|
||||
"n_validated": 10,
|
||||
"average_errors_percent": {
|
||||
"mass": 3.718643367122823,
|
||||
"stress": 2.020364475341075,
|
||||
"stiffness": 7.782164972196007
|
||||
},
|
||||
"results": [
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 38.72700594236812,
|
||||
"tip_thickness": 58.52142919229749
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.15462589263916016,
|
||||
90.49411010742188,
|
||||
-19956.513671875
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.1594800904665372,
|
||||
89.4502578125,
|
||||
-20960.59592691965
|
||||
],
|
||||
"errors_percent": [
|
||||
3.0437641546206433,
|
||||
1.1669639869679682,
|
||||
4.790332577114896
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 56.59969709057025,
|
||||
"tip_thickness": 47.959754525911094
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1316341757774353,
|
||||
80.95538330078125,
|
||||
-15403.2138671875
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.1370984916826118,
|
||||
80.2043046875,
|
||||
-16381.0655256764
|
||||
],
|
||||
"errors_percent": [
|
||||
3.9856863763509414,
|
||||
0.9364567353431696,
|
||||
5.969402032829749
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 27.800932022121827,
|
||||
"tip_thickness": 34.67983561008608
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1059565469622612,
|
||||
75.57935333251953,
|
||||
-8278.44921875
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.10630918746092984,
|
||||
75.5471015625,
|
||||
-8142.120566330409
|
||||
],
|
||||
"errors_percent": [
|
||||
0.3317121568615468,
|
||||
0.0426909429382223,
|
||||
1.6743629784032203
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 50.05575058716044,
|
||||
"tip_thickness": 51.242177333881365
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.13515426218509674,
|
||||
73.69579315185547,
|
||||
-15871.068359375
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.14318368576930707,
|
||||
73.3545859375,
|
||||
-17662.840771637857
|
||||
],
|
||||
"errors_percent": [
|
||||
5.607778247269787,
|
||||
0.46514776137674774,
|
||||
10.14430484557161
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 29.09124836035503,
|
||||
"tip_thickness": 35.50213529560301
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10616718232631683,
|
||||
75.49954986572266,
|
||||
-8333.7919921875
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.10827058249925942,
|
||||
72.4169921875,
|
||||
-8632.595914157022
|
||||
],
|
||||
"errors_percent": [
|
||||
1.9427254609597853,
|
||||
4.256677314409008,
|
||||
3.461344941207066
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 41.59725093210579,
|
||||
"tip_thickness": 38.736874205941255
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10606641322374344,
|
||||
77.42456817626953,
|
||||
-8482.6328125
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.11718762744364532,
|
||||
75.1669609375,
|
||||
-11092.555729424334
|
||||
],
|
||||
"errors_percent": [
|
||||
9.490092480326041,
|
||||
3.0034568520692204,
|
||||
23.52859864387429
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 50.59264473611897,
|
||||
"tip_thickness": 34.18481581956125
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.11001653969287872,
|
||||
78.32686614990234,
|
||||
-9909.66015625
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.11190565081078178,
|
||||
76.7876328125,
|
||||
-10422.469553635548
|
||||
],
|
||||
"errors_percent": [
|
||||
1.6881284405354184,
|
||||
2.0045328668496007,
|
||||
4.920229267608377
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 34.60723242676091,
|
||||
"tip_thickness": 40.99085529881075
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.11470890045166016,
|
||||
71.76973724365234,
|
||||
-10232.564453125
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.12047991649273775,
|
||||
70.5054453125,
|
||||
-11692.113952912616
|
||||
],
|
||||
"errors_percent": [
|
||||
4.790023274481149,
|
||||
1.7931833854089492,
|
||||
12.483195987189537
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 42.8034992108518,
|
||||
"tip_thickness": 53.55527884179041
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.1554829478263855,
|
||||
89.65568542480469,
|
||||
-20128.802734375
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.14802894076279258,
|
||||
92.6986484375,
|
||||
-18351.580922756133
|
||||
],
|
||||
"errors_percent": [
|
||||
5.03550658755136,
|
||||
3.282640107473584,
|
||||
9.684298149022656
|
||||
]
|
||||
},
|
||||
{
|
||||
"params": {
|
||||
"support_angle": 49.620728443102124,
|
||||
"tip_thickness": 31.393512381599933
|
||||
},
|
||||
"nn_objectives": [
|
||||
0.10854113101959229,
|
||||
78.32325744628906,
|
||||
-9371.779296875
|
||||
],
|
||||
"fea_objectives": [
|
||||
0.107178869906846,
|
||||
75.856484375,
|
||||
-9263.802242979662
|
||||
],
|
||||
"errors_percent": [
|
||||
1.2710164922715559,
|
||||
3.2518948005742834,
|
||||
1.1655802991386794
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
60
studies/bracket_pareto_3obj/MODEL_INTROSPECTION.md
Normal file
60
studies/bracket_pareto_3obj/MODEL_INTROSPECTION.md
Normal file
@@ -0,0 +1,60 @@
|
||||
# Model Introspection Report
|
||||
|
||||
**Study**: bracket_pareto_3obj
|
||||
**Generated**: 2025-12-06 14:43
|
||||
**Introspection Version**: 1.0
|
||||
|
||||
---
|
||||
|
||||
## 1. Files Discovered
|
||||
|
||||
| Type | File | Status |
|
||||
|------|------|--------|
|
||||
| Part (.prt) | Bracket.prt | ✓ Found |
|
||||
| Simulation (.sim) | Bracket_sim1.sim | ✓ Found |
|
||||
| FEM (.fem) | Bracket_fem1.fem | ✓ Found |
|
||||
|
||||
---
|
||||
|
||||
## 2. Expressions (Potential Design Variables)
|
||||
|
||||
*Run introspection to discover expressions.*
|
||||
|
||||
---
|
||||
|
||||
## 3. Solutions
|
||||
|
||||
*Run introspection to discover solutions.*
|
||||
|
||||
---
|
||||
|
||||
## 4. Available Results
|
||||
|
||||
| Result Type | Available | Subcases |
|
||||
|-------------|-----------|----------|
|
||||
| Displacement | ? | - |
|
||||
| Stress | ? | - |
|
||||
| SPC Forces | ? | - |
|
||||
|
||||
---
|
||||
|
||||
## 5. Optimization Configuration
|
||||
|
||||
### Selected Design Variables
|
||||
|
||||
- `support_angle`: [20, 70] degrees
|
||||
- `tip_thickness`: [30, 60] mm
|
||||
|
||||
### Selected Objectives
|
||||
|
||||
- Minimize `mass` using `extract_mass_from_bdf`
|
||||
- Minimize `stress` using `extract_solid_stress`
|
||||
- Maximize `stiffness` using `extract_displacement`
|
||||
|
||||
### Selected Constraints
|
||||
|
||||
- `stress_limit` less_than 300 MPa
|
||||
|
||||
---
|
||||
|
||||
*Ready to create optimization study? Run `python run_optimization.py --discover` to proceed.*
|
||||
130
studies/bracket_pareto_3obj/README.md
Normal file
130
studies/bracket_pareto_3obj/README.md
Normal file
@@ -0,0 +1,130 @@
|
||||
# bracket_pareto_3obj
|
||||
|
||||
Three-objective Pareto optimization: minimize mass, minimize stress, maximize stiffness
|
||||
|
||||
**Generated**: 2025-12-06 14:43
|
||||
**Protocol**: Multi-Objective NSGA-II
|
||||
**Trials**: 100
|
||||
|
||||
---
|
||||
|
||||
## 1. Engineering Problem
|
||||
|
||||
Three-objective Pareto optimization: minimize mass, minimize stress, maximize stiffness
|
||||
|
||||
---
|
||||
|
||||
## 2. Mathematical Formulation
|
||||
|
||||
### Design Variables
|
||||
|
||||
| Parameter | Bounds | Units | Description |
|
||||
|-----------|--------|-------|-------------|
|
||||
| `support_angle` | [20, 70] | degrees | Angle of support arm relative to base |
|
||||
| `tip_thickness` | [30, 60] | mm | Thickness at bracket tip where load is applied |
|
||||
|
||||
|
||||
### Objectives
|
||||
|
||||
| Objective | Goal | Extractor | Weight |
|
||||
|-----------|------|-----------|--------|
|
||||
| mass | minimize | `extract_mass_from_bdf` | 1.0 |
|
||||
| stress | minimize | `extract_solid_stress` | 1.0 |
|
||||
| stiffness | maximize | `extract_displacement` | 1.0 |
|
||||
|
||||
|
||||
### Constraints
|
||||
|
||||
| Constraint | Type | Threshold | Units |
|
||||
|------------|------|-----------|-------|
|
||||
| stress_limit | less_than | 300 | MPa |
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 3. Optimization Algorithm
|
||||
|
||||
- **Protocol**: protocol_11_multi
|
||||
- **Sampler**: NSGAIISampler
|
||||
- **Trials**: 100
|
||||
- **Neural Acceleration**: Disabled
|
||||
|
||||
---
|
||||
|
||||
## 4. Simulation Pipeline
|
||||
|
||||
```
|
||||
Design Variables → NX Expression Update → Nastran Solve → Result Extraction → Objective Evaluation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Result Extraction Methods
|
||||
|
||||
| Result | Extractor | Source |
|
||||
|--------|-----------|--------|
|
||||
| mass | `extract_mass_from_bdf` | OP2/DAT |
|
||||
| stress | `extract_solid_stress` | OP2/DAT |
|
||||
| stiffness | `extract_displacement` | OP2/DAT |
|
||||
|
||||
---
|
||||
|
||||
## 6. Study File Structure
|
||||
|
||||
```
|
||||
bracket_pareto_3obj/
|
||||
├── 1_setup/
|
||||
│ ├── model/
|
||||
│ │ ├── Bracket.prt
|
||||
│ │ ├── Bracket_sim1.sim
|
||||
│ │ └── Bracket_fem1.fem
|
||||
│ ├── optimization_config.json
|
||||
│ └── workflow_config.json
|
||||
├── 2_results/
|
||||
│ ├── study.db
|
||||
│ └── optimization.log
|
||||
├── run_optimization.py
|
||||
├── reset_study.py
|
||||
├── README.md
|
||||
├── STUDY_REPORT.md
|
||||
└── MODEL_INTROSPECTION.md
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. Quick Start
|
||||
|
||||
```bash
|
||||
# 1. Discover model outputs
|
||||
python run_optimization.py --discover
|
||||
|
||||
# 2. Validate setup with single trial
|
||||
python run_optimization.py --validate
|
||||
|
||||
# 3. Run integration test (3 trials)
|
||||
python run_optimization.py --test
|
||||
|
||||
# 4. Run full optimization
|
||||
python run_optimization.py --run --trials 100
|
||||
|
||||
# 5. Resume if interrupted
|
||||
python run_optimization.py --run --trials 50 --resume
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. Results Location
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `2_results/study.db` | Optuna SQLite database |
|
||||
| `2_results/optimization.log` | Structured log file |
|
||||
| `2_results/pareto_front.json` | Pareto-optimal solutions |
|
||||
|
||||
---
|
||||
|
||||
## 9. References
|
||||
|
||||
- [Atomizer Documentation](../../docs/)
|
||||
- [Protocol protocol_11_multi](../../docs/protocols/system/)
|
||||
- [Extractor Library](../../docs/protocols/system/SYS_12_EXTRACTOR_LIBRARY.md)
|
||||
60
studies/bracket_pareto_3obj/STUDY_REPORT.md
Normal file
60
studies/bracket_pareto_3obj/STUDY_REPORT.md
Normal file
@@ -0,0 +1,60 @@
|
||||
# Study Report: bracket_pareto_3obj
|
||||
|
||||
**Status**: Not Started
|
||||
**Created**: 2025-12-06 14:43
|
||||
**Last Updated**: 2025-12-06 14:43
|
||||
|
||||
---
|
||||
|
||||
## 1. Optimization Progress
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Total Trials | 0 |
|
||||
| Successful Trials | 0 |
|
||||
| Best Objective | - |
|
||||
| Duration | - |
|
||||
|
||||
---
|
||||
|
||||
## 2. Best Solutions
|
||||
|
||||
*No optimization runs completed yet.*
|
||||
|
||||
---
|
||||
|
||||
## 3. Pareto Front (if multi-objective)
|
||||
|
||||
*No Pareto front generated yet.*
|
||||
|
||||
---
|
||||
|
||||
## 4. Design Variable Sensitivity
|
||||
|
||||
*Analysis pending optimization runs.*
|
||||
|
||||
---
|
||||
|
||||
## 5. Constraint Satisfaction
|
||||
|
||||
*Analysis pending optimization runs.*
|
||||
|
||||
---
|
||||
|
||||
## 6. Recommendations
|
||||
|
||||
*Recommendations will be added after optimization runs.*
|
||||
|
||||
---
|
||||
|
||||
## 7. Next Steps
|
||||
|
||||
1. [ ] Run `python run_optimization.py --discover`
|
||||
2. [ ] Run `python run_optimization.py --validate`
|
||||
3. [ ] Run `python run_optimization.py --test`
|
||||
4. [ ] Run `python run_optimization.py --run --trials 100`
|
||||
5. [ ] Analyze results and update this report
|
||||
|
||||
---
|
||||
|
||||
*Generated by StudyWizard*
|
||||
48
studies/bracket_pareto_3obj/reset_study.py
Normal file
48
studies/bracket_pareto_3obj/reset_study.py
Normal file
@@ -0,0 +1,48 @@
|
||||
"""
|
||||
Reset study - Delete results database and logs.
|
||||
|
||||
Usage:
|
||||
python reset_study.py
|
||||
python reset_study.py --confirm # Skip confirmation
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--confirm', action='store_true', help='Skip confirmation')
|
||||
args = parser.parse_args()
|
||||
|
||||
study_dir = Path(__file__).parent
|
||||
results_dir = study_dir / "2_results"
|
||||
|
||||
if not args.confirm:
|
||||
print(f"This will delete all results in: {results_dir}")
|
||||
response = input("Are you sure? (y/N): ")
|
||||
if response.lower() != 'y':
|
||||
print("Cancelled.")
|
||||
return
|
||||
|
||||
# Delete database files
|
||||
for f in results_dir.glob("*.db"):
|
||||
f.unlink()
|
||||
print(f"Deleted: {f.name}")
|
||||
|
||||
# Delete log files
|
||||
for f in results_dir.glob("*.log"):
|
||||
f.unlink()
|
||||
print(f"Deleted: {f.name}")
|
||||
|
||||
# Delete JSON results
|
||||
for f in results_dir.glob("*.json"):
|
||||
f.unlink()
|
||||
print(f"Deleted: {f.name}")
|
||||
|
||||
print("Study reset complete.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1095
studies/bracket_pareto_3obj/run_nn_optimization.py
Normal file
1095
studies/bracket_pareto_3obj/run_nn_optimization.py
Normal file
File diff suppressed because it is too large
Load Diff
245
studies/bracket_pareto_3obj/run_optimization.py
Normal file
245
studies/bracket_pareto_3obj/run_optimization.py
Normal file
@@ -0,0 +1,245 @@
|
||||
"""
|
||||
bracket_pareto_3obj - Optimization Script
|
||||
============================================================
|
||||
|
||||
Three-objective Pareto optimization: minimize mass, minimize stress, maximize stiffness
|
||||
|
||||
Protocol: Multi-Objective NSGA-II
|
||||
|
||||
Staged Workflow:
|
||||
----------------
|
||||
1. DISCOVER: python run_optimization.py --discover
|
||||
2. VALIDATE: python run_optimization.py --validate
|
||||
3. TEST: python run_optimization.py --test
|
||||
4. RUN: python run_optimization.py --run --trials 100
|
||||
|
||||
Generated by StudyWizard on 2025-12-06 14:43
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import json
|
||||
import argparse
|
||||
from datetime import datetime
|
||||
from typing import Optional, Tuple, List
|
||||
|
||||
# Add parent directory to path
|
||||
project_root = Path(__file__).resolve().parents[2]
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
import optuna
|
||||
from optuna.samplers import NSGAIISampler
|
||||
|
||||
# Core imports
|
||||
from optimization_engine.nx_solver import NXSolver
|
||||
from optimization_engine.logger import get_logger
|
||||
|
||||
# Extractor imports
|
||||
from optimization_engine.extractors.bdf_mass_extractor import extract_mass_from_bdf
|
||||
from optimization_engine.extractors.extract_displacement import extract_displacement
|
||||
from optimization_engine.extractors.extract_von_mises_stress import extract_solid_stress
|
||||
|
||||
|
||||
def load_config(config_file: Path) -> dict:
|
||||
"""Load configuration from JSON file."""
|
||||
with open(config_file, 'r') as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def clean_nastran_files(model_dir: Path, logger) -> List[Path]:
|
||||
"""Remove old Nastran solver output files."""
|
||||
patterns = ['*.op2', '*.f06', '*.log', '*.f04', '*.pch', '*.DBALL', '*.MASTER', '_temp*.txt']
|
||||
deleted = []
|
||||
|
||||
for pattern in patterns:
|
||||
for f in model_dir.glob(pattern):
|
||||
try:
|
||||
f.unlink()
|
||||
deleted.append(f)
|
||||
logger.info(f" Deleted: {f.name}")
|
||||
except Exception as e:
|
||||
logger.warning(f" Failed to delete {f.name}: {e}")
|
||||
|
||||
return deleted
|
||||
|
||||
|
||||
def objective(trial: optuna.Trial, config: dict, nx_solver: NXSolver,
|
||||
model_dir: Path, logger) -> Tuple[float, float, float]:
|
||||
"""
|
||||
Objective function for optimization.
|
||||
|
||||
Returns tuple of objectives for multi-objective optimization.
|
||||
"""
|
||||
# Sample design variables
|
||||
design_vars = {}
|
||||
for var in config['design_variables']:
|
||||
param_name = var['parameter']
|
||||
bounds = var['bounds']
|
||||
design_vars[param_name] = trial.suggest_float(param_name, bounds[0], bounds[1])
|
||||
|
||||
logger.trial_start(trial.number, design_vars)
|
||||
|
||||
try:
|
||||
# Get file paths
|
||||
sim_file = model_dir / config['simulation']['sim_file']
|
||||
|
||||
# Run FEA simulation
|
||||
result = nx_solver.run_simulation(
|
||||
sim_file=sim_file,
|
||||
working_dir=model_dir,
|
||||
expression_updates=design_vars,
|
||||
solution_name=config['simulation'].get('solution_name'),
|
||||
cleanup=True
|
||||
)
|
||||
|
||||
if not result['success']:
|
||||
logger.trial_failed(trial.number, f"Simulation failed: {result.get('error', 'Unknown')}")
|
||||
return (float('inf'), float('inf'), float('inf'))
|
||||
|
||||
op2_file = result['op2_file']
|
||||
dat_file = model_dir / config['simulation']['dat_file']
|
||||
|
||||
# Extract results
|
||||
obj_mass = extract_mass_from_bdf(str(dat_file))
|
||||
logger.info(f' mass: {obj_mass}')
|
||||
|
||||
stress_result = extract_solid_stress(op2_file, subcase=1, element_type='chexa')
|
||||
obj_stress = stress_result.get('max_von_mises', float('inf')) / 1000.0 # kPa -> MPa
|
||||
logger.info(f' stress: {obj_stress:.2f} MPa')
|
||||
|
||||
disp_result = extract_displacement(op2_file, subcase=1)
|
||||
max_displacement = disp_result['max_displacement']
|
||||
# For stiffness maximization, use inverse of displacement
|
||||
applied_force = 1000.0 # N - adjust based on your model
|
||||
obj_stiffness = -applied_force / max(abs(max_displacement), 1e-6)
|
||||
logger.info(f' stiffness: {obj_stiffness}')
|
||||
|
||||
|
||||
# Check constraints
|
||||
feasible = True
|
||||
constraint_results = {}
|
||||
# Check stress_limit (stress from OP2 is in kPa for mm/kg units, convert to MPa)
|
||||
const_stress_limit = extract_solid_stress(op2_file, element_type='chexa')
|
||||
stress_mpa = const_stress_limit.get('max_von_mises', float('inf')) / 1000.0 # kPa -> MPa
|
||||
constraint_results['stress_limit'] = stress_mpa
|
||||
if stress_mpa > 300:
|
||||
feasible = False
|
||||
logger.warning(f' Constraint violation: stress_limit = {stress_mpa:.1f} MPa vs 300 MPa')
|
||||
|
||||
|
||||
# Set user attributes
|
||||
trial.set_user_attr('mass', obj_mass)
|
||||
trial.set_user_attr('stress', obj_stress)
|
||||
trial.set_user_attr('stiffness', obj_stiffness)
|
||||
trial.set_user_attr('feasible', feasible)
|
||||
|
||||
objectives = {'mass': obj_mass, 'stress': obj_stress, 'stiffness': obj_stiffness}
|
||||
logger.trial_complete(trial.number, objectives, constraint_results, feasible)
|
||||
|
||||
return (obj_mass, obj_stress, obj_stiffness)
|
||||
|
||||
except Exception as e:
|
||||
logger.trial_failed(trial.number, str(e))
|
||||
return (float('inf'), float('inf'), float('inf'))
|
||||
|
||||
|
||||
def main():
|
||||
"""Main optimization workflow."""
|
||||
parser = argparse.ArgumentParser(description='bracket_pareto_3obj')
|
||||
|
||||
stage_group = parser.add_mutually_exclusive_group()
|
||||
stage_group.add_argument('--discover', action='store_true', help='Discover model outputs')
|
||||
stage_group.add_argument('--validate', action='store_true', help='Run single validation trial')
|
||||
stage_group.add_argument('--test', action='store_true', help='Run 3-trial test')
|
||||
stage_group.add_argument('--run', action='store_true', help='Run optimization')
|
||||
|
||||
parser.add_argument('--trials', type=int, default=100, help='Number of trials')
|
||||
parser.add_argument('--resume', action='store_true', help='Resume existing study')
|
||||
parser.add_argument('--clean', action='store_true', help='Clean old files first')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not any([args.discover, args.validate, args.test, args.run]):
|
||||
print("No stage specified. Use --discover, --validate, --test, or --run")
|
||||
return 1
|
||||
|
||||
# Setup paths
|
||||
study_dir = Path(__file__).parent
|
||||
config_path = study_dir / "1_setup" / "optimization_config.json"
|
||||
model_dir = study_dir / "1_setup" / "model"
|
||||
results_dir = study_dir / "2_results"
|
||||
results_dir.mkdir(exist_ok=True)
|
||||
|
||||
study_name = "bracket_pareto_3obj"
|
||||
|
||||
# Initialize
|
||||
logger = get_logger(study_name, study_dir=results_dir)
|
||||
config = load_config(config_path)
|
||||
nx_solver = NXSolver(nastran_version="2506")
|
||||
|
||||
if args.clean:
|
||||
clean_nastran_files(model_dir, logger)
|
||||
|
||||
# Run appropriate stage
|
||||
if args.discover or args.validate or args.test:
|
||||
# Run limited trials for these stages
|
||||
n = 1 if args.discover or args.validate else 3
|
||||
storage = f"sqlite:///{results_dir / 'study_test.db'}"
|
||||
|
||||
study = optuna.create_study(
|
||||
study_name=f"{study_name}_test",
|
||||
storage=storage,
|
||||
sampler=NSGAIISampler(population_size=5, seed=42),
|
||||
directions=['minimize'] * 3,
|
||||
load_if_exists=False
|
||||
)
|
||||
|
||||
study.optimize(
|
||||
lambda trial: objective(trial, config, nx_solver, model_dir, logger),
|
||||
n_trials=n,
|
||||
show_progress_bar=True
|
||||
)
|
||||
|
||||
logger.info(f"Completed {len(study.trials)} trial(s)")
|
||||
return 0
|
||||
|
||||
# Full optimization run
|
||||
storage = f"sqlite:///{results_dir / 'study.db'}"
|
||||
|
||||
if args.resume:
|
||||
study = optuna.load_study(
|
||||
study_name=study_name,
|
||||
storage=storage,
|
||||
sampler=NSGAIISampler(population_size=20, seed=42)
|
||||
)
|
||||
else:
|
||||
study = optuna.create_study(
|
||||
study_name=study_name,
|
||||
storage=storage,
|
||||
sampler=NSGAIISampler(population_size=20, seed=42),
|
||||
directions=['minimize'] * 3,
|
||||
load_if_exists=True
|
||||
)
|
||||
|
||||
logger.study_start(study_name, args.trials, "NSGAIISampler")
|
||||
|
||||
study.optimize(
|
||||
lambda trial: objective(trial, config, nx_solver, model_dir, logger),
|
||||
n_trials=args.trials,
|
||||
show_progress_bar=True
|
||||
)
|
||||
|
||||
n_complete = len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE])
|
||||
logger.study_complete(study_name, len(study.trials), n_complete)
|
||||
|
||||
# Report results
|
||||
pareto_trials = study.best_trials
|
||||
logger.info(f"\nOptimization Complete!")
|
||||
logger.info(f"Total trials: {len(study.trials)}")
|
||||
logger.info(f"Successful: {n_complete}")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
Reference in New Issue
Block a user