feat: Complete working optimization pipeline with stress extraction
COMPLETE PIPELINE VALIDATED: - Stress extraction: 197.65 MPa (CTETRA elements) ✓ - Displacement extraction: 0.322 mm ✓ - Model parameter updates in .prt files ✓ - Optuna optimization with TPE sampler ✓ - Constraint handling (displacement < 1.0 mm) ✓ - Results saved to CSV/JSON ✓ Test Results (5 trials): - All extractors working correctly - Parameters updated successfully - Constraints validated - History and summary files generated New Files: - examples/test_stress_displacement_optimization.py Complete pipeline test with stress + displacement - examples/test_displacement_optimization.py Displacement-only optimization test - examples/run_optimization_real.py Full example with all extractors - examples/check_op2.py OP2 diagnostic utility - examples/bracket/optimization_config_stress_displacement.json Config: minimize stress, constrain displacement - examples/bracket/optimization_config_displacement_only.json Config: minimize displacement only Updated: - .gitignore: Exclude NX output files and optimization results - examples/bracket/optimization_config.json: Updated paths Next Step: Integrate NX solver execution for real optimization
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examples/run_optimization_real.py
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166
examples/run_optimization_real.py
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"""
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Example: Running Complete Optimization WITH REAL OP2 EXTRACTION
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This version uses real pyNastran extractors instead of dummy data.
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Requirements:
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- conda activate test_env (with pyNastran and optuna installed)
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What this does:
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1. Updates NX model parameters in the .prt file
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2. Uses existing OP2 results (simulation step skipped for now)
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3. Extracts REAL mass, stress, displacement from OP2
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4. Runs Optuna optimization
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Note: Since we're using the same OP2 file for all trials (no re-solving),
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the results will be constant. This is just to test the pipeline.
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For real optimization, you'd need to run NX solver for each trial.
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"""
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from pathlib import Path
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import sys
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# Add project root to path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from optimization_engine.runner import OptimizationRunner
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from optimization_engine.nx_updater import update_nx_model
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from optimization_engine.result_extractors.extractors import (
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mass_extractor,
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stress_extractor,
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displacement_extractor
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)
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# ==================================================
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# STEP 1: Define model updater function
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# ==================================================
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def bracket_model_updater(design_vars: dict):
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"""
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Update the bracket model with new design variable values.
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Args:
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design_vars: Dict like {'tip_thickness': 22.5, 'support_angle': 35.0}
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"""
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prt_file = project_root / "examples/bracket/Bracket.prt"
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print(f"\n[MODEL UPDATE] Updating {prt_file.name} with:")
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for name, value in design_vars.items():
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print(f" {name} = {value:.4f}")
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# Update the .prt file with new parameter values
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update_nx_model(prt_file, design_vars, backup=False)
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print("[MODEL UPDATE] Complete")
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# ==================================================
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# STEP 2: Define simulation runner function
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# ==================================================
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def bracket_simulation_runner() -> Path:
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"""
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Run NX simulation and return path to result files.
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For this demo, we just return the existing OP2 file.
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In production, this would:
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1. Run NX solver with updated model
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2. Wait for completion
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3. Return path to new OP2 file
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"""
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print("\n[SIMULATION] Running NX Nastran solver...")
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print("[SIMULATION] (Using existing OP2 for demo - no actual solve)")
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# Return path to existing OP2 file
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result_file = project_root / "examples/bracket/bracket_sim1-solution_1.op2"
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if not result_file.exists():
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raise FileNotFoundError(f"Result file not found: {result_file}")
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print(f"[SIMULATION] Results: {result_file.name}")
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return result_file
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# ==================================================
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# MAIN: Run optimization
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# ==================================================
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if __name__ == "__main__":
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print("="*60)
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print("ATOMIZER - REAL OPTIMIZATION TEST")
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print("="*60)
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# Path to optimization configuration
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config_path = project_root / "examples/bracket/optimization_config.json"
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if not config_path.exists():
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print(f"Error: Configuration file not found: {config_path}")
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print("Please run the MCP build_optimization_config tool first.")
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sys.exit(1)
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print(f"\nConfiguration: {config_path}")
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# Use REAL extractors
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print("\nUsing REAL OP2 extractors (pyNastran)")
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extractors = {
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'mass_extractor': mass_extractor,
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'stress_extractor': stress_extractor,
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'displacement_extractor': displacement_extractor
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}
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# Create optimization runner
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runner = OptimizationRunner(
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config_path=config_path,
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model_updater=bracket_model_updater,
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simulation_runner=bracket_simulation_runner,
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result_extractors=extractors
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)
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# Run optimization with just 5 trials for testing
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print("\n" + "="*60)
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print("Starting optimization with 5 trials (test mode)")
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print("="*60)
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print("\nNOTE: Since we're using the same OP2 file for all trials")
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print("(not re-running solver), results will be constant.")
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print("This is just to test the pipeline integration.")
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print("="*60)
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# Override n_trials for demo
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runner.config['optimization_settings']['n_trials'] = 5
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try:
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# Run!
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study = runner.run(study_name="bracket_real_extraction_test")
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print("\n" + "="*60)
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print("TEST COMPLETE - PIPELINE WORKS!")
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print("="*60)
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print(f"\nBest parameters found:")
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for param, value in study.best_params.items():
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print(f" {param}: {value:.4f}")
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print(f"\nBest objective value: {study.best_value:.6f}")
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print(f"\nResults saved to: {runner.output_dir}")
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print(" - history.csv (all trials)")
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print(" - history.json (detailed results)")
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print(" - optimization_summary.json (best results)")
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print("\n" + "="*60)
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print("NEXT STEPS:")
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print("="*60)
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print("1. Check the history.csv to see extracted values")
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print("2. Integrate NX solver execution (batch mode)")
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print("3. Run real optimization with solver re-runs")
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print("="*60)
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except Exception as e:
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print(f"\n{'='*60}")
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print("ERROR DURING OPTIMIZATION")
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print("="*60)
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print(f"Error: {e}")
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print("\nMake sure you're running in test_env with:")
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print(" - pyNastran installed")
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print(" - optuna installed")
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print(" - pandas installed")
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import traceback
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traceback.print_exc()
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