docs: Complete M1 mirror optimization campaign V11-V15
## M1 Mirror Campaign Summary - V11-V15 optimization campaign completed (~1,400 FEA evaluations) - Best design: V14 Trial #725 with Weighted Sum = 121.72 - V15 NSGA-II confirmed V14 TPE found optimal solution - Campaign improved from WS=129.33 (V11) to WS=121.72 (V14): -5.9% ## Key Results - 40° tracking: 5.99 nm (target 4.0 nm) - 60° tracking: 13.10 nm (target 10.0 nm) - Manufacturing: 26.28 nm (target 20.0 nm) - Targets not achievable within current design space ## Documentation Added - V15 STUDY_REPORT.md: Detailed NSGA-II results analysis - M1_MIRROR_CAMPAIGN_SUMMARY.md: Full V11-V15 campaign overview - Updated CLAUDE.md, ATOMIZER_CONTEXT.md with NXSolver patterns - Updated 01_CHEATSHEET.md with --resume guidance - Updated OP_01_CREATE_STUDY.md with FEARunner template ## Studies Added - m1_mirror_adaptive_V13: TPE validation (291 trials) - m1_mirror_adaptive_V14: TPE intensive (785 trials, BEST) - m1_mirror_adaptive_V15: NSGA-II exploration (126 new FEA) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Antoine
@@ -197,62 +197,132 @@ Create `optimization_config.json`:
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### Step 6: Generate run_optimization.py
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**CRITICAL**: Always use the `FEARunner` class pattern with proper `NXSolver` initialization.
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```python
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#!/usr/bin/env python
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#!/usr/bin/env python3
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"""
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{study_name} - Optimization Runner
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Generated by Atomizer LLM
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"""
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import sys
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import re
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import json
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from pathlib import Path
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from typing import Dict, Optional, Any
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# Add optimization engine to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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import optuna
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from optimization_engine.nx_solver import NXSolver
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from optimization_engine.extractors import extract_displacement, extract_solid_stress
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from optimization_engine.utils import ensure_nx_running
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from optimization_engine.extractors import extract_solid_stress
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# Paths
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STUDY_DIR = Path(__file__).parent
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MODEL_DIR = STUDY_DIR / "1_setup" / "model"
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RESULTS_DIR = STUDY_DIR / "2_results"
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SETUP_DIR = STUDY_DIR / "1_setup"
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ITERATIONS_DIR = STUDY_DIR / "2_iterations"
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RESULTS_DIR = STUDY_DIR / "3_results"
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CONFIG_PATH = SETUP_DIR / "optimization_config.json"
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def objective(trial):
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"""Optimization objective function."""
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# Sample design variables
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thickness = trial.suggest_float("thickness", 2.0, 10.0)
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# Ensure directories exist
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ITERATIONS_DIR.mkdir(exist_ok=True)
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RESULTS_DIR.mkdir(exist_ok=True)
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# Update NX model and solve
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nx_solver = NXSolver(...)
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result = nx_solver.run_simulation(
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sim_file=MODEL_DIR / "bracket.sim",
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working_dir=MODEL_DIR,
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expression_updates={"thickness": thickness}
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)
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if not result['success']:
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raise optuna.TrialPruned("Simulation failed")
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class FEARunner:
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"""Runs actual FEA simulations. Always use this pattern!"""
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# Extract results using library extractors
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op2_file = result['op2_file']
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stress_result = extract_solid_stress(op2_file)
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max_stress = stress_result['max_von_mises']
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def __init__(self, config: Dict[str, Any]):
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self.config = config
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self.nx_solver = None
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self.nx_manager = None
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self.master_model_dir = SETUP_DIR / "model"
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# Check constraint
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if max_stress > 250.0:
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raise optuna.TrialPruned(f"Stress constraint violated: {max_stress} MPa")
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def setup(self):
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"""Setup NX and solver. Called lazily on first use."""
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study_name = self.config.get('study_name', 'my_study')
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# Return objective
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mass = extract_mass(...)
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return mass
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# Ensure NX is running
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self.nx_manager, nx_was_started = ensure_nx_running(
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session_id=study_name,
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auto_start=True,
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start_timeout=120
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)
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if __name__ == "__main__":
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# Run optimization
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import optuna
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study = optuna.create_study(direction="minimize")
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study.optimize(objective, n_trials=50)
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# CRITICAL: Initialize NXSolver with named parameters, NOT config dict
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nx_settings = self.config.get('nx_settings', {})
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nx_install_dir = nx_settings.get('nx_install_path', 'C:\\Program Files\\Siemens\\NX2506')
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# Extract version from path
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version_match = re.search(r'NX(\d+)', nx_install_dir)
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nastran_version = version_match.group(1) if version_match else "2506"
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self.nx_solver = NXSolver(
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master_model_dir=str(self.master_model_dir),
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nx_install_dir=nx_install_dir,
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nastran_version=nastran_version,
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timeout=nx_settings.get('simulation_timeout_s', 600),
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use_iteration_folders=True,
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study_name=study_name
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)
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def run_fea(self, params: Dict[str, float], iter_num: int) -> Optional[Dict]:
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"""Run FEA simulation and extract results."""
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if self.nx_solver is None:
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self.setup()
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# Create expression updates
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expressions = {var['expression_name']: params[var['name']]
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for var in self.config['design_variables']}
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# Create iteration folder with model copies
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iter_folder = self.nx_solver.create_iteration_folder(
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iterations_base_dir=ITERATIONS_DIR,
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iteration_number=iter_num,
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expression_updates=expressions
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)
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# Run simulation
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nx_settings = self.config.get('nx_settings', {})
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sim_file = iter_folder / nx_settings.get('sim_file', 'model.sim')
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result = self.nx_solver.run_simulation(
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sim_file=sim_file,
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working_dir=iter_folder,
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expression_updates=expressions,
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solution_name=nx_settings.get('solution_name', 'Solution 1'),
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cleanup=False
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)
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if not result['success']:
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return None
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# Extract results
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op2_file = result['op2_file']
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stress_result = extract_solid_stress(op2_file)
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return {
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'params': params,
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'max_stress': stress_result['max_von_mises'],
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'op2_file': op2_file
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}
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# Optimizer class would use FEARunner...
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# See m1_mirror_adaptive_V14/run_optimization.py for full example
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```
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**WRONG** - causes `TypeError: expected str, bytes or os.PathLike object, not dict`:
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```python
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self.nx_solver = NXSolver(self.config) # ❌ NEVER DO THIS
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```
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**Reference implementations**:
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- `studies/m1_mirror_adaptive_V14/run_optimization.py` (TPE single-objective)
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- `studies/m1_mirror_adaptive_V15/run_optimization.py` (NSGA-II multi-objective)
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### Step 7: Generate Documentation
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**README.md** (11 sections required):
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@@ -400,4 +470,5 @@ Generated config:
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| Version | Date | Changes |
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|---------|------|---------|
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| 1.1 | 2025-12-12 | Added FEARunner class pattern, NXSolver initialization warning |
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| 1.0 | 2025-12-05 | Initial release |
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