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
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.claude/skills/modules/extractors-catalog.md
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.claude/skills/modules/extractors-catalog.md
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# Extractors Catalog Module
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**Last Updated**: December 5, 2025
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**Version**: 1.0
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**Type**: Optional Module
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This module documents all available extractors in the Atomizer framework. Load this when the user asks about result extraction or needs to understand what extractors are available.
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---
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## When to Load
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- User asks "what extractors are available?"
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- User needs to extract results from OP2/BDF files
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- Setting up a new study with custom extraction needs
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- Debugging extraction issues
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---
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## PR.1 Extractor Catalog
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| ID | Extractor | Module | Function | Input | Output | Returns |
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|----|-----------|--------|----------|-------|--------|---------|
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| E1 | **Displacement** | `optimization_engine.extractors.extract_displacement` | `extract_displacement(op2_file, subcase=1)` | `.op2` | mm | `{'max_displacement': float, 'max_disp_node': int, 'max_disp_x/y/z': float}` |
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| E2 | **Frequency** | `optimization_engine.extractors.extract_frequency` | `extract_frequency(op2_file, subcase=1, mode_number=1)` | `.op2` | Hz | `{'frequency': float, 'mode_number': int, 'eigenvalue': float, 'all_frequencies': list}` |
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| E3 | **Von Mises Stress** | `optimization_engine.extractors.extract_von_mises_stress` | `extract_solid_stress(op2_file, subcase=1, element_type='cquad4')` | `.op2` | MPa | `{'max_von_mises': float, 'max_stress_element': int}` |
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| E4 | **BDF Mass** | `optimization_engine.extractors.bdf_mass_extractor` | `extract_mass_from_bdf(bdf_file)` | `.dat`/`.bdf` | kg | `float` (mass in kg) |
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| E5 | **CAD Expression Mass** | `optimization_engine.extractors.extract_mass_from_expression` | `extract_mass_from_expression(prt_file, expression_name='p173')` | `.prt` + `_temp_mass.txt` | kg | `float` (mass in kg) |
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| E6 | **Field Data** | `optimization_engine.extractors.field_data_extractor` | `FieldDataExtractor(field_file, result_column, aggregation)` | `.fld`/`.csv` | varies | `{'value': float, 'stats': dict}` |
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| E7 | **Stiffness** | `optimization_engine.extractors.stiffness_calculator` | `StiffnessCalculator(field_file, op2_file, force_component, displacement_component)` | `.fld` + `.op2` | N/mm | `{'stiffness': float, 'displacement': float, 'force': float}` |
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| E11 | **Part Mass & Material** | `optimization_engine.extractors.extract_part_mass_material` | `extract_part_mass_material(prt_file)` | `.prt` | kg + dict | `{'mass_kg': float, 'volume_mm3': float, 'material': {'name': str}, ...}` |
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**For Zernike extractors (E8-E10)**, see the [zernike-optimization module](./zernike-optimization.md).
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---
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## PR.2 Extractor Code Snippets (COPY-PASTE)
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### E1: Displacement Extraction
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```python
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from optimization_engine.extractors.extract_displacement import extract_displacement
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disp_result = extract_displacement(op2_file, subcase=1)
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max_displacement = disp_result['max_displacement'] # mm
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max_node = disp_result['max_disp_node'] # Node ID
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```
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**Return Dictionary**:
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```python
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{
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'max_displacement': 0.523, # Maximum magnitude (mm)
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'max_disp_node': 1234, # Node ID with max displacement
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'max_disp_x': 0.123, # X component at max node
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'max_disp_y': 0.456, # Y component at max node
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'max_disp_z': 0.234 # Z component at max node
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}
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```
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### E2: Frequency Extraction
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```python
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from optimization_engine.extractors.extract_frequency import extract_frequency
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# Get first mode frequency
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freq_result = extract_frequency(op2_file, subcase=1, mode_number=1)
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frequency = freq_result['frequency'] # Hz
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# Get all frequencies
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all_freqs = freq_result['all_frequencies'] # List of all mode frequencies
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```
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**Return Dictionary**:
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```python
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{
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'frequency': 125.4, # Requested mode frequency (Hz)
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'mode_number': 1, # Mode number requested
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'eigenvalue': 6.21e5, # Eigenvalue (rad/s)^2
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'all_frequencies': [125.4, 234.5, 389.2, ...] # All mode frequencies
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}
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```
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### E3: Stress Extraction
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```python
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from optimization_engine.extractors.extract_von_mises_stress import extract_solid_stress
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# For shell elements (CQUAD4, CTRIA3)
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stress_result = extract_solid_stress(op2_file, subcase=1, element_type='cquad4')
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# For solid elements (CTETRA, CHEXA)
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stress_result = extract_solid_stress(op2_file, subcase=1, element_type='ctetra')
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max_stress = stress_result['max_von_mises'] # MPa
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```
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**Return Dictionary**:
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```python
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{
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'max_von_mises': 187.5, # Maximum von Mises stress (MPa)
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'max_stress_element': 5678, # Element ID with max stress
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'mean_stress': 45.2, # Mean stress across all elements
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'stress_distribution': {...} # Optional: full distribution data
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}
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```
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### E4: BDF Mass Extraction
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```python
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from optimization_engine.extractors.bdf_mass_extractor import extract_mass_from_bdf
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mass_kg = extract_mass_from_bdf(str(dat_file)) # kg
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```
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**Note**: Calculates mass from element properties and material density in the BDF/DAT file.
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### E5: CAD Expression Mass
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```python
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from optimization_engine.extractors.extract_mass_from_expression import extract_mass_from_expression
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mass_kg = extract_mass_from_expression(model_file, expression_name="p173") # kg
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```
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**Note**: Requires `_temp_mass.txt` to be written by solve journal. The expression name is the NX expression that contains the mass value.
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### E6: Field Data Extraction
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```python
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from optimization_engine.extractors.field_data_extractor import FieldDataExtractor
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# Create extractor
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extractor = FieldDataExtractor(
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field_file="results.fld",
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result_column="Temperature",
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aggregation="max" # or "min", "mean", "sum"
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)
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result = extractor.extract()
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value = result['value'] # Aggregated value
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stats = result['stats'] # Full statistics
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```
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### E7: Stiffness Calculation
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```python
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# Simple stiffness from displacement (most common)
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applied_force = 1000.0 # N - MUST MATCH YOUR MODEL'S APPLIED LOAD
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stiffness = applied_force / max(abs(max_displacement), 1e-6) # N/mm
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# Or using StiffnessCalculator for complex cases
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from optimization_engine.extractors.stiffness_calculator import StiffnessCalculator
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calc = StiffnessCalculator(
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field_file="displacement.fld",
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op2_file="results.op2",
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force_component="Fz",
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displacement_component="Tz"
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)
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result = calc.calculate()
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stiffness = result['stiffness'] # N/mm
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```
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### E11: Part Mass & Material Extraction
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```python
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from optimization_engine.extractors import extract_part_mass_material, extract_part_mass
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# Full extraction with all properties
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result = extract_part_mass_material(prt_file)
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mass_kg = result['mass_kg'] # kg
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volume = result['volume_mm3'] # mm³
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area = result['surface_area_mm2'] # mm²
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cog = result['center_of_gravity_mm'] # [x, y, z] mm
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material = result['material']['name'] # e.g., "Aluminum_2014"
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# Simple mass-only extraction
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mass_kg = extract_part_mass(prt_file) # kg
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```
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**Return Dictionary**:
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```python
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{
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'mass_kg': 0.1098, # Mass in kg
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'mass_g': 109.84, # Mass in grams
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'volume_mm3': 39311.99, # Volume in mm³
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'surface_area_mm2': 10876.71, # Surface area in mm²
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'center_of_gravity_mm': [0, 42.3, 39.6], # CoG in mm
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'material': {
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'name': 'Aluminum_2014', # Material name (or None)
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'density': None, # Density if available
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'density_unit': 'kg/mm^3'
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},
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'num_bodies': 1 # Number of solid bodies
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}
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```
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**Prerequisites**: Run the NX journal first to create the temp file:
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```bash
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run_journal.exe nx_journals/extract_part_mass_material.py -args model.prt
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```
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---
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## Extractor Selection Guide
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| Need | Extractor | When to Use |
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|------|-----------|-------------|
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| Max deflection | E1 | Static analysis displacement check |
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| Natural frequency | E2 | Modal analysis, resonance avoidance |
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| Peak stress | E3 | Strength validation, fatigue life |
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| FEM mass | E4 | When mass is from mesh elements |
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| CAD mass | E5 | When mass is from NX expression |
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| Temperature/Custom | E6 | Thermal or custom field results |
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| k = F/δ | E7 | Stiffness maximization |
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| Wavefront error | E8-E10 | Telescope/mirror optimization |
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| Part mass + material | E11 | Direct from .prt file with material info |
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---
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## Engineering Result Types
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| Result Type | Nastran SOL | Output File | Extractor |
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|-------------|-------------|-------------|-----------|
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| Static Stress | SOL 101 | `.op2` | E3: `extract_solid_stress` |
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| Displacement | SOL 101 | `.op2` | E1: `extract_displacement` |
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| Natural Frequency | SOL 103 | `.op2` | E2: `extract_frequency` |
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| Buckling Load | SOL 105 | `.op2` | `extract_buckling` |
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| Modal Shapes | SOL 103 | `.op2` | `extract_mode_shapes` |
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| Mass | - | `.dat`/`.bdf` | E4: `bdf_mass_extractor` |
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| Stiffness | SOL 101 | `.fld` + `.op2` | E7: `stiffness_calculator` |
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---
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## Common Objective Formulations
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### Stiffness Maximization
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- k = F/δ (force/displacement)
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- Maximize k or minimize 1/k (compliance)
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- Requires consistent load magnitude across trials
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### Mass Minimization
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- Extract from BDF element properties + material density
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- Units: typically kg (NX uses kg-mm-s)
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### Stress Constraints
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- Von Mises < σ_yield / safety_factor
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- Account for stress concentrations
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### Frequency Constraints
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- f₁ > threshold (avoid resonance)
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- Often paired with mass minimization
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---
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## Adding New Extractors
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When the study needs result extraction not covered by existing extractors (E1-E10):
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```
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STEP 1: Check existing extractors in this catalog
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├── If exists → IMPORT and USE it (done!)
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└── If missing → Continue to STEP 2
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STEP 2: Create extractor in optimization_engine/extractors/
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├── File: extract_{feature}.py
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├── Follow existing extractor patterns
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└── Include comprehensive docstrings
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STEP 3: Add to __init__.py
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└── Export functions in optimization_engine/extractors/__init__.py
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STEP 4: Update this module
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├── Add to Extractor Catalog table
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└── Add code snippet
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STEP 5: Document in SYS_12_EXTRACTOR_LIBRARY.md
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```
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See [EXT_01_CREATE_EXTRACTOR](../../docs/protocols/extensions/EXT_01_CREATE_EXTRACTOR.md) for full guide.
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---
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## Cross-References
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- **System Protocol**: [SYS_12_EXTRACTOR_LIBRARY](../../docs/protocols/system/SYS_12_EXTRACTOR_LIBRARY.md)
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- **Extension Guide**: [EXT_01_CREATE_EXTRACTOR](../../docs/protocols/extensions/EXT_01_CREATE_EXTRACTOR.md)
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- **Zernike Extractors**: [zernike-optimization module](./zernike-optimization.md)
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- **Core Skill**: [study-creation-core](../core/study-creation-core.md)
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.claude/skills/modules/neural-acceleration.md
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# Neural Acceleration Module
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**Last Updated**: December 5, 2025
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**Version**: 1.0
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**Type**: Optional Module
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This module provides guidance for AtomizerField neural network surrogate acceleration, enabling 1000x faster optimization by replacing expensive FEA evaluations with instant neural predictions.
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---
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## When to Load
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- User needs >50 optimization trials
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- User mentions "neural", "surrogate", "NN", "machine learning"
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- User wants faster optimization
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- Exporting training data for neural networks
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---
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## Overview
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**Key Innovation**: Train once on FEA data, then explore 50,000+ designs in the time it takes to run 50 FEA trials.
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| Metric | Traditional FEA | Neural Network | Improvement |
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|--------|-----------------|----------------|-------------|
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| Time per evaluation | 10-30 minutes | 4.5 milliseconds | **2,000-500,000x** |
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| Trials per hour | 2-6 | 800,000+ | **1000x** |
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| Design exploration | ~50 designs | ~50,000 designs | **1000x** |
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---
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## Training Data Export (PR.9)
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Enable training data export in your optimization config:
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```json
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{
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"training_data_export": {
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"enabled": true,
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"export_dir": "atomizer_field_training_data/my_study"
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}
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}
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```
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### Using TrainingDataExporter
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```python
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from optimization_engine.training_data_exporter import TrainingDataExporter
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training_exporter = TrainingDataExporter(
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export_dir=export_dir,
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study_name=study_name,
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design_variable_names=['param1', 'param2'],
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objective_names=['stiffness', 'mass'],
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constraint_names=['mass_limit'],
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metadata={'atomizer_version': '2.0', 'optimization_algorithm': 'NSGA-II'}
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)
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# In objective function:
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training_exporter.export_trial(
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trial_number=trial.number,
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design_variables=design_vars,
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results={'objectives': {...}, 'constraints': {...}},
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simulation_files={'dat_file': dat_path, 'op2_file': op2_path}
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)
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# After optimization:
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training_exporter.finalize()
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```
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### Training Data Structure
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```
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atomizer_field_training_data/{study_name}/
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├── trial_0001/
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│ ├── input/model.bdf # Nastran input (mesh + params)
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│ ├── output/model.op2 # Binary results
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│ └── metadata.json # Design params + objectives
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||||
├── trial_0002/
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│ └── ...
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└── study_summary.json # Study-level metadata
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```
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||||
**Recommended**: 100-500 FEA samples for good generalization.
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||||
|
||||
---
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||||
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||||
## Neural Configuration
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||||
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||||
### Full Configuration Example
|
||||
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||||
```json
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||||
{
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"study_name": "bracket_neural_optimization",
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||||
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||||
"surrogate_settings": {
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"enabled": true,
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"model_type": "parametric_gnn",
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"model_path": "models/bracket_surrogate.pt",
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"confidence_threshold": 0.85,
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"validation_frequency": 10,
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||||
"fallback_to_fea": true
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||||
},
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||||
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||||
"training_data_export": {
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"enabled": true,
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"export_dir": "atomizer_field_training_data/bracket_study",
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"export_bdf": true,
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||||
"export_op2": true,
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||||
"export_fields": ["displacement", "stress"]
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||||
},
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||||
|
||||
"neural_optimization": {
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||||
"initial_fea_trials": 50,
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||||
"neural_trials": 5000,
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||||
"retraining_interval": 500,
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||||
"uncertainty_threshold": 0.15
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||||
}
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||||
}
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||||
```
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||||
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||||
### Configuration Parameters
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||||
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||||
| Parameter | Type | Default | Description |
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||||
|-----------|------|---------|-------------|
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||||
| `enabled` | bool | false | Enable neural surrogate |
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||||
| `model_type` | string | "parametric_gnn" | Model architecture |
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| `model_path` | string | - | Path to trained model |
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| `confidence_threshold` | float | 0.85 | Min confidence for predictions |
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| `validation_frequency` | int | 10 | FEA validation every N trials |
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||||
| `fallback_to_fea` | bool | true | Use FEA when uncertain |
|
||||
|
||||
---
|
||||
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||||
## Model Types
|
||||
|
||||
### Parametric Predictor GNN (Recommended)
|
||||
|
||||
Direct optimization objective prediction - fastest option.
|
||||
|
||||
```
|
||||
Design Parameters (ND) → Design Encoder (MLP) → GNN Backbone → Scalar Heads
|
||||
|
||||
Output (objectives):
|
||||
├── mass (grams)
|
||||
├── frequency (Hz)
|
||||
├── max_displacement (mm)
|
||||
└── max_stress (MPa)
|
||||
```
|
||||
|
||||
**Use When**: You only need scalar objectives, not full field predictions.
|
||||
|
||||
### Field Predictor GNN
|
||||
|
||||
Full displacement/stress field prediction.
|
||||
|
||||
```
|
||||
Input Features (12D per node):
|
||||
├── Node coordinates (x, y, z)
|
||||
├── Material properties (E, nu, rho)
|
||||
├── Boundary conditions (fixed/free per DOF)
|
||||
└── Load information (force magnitude, direction)
|
||||
|
||||
Output (per node):
|
||||
├── Displacement (6 DOF: Tx, Ty, Tz, Rx, Ry, Rz)
|
||||
└── Von Mises stress (1 value)
|
||||
```
|
||||
|
||||
**Use When**: You need field visualization or complex derived quantities.
|
||||
|
||||
### Ensemble Models
|
||||
|
||||
Multiple models for uncertainty quantification.
|
||||
|
||||
```python
|
||||
# Run N models
|
||||
predictions = [model_i(x) for model_i in ensemble]
|
||||
|
||||
# Statistics
|
||||
mean_prediction = np.mean(predictions)
|
||||
uncertainty = np.std(predictions)
|
||||
|
||||
# Decision
|
||||
if uncertainty > threshold:
|
||||
result = run_fea(x) # Fall back to FEA
|
||||
else:
|
||||
result = mean_prediction
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Hybrid FEA/Neural Workflow
|
||||
|
||||
### Phase 1: FEA Exploration (50-100 trials)
|
||||
- Run standard FEA optimization
|
||||
- Export training data automatically
|
||||
- Build landscape understanding
|
||||
|
||||
### Phase 2: Neural Training
|
||||
- Parse collected data
|
||||
- Train parametric predictor
|
||||
- Validate accuracy
|
||||
|
||||
### Phase 3: Neural Acceleration (1000s of trials)
|
||||
- Use neural network for rapid exploration
|
||||
- Periodic FEA validation
|
||||
- Retrain if distribution shifts
|
||||
|
||||
### Phase 4: FEA Refinement (10-20 trials)
|
||||
- Validate top candidates with FEA
|
||||
- Ensure results are physically accurate
|
||||
- Generate final Pareto front
|
||||
|
||||
---
|
||||
|
||||
## Training Pipeline
|
||||
|
||||
### Step 1: Collect Training Data
|
||||
|
||||
Run optimization with export enabled:
|
||||
```bash
|
||||
python run_optimization.py --train --trials 100
|
||||
```
|
||||
|
||||
### Step 2: Parse to Neural Format
|
||||
|
||||
```bash
|
||||
cd atomizer-field
|
||||
python batch_parser.py ../atomizer_field_training_data/my_study
|
||||
```
|
||||
|
||||
### Step 3: Train Model
|
||||
|
||||
**Parametric Predictor** (recommended):
|
||||
```bash
|
||||
python train_parametric.py \
|
||||
--train_dir ../training_data/parsed \
|
||||
--val_dir ../validation_data/parsed \
|
||||
--epochs 200 \
|
||||
--hidden_channels 128 \
|
||||
--num_layers 4
|
||||
```
|
||||
|
||||
**Field Predictor**:
|
||||
```bash
|
||||
python train.py \
|
||||
--train_dir ../training_data/parsed \
|
||||
--epochs 200 \
|
||||
--model FieldPredictorGNN \
|
||||
--hidden_channels 128 \
|
||||
--num_layers 6 \
|
||||
--physics_loss_weight 0.3
|
||||
```
|
||||
|
||||
### Step 4: Validate
|
||||
|
||||
```bash
|
||||
python validate.py --checkpoint runs/my_model/checkpoint_best.pt
|
||||
```
|
||||
|
||||
Expected output:
|
||||
```
|
||||
Validation Results:
|
||||
├── Mean Absolute Error: 2.3% (mass), 1.8% (frequency)
|
||||
├── R² Score: 0.987
|
||||
├── Inference Time: 4.5ms ± 0.8ms
|
||||
└── Physics Violations: 0.2%
|
||||
```
|
||||
|
||||
### Step 5: Deploy
|
||||
|
||||
Update config to use trained model:
|
||||
```json
|
||||
{
|
||||
"neural_surrogate": {
|
||||
"enabled": true,
|
||||
"model_checkpoint": "atomizer-field/runs/my_model/checkpoint_best.pt",
|
||||
"confidence_threshold": 0.85
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Uncertainty Thresholds
|
||||
|
||||
| Uncertainty | Action |
|
||||
|-------------|--------|
|
||||
| < 5% | Use neural prediction |
|
||||
| 5-15% | Use neural, flag for validation |
|
||||
| > 15% | Fall back to FEA |
|
||||
|
||||
---
|
||||
|
||||
## Accuracy Expectations
|
||||
|
||||
| Problem Type | Expected R² | Samples Needed |
|
||||
|--------------|-------------|----------------|
|
||||
| Well-behaved | > 0.95 | 50-100 |
|
||||
| Moderate nonlinear | > 0.90 | 100-200 |
|
||||
| Highly nonlinear | > 0.85 | 200-500 |
|
||||
|
||||
---
|
||||
|
||||
## AtomizerField Components
|
||||
|
||||
```
|
||||
atomizer-field/
|
||||
├── neural_field_parser.py # BDF/OP2 parsing
|
||||
├── field_predictor.py # Field GNN
|
||||
├── parametric_predictor.py # Parametric GNN
|
||||
├── train.py # Field training
|
||||
├── train_parametric.py # Parametric training
|
||||
├── validate.py # Model validation
|
||||
├── physics_losses.py # Physics-informed loss
|
||||
└── batch_parser.py # Batch data conversion
|
||||
|
||||
optimization_engine/
|
||||
├── neural_surrogate.py # Atomizer integration
|
||||
└── runner_with_neural.py # Neural runner
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
| Symptom | Cause | Solution |
|
||||
|---------|-------|----------|
|
||||
| High prediction error | Insufficient training data | Collect more FEA samples |
|
||||
| Out-of-distribution warnings | Design outside training range | Retrain with expanded range |
|
||||
| Slow inference | Large mesh | Use parametric predictor instead |
|
||||
| Physics violations | Low physics loss weight | Increase `physics_loss_weight` |
|
||||
|
||||
---
|
||||
|
||||
## Cross-References
|
||||
|
||||
- **System Protocol**: [SYS_14_NEURAL_ACCELERATION](../../docs/protocols/system/SYS_14_NEURAL_ACCELERATION.md)
|
||||
- **Operations**: [OP_05_EXPORT_TRAINING_DATA](../../docs/protocols/operations/OP_05_EXPORT_TRAINING_DATA.md)
|
||||
- **Core Skill**: [study-creation-core](../core/study-creation-core.md)
|
||||
209
.claude/skills/modules/nx-docs-lookup.md
Normal file
209
.claude/skills/modules/nx-docs-lookup.md
Normal file
@@ -0,0 +1,209 @@
|
||||
# NX Documentation Lookup Module
|
||||
|
||||
## Overview
|
||||
|
||||
This module provides on-demand access to Siemens NX Open and Simcenter documentation via the Dalidou MCP server. Use these tools when building new extractors, NX automation scripts, or debugging NX-related issues.
|
||||
|
||||
## CRITICAL: When to AUTO-SEARCH Documentation
|
||||
|
||||
**You MUST call `siemens_docs_search` BEFORE writing any code that uses NX Open APIs.**
|
||||
|
||||
### Automatic Search Triggers
|
||||
|
||||
| User Request | Action Required |
|
||||
|--------------|-----------------|
|
||||
| "Create extractor for {X}" | → `siemens_docs_search("{X} NXOpen")` |
|
||||
| "Get {property} from part" | → `siemens_docs_search("{property} NXOpen.Part")` |
|
||||
| "Extract {data} from FEM" | → `siemens_docs_search("{data} NXOpen.CAE")` |
|
||||
| "How do I {action} in NX" | → `siemens_docs_search("{action} NXOpen")` |
|
||||
| Any code with `NXOpen.*` | → Search before writing |
|
||||
|
||||
### Example: User asks "Create an extractor for inertia values"
|
||||
|
||||
```
|
||||
STEP 1: Immediately search
|
||||
→ siemens_docs_search("inertia mass properties NXOpen")
|
||||
|
||||
STEP 2: Review results, fetch details
|
||||
→ siemens_docs_fetch("NXOpen.MeasureManager")
|
||||
|
||||
STEP 3: Now write code with correct API calls
|
||||
```
|
||||
|
||||
**DO NOT guess NX Open API names.** Always search first.
|
||||
|
||||
## When to Load
|
||||
|
||||
Load this module when:
|
||||
- Creating new NX Open scripts or extractors
|
||||
- Working with `NXOpen.*` namespaces
|
||||
- Debugging NX automation errors
|
||||
- User mentions "NX API", "NX Open", "Simcenter docs"
|
||||
- Building features that interact with NX/Simcenter
|
||||
|
||||
## Available MCP Tools
|
||||
|
||||
### `siemens_docs_search`
|
||||
|
||||
**Purpose**: Search across NX Open, Simcenter, and Teamcenter documentation
|
||||
|
||||
**When to use**:
|
||||
- Finding which class/method performs a specific task
|
||||
- Discovering available APIs for a feature
|
||||
- Looking up Nastran card references
|
||||
|
||||
**Examples**:
|
||||
```
|
||||
siemens_docs_search("get node coordinates FEM")
|
||||
siemens_docs_search("CQUAD4 element properties")
|
||||
siemens_docs_search("NXOpen.CAE mesh creation")
|
||||
siemens_docs_search("extract stress results OP2")
|
||||
```
|
||||
|
||||
### `siemens_docs_fetch`
|
||||
|
||||
**Purpose**: Fetch a specific documentation page with full content
|
||||
|
||||
**When to use**:
|
||||
- Need complete class reference
|
||||
- Getting detailed method signatures
|
||||
- Reading full examples
|
||||
|
||||
**Examples**:
|
||||
```
|
||||
siemens_docs_fetch("NXOpen.CAE.FemPart")
|
||||
siemens_docs_fetch("Nastran Quick Reference CQUAD4")
|
||||
```
|
||||
|
||||
### `siemens_auth_status`
|
||||
|
||||
**Purpose**: Check if the Siemens SSO session is valid
|
||||
|
||||
**When to use**:
|
||||
- Before a series of documentation lookups
|
||||
- When fetch requests fail
|
||||
- Debugging connection issues
|
||||
|
||||
### `siemens_login`
|
||||
|
||||
**Purpose**: Re-authenticate with Siemens if session expired
|
||||
|
||||
**When to use**:
|
||||
- After `siemens_auth_status` shows expired
|
||||
- When documentation fetches return auth errors
|
||||
|
||||
## Workflow: Building New Extractor
|
||||
|
||||
When creating a new extractor that uses NX Open APIs:
|
||||
|
||||
### Step 1: Search for Relevant APIs
|
||||
```
|
||||
→ siemens_docs_search("element stress results OP2")
|
||||
```
|
||||
Review results to identify candidate classes/methods.
|
||||
|
||||
### Step 2: Fetch Detailed Documentation
|
||||
```
|
||||
→ siemens_docs_fetch("NXOpen.CAE.Result")
|
||||
```
|
||||
Get full class documentation with method signatures.
|
||||
|
||||
### Step 3: Understand Data Formats
|
||||
```
|
||||
→ siemens_docs_search("CQUAD4 stress output format")
|
||||
```
|
||||
Understand Nastran output structure.
|
||||
|
||||
### Step 4: Build Extractor
|
||||
Following EXT_01 template, create the extractor with:
|
||||
- Proper API calls based on documentation
|
||||
- Docstring referencing the APIs used
|
||||
- Error handling for common NX exceptions
|
||||
|
||||
### Step 5: Document API Usage
|
||||
In the extractor docstring:
|
||||
```python
|
||||
def extract_element_stress(op2_path: Path) -> Dict:
|
||||
"""
|
||||
Extract element stress results from OP2 file.
|
||||
|
||||
NX Open APIs Used:
|
||||
- NXOpen.CAE.Result.AskElementStress
|
||||
- NXOpen.CAE.ResultAccess.AskResultValues
|
||||
|
||||
Nastran Cards:
|
||||
- CQUAD4, CTRIA3 (shell elements)
|
||||
- STRESS case control
|
||||
"""
|
||||
```
|
||||
|
||||
## Workflow: Debugging NX Errors
|
||||
|
||||
When encountering NX Open errors:
|
||||
|
||||
### Step 1: Search for Correct API
|
||||
```
|
||||
Error: AttributeError: 'FemPart' object has no attribute 'GetNodes'
|
||||
|
||||
→ siemens_docs_search("FemPart get nodes")
|
||||
```
|
||||
|
||||
### Step 2: Fetch Correct Class Reference
|
||||
```
|
||||
→ siemens_docs_fetch("NXOpen.CAE.FemPart")
|
||||
```
|
||||
Find the actual method name and signature.
|
||||
|
||||
### Step 3: Apply Fix
|
||||
Document the correction:
|
||||
```python
|
||||
# Wrong: femPart.GetNodes()
|
||||
# Right: femPart.BaseFEModel.FemMesh.Nodes
|
||||
```
|
||||
|
||||
## Common Search Patterns
|
||||
|
||||
| Task | Search Query |
|
||||
|------|--------------|
|
||||
| Mesh operations | `siemens_docs_search("NXOpen.CAE mesh")` |
|
||||
| Result extraction | `siemens_docs_search("CAE result OP2")` |
|
||||
| Geometry access | `siemens_docs_search("NXOpen.Features body")` |
|
||||
| Material properties | `siemens_docs_search("Nastran MAT1 material")` |
|
||||
| Load application | `siemens_docs_search("CAE load force")` |
|
||||
| Constraint setup | `siemens_docs_search("CAE boundary condition")` |
|
||||
| Expressions/Parameters | `siemens_docs_search("NXOpen Expression")` |
|
||||
| Part manipulation | `siemens_docs_search("NXOpen.Part")` |
|
||||
|
||||
## Key NX Open Namespaces
|
||||
|
||||
| Namespace | Domain |
|
||||
|-----------|--------|
|
||||
| `NXOpen.CAE` | FEA, meshing, results |
|
||||
| `NXOpen.Features` | Parametric features |
|
||||
| `NXOpen.Assemblies` | Assembly operations |
|
||||
| `NXOpen.Part` | Part-level operations |
|
||||
| `NXOpen.UF` | User Function (legacy) |
|
||||
| `NXOpen.GeometricUtilities` | Geometry helpers |
|
||||
|
||||
## Integration with Extractors
|
||||
|
||||
All extractors in `optimization_engine/extractors/` should:
|
||||
|
||||
1. **Search before coding**: Use `siemens_docs_search` to find correct APIs
|
||||
2. **Document API usage**: List NX Open APIs in docstring
|
||||
3. **Handle NX exceptions**: Catch `NXOpen.NXException` appropriately
|
||||
4. **Follow 20-line rule**: If extraction is complex, check if existing extractor handles it
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
| Issue | Solution |
|
||||
|-------|----------|
|
||||
| Auth errors | Run `siemens_auth_status`, then `siemens_login` if needed |
|
||||
| No results | Try broader search terms, check namespace spelling |
|
||||
| Incomplete docs | Fetch the parent class for full context |
|
||||
| Network errors | Verify Dalidou is accessible: `ping dalidou.local` |
|
||||
|
||||
---
|
||||
|
||||
*Module Version: 1.0*
|
||||
*MCP Server: dalidou.local:5000*
|
||||
364
.claude/skills/modules/zernike-optimization.md
Normal file
364
.claude/skills/modules/zernike-optimization.md
Normal file
@@ -0,0 +1,364 @@
|
||||
# Zernike Optimization Module
|
||||
|
||||
**Last Updated**: December 5, 2025
|
||||
**Version**: 1.0
|
||||
**Type**: Optional Module
|
||||
|
||||
This module provides specialized guidance for telescope mirror and optical surface optimization using Zernike polynomial decomposition.
|
||||
|
||||
---
|
||||
|
||||
## When to Load
|
||||
|
||||
- User mentions "telescope", "mirror", "optical", "wavefront"
|
||||
- Optimization involves surface deformation analysis
|
||||
- Need to extract Zernike coefficients from FEA results
|
||||
- Working with multi-subcase elevation angle comparisons
|
||||
|
||||
---
|
||||
|
||||
## Zernike Extractors (E8-E10)
|
||||
|
||||
| ID | Extractor | Function | Input | Output | Use Case |
|
||||
|----|-----------|----------|-------|--------|----------|
|
||||
| E8 | **Zernike WFE** | `extract_zernike_from_op2()` | `.op2` + `.bdf` | nm | Single subcase wavefront error |
|
||||
| E9 | **Zernike Relative** | `extract_zernike_relative_rms()` | `.op2` + `.bdf` | nm | Compare target vs reference subcase |
|
||||
| E10 | **Zernike Helpers** | `ZernikeObjectiveBuilder` | `.op2` | nm | Multi-subcase optimization builder |
|
||||
|
||||
---
|
||||
|
||||
## E8: Single Subcase Zernike Extraction
|
||||
|
||||
Extract Zernike coefficients and RMS metrics for a single subcase (e.g., one elevation angle).
|
||||
|
||||
```python
|
||||
from optimization_engine.extractors.extract_zernike import extract_zernike_from_op2
|
||||
|
||||
# Extract Zernike coefficients and RMS metrics for a single subcase
|
||||
result = extract_zernike_from_op2(
|
||||
op2_file,
|
||||
bdf_file=None, # Auto-detect from op2 location
|
||||
subcase="20", # Subcase label (e.g., "20" = 20 deg elevation)
|
||||
displacement_unit="mm"
|
||||
)
|
||||
|
||||
global_rms = result['global_rms_nm'] # Total surface RMS in nm
|
||||
filtered_rms = result['filtered_rms_nm'] # RMS with low orders removed
|
||||
coefficients = result['coefficients'] # List of 50 Zernike coefficients
|
||||
```
|
||||
|
||||
**Return Dictionary**:
|
||||
```python
|
||||
{
|
||||
'global_rms_nm': 45.2, # Total surface RMS (nm)
|
||||
'filtered_rms_nm': 12.8, # RMS with J1-J4 (piston, tip, tilt, defocus) removed
|
||||
'coefficients': [0.0, 12.3, ...], # 50 Zernike coefficients (Noll indexing)
|
||||
'n_nodes': 5432, # Number of surface nodes
|
||||
'rms_per_mode': {...} # RMS contribution per Zernike mode
|
||||
}
|
||||
```
|
||||
|
||||
**When to Use**:
|
||||
- Single elevation angle analysis
|
||||
- Polishing orientation (zenith) wavefront error
|
||||
- Absolute surface quality metrics
|
||||
|
||||
---
|
||||
|
||||
## E9: Relative RMS Between Subcases
|
||||
|
||||
Compare wavefront error between two subcases (e.g., 40° vs 20° reference).
|
||||
|
||||
```python
|
||||
from optimization_engine.extractors.extract_zernike import extract_zernike_relative_rms
|
||||
|
||||
# Compare wavefront error between subcases (e.g., 40 deg vs 20 deg reference)
|
||||
result = extract_zernike_relative_rms(
|
||||
op2_file,
|
||||
bdf_file=None,
|
||||
target_subcase="40", # Target orientation
|
||||
reference_subcase="20", # Reference (usually polishing orientation)
|
||||
displacement_unit="mm"
|
||||
)
|
||||
|
||||
relative_rms = result['relative_filtered_rms_nm'] # Differential WFE in nm
|
||||
delta_coeffs = result['delta_coefficients'] # Coefficient differences
|
||||
```
|
||||
|
||||
**Return Dictionary**:
|
||||
```python
|
||||
{
|
||||
'relative_filtered_rms_nm': 8.7, # Differential WFE (target - reference)
|
||||
'delta_coefficients': [...], # Coefficient differences
|
||||
'target_rms_nm': 52.3, # Target subcase absolute RMS
|
||||
'reference_rms_nm': 45.2, # Reference subcase absolute RMS
|
||||
'improvement_percent': -15.7 # Negative = worse than reference
|
||||
}
|
||||
```
|
||||
|
||||
**When to Use**:
|
||||
- Comparing performance across elevation angles
|
||||
- Minimizing deformation relative to polishing orientation
|
||||
- Multi-angle telescope mirror optimization
|
||||
|
||||
---
|
||||
|
||||
## E10: Multi-Subcase Objective Builder
|
||||
|
||||
Build objectives for multiple subcases in a single extractor (most efficient for complex optimization).
|
||||
|
||||
```python
|
||||
from optimization_engine.extractors.zernike_helpers import ZernikeObjectiveBuilder
|
||||
|
||||
# Build objectives for multiple subcases in one extractor
|
||||
builder = ZernikeObjectiveBuilder(
|
||||
op2_finder=lambda: model_dir / "ASSY_M1-solution_1.op2"
|
||||
)
|
||||
|
||||
# Add relative objectives (target vs reference)
|
||||
builder.add_relative_objective(
|
||||
"40", "20", # 40° vs 20° reference
|
||||
metric="relative_filtered_rms_nm",
|
||||
weight=5.0
|
||||
)
|
||||
builder.add_relative_objective(
|
||||
"60", "20", # 60° vs 20° reference
|
||||
metric="relative_filtered_rms_nm",
|
||||
weight=5.0
|
||||
)
|
||||
|
||||
# Add absolute objective for polishing orientation
|
||||
builder.add_subcase_objective(
|
||||
"90", # Zenith (polishing orientation)
|
||||
metric="rms_filter_j1to3", # Only remove piston, tip, tilt
|
||||
weight=1.0
|
||||
)
|
||||
|
||||
# Evaluate all at once (efficient - parses OP2 only once)
|
||||
results = builder.evaluate_all()
|
||||
# Returns: {'rel_40_vs_20': 4.2, 'rel_60_vs_20': 8.7, 'rms_90': 15.3}
|
||||
```
|
||||
|
||||
**When to Use**:
|
||||
- Multi-objective telescope optimization
|
||||
- Multiple elevation angles to optimize
|
||||
- Weighted combination of absolute and relative WFE
|
||||
|
||||
---
|
||||
|
||||
## Zernike Modes Reference
|
||||
|
||||
| Noll Index | Name | Physical Meaning | Correctability |
|
||||
|------------|------|------------------|----------------|
|
||||
| J1 | Piston | Constant offset | Easily corrected |
|
||||
| J2 | Tip | X-tilt | Easily corrected |
|
||||
| J3 | Tilt | Y-tilt | Easily corrected |
|
||||
| J4 | Defocus | Power error | Easily corrected |
|
||||
| J5 | Astigmatism (0°) | Cylindrical error | Correctable |
|
||||
| J6 | Astigmatism (45°) | Cylindrical error | Correctable |
|
||||
| J7 | Coma (x) | Off-axis aberration | Harder to correct |
|
||||
| J8 | Coma (y) | Off-axis aberration | Harder to correct |
|
||||
| J9-J10 | Trefoil | Triangular error | Hard to correct |
|
||||
| J11+ | Higher order | Complex aberrations | Very hard to correct |
|
||||
|
||||
**Filtering Convention**:
|
||||
- `filtered_rms`: Removes J1-J4 (piston, tip, tilt, defocus) - standard
|
||||
- `rms_filter_j1to3`: Removes only J1-J3 (keeps defocus) - for focus-sensitive applications
|
||||
|
||||
---
|
||||
|
||||
## Common Zernike Optimization Patterns
|
||||
|
||||
### Pattern 1: Minimize Relative WFE Across Elevations
|
||||
|
||||
```python
|
||||
# Objective: Minimize max relative WFE across all elevation angles
|
||||
objectives = [
|
||||
{"name": "rel_40_vs_20", "goal": "minimize"},
|
||||
{"name": "rel_60_vs_20", "goal": "minimize"},
|
||||
]
|
||||
|
||||
# Use weighted sum or multi-objective
|
||||
def objective(trial):
|
||||
results = builder.evaluate_all()
|
||||
return (results['rel_40_vs_20'], results['rel_60_vs_20'])
|
||||
```
|
||||
|
||||
### Pattern 2: Single Elevation + Mass
|
||||
|
||||
```python
|
||||
# Objective: Minimize WFE at 45° while minimizing mass
|
||||
objectives = [
|
||||
{"name": "wfe_45", "goal": "minimize"}, # Wavefront error
|
||||
{"name": "mass", "goal": "minimize"}, # Mirror mass
|
||||
]
|
||||
```
|
||||
|
||||
### Pattern 3: Weighted Multi-Angle
|
||||
|
||||
```python
|
||||
# Weighted combination of multiple angles
|
||||
def combined_wfe(trial):
|
||||
results = builder.evaluate_all()
|
||||
weighted_wfe = (
|
||||
5.0 * results['rel_40_vs_20'] +
|
||||
5.0 * results['rel_60_vs_20'] +
|
||||
1.0 * results['rms_90']
|
||||
)
|
||||
return weighted_wfe
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Telescope Mirror Study Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"study_name": "m1_mirror_optimization",
|
||||
"description": "Minimize wavefront error across elevation angles",
|
||||
|
||||
"objectives": [
|
||||
{
|
||||
"name": "wfe_40_vs_20",
|
||||
"goal": "minimize",
|
||||
"unit": "nm",
|
||||
"extraction": {
|
||||
"action": "extract_zernike_relative_rms",
|
||||
"params": {
|
||||
"target_subcase": "40",
|
||||
"reference_subcase": "20"
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
|
||||
"simulation": {
|
||||
"analysis_types": ["static"],
|
||||
"subcases": ["20", "40", "60", "90"],
|
||||
"solution_name": null
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
1. **Parse OP2 Once**: Use `ZernikeObjectiveBuilder` to parse the OP2 file only once per trial
|
||||
2. **Subcase Labels**: Match exact subcase labels from NX simulation
|
||||
3. **Node Selection**: Zernike extraction uses surface nodes only (auto-detected from BDF)
|
||||
4. **Memory**: Large meshes (>50k nodes) may require chunked processing
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
| Symptom | Cause | Solution |
|
||||
|---------|-------|----------|
|
||||
| "Subcase not found" | Wrong subcase label | Check NX .sim for exact labels |
|
||||
| High J1-J4 coefficients | Rigid body motion not constrained | Check boundary conditions |
|
||||
| NaN in coefficients | Insufficient nodes for polynomial order | Reduce max Zernike order |
|
||||
| Inconsistent RMS | Different node sets per subcase | Verify mesh consistency |
|
||||
| "Billion nm" RMS values | Node merge failed in AFEM | Check `MergeOccurrenceNodes = True` |
|
||||
| Corrupt OP2 data | All-zero displacements | Validate OP2 before processing |
|
||||
|
||||
---
|
||||
|
||||
## Assembly FEM (AFEM) Structure for Mirrors
|
||||
|
||||
Telescope mirror assemblies in NX typically consist of:
|
||||
|
||||
```
|
||||
ASSY_M1.prt # Master assembly part
|
||||
ASSY_M1_assyfem1.afm # Assembly FEM container
|
||||
ASSY_M1_assyfem1_sim1.sim # Simulation file (solve this)
|
||||
M1_Blank.prt # Mirror blank part
|
||||
M1_Blank_fem1.fem # Mirror blank mesh
|
||||
M1_Vertical_Support_Skeleton.prt # Support structure
|
||||
```
|
||||
|
||||
**Key Point**: Expressions in master `.prt` propagate through assembly → AFEM updates automatically.
|
||||
|
||||
---
|
||||
|
||||
## Multi-Subcase Gravity Analysis
|
||||
|
||||
For telescope mirrors, analyze multiple gravity orientations:
|
||||
|
||||
| Subcase | Elevation Angle | Purpose |
|
||||
|---------|-----------------|---------|
|
||||
| 1 | 90° (zenith) | Polishing orientation - manufacturing reference |
|
||||
| 2 | 20° | Low elevation - reference for relative metrics |
|
||||
| 3 | 40° | Mid-low elevation |
|
||||
| 4 | 60° | Mid-high elevation |
|
||||
|
||||
**CRITICAL**: NX subcase numbers don't always match angle labels! Use explicit mapping:
|
||||
|
||||
```json
|
||||
"subcase_labels": {
|
||||
"1": "90deg",
|
||||
"2": "20deg",
|
||||
"3": "40deg",
|
||||
"4": "60deg"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Lessons Learned (M1 Mirror V1-V9)
|
||||
|
||||
### 1. TPE Sampler Seed Issue
|
||||
|
||||
**Problem**: Resuming study with fixed seed causes duplicate parameters.
|
||||
|
||||
**Solution**:
|
||||
```python
|
||||
if is_new_study:
|
||||
sampler = TPESampler(seed=42)
|
||||
else:
|
||||
sampler = TPESampler() # No seed for resume
|
||||
```
|
||||
|
||||
### 2. OP2 Data Validation
|
||||
|
||||
**Always validate before processing**:
|
||||
```python
|
||||
unique_values = len(np.unique(disp_z))
|
||||
if unique_values < 10:
|
||||
raise RuntimeError("CORRUPT OP2: insufficient unique values")
|
||||
|
||||
if np.abs(disp_z).max() > 1e6:
|
||||
raise RuntimeError("CORRUPT OP2: unrealistic displacement")
|
||||
```
|
||||
|
||||
### 3. Reference Subcase Selection
|
||||
|
||||
Use lowest operational elevation (typically 20°) as reference. Higher elevations show positive relative WFE as gravity effects increase.
|
||||
|
||||
### 4. Optical Convention
|
||||
|
||||
For mirror surface to wavefront error:
|
||||
```python
|
||||
WFE = 2 * surface_displacement # Reflection doubles path difference
|
||||
wfe_nm = 2.0 * displacement_mm * 1e6 # Convert mm to nm
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Typical Mirror Design Variables
|
||||
|
||||
| Parameter | Description | Typical Range |
|
||||
|-----------|-------------|---------------|
|
||||
| `whiffle_min` | Whiffle tree minimum dimension | 35-55 mm |
|
||||
| `whiffle_outer_to_vertical` | Whiffle arm angle | 68-80 deg |
|
||||
| `inner_circular_rib_dia` | Rib diameter | 480-620 mm |
|
||||
| `lateral_inner_angle` | Lateral support angle | 25-28.5 deg |
|
||||
| `blank_backface_angle` | Mirror blank geometry | 3.5-5.0 deg |
|
||||
|
||||
---
|
||||
|
||||
## Cross-References
|
||||
|
||||
- **Extractor Catalog**: [extractors-catalog module](./extractors-catalog.md)
|
||||
- **System Protocol**: [SYS_12_EXTRACTOR_LIBRARY](../../docs/protocols/system/SYS_12_EXTRACTOR_LIBRARY.md)
|
||||
- **Core Skill**: [study-creation-core](../core/study-creation-core.md)
|
||||
Reference in New Issue
Block a user