ec5e42d733
Major improvements to telescope mirror optimization workflow: Assembly FEM Workflow (solve_simulation.py): - Fixed multi-part assembly FEM update sequence - Use ImportFromFile() for reliable expression updates - Add DuplicateNodesCheckBuilder with MergeOccurrenceNodes=True - Switch to Foreground solve mode for multi-subcase solutions - Add detailed logging and diagnostics for node merge operations Zernike RMS Calculation: - CRITICAL FIX: Use correct surface-based RMS formula - Global RMS = sqrt(mean(W^2)) from actual WFE values - Filtered RMS = sqrt(mean(W_residual^2)) after removing low-order fit - This matches zernike_Post_Script_NX.py (optical standard) - Previous WRONG formula was: sqrt(sum(coeffs^2)) - Add compute_rms_filter_j1to3() for optician workload metric Subcase Mapping: - Fix subcase mapping to match NX model: - Subcase 1 = 90 deg (polishing orientation) - Subcase 2 = 20 deg (reference) - Subcase 3 = 40 deg - Subcase 4 = 60 deg New Study: M1 Mirror Zernike Optimization - Full optimization config with 11 design variables - 3 objectives: rel_filtered_rms_40_vs_20, rel_filtered_rms_60_vs_20, mfg_90_optician_workload - Neural surrogate support for accelerated optimization Documentation: - Update ZERNIKE_INTEGRATION.md with correct RMS formula - Update ASSEMBLY_FEM_WORKFLOW.md with expression import and node merge details - Add reference scripts from original zernike_Post_Script_NX.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
135 lines
4.0 KiB
Markdown
135 lines
4.0 KiB
Markdown
# M1 Mirror Zernike Optimization Dashboard
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## Study Overview
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**Objective**: Optimize telescope primary mirror (M1) support structure to minimize wavefront error across different gravity orientations.
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**Method**: Hybrid FEA + Neural Network acceleration using Zernike polynomial decomposition.
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## Quick Start
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### 1. Prepare Model Files
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Copy your NX model files to:
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```
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studies/m1_mirror_zernike_optimization/1_setup/model/
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```
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Required files:
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- `ASSY_M1.prt` (or your assembly name)
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- `ASSY_M1_assyfem1.afm`
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- `ASSY_M1_assyfem1_sim1.sim`
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- Associated `.fem` and `_i.prt` files
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### 2. Run FEA Trials (Build Training Data)
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```bash
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cd studies/m1_mirror_zernike_optimization
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python run_optimization.py --run --trials 40
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```
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This will:
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- Run ~40 FEA trials (10-15 min each = ~8-10 hours)
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- Extract 50 Zernike coefficients for each subcase (20/40/60/90 deg)
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- Store all data in Optuna database
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### 3. Train Neural Surrogate
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```bash
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python run_optimization.py --train-surrogate
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```
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Trains MLP to predict 200 outputs (50 coefficients x 4 subcases) from design variables.
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### 4. Run Neural-Accelerated Optimization
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```bash
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python run_optimization.py --run --trials 1000 --enable-nn
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```
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1000 trials in ~seconds!
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### 5. View Results
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**Optuna Dashboard:**
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```bash
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optuna-dashboard sqlite:///2_results/study.db --port 8081
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```
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Open http://localhost:8081
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## Design Variables
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| Variable | Range | Baseline | Units | Status |
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|----------|-------|----------|-------|--------|
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| whiffle_min | 35-55 | 40.55 | mm | **Enabled** |
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| whiffle_outer_to_vertical | 68-80 | 75.67 | deg | **Enabled** |
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| inner_circular_rib_dia | 480-620 | 534.00 | mm | **Enabled** |
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| whiffle_triangle_closeness | 50-65 | 60.00 | mm | Disabled |
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| blank_backface_angle | 3.5-5.0 | 4.23 | deg | Disabled |
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| lateral_inner_angle | 25-28.5 | 26.79 | deg | Disabled |
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| lateral_outer_angle | 13-17 | 14.64 | deg | Disabled |
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| lateral_outer_pivot | 9-12 | 10.40 | mm | Disabled |
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| lateral_inner_pivot | 9-12 | 10.07 | mm | Disabled |
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| lateral_middle_pivot | 18-23 | 20.73 | mm | Disabled |
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| lateral_closeness | 9.5-12.5 | 11.02 | mm | Disabled |
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Edit `1_setup/optimization_config.json` to enable/disable variables.
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## Objectives
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| Objective | Weight | Target | Description |
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|-----------|--------|--------|-------------|
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| rel_filtered_rms_40_vs_20 | 5 | 4 nm | WFE at 40° relative to 20° reference |
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| rel_filtered_rms_60_vs_20 | 5 | 10 nm | WFE at 60° relative to 20° reference |
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| mfg_90_optician_workload | 1 | 20 nm | Polishing workload at 90° orientation |
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**Strategy**: Weighted sum minimization (normalized by targets)
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## Neural Surrogate Architecture
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- **Input**: Design variables (3-11 depending on enabled)
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- **Output**: 200 values (50 Zernike coefficients × 4 subcases)
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- **Architecture**: MLP with 4 layers, 128 hidden units
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- **Training**: ~40 FEA samples, 200 epochs
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## File Structure
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```
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m1_mirror_zernike_optimization/
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├── 1_setup/
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│ ├── optimization_config.json # Configuration
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│ └── model/ # NX model files (add yours here)
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├── 2_results/
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│ ├── study.db # Optuna database
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│ └── zernike_surrogate/ # Trained neural model
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│ └── checkpoint_best.pt
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├── run_optimization.py # Main script
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└── DASHBOARD.md # This file
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```
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## Commands Reference
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```bash
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# Run FEA optimization
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python run_optimization.py --run --trials 40
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# Train neural surrogate
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python run_optimization.py --train-surrogate
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# Run with neural acceleration
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python run_optimization.py --run --trials 1000 --enable-nn
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# Check status
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python run_optimization.py --status
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# Launch Optuna dashboard
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optuna-dashboard sqlite:///2_results/study.db --port 8081
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```
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## Tips
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1. **Start small**: Run 5-10 FEA trials first to verify everything works
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2. **Check Zernike extraction**: Verify OP2 has correct subcases (20/40/60/90)
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3. **Enable variables gradually**: Start with 3, add more after initial exploration
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4. **Neural validation**: After finding good neural designs, verify top candidates with FEA
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