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Atomizer/studies/Simple_Bracket/bracket_stiffness_optimization_atomizerfield/README.md

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feat: Add dashboard chat integration and MCP server Major changes: - Dashboard: WebSocket-based chat with session management - Dashboard: New chat components (ChatPane, ChatInput, ModeToggle) - Dashboard: Enhanced UI with parallel coordinates chart - MCP Server: New atomizer-tools server for Claude integration - Extractors: Enhanced Zernike OPD extractor - Reports: Improved report generator New studies (configs and scripts only): - M1 Mirror: Cost reduction campaign studies - Simple Beam, Simple Bracket, UAV Arm studies Note: Large iteration data (2_iterations/, best_design_archive/) excluded via .gitignore - kept on local Gitea only. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-13 15:53:55 -05:00
# Bracket Stiffness Optimization - AtomizerField
Multi-objective bracket geometry optimization with neural network acceleration.
**Created**: 2025-11-26
**Protocol**: Protocol 11 (Multi-Objective NSGA-II)
**Status**: Ready to Run
---
## 1. Engineering Problem
### 1.1 Objective
Optimize an L-bracket mounting structure to maximize structural stiffness while minimizing mass, subject to manufacturing constraints.
### 1.2 Physical System
- **Component**: L-shaped mounting bracket
- **Material**: Steel (density ρ defined in NX model)
- **Loading**: Static force applied in Z-direction
- **Boundary Conditions**: Fixed support at mounting face
- **Analysis Type**: Linear static (Nastran SOL 101)
---
## 2. Mathematical Formulation
### 2.1 Objectives
| Objective | Goal | Weight | Formula | Units |
|-----------|------|--------|---------|-------|
| Stiffness | maximize | 1.0 | $k = \frac{F}{\delta_{max}}$ | N/mm |
| Mass | minimize | 0.1 | $m = \sum_{e} \rho_e V_e$ | kg |
Where:
- $k$ = structural stiffness
- $F$ = applied force magnitude (N)
- $\delta_{max}$ = maximum absolute displacement (mm)
- $\rho_e$ = element material density (kg/mm³)
- $V_e$ = element volume (mm³)
### 2.2 Design Variables
| Parameter | Symbol | Bounds | Units | Description |
|-----------|--------|--------|-------|-------------|
| Support Angle | $\theta$ | [20, 70] | degrees | Angle of the support arm relative to base |
| Tip Thickness | $t$ | [30, 60] | mm | Thickness at the bracket tip |
**Design Space**:
$$\mathbf{x} = [\theta, t]^T \in \mathbb{R}^2$$
$$20 \leq \theta \leq 70$$
$$30 \leq t \leq 60$$
### 2.3 Constraints
| Constraint | Type | Formula | Threshold | Handling |
|------------|------|---------|-----------|----------|
| Mass Limit | Inequality | $g_1(\mathbf{x}) = m - m_{max}$ | $m_{max} = 0.2$ kg | Infeasible if violated |
**Feasible Region**:
$$\mathcal{F} = \{\mathbf{x} : g_1(\mathbf{x}) \leq 0\}$$
### 2.4 Multi-Objective Formulation
**Pareto Optimization Problem**:
$$\max_{\mathbf{x} \in \mathcal{F}} \quad k(\mathbf{x})$$
$$\min_{\mathbf{x} \in \mathcal{F}} \quad m(\mathbf{x})$$
**Pareto Dominance**: Solution $\mathbf{x}_1$ dominates $\mathbf{x}_2$ if:
- $k(\mathbf{x}_1) \geq k(\mathbf{x}_2)$ and $m(\mathbf{x}_1) \leq m(\mathbf{x}_2)$
- With at least one strict inequality
---
## 3. Optimization Algorithm
### 3.1 NSGA-II Configuration
| Parameter | Value | Description |
|-----------|-------|-------------|
| Algorithm | NSGA-II | Non-dominated Sorting Genetic Algorithm II |
| Population | auto | Managed by Optuna |
| Directions | `['maximize', 'minimize']` | (stiffness, mass) |
| Sampler | `NSGAIISampler` | Multi-objective sampler |
| Trials | 100 | 50 FEA + 50 neural |
**NSGA-II Properties**:
- Fast non-dominated sorting: $O(MN^2)$ where $M$ = objectives, $N$ = population
- Crowding distance for diversity preservation
- Binary tournament selection with crowding comparison
### 3.2 Return Format
```python
def objective(trial) -> Tuple[float, float]:
# ... simulation and extraction ...
return (stiffness, mass) # Tuple, NOT negated
```
---
## 4. Simulation Pipeline
### 4.1 Trial Execution Flow
```
┌─────────────────────────────────────────────────────────────────────┐
│ TRIAL n EXECUTION │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ 1. OPTUNA SAMPLES (NSGA-II) │
│ θ = trial.suggest_float("support_angle", 20, 70) │
│ t = trial.suggest_float("tip_thickness", 30, 60) │
│ │
│ 2. NX PARAMETER UPDATE │
│ Module: optimization_engine/nx_updater.py │
│ Action: Bracket.prt expressions ← {θ, t} │
│ │
│ 3. HOOK: PRE_SOLVE │
│ → Log trial start, validate design bounds │
│ │
│ 4. NX SIMULATION (Nastran SOL 101) │
│ Module: optimization_engine/solve_simulation.py │
│ Input: Bracket_sim1.sim │
│ Output: .dat, .op2, .f06 │
│ │
│ 5. HOOK: POST_SOLVE │
│ → Run export_displacement_field.py │
│ → Generate export_field_dz.fld │
│ │
│ 6. RESULT EXTRACTION │
│ Mass ← bdf_mass_extractor(.dat) │
│ Stiffness ← stiffness_calculator(.fld, .op2) │
│ │
│ 7. HOOK: POST_EXTRACTION │
│ → Export BDF/OP2 to training data directory │
│ │
│ 8. CONSTRAINT EVALUATION │
│ mass ≤ 0.2 kg → feasible/infeasible │
│ │
│ 9. RETURN TO OPTUNA │
│ return (stiffness, mass) │
│ │
└─────────────────────────────────────────────────────────────────────┘
```
### 4.2 Hooks Configuration
| Hook Point | Function | Purpose |
|------------|----------|---------|
| `PRE_SOLVE` | `log_trial_start()` | Log design variables, trial number |
| `POST_SOLVE` | `export_field_data()` | Run NX journal for .fld export |
| `POST_EXTRACTION` | `export_training_data()` | Save BDF/OP2 for neural training |
---
## 5. Result Extraction Methods
### 5.1 Mass Extraction
| Attribute | Value |
|-----------|-------|
| **Extractor** | `bdf_mass_extractor` |
| **Module** | `optimization_engine.extractors.bdf_mass_extractor` |
| **Function** | `extract_mass_from_bdf()` |
| **Source** | `bracket_sim1-solution_1.dat` |
| **Output** | kg |
**Algorithm**:
$$m = \sum_{e=1}^{N_{elem}} m_e = \sum_{e=1}^{N_{elem}} \rho_e \cdot V_e$$
Where element volume $V_e$ is computed from BDF geometry (CTETRA, CHEXA, etc.).
**Code**:
```python
from optimization_engine.extractors.bdf_mass_extractor import extract_mass_from_bdf
mass_kg = extract_mass_from_bdf("1_setup/model/bracket_sim1-solution_1.dat")
```
### 5.2 Stiffness Extraction
| Attribute | Value |
|-----------|-------|
| **Extractor** | `stiffness_calculator` |
| **Module** | `optimization_engine.extractors.stiffness_calculator` |
| **Displacement Source** | `export_field_dz.fld` |
| **Force Source** | `bracket_sim1-solution_1.op2` |
| **Components** | Force: $F_z$, Displacement: $\delta_z$ |
| **Output** | N/mm |
**Algorithm**:
$$k = \frac{F_z}{\delta_{max,z}}$$
Where:
- $F_z$ = applied force in Z-direction (extracted from OP2 OLOAD resultant)
- $\delta_{max,z} = \max_{i \in nodes} |u_{z,i}|$ (from field export)
**Code**:
```python
from optimization_engine.extractors.stiffness_calculator import StiffnessCalculator
calculator = StiffnessCalculator(
field_file="1_setup/model/export_field_dz.fld",
op2_file="1_setup/model/bracket_sim1-solution_1.op2",
force_component="fz",
displacement_component="z"
)
result = calculator.calculate()
stiffness = result['stiffness'] # N/mm
```
### 5.3 Field Data Format
**NX Field Export** (.fld):
```
FIELD: [ResultProbe] : [TABLE]
RESULT TYPE: Displacement
COMPONENT: Z
START DATA
step, node_id, value
0, 396, -0.086716040968895
0, 397, -0.091234567890123
...
END DATA
```
---
## 6. Neural Acceleration (AtomizerField)
### 6.1 Configuration
| Setting | Value | Description |
|---------|-------|-------------|
| `enabled` | `true` | Neural surrogate active |
| `min_training_points` | 50 | FEA trials before auto-training |
| `auto_train` | `true` | Trigger training automatically |
| `epochs` | 100 | Training epochs |
| `validation_split` | 0.2 | 20% holdout for validation |
| `retrain_threshold` | 25 | Retrain after N new FEA points |
| `model_type` | `parametric` | Input: design params only |
### 6.2 Surrogate Model
**Input**: $\mathbf{x} = [\theta, t]^T \in \mathbb{R}^2$
**Output**: $\hat{\mathbf{y}} = [\hat{k}, \hat{m}]^T \in \mathbb{R}^2$
**Architecture**: Parametric neural network (MLP)
**Training Objective**:
$$\mathcal{L} = \frac{1}{N} \sum_{i=1}^{N} \left[ (k_i - \hat{k}_i)^2 + (m_i - \hat{m}_i)^2 \right]$$
### 6.3 Training Data Location
```
atomizer_field_training_data/bracket_stiffness_optimization_atomizerfield/
├── trial_0001/
│ ├── input/model.bdf # Mesh + design parameters
│ ├── output/model.op2 # FEA displacement/stress results
│ └── metadata.json # {support_angle, tip_thickness, stiffness, mass}
├── trial_0002/
└── ...
```
### 6.4 Expected Performance
| Metric | Value |
|--------|-------|
| FEA time per trial | 10-30 min |
| Neural time per trial | ~4.5 ms |
| Speedup | ~2,200x |
| Expected R² | > 0.95 (after 50 samples) |
---
## 7. Study File Structure
```
bracket_stiffness_optimization_atomizerfield/
│
├── 1_setup/ # INPUT CONFIGURATION
│ ├── model/ # NX Model Files
│ │ ├── Bracket.prt # Parametric part
│ │ │ └── Expressions: support_angle, tip_thickness
│ │ ├── Bracket_sim1.sim # Simulation (SOL 101)
│ │ ├── Bracket_fem1.fem # FEM mesh (auto-updated)
│ │ ├── bracket_sim1-solution_1.dat # Nastran BDF input
│ │ ├── bracket_sim1-solution_1.op2 # Binary results
│ │ ├── bracket_sim1-solution_1.f06 # Text summary
│ │ ├── export_displacement_field.py # Field export journal
│ │ └── export_field_dz.fld # Z-displacement field
│ │
│ ├── optimization_config.json # Study configuration
│ └── workflow_config.json # Workflow metadata
│
├── 2_results/ # OUTPUT (auto-generated)
│ ├── study.db # Optuna SQLite database
│ ├── optimization_history.json # Trial history
│ ├── pareto_front.json # Pareto-optimal solutions
│ ├── optimization.log # Structured log
│ └── reports/ # Generated reports
│ └── optimization_report.md # Full results report
│
├── run_optimization.py # Entry point
├── reset_study.py # Database reset
└── README.md # This blueprint
```
---
## 8. Results Location
After optimization completes, results will be generated in `2_results/`:
| File | Description | Format |
|------|-------------|--------|
| `study.db` | Optuna database with all trials | SQLite |
| `optimization_history.json` | Full trial history | JSON |
| `pareto_front.json` | Pareto-optimal solutions | JSON |
| `optimization.log` | Execution log | Text |
| `reports/optimization_report.md` | **Full Results Report** | Markdown |
### 8.1 Results Report Contents
The generated `optimization_report.md` will contain:
1. **Optimization Summary** - Best solutions, convergence status
2. **Pareto Front Analysis** - All non-dominated solutions with trade-off visualization
3. **Parameter Correlations** - Design variable vs objective relationships
4. **Convergence History** - Objective values over trials
5. **Constraint Satisfaction** - Feasibility statistics
6. **Neural Surrogate Performance** - Training loss, validation R², prediction accuracy
7. **Algorithm Statistics** - NSGA-II population diversity, hypervolume indicator
8. **Recommendations** - Suggested optimal configurations
---
## 9. Quick Start
### Staged Workflow (Recommended)
```bash
# STAGE 1: DISCOVER - Clean old files, run ONE solve, discover available outputs
python run_optimization.py --discover
# STAGE 2: VALIDATE - Run single trial to validate extraction works
python run_optimization.py --validate
# STAGE 3: TEST - Run 3-trial integration test
python run_optimization.py --test
# STAGE 4: TRAIN - Collect FEA training data for neural surrogate
python run_optimization.py --train --trials 50
# STAGE 5: RUN - Official optimization
python run_optimization.py --run --trials 100
# With neural acceleration (after training)
python run_optimization.py --run --trials 100 --enable-nn --resume
```
### Stage Descriptions
| Stage | Command | Purpose | When to Use |
|-------|---------|---------|-------------|
| **DISCOVER** | `--discover` | Scan model, clean files, run 1 solve, report outputs | First time setup |
| **VALIDATE** | `--validate` | Run 1 trial with full extraction pipeline | After discover |
| **TEST** | `--test` | Run 3 trials, check consistency | Before long runs |
| **TRAIN** | `--train` | Collect FEA data for neural network | Building surrogate |
| **RUN** | `--run` | Official optimization | Production runs |
### Additional Options
```bash
# Clean old Nastran files before any stage
python run_optimization.py --discover --clean
# Resume from existing study
python run_optimization.py --run --trials 50 --resume
# Reset study (delete database)
python reset_study.py
python reset_study.py --clean # Also clean Nastran files
```
---
## 10. Dashboard Access
### Live Monitoring
| Dashboard | URL | Purpose |
|-----------|-----|---------|
| **Atomizer Dashboard** | [http://localhost:3003](http://localhost:3003) | Live optimization monitoring, Pareto plots |
| **Optuna Dashboard** | [http://localhost:8081](http://localhost:8081) | Trial history, hyperparameter importance |
### Starting Dashboards
```bash
# Start Atomizer Dashboard (from project root)
cd atomizer-dashboard/frontend && npm run dev
cd atomizer-dashboard/backend && python -m uvicorn api.main:app --port 8000
# Start Optuna Dashboard (for this study)
optuna-dashboard sqlite:///2_results/study.db --port 8081
```
### What You'll See
**Atomizer Dashboard** (localhost:3003):
- Real-time Pareto front visualization
- Parallel coordinates plot for design variables
- Trial progress and success/failure rates
- Study comparison across multiple optimizations
**Optuna Dashboard** (localhost:8081):
- Trial history with all parameters and objectives
- Hyperparameter importance analysis
- Optimization history plots
- Slice plots for parameter sensitivity
---
## 11. Configuration Reference
**File**: `1_setup/optimization_config.json`
| Section | Key | Description |
|---------|-----|-------------|
| `optimization_settings.protocol` | `protocol_11_multi_objective` | Algorithm selection |
| `optimization_settings.sampler` | `NSGAIISampler` | Optuna sampler |
| `optimization_settings.n_trials` | `100` | Total trials |
| `design_variables[]` | `[support_angle, tip_thickness]` | Params to optimize |
| `objectives[]` | `[stiffness, mass]` | Objectives with goals |
| `constraints[]` | `[mass_limit]` | Constraints with thresholds |
| `result_extraction.*` | Extractor configs | How to get results |
| `neural_acceleration.*` | Neural settings | AtomizerField config |
| `training_data_export.*` | Export settings | Training data location |
---
## 12. References
- **Deb, K. et al.** (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. *IEEE Transactions on Evolutionary Computation*.
- **pyNastran Documentation**: BDF/OP2 parsing
- **Optuna Documentation**: Multi-objective optimization with NSGA-II
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