Atomizer/studies/Simple_Bracket/bracket_stiffness_optimization/README.md

# Bracket Stiffness Optimization Study

Multi-objective optimization to maximize bracket stiffness while minimizing mass.

## Objectives

1. **Maximize Stiffness** (Primary)
   - Structural stiffness calculated as k = F/δ
   - Units: N/mm
   - Higher is better

2. **Minimize Mass** (Secondary + Constraint)
   - Total bracket mass
   - Units: kg
   - **Hard Constraint**: Mass ≤ 0.2 kg (200 grams)

## Design Variables

| Variable | Min | Max | Initial | Unit | Description |
|----------|-----|-----|---------|------|-------------|
| `support_angle` | 30.0 | 90.0 | 60.0 | degrees | Angle of support arm relative to base |
| `tip_thickness` | 15.0 | 40.0 | 25.0 | mm | Thickness of bracket tip where load is applied |

## Study Structure

```
bracket_stiffness_optimization/
├── README.md                           # This file
├── optimization_config.json            # Optimization settings
├── workflow_config.json                # Workflow definition
├── run_optimization.py                 # Main runner script
├── bracket_stiffness_extractor.py     # Results extractor
│
├── 1_setup/
│   └── model/
│       ├── Bracket.prt                 # NX part file
│       ├── Bracket_fem1_i.prt          # Idealized geometry
│       ├── Bracket_fem1.fem            # FEM definition
│       ├── Bracket_sim1.sim            # Simulation file
│       └── export_displacement_field.py # NX journal for field export
│
└── 2_results/
    ├── study.db                        # Optuna study database
    ├── optimization_summary.json       # Results summary
    ├── workflow.log                    # Execution log
    └── errors.log                      # Error log
```

## Generic Extractors Used

This study uses **reusable generic extractors** from `optimization_engine/extractors/`:

1. **`field_data_extractor.py`**
   - Parses NX exported field data (.fld files)
   - Works for: displacement, stress, strain, temperature, any scalar field
   - Multiple aggregation methods: max_abs, max, min, mean, std

2. **`op2_extractor.py`**
   - Extracts data from Nastran OP2 files using pyNastran
   - Mass properties (with unit conversion ton→kg)
   - Grid point forces (fx, fy, fz, resultant)
   - ~10000x faster than F06 parsing

3. **`stiffness_calculator.py`**
   - Generic stiffness calculator: k = F/δ
   - Works for any structure: bracket, beam, plate, etc.
   - Combines field data and OP2 extractors

The `bracket_stiffness_extractor.py` is a thin wrapper that orchestrates these generic tools with bracket-specific parameters.

## Workflow

Each optimization trial follows these steps:

1. **Update Design Variables**
   - Modify NX model expressions: `support_angle`, `tip_thickness`

2. **Solve Simulation**
   - Run NX Nastran SOL 101 (linear static analysis)
   - Output: `.op2` and `.f06` files

3. **Export Displacement Field**
   - Execute NX journal to export z-displacement
   - Output: `export_field_dz.fld`

4. **Extract Results**
   - Parse displacement field (max absolute z-displacement)
   - Extract applied force from OP2
   - Calculate stiffness: k = Force / Displacement
   - Extract mass from OP2 grid point weight

5. **Evaluate Constraints**
   - Check: mass ≤ 0.2 kg
   - If violated: prune trial

6. **Report Results**
   - Send to Optuna study database
   - Broadcast to dashboard via WebSocket

## Running the Optimization

### Basic Usage

```bash
cd studies/bracket_stiffness_optimization
python run_optimization.py
```

### Advanced Options

```bash
# Custom number of trials
python run_optimization.py --trials 100

# Enable real-time dashboard
python run_optimization.py --dashboard

# Both
python run_optimization.py --trials 50 --dashboard
```

### Testing Before Full Run

Test the extractors with a single trial first:

```bash
# Test extractor independently
python bracket_stiffness_extractor.py
```

## Results Analysis

### View in Dashboard

The React dashboard provides real-time monitoring:
- Convergence plot (stiffness over trials)
- Parameter space exploration
- Pareto front visualization
- Mass constraint violations

Access at: `http://localhost:3001`

### Database Query

Results are stored in SQLite database `2_results/study.db`:

```python
import optuna

study = optuna.load_study(
    study_name="bracket_stiffness_optimization",
    storage="sqlite:///2_results/study.db"
)

# Get Pareto-optimal solutions
best_trials = study.best_trials

for trial in best_trials:
    stiffness_neg, mass = trial.values
    stiffness = -stiffness_neg
    print(f"Trial {trial.number}: Stiffness={stiffness:.2f} N/mm, Mass={mass:.6f} kg")
    print(f"  Params: {trial.params}")
```

### Export Results

```python
# Export to CSV
import pandas as pd

df = study.trials_dataframe()
df.to_csv('2_results/trials.csv', index=False)

# Export to JSON
import json
with open('2_results/trials.json', 'w') as f:
    json.dump([t.params | {'values': t.values} for t in study.trials], f, indent=2)
```

## Optimization Settings

- **Framework**: Protocol 10 - Intelligent Multi-Strategy Optimization (IMSO)
- **Adaptive Features**:
  - Landscape characterization (analyzes problem structure)
  - Strategy selection (picks best algorithm automatically)
  - Dynamic switching (changes strategy when stagnating)
  - Adaptive surrogate modeling
- **Strategies Available**: TPE, CMA-ES, QMC, Random, NSGA-II
- **Default trials**: 50
- **Parallelization**: 1 job (sequential)

### How Protocol 10 Works:

1. **Characterization Phase** (first 10 trials)
   - Random sampling to explore landscape
   - Analyzes: smoothness, multimodality, noise, dimensionality

2. **Strategy Selection**
   - Automatically picks best optimizer based on landscape
   - Example: Smooth → CMA-ES, Multimodal → TPE

3. **Adaptive Optimization**
   - Monitors progress every 10 trials
   - Switches strategies if stagnating
   - All history kept for surrogate modeling

## Expected Performance

- **Trial duration**: ~2-5 minutes (depends on mesh size)
- **50 trials**: ~2-4 hours
- **Infeasibility rate**: ~20-30% (trials violating mass constraint, but kept for surrogate)

## Constraints

1. **Mass Constraint**: mass ≤ 0.2 kg
   - Trials exceeding this are **NOT pruned** - they complete normally
   - Kept in history for surrogate modeling (valuable search information)
   - Marked as infeasible in database with `constraint_satisfied=False` attribute
   - **Not eligible for Pareto front** - only feasible solutions reported as optimal
   - This approach preserves knowledge while enforcing hard constraints

## Notes

- Simulation uses NX Nastran SOL 101 (linear static)
- Force units: Newtons (N)
- Displacement units: millimeters (mm)
- Mass units: kilograms (kg), converted from ton-mm-sec system
- Stiffness units: N/mm

## Troubleshooting

### NX Session Issues
If NX hangs or crashes:
```bash
# Kill all NX processes
taskkill /F /IM NXBIN.exe
```

### Extractor Failures
Check that:
- ResultProbe is defined in simulation for z-displacement
- OP2 file is generated (check solver settings)
- Field export journal has correct path

### Database Locked
If database is locked:
```bash
# Close all connections and restart
rm 2_results/study.db-journal
```

## References

- Generic extractors: `optimization_engine/extractors/`
- NX solver: `optimization_engine/nx_solver.py`
- pyNastran docs: https://pynastran-git.readthedocs.io/