feat(hydrotech-beam): Phase 1 LHS DoE study code
Implements the optimization study code for Phase 1 (LHS DoE) of the Hydrotech Beam structural optimization. Files added: - run_doe.py: Main entry point — Optuna study with SQLite persistence, Deb's feasibility rules, CSV/JSON export, Phase 1→2 gate check - sampling.py: 50-point LHS via scipy.stats.qmc with stratified integer sampling ensuring all 11 hole_count levels (5-15) are covered - geometric_checks.py: Pre-flight feasibility filter — hole overlap (corrected formula: span/(n-1) - d ≥ 30mm) and web clearance checks - nx_interface.py: NX automation module with stub solver for development and NXOpen template for Windows/dalidou integration - requirements.txt: optuna, scipy, numpy, pandas Key design decisions: - Baseline enqueued as Trial 0 (LAC lesson) - All 4 DV expression names from binary introspection (exact spelling) - Pre-flight geometric filter saves compute and prevents NX crashes - No surrogates (LAC lesson: direct FEA via TPE beats surrogate+L-BFGS) - SQLite persistence enables resume after interruption Tested end-to-end with stub solver: 51 trials, 12 geometric rejects, 39 solved, correct CSV/JSON output. Ref: OPTIMIZATION_STRATEGY.md, auditor review 2026-02-10
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## Purpose
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Map the design space of the Hydrotech sandwich I-beam to identify feasible regions, characterize variable sensitivities, and converge on a minimum-mass design that satisfies displacement and stress constraints.
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Map the design space of the Hydrotech sandwich I-beam to identify feasible regions, characterize variable sensitivities, and prepare data for Phase 2 (TPE optimization). This is Phase 1 of the two-phase strategy (DEC-HB-002).
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## Quick Facts
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@@ -13,59 +13,175 @@ Map the design space of the Hydrotech sandwich I-beam to identify feasible regio
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| **Objective** | Minimize mass (kg) |
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| **Constraints** | Tip displacement ≤ 10 mm, Von Mises stress ≤ 130 MPa |
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| **Design variables** | 4 (3 continuous + 1 integer) |
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| **Algorithm** | Phase 1: LHS DoE (50 trials) → Phase 2: TPE (60-100 trials) |
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| **Total budget** | 114–156 NX evaluations |
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| **Estimated compute** | ~4–5 hours |
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| **Status** | DRAFT — Awaiting pre-flight checks and review |
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| **Algorithm** | Phase 1: LHS DoE (50 trials + 1 baseline) |
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| **Total budget** | 51 evaluations |
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| **Constraint handling** | Deb's feasibility rules (Optuna constraint interface) |
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| **Status** | Code complete — ready for execution |
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## Design Variables
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## Design Variables (confirmed via NX binary introspection)
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| ID | Name | Range | Type | Baseline |
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|----|------|-------|------|----------|
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| DV1 | `beam_half_core_thickness` | 10–40 mm | Continuous | 20 mm |
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| DV2 | `beam_face_thickness` | 10–40 mm | Continuous | 20 mm |
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| DV3 | `holes_diameter` | 150–450 mm | Continuous | 300 mm |
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| DV4 | `hole_count` | 5–15 | Integer | 10 |
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| ID | NX Expression | Range | Type | Baseline | Unit |
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|----|--------------|-------|------|----------|------|
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| DV1 | `beam_half_core_thickness` | 10–40 | Continuous | 25.162 | mm |
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| DV2 | `beam_face_thickness` | 10–40 | Continuous | 21.504 | mm |
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| DV3 | `holes_diameter` | 150–450 | Continuous | 300 | mm |
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| DV4 | `hole_count` | 5–15 | Integer | 10 | — |
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## Baseline Performance
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## Usage
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| Metric | Value | Constraint | Status |
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|--------|-------|------------|--------|
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| Mass | ~974 kg | minimize | Overbuilt |
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| Tip displacement | ~22 mm | ≤ 10 mm | ❌ FAILS |
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| VM stress | unknown | ≤ 130 MPa | ⚠️ TBD |
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### Prerequisites
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## Key Decisions
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```bash
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pip install -r requirements.txt
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```
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- **Single-objective** formulation (DEC-HB-001)
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- **Two-phase** algorithm: LHS → TPE (DEC-HB-002)
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- **True integer** handling for hole_count (DEC-HB-003)
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- **Deb's feasibility rules** for constraint handling (infeasible baseline)
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- **Baseline enqueued** as Trial 0 (LAC lesson)
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Requires: Python 3.10+, optuna, scipy, numpy, pandas.
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### Development Run (stub solver)
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```bash
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# From the study directory:
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cd projects/hydrotech-beam/studies/01_doe_landscape/
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# Run with synthetic results (for pipeline testing):
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python run_doe.py --backend stub
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# With verbose logging:
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python run_doe.py --backend stub -v
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# Custom study name:
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python run_doe.py --backend stub --study-name my_test_run
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```
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### Production Run (NXOpen on Windows)
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```bash
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# On dalidou (Windows node with NX):
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python run_doe.py --backend nxopen --model-dir "C:/path/to/syncthing/Beam"
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```
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### Resume Interrupted Study
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```bash
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python run_doe.py --backend stub --resume --study-name hydrotech_beam_doe_phase1
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```
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### CLI Options
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| Flag | Default | Description |
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|------|---------|-------------|
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| `--backend` | `stub` | `stub` (testing) or `nxopen` (real NX) |
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| `--model-dir` | — | Path to NX model files (required for `nxopen`) |
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| `--study-name` | `hydrotech_beam_doe_phase1` | Optuna study name |
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| `--n-samples` | 50 | Number of LHS sample points |
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| `--seed` | 42 | Random seed for reproducibility |
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| `--results-dir` | `results/` | Output directory |
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| `--resume` | false | Resume existing study |
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| `-v` | false | Verbose (DEBUG) logging |
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## Files
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| File | Description |
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|------|-------------|
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| `OPTIMIZATION_STRATEGY.md` | Full strategy document (problem formulation, algorithm selection, risk mitigation) |
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| `atomizer_spec_draft.json` | AtomizerSpec configuration skeleton (DRAFT — open items must be resolved) |
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| `README.md` | This file |
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| `run_doe.py` | Main entry point — orchestrates the DoE study |
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| `sampling.py` | LHS generation with stratified integer sampling |
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| `geometric_checks.py` | Pre-flight geometric feasibility filter |
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| `nx_interface.py` | NX automation module (stub + NXOpen template) |
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| `requirements.txt` | Python dependencies |
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| `OPTIMIZATION_STRATEGY.md` | Full strategy document |
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| `results/` | Output directory (CSV, JSON, Optuna DB) |
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## Open Items Before Execution
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## Output Files
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1. Beam web length needed (geometric feasibility of hole patterns)
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2. Displacement extraction method (sensor vs .op2 node parsing)
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3. Stress extraction scope (whole model vs element group)
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4. Baseline stress measurement
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5. SOL 101 runtime benchmark
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6. Corner test validation (16 bound combinations)
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After a study run, `results/` will contain:
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| File | Description |
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|------|-------------|
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| `doe_results.csv` | All trials — DVs, objectives, constraints, status |
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| `doe_summary.json` | Study metadata, statistics, best feasible design |
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| `optuna_study.db` | SQLite database (Optuna persistence, resume support) |
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## Architecture
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```
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run_doe.py
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├── sampling.py Generate 50 LHS + 1 baseline
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│ └── scipy.stats.qmc.LatinHypercube
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├── geometric_checks.py Pre-flight feasibility filter
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│ ├── Hole overlap: span/(n-1) - d ≥ 30mm
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│ └── Web clearance: 500 - 2·face - d > 0
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├── nx_interface.py NX solver (stub or NXOpen)
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│ ├── NXStubSolver → synthetic results (development)
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│ └── NXOpenSolver → real NX Nastran SOL 101 (production)
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└── Optuna study
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├── SQLite storage (resume support)
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├── Enqueued trials (deterministic LHS)
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└── Deb's feasibility rules (constraint interface)
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```
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## Pipeline per Trial
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```
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Trial N
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│
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├── 1. Suggest DVs (from enqueued LHS point)
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│
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├── 2. Geometric pre-check
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│ ├── FAIL → record as infeasible, skip NX
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│ └── PASS ↓
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│
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├── 3. NX evaluation
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│ ├── Update expressions (exact NX names)
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│ ├── Rebuild model
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│ ├── Solve SOL 101
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│ └── Extract: mass (p173), displacement, stress
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│
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├── 4. Record results + constraint violations
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│
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└── 5. Log to CSV + Optuna DB
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```
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## Phase 1 → Phase 2 Gate Criteria
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Before proceeding to Phase 2 (TPE optimization), these checks must pass:
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| Check | Threshold | Action if FAIL |
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|-------|-----------|----------------|
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| Feasible points found | ≥ 5 | Expand bounds or relax constraints (escalate to CEO) |
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| NX solve success rate | ≥ 80% | Investigate failures, fix model, re-run |
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| No systematic crashes at bounds | Visual check | Tighten bounds away from failure region |
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## Key Design Decisions
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- **Baseline as Trial 0** — LAC lesson: always validate baseline first
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- **Pre-flight geometric filter** — catches infeasible geometry before NX (saves compute, avoids crashes)
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- **Stratified integer sampling** — ensures all 11 hole_count levels (5-15) are covered
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- **Deb's feasibility rules** — no penalty weight tuning; feasible always beats infeasible
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- **SQLite persistence** — study can be interrupted and resumed
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- **No surrogates** — LAC lesson: direct FEA via TPE beats surrogate + L-BFGS
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## NX Integration Notes
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The `nx_interface.py` module provides:
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- **`NXStubSolver`** — synthetic results from simplified beam mechanics (for pipeline testing)
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- **`NXOpenSolver`** — template for real NXOpen Python API integration (to be completed on Windows)
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Expression names are exact from binary introspection. Critical: `beam_lenght` has a typo in NX (no 'h') — use exact spelling.
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### Outputs to extract from NX:
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| Output | Source | Unit | Notes |
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|--------|--------|------|-------|
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| Mass | Expression `p173` | kg | Direct read |
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| Tip displacement | SOL 101 results | mm | TBD: sensor or .op2 parsing |
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| Max VM stress | SOL 101 results | MPa | ⚠️ pyNastran returns kPa — divide by 1000 |
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## References
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- [BREAKDOWN.md](../../BREAKDOWN.md) — Tech Lead's technical analysis
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- [DECISIONS.md](../../DECISIONS.md) — Decision log
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- [CONTEXT.md](../../CONTEXT.md) — Project context
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- [OPTIMIZATION_STRATEGY.md](./OPTIMIZATION_STRATEGY.md) — Full strategy document
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- [../../BREAKDOWN.md](../../BREAKDOWN.md) — Tech Lead's technical analysis
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- [../../DECISIONS.md](../../DECISIONS.md) — Decision log
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- [../../CONTEXT.md](../../CONTEXT.md) — Project context & expression map
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---
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*Created by ⚡ Optimizer Agent | 2026-02-09*
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*Code: 🏗️ Study Builder (Technical Lead) | Strategy: ⚡ Optimizer Agent | 2026-02-10*
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@@ -0,0 +1,205 @@
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"""Geometric feasibility pre-flight checks for Hydrotech Beam.
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Validates trial design variable combinations against physical geometry
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constraints BEFORE sending to NX. Catches infeasible combos that would
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cause NX rebuild failures or physically impossible geometries.
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References:
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OPTIMIZATION_STRATEGY.md §4.2 — Hole overlap analysis
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Auditor review 2026-02-10 — Corrected spacing formula
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import NamedTuple
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# ---------------------------------------------------------------------------
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# Fixed geometry constants (from NX introspection — CONTEXT.md)
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# ---------------------------------------------------------------------------
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BEAM_HALF_HEIGHT: float = 250.0 # mm — fixed, expression `beam_half_height`
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HOLE_SPAN: float = 4000.0 # mm — expression `p6`, total distribution length
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MIN_LIGAMENT: float = 30.0 # mm — minimum material between holes (mesh + structural)
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class FeasibilityResult(NamedTuple):
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"""Result of a geometric feasibility check."""
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feasible: bool
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reason: str
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ligament: float # mm — material between adjacent holes
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web_clearance: float # mm — clearance between hole edge and faces
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@dataclass(frozen=True)
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class DesignPoint:
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"""A single design variable combination."""
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beam_half_core_thickness: float # mm — DV1
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beam_face_thickness: float # mm — DV2
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holes_diameter: float # mm — DV3
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hole_count: int # — DV4
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def compute_ligament(holes_diameter: float, hole_count: int) -> float:
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"""Compute ligament width (material between adjacent holes).
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The NX pattern places `n` holes across `hole_span` mm using `n-1`
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intervals (holes at both endpoints of the span).
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Formula (corrected per auditor review):
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spacing = hole_span / (hole_count - 1)
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ligament = spacing - holes_diameter
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Args:
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holes_diameter: Hole diameter in mm.
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hole_count: Number of holes (integer ≥ 2).
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Returns:
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Ligament width in mm. Negative means overlap.
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"""
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if hole_count < 2:
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# Single hole — no overlap possible, return large ligament
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return HOLE_SPAN
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spacing = HOLE_SPAN / (hole_count - 1)
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return spacing - holes_diameter
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def compute_web_clearance(
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beam_face_thickness: float, holes_diameter: float
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) -> float:
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"""Compute clearance between hole edge and face sheets.
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Total beam height = 2 × beam_half_height = 500 mm (fixed).
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Web clear height = total_height - 2 × face_thickness.
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Clearance = web_clear_height - holes_diameter.
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Args:
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beam_face_thickness: Face sheet thickness in mm.
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holes_diameter: Hole diameter in mm.
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Returns:
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Web clearance in mm. ≤ 0 means hole doesn't fit.
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"""
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total_height = 2.0 * BEAM_HALF_HEIGHT # 500 mm
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web_clear_height = total_height - 2.0 * beam_face_thickness
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return web_clear_height - holes_diameter
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def check_feasibility(point: DesignPoint) -> FeasibilityResult:
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"""Run all geometric feasibility checks on a design point.
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Checks (in order):
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1. Hole overlap — ligament between adjacent holes ≥ MIN_LIGAMENT
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2. Web clearance — hole fits within the web (between face sheets)
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Args:
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point: Design variable combination to check.
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Returns:
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FeasibilityResult with feasibility status and diagnostic info.
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"""
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ligament = compute_ligament(point.holes_diameter, point.hole_count)
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web_clearance = compute_web_clearance(
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point.beam_face_thickness, point.holes_diameter
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)
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# Check 1: Hole overlap
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if ligament < MIN_LIGAMENT:
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return FeasibilityResult(
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feasible=False,
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reason=(
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f"Hole overlap: ligament={ligament:.1f}mm < "
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f"{MIN_LIGAMENT}mm minimum "
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f"(d={point.holes_diameter}mm, n={point.hole_count})"
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),
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ligament=ligament,
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web_clearance=web_clearance,
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)
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# Check 2: Web clearance
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if web_clearance <= 0:
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return FeasibilityResult(
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feasible=False,
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reason=(
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f"Hole exceeds web: clearance={web_clearance:.1f}mm ≤ 0 "
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f"(face={point.beam_face_thickness}mm, "
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f"d={point.holes_diameter}mm)"
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),
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ligament=ligament,
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web_clearance=web_clearance,
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)
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return FeasibilityResult(
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feasible=True,
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reason="OK",
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ligament=ligament,
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web_clearance=web_clearance,
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)
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def check_feasibility_from_values(
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beam_half_core_thickness: float,
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beam_face_thickness: float,
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holes_diameter: float,
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hole_count: int,
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) -> FeasibilityResult:
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"""Convenience wrapper — check feasibility from raw DV values.
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Args:
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beam_half_core_thickness: Core half-thickness in mm (DV1).
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beam_face_thickness: Face sheet thickness in mm (DV2).
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holes_diameter: Hole diameter in mm (DV3).
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hole_count: Number of holes (DV4).
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Returns:
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FeasibilityResult with feasibility status and diagnostic info.
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"""
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point = DesignPoint(
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beam_half_core_thickness=beam_half_core_thickness,
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beam_face_thickness=beam_face_thickness,
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holes_diameter=holes_diameter,
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hole_count=hole_count,
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)
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return check_feasibility(point)
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# ---------------------------------------------------------------------------
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# Quick self-test
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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# Baseline: should be feasible
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baseline = DesignPoint(
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beam_half_core_thickness=25.162,
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beam_face_thickness=21.504,
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holes_diameter=300.0,
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hole_count=10,
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)
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result = check_feasibility(baseline)
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print(f"Baseline: {result}")
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assert result.feasible, f"Baseline should be feasible! Got: {result}"
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# Worst case: n=15, d=450 — should be infeasible (overlap)
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worst_overlap = DesignPoint(
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beam_half_core_thickness=25.0,
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beam_face_thickness=20.0,
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holes_diameter=450.0,
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hole_count=15,
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)
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result = check_feasibility(worst_overlap)
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print(f"Worst overlap: {result}")
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assert not result.feasible, "n=15, d=450 should be infeasible"
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# Web clearance fail: face=40, d=450
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web_fail = DesignPoint(
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beam_half_core_thickness=25.0,
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beam_face_thickness=40.0,
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holes_diameter=450.0,
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hole_count=5,
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)
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result = check_feasibility(web_fail)
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print(f"Web clearance fail: {result}")
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assert not result.feasible, "face=40, d=450 should fail web clearance"
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print("\nAll self-tests passed ✓")
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398
projects/hydrotech-beam/studies/01_doe_landscape/nx_interface.py
Normal file
398
projects/hydrotech-beam/studies/01_doe_landscape/nx_interface.py
Normal file
@@ -0,0 +1,398 @@
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"""NX automation interface for Hydrotech Beam optimization.
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Stub/template module for NXOpen Python API integration. The actual NX
|
||||
automation runs on Windows (dalidou node) via Syncthing-synced model files.
|
||||
|
||||
This module defines the interface contract. The NXOpen-specific implementation
|
||||
will be filled in when running on the Windows side.
|
||||
|
||||
NX Expression Names (confirmed via binary introspection — CONTEXT.md):
|
||||
Design Variables:
|
||||
- beam_half_core_thickness (mm, continuous)
|
||||
- beam_face_thickness (mm, continuous)
|
||||
- holes_diameter (mm, continuous)
|
||||
- hole_count (integer, links to Pattern_p7)
|
||||
Outputs:
|
||||
- p173 (mass in kg, body_property147.mass)
|
||||
Fixed:
|
||||
- beam_lenght (⚠️ TYPO in NX — no 'h', 5000 mm)
|
||||
- beam_half_height (250 mm)
|
||||
- beam_half_width (150 mm)
|
||||
|
||||
References:
|
||||
CONTEXT.md — Full expression map
|
||||
OPTIMIZATION_STRATEGY.md §8.2 — Extractor requirements
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
from typing import Protocol
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Data types
|
||||
# ---------------------------------------------------------------------------
|
||||
@dataclass(frozen=True)
|
||||
class TrialInput:
|
||||
"""Design variable values for a single trial."""
|
||||
|
||||
beam_half_core_thickness: float # mm — DV1
|
||||
beam_face_thickness: float # mm — DV2
|
||||
holes_diameter: float # mm — DV3
|
||||
hole_count: int # — DV4
|
||||
|
||||
|
||||
@dataclass
|
||||
class TrialResult:
|
||||
"""Results extracted from NX after a trial solve.
|
||||
|
||||
All values populated after a successful SOL 101 solve.
|
||||
On failure, success=False and error_message explains the failure.
|
||||
"""
|
||||
|
||||
success: bool
|
||||
mass: float = float("nan") # kg — from expression `p173`
|
||||
tip_displacement: float = float("nan") # mm — from SOL 101 results
|
||||
max_von_mises: float = float("nan") # MPa — from SOL 101 results
|
||||
error_message: str = ""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# NX expression name constants
|
||||
# ---------------------------------------------------------------------------
|
||||
# ⚠️ These are EXACT NX expression names from binary introspection.
|
||||
# Do NOT change spelling — `beam_lenght` has a typo (no 'h') in NX.
|
||||
EXPR_HALF_CORE_THICKNESS = "beam_half_core_thickness"
|
||||
EXPR_FACE_THICKNESS = "beam_face_thickness"
|
||||
EXPR_HOLES_DIAMETER = "holes_diameter"
|
||||
EXPR_HOLE_COUNT = "hole_count"
|
||||
EXPR_MASS = "p173" # body_property147.mass, kg
|
||||
EXPR_BEAM_LENGTH = "beam_lenght" # ⚠️ TYPO IN NX — intentional
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Interface protocol
|
||||
# ---------------------------------------------------------------------------
|
||||
class NXSolverInterface(Protocol):
|
||||
"""Protocol for NX solver backends.
|
||||
|
||||
Implementors must provide the full pipeline:
|
||||
1. Update expressions → 2. Rebuild model → 3. Solve SOL 101 → 4. Extract results
|
||||
"""
|
||||
|
||||
def evaluate(self, trial_input: TrialInput) -> TrialResult:
|
||||
"""Run a full NX evaluation cycle for one trial.
|
||||
|
||||
Args:
|
||||
trial_input: Design variable values.
|
||||
|
||||
Returns:
|
||||
TrialResult with extracted outputs or failure info.
|
||||
"""
|
||||
...
|
||||
|
||||
def close(self) -> None:
|
||||
"""Clean up NX session resources.
|
||||
|
||||
⚠️ LAC CRITICAL: NEVER kill NX processes directly.
|
||||
Use NXSessionManager.close_nx_if_allowed() only.
|
||||
"""
|
||||
...
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Stub implementation (for development/testing without NX)
|
||||
# ---------------------------------------------------------------------------
|
||||
class NXStubSolver:
|
||||
"""Stub NX solver for development and testing.
|
||||
|
||||
Returns synthetic results based on simple analytical approximations
|
||||
of the beam behavior. NOT physically accurate — use only for
|
||||
testing the optimization pipeline.
|
||||
|
||||
The stub uses rough scaling relationships:
|
||||
- Mass ∝ (core + face) and inversely with hole area
|
||||
- Displacement ∝ 1/I where I depends on core and face thickness
|
||||
- Stress ∝ M*y/I (bending stress approximation)
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
"""Initialize stub solver."""
|
||||
logger.warning(
|
||||
"Using NX STUB solver — results are synthetic approximations. "
|
||||
"Replace with NXOpenSolver for real evaluations."
|
||||
)
|
||||
|
||||
def evaluate(self, trial_input: TrialInput) -> TrialResult:
|
||||
"""Return synthetic results based on simplified beam mechanics.
|
||||
|
||||
Args:
|
||||
trial_input: Design variable values.
|
||||
|
||||
Returns:
|
||||
TrialResult with approximate values.
|
||||
"""
|
||||
try:
|
||||
return self._compute_approximate(trial_input)
|
||||
except Exception as e:
|
||||
logger.error("Stub evaluation failed: %s", e)
|
||||
return TrialResult(
|
||||
success=False,
|
||||
error_message=f"Stub evaluation error: {e}",
|
||||
)
|
||||
|
||||
def _compute_approximate(self, inp: TrialInput) -> TrialResult:
|
||||
"""Simple analytical approximation of beam response.
|
||||
|
||||
This is a ROUGH approximation for pipeline testing only.
|
||||
Real physics requires NX Nastran SOL 101.
|
||||
"""
|
||||
import math
|
||||
|
||||
# Geometry
|
||||
L = 5000.0 # mm — beam length
|
||||
b = 300.0 # mm — beam width (2 × beam_half_width)
|
||||
h_core = inp.beam_half_core_thickness # mm — half core
|
||||
t_face = inp.beam_face_thickness # mm — face thickness
|
||||
d_hole = inp.holes_diameter # mm
|
||||
n_holes = inp.hole_count
|
||||
|
||||
# Total height and section properties (simplified I-beam)
|
||||
h_total = 500.0 # mm — 2 × beam_half_height (fixed)
|
||||
|
||||
# Approximate second moment of area (sandwich beam)
|
||||
# I ≈ b*h_total^3/12 - b*(h_total-2*t_face)^3/12 + web contribution
|
||||
h_inner = h_total - 2.0 * t_face
|
||||
I_section = (b * h_total**3 / 12.0) - (b * max(h_inner, 0.0) ** 3 / 12.0)
|
||||
|
||||
# Add core contribution
|
||||
I_section += 2.0 * h_core * h_total**2 / 4.0 # approximate
|
||||
|
||||
# Hole area reduction (mass)
|
||||
hole_area = n_holes * math.pi * (d_hole / 2.0) ** 2 # mm²
|
||||
|
||||
# Approximate mass (steel: 7.3 g/cm³ = 7.3e-6 kg/mm³)
|
||||
rho = 7.3e-6 # kg/mm³
|
||||
# Gross cross-section area (very simplified)
|
||||
A_gross = 2.0 * b * t_face + 2.0 * h_core * h_total
|
||||
# Remove holes from web
|
||||
web_thickness = 2.0 * h_core # approximate web thickness
|
||||
A_holes = n_holes * math.pi * (d_hole / 2.0) ** 2
|
||||
V_solid = A_gross * L
|
||||
V_holes = A_holes * web_thickness
|
||||
mass = rho * (V_solid - min(V_holes, V_solid * 0.8))
|
||||
|
||||
# Approximate tip displacement (cantilever, point load)
|
||||
# δ = PL³/(3EI)
|
||||
P = 10000.0 * 9.81 # 10,000 kgf → N
|
||||
E = 200000.0 # MPa (steel)
|
||||
if I_section > 0:
|
||||
displacement = P * L**3 / (3.0 * E * I_section)
|
||||
else:
|
||||
displacement = 9999.0
|
||||
|
||||
# Approximate max bending stress
|
||||
# σ = M*y/I where M = P*L, y = h_total/2
|
||||
M_max = P * L # N·mm
|
||||
y_max = h_total / 2.0
|
||||
if I_section > 0:
|
||||
stress = M_max * y_max / I_section # MPa
|
||||
else:
|
||||
stress = 9999.0
|
||||
|
||||
return TrialResult(
|
||||
success=True,
|
||||
mass=mass,
|
||||
tip_displacement=displacement,
|
||||
max_von_mises=stress,
|
||||
)
|
||||
|
||||
def close(self) -> None:
|
||||
"""No-op for stub solver."""
|
||||
logger.info("Stub solver closed.")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# NXOpen implementation template (to be completed on Windows/dalidou)
|
||||
# ---------------------------------------------------------------------------
|
||||
class NXOpenSolver:
|
||||
"""Real NXOpen-based solver — TEMPLATE, not yet functional.
|
||||
|
||||
This class provides the correct structure for NXOpen integration.
|
||||
Expression update code uses the exact names from binary introspection.
|
||||
|
||||
To complete:
|
||||
1. Set NX_MODEL_DIR to the Syncthing-synced model directory
|
||||
2. Implement _open_session() with NXOpen.Session
|
||||
3. Implement _solve() to trigger SOL 101
|
||||
4. Implement _extract_displacement() and _extract_stress()
|
||||
from .op2 results or NX result sensors
|
||||
"""
|
||||
|
||||
def __init__(self, model_dir: str) -> None:
|
||||
"""Initialize NXOpen solver.
|
||||
|
||||
Args:
|
||||
model_dir: Path to directory containing Beam.prt, etc.
|
||||
"""
|
||||
self.model_dir = model_dir
|
||||
self._session = None # NXOpen.Session
|
||||
self._part = None # NXOpen.Part
|
||||
logger.info("NXOpenSolver initialized with model_dir=%s", model_dir)
|
||||
|
||||
def evaluate(self, trial_input: TrialInput) -> TrialResult:
|
||||
"""Full NX evaluation pipeline.
|
||||
|
||||
Pipeline:
|
||||
1. Update expressions (beam_half_core_thickness, etc.)
|
||||
2. Rebuild model (triggers re-mesh of idealized part)
|
||||
3. Solve SOL 101
|
||||
4. Extract mass (p173), displacement, stress
|
||||
"""
|
||||
raise NotImplementedError(
|
||||
"NXOpenSolver.evaluate() is a template — implement on Windows "
|
||||
"with NXOpen Python API. See docstring for pipeline."
|
||||
)
|
||||
|
||||
def _update_expressions(self, trial_input: TrialInput) -> None:
|
||||
"""Update NX expressions for a trial.
|
||||
|
||||
⚠️ Expression names are EXACT from binary introspection.
|
||||
⚠️ `beam_lenght` has a typo (no 'h') — do NOT correct it.
|
||||
|
||||
This is the correct NXOpen code pattern (to be run on Windows):
|
||||
|
||||
```python
|
||||
import NXOpen
|
||||
|
||||
session = NXOpen.Session.GetSession()
|
||||
part = session.Parts.Work
|
||||
|
||||
# Update design variables
|
||||
expressions = {
|
||||
"beam_half_core_thickness": trial_input.beam_half_core_thickness,
|
||||
"beam_face_thickness": trial_input.beam_face_thickness,
|
||||
"holes_diameter": trial_input.holes_diameter,
|
||||
"hole_count": trial_input.hole_count,
|
||||
}
|
||||
|
||||
for expr_name, value in expressions.items():
|
||||
expr = part.Expressions.FindObject(expr_name)
|
||||
unit = expr.Units
|
||||
part.Expressions.EditWithUnits(expr, unit, str(value))
|
||||
|
||||
# Rebuild (update model to reflect new expressions)
|
||||
session.UpdateManager.DoUpdate(
|
||||
session.SetUndoMark(NXOpen.Session.MarkVisibility.Invisible, "Update")
|
||||
)
|
||||
```
|
||||
"""
|
||||
raise NotImplementedError("Template — implement with NXOpen")
|
||||
|
||||
def _solve(self) -> bool:
|
||||
"""Trigger NX Nastran SOL 101 solve.
|
||||
|
||||
```python
|
||||
# Open the sim file
|
||||
sim_part = session.Parts.OpenDisplay(
|
||||
os.path.join(self.model_dir, "Beam_sim1.sim"), None
|
||||
)
|
||||
|
||||
# Get the solution and solve
|
||||
sim_simulation = sim_part.Simulation
|
||||
solution = sim_simulation.Solutions[0] # First solution
|
||||
solution.Solve()
|
||||
|
||||
# Check solve status
|
||||
return solution.SolveStatus == NXOpen.CAE.Solution.Status.Solved
|
||||
```
|
||||
"""
|
||||
raise NotImplementedError("Template — implement with NXOpen")
|
||||
|
||||
def _extract_mass(self) -> float:
|
||||
"""Extract mass from NX expression p173.
|
||||
|
||||
```python
|
||||
mass_expr = part.Expressions.FindObject("p173")
|
||||
return mass_expr.Value # kg
|
||||
```
|
||||
"""
|
||||
raise NotImplementedError("Template — implement with NXOpen")
|
||||
|
||||
def _extract_displacement(self) -> float:
|
||||
"""Extract tip displacement from SOL 101 results.
|
||||
|
||||
Options (TBD — need to determine best approach):
|
||||
1. NX result sensor at beam tip → read value directly
|
||||
2. Parse .op2 file with pyNastran → find max displacement at tip nodes
|
||||
3. NX post-processing API → query displacement field
|
||||
|
||||
Returns:
|
||||
Tip displacement in mm.
|
||||
"""
|
||||
raise NotImplementedError(
|
||||
"Template — extraction method TBD. "
|
||||
"Options: result sensor, .op2 parsing, or NX post-processing API."
|
||||
)
|
||||
|
||||
def _extract_stress(self) -> float:
|
||||
"""Extract max von Mises stress from SOL 101 results.
|
||||
|
||||
⚠️ LAC LESSON: pyNastran returns stress in kPa for NX kg-mm-s
|
||||
unit system. Divide by 1000 for MPa.
|
||||
|
||||
Options (TBD):
|
||||
1. NX result sensor for max VM stress → read value directly
|
||||
2. Parse .op2 with pyNastran → max elemental nodal VM stress
|
||||
3. NX post-processing API → query stress field
|
||||
|
||||
Returns:
|
||||
Max von Mises stress in MPa.
|
||||
"""
|
||||
raise NotImplementedError(
|
||||
"Template — extraction method TBD. "
|
||||
"Remember: pyNastran stress is in kPa → divide by 1000 for MPa."
|
||||
)
|
||||
|
||||
def close(self) -> None:
|
||||
"""Close NX session gracefully.
|
||||
|
||||
⚠️ LAC CRITICAL: NEVER kill NX processes directly.
|
||||
Use NXSessionManager.close_nx_if_allowed() only.
|
||||
If NX hangs, implement a timeout (10 min per trial) and let
|
||||
NX time out gracefully.
|
||||
"""
|
||||
raise NotImplementedError("Template — implement graceful shutdown")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Factory
|
||||
# ---------------------------------------------------------------------------
|
||||
def create_solver(
|
||||
backend: str = "stub",
|
||||
model_dir: str = "",
|
||||
) -> NXStubSolver | NXOpenSolver:
|
||||
"""Create an NX solver instance.
|
||||
|
||||
Args:
|
||||
backend: "stub" for development, "nxopen" for real NX (Windows only).
|
||||
model_dir: Path to NX model directory (required for nxopen backend).
|
||||
|
||||
Returns:
|
||||
Solver instance implementing the NXSolverInterface protocol.
|
||||
|
||||
Raises:
|
||||
ValueError: If backend is unknown.
|
||||
"""
|
||||
if backend == "stub":
|
||||
return NXStubSolver()
|
||||
elif backend == "nxopen":
|
||||
if not model_dir:
|
||||
raise ValueError("model_dir required for nxopen backend")
|
||||
return NXOpenSolver(model_dir=model_dir)
|
||||
else:
|
||||
raise ValueError(f"Unknown backend: {backend!r}. Use 'stub' or 'nxopen'.")
|
||||
@@ -0,0 +1,7 @@
|
||||
# Hydrotech Beam — Phase 1 LHS DoE Study
|
||||
# Python 3.10+
|
||||
|
||||
optuna>=3.5.0
|
||||
scipy>=1.10.0
|
||||
numpy>=1.24.0
|
||||
pandas>=2.0.0
|
||||
659
projects/hydrotech-beam/studies/01_doe_landscape/run_doe.py
Normal file
659
projects/hydrotech-beam/studies/01_doe_landscape/run_doe.py
Normal file
@@ -0,0 +1,659 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Phase 1 LHS DoE — Hydrotech Beam Optimization.
|
||||
|
||||
Main entry point for the Design of Experiments landscape mapping study.
|
||||
Generates 50 LHS sample points + 1 baseline (Trial 0), runs pre-flight
|
||||
geometric checks, evaluates via NX (or stub), and manages the Optuna study.
|
||||
|
||||
Usage:
|
||||
# Dry run with stub solver (development/testing):
|
||||
python run_doe.py --backend stub --study-name hydrotech_doe_dev
|
||||
|
||||
# Real run with NXOpen (on Windows/dalidou):
|
||||
python run_doe.py --backend nxopen --model-dir /path/to/nx/models
|
||||
|
||||
# Resume interrupted study:
|
||||
python run_doe.py --backend stub --study-name hydrotech_doe_dev --resume
|
||||
|
||||
References:
|
||||
OPTIMIZATION_STRATEGY.md — Full strategy document
|
||||
CONTEXT.md — Confirmed expression names and values
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
import optuna
|
||||
|
||||
from geometric_checks import (
|
||||
DesignPoint,
|
||||
FeasibilityResult,
|
||||
check_feasibility,
|
||||
)
|
||||
from nx_interface import TrialInput, TrialResult, create_solver
|
||||
from sampling import DV_DEFINITIONS, generate_lhs_samples, points_to_dicts
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Configuration
|
||||
# ---------------------------------------------------------------------------
|
||||
DEFAULT_STUDY_NAME = "hydrotech_beam_doe_phase1"
|
||||
DEFAULT_N_SAMPLES = 50
|
||||
DEFAULT_SEED = 42
|
||||
DEFAULT_RESULTS_DIR = "results"
|
||||
DEFAULT_DB_PATH = "results/optuna_study.db"
|
||||
|
||||
# Constraint limits (hard limits — OPTIMIZATION_STRATEGY.md §1.2)
|
||||
DISPLACEMENT_LIMIT = 10.0 # mm
|
||||
STRESS_LIMIT = 130.0 # MPa
|
||||
|
||||
# Infeasible placeholder values (for geometric pre-check failures)
|
||||
INFEASIBLE_MASS = 99999.0 # kg
|
||||
INFEASIBLE_DISPLACEMENT = 99999.0 # mm
|
||||
INFEASIBLE_STRESS = 99999.0 # MPa
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Optuna constraint callback
|
||||
# ---------------------------------------------------------------------------
|
||||
def constraints_func(trial: optuna.trial.FrozenTrial) -> list[float]:
|
||||
"""Compute constraint violations for Deb's feasibility rules.
|
||||
|
||||
Returns list of constraint values where:
|
||||
value ≤ 0 → feasible (constraint satisfied)
|
||||
value > 0 → infeasible (constraint violated)
|
||||
|
||||
This implements Deb's feasibility rules (Deb 2000):
|
||||
1. Feasible vs feasible → lower objective wins
|
||||
2. Feasible vs infeasible → feasible wins
|
||||
3. Infeasible vs infeasible → lower total violation wins
|
||||
|
||||
References:
|
||||
OPTIMIZATION_STRATEGY.md §3.2
|
||||
|
||||
Args:
|
||||
trial: Optuna frozen trial with user attributes set.
|
||||
|
||||
Returns:
|
||||
[displacement_violation, stress_violation]
|
||||
"""
|
||||
disp = trial.user_attrs.get("tip_displacement", INFEASIBLE_DISPLACEMENT)
|
||||
stress = trial.user_attrs.get("max_von_mises", INFEASIBLE_STRESS)
|
||||
return [
|
||||
disp - DISPLACEMENT_LIMIT, # ≤ 0 means displacement ≤ 10 mm
|
||||
stress - STRESS_LIMIT, # ≤ 0 means stress ≤ 130 MPa
|
||||
]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Trial evaluation
|
||||
# ---------------------------------------------------------------------------
|
||||
def evaluate_trial(
|
||||
trial: optuna.Trial,
|
||||
solver: Any,
|
||||
) -> float:
|
||||
"""Evaluate a single trial: geometric check → NX solve → extract.
|
||||
|
||||
Args:
|
||||
trial: Optuna trial (with parameters already suggested/enqueued).
|
||||
solver: NX solver instance (stub or real).
|
||||
|
||||
Returns:
|
||||
Objective value (mass in kg). Returns INFEASIBLE_MASS for
|
||||
geometrically infeasible or failed evaluations.
|
||||
"""
|
||||
# Extract design variables from trial
|
||||
dv1 = trial.suggest_float(
|
||||
"beam_half_core_thickness", 10.0, 40.0,
|
||||
)
|
||||
dv2 = trial.suggest_float(
|
||||
"beam_face_thickness", 10.0, 40.0,
|
||||
)
|
||||
dv3 = trial.suggest_float(
|
||||
"holes_diameter", 150.0, 450.0,
|
||||
)
|
||||
dv4 = trial.suggest_int(
|
||||
"hole_count", 5, 15,
|
||||
)
|
||||
|
||||
trial_num = trial.number
|
||||
logger.info(
|
||||
"Trial %d: DV1=%.2f, DV2=%.2f, DV3=%.1f, DV4=%d",
|
||||
trial_num, dv1, dv2, dv3, dv4,
|
||||
)
|
||||
|
||||
# Store DVs in user attributes for logging
|
||||
trial.set_user_attr("beam_half_core_thickness", dv1)
|
||||
trial.set_user_attr("beam_face_thickness", dv2)
|
||||
trial.set_user_attr("holes_diameter", dv3)
|
||||
trial.set_user_attr("hole_count", dv4)
|
||||
|
||||
# Pre-flight geometric check
|
||||
point = DesignPoint(
|
||||
beam_half_core_thickness=dv1,
|
||||
beam_face_thickness=dv2,
|
||||
holes_diameter=dv3,
|
||||
hole_count=dv4,
|
||||
)
|
||||
geo_result: FeasibilityResult = check_feasibility(point)
|
||||
trial.set_user_attr("geo_feasible", geo_result.feasible)
|
||||
trial.set_user_attr("ligament", geo_result.ligament)
|
||||
trial.set_user_attr("web_clearance", geo_result.web_clearance)
|
||||
|
||||
if not geo_result.feasible:
|
||||
logger.warning(
|
||||
"Trial %d: GEOMETRICALLY INFEASIBLE — %s",
|
||||
trial_num, geo_result.reason,
|
||||
)
|
||||
trial.set_user_attr("status", "geo_infeasible")
|
||||
trial.set_user_attr("geo_reason", geo_result.reason)
|
||||
trial.set_user_attr("tip_displacement", INFEASIBLE_DISPLACEMENT)
|
||||
trial.set_user_attr("max_von_mises", INFEASIBLE_STRESS)
|
||||
trial.set_user_attr("mass", INFEASIBLE_MASS)
|
||||
return INFEASIBLE_MASS
|
||||
|
||||
# NX evaluation
|
||||
trial_input = TrialInput(
|
||||
beam_half_core_thickness=dv1,
|
||||
beam_face_thickness=dv2,
|
||||
holes_diameter=dv3,
|
||||
hole_count=dv4,
|
||||
)
|
||||
|
||||
t_start = time.monotonic()
|
||||
nx_result: TrialResult = solver.evaluate(trial_input)
|
||||
t_elapsed = time.monotonic() - t_start
|
||||
|
||||
trial.set_user_attr("solve_time_s", round(t_elapsed, 2))
|
||||
|
||||
if not nx_result.success:
|
||||
logger.error(
|
||||
"Trial %d: NX SOLVE FAILED — %s (%.1fs)",
|
||||
trial_num, nx_result.error_message, t_elapsed,
|
||||
)
|
||||
trial.set_user_attr("status", "solve_failed")
|
||||
trial.set_user_attr("error_message", nx_result.error_message)
|
||||
trial.set_user_attr("tip_displacement", INFEASIBLE_DISPLACEMENT)
|
||||
trial.set_user_attr("max_von_mises", INFEASIBLE_STRESS)
|
||||
trial.set_user_attr("mass", INFEASIBLE_MASS)
|
||||
return INFEASIBLE_MASS
|
||||
|
||||
# Record successful results
|
||||
trial.set_user_attr("status", "solved")
|
||||
trial.set_user_attr("mass", nx_result.mass)
|
||||
trial.set_user_attr("tip_displacement", nx_result.tip_displacement)
|
||||
trial.set_user_attr("max_von_mises", nx_result.max_von_mises)
|
||||
|
||||
# Check constraint feasibility
|
||||
disp_ok = nx_result.tip_displacement <= DISPLACEMENT_LIMIT
|
||||
stress_ok = nx_result.max_von_mises <= STRESS_LIMIT
|
||||
trial.set_user_attr("displacement_feasible", disp_ok)
|
||||
trial.set_user_attr("stress_feasible", stress_ok)
|
||||
trial.set_user_attr("fully_feasible", disp_ok and stress_ok)
|
||||
|
||||
logger.info(
|
||||
"Trial %d: mass=%.2f kg, disp=%.2f mm%s, stress=%.1f MPa%s (%.1fs)",
|
||||
trial_num,
|
||||
nx_result.mass,
|
||||
nx_result.tip_displacement,
|
||||
" ✓" if disp_ok else " ✗",
|
||||
nx_result.max_von_mises,
|
||||
" ✓" if stress_ok else " ✗",
|
||||
t_elapsed,
|
||||
)
|
||||
|
||||
return nx_result.mass
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Results export
|
||||
# ---------------------------------------------------------------------------
|
||||
def export_csv(study: optuna.Study, output_path: str) -> None:
|
||||
"""Export all trial results to CSV.
|
||||
|
||||
Columns: trial_number, status, beam_half_core_thickness, beam_face_thickness,
|
||||
holes_diameter, hole_count, mass, tip_displacement, max_von_mises,
|
||||
geo_feasible, displacement_feasible, stress_feasible, fully_feasible,
|
||||
ligament, web_clearance, solve_time_s
|
||||
|
||||
Args:
|
||||
study: Completed Optuna study.
|
||||
output_path: Path to output CSV file.
|
||||
"""
|
||||
fieldnames = [
|
||||
"trial_number",
|
||||
"status",
|
||||
"beam_half_core_thickness",
|
||||
"beam_face_thickness",
|
||||
"holes_diameter",
|
||||
"hole_count",
|
||||
"mass_kg",
|
||||
"tip_displacement_mm",
|
||||
"max_von_mises_MPa",
|
||||
"geo_feasible",
|
||||
"displacement_feasible",
|
||||
"stress_feasible",
|
||||
"fully_feasible",
|
||||
"ligament_mm",
|
||||
"web_clearance_mm",
|
||||
"solve_time_s",
|
||||
]
|
||||
|
||||
with open(output_path, "w", newline="") as f:
|
||||
writer = csv.DictWriter(f, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
|
||||
for trial in study.trials:
|
||||
ua = trial.user_attrs
|
||||
writer.writerow({
|
||||
"trial_number": trial.number,
|
||||
"status": ua.get("status", "unknown"),
|
||||
"beam_half_core_thickness": ua.get("beam_half_core_thickness", ""),
|
||||
"beam_face_thickness": ua.get("beam_face_thickness", ""),
|
||||
"holes_diameter": ua.get("holes_diameter", ""),
|
||||
"hole_count": ua.get("hole_count", ""),
|
||||
"mass_kg": ua.get("mass", ""),
|
||||
"tip_displacement_mm": ua.get("tip_displacement", ""),
|
||||
"max_von_mises_MPa": ua.get("max_von_mises", ""),
|
||||
"geo_feasible": ua.get("geo_feasible", ""),
|
||||
"displacement_feasible": ua.get("displacement_feasible", ""),
|
||||
"stress_feasible": ua.get("stress_feasible", ""),
|
||||
"fully_feasible": ua.get("fully_feasible", ""),
|
||||
"ligament_mm": ua.get("ligament", ""),
|
||||
"web_clearance_mm": ua.get("web_clearance", ""),
|
||||
"solve_time_s": ua.get("solve_time_s", ""),
|
||||
})
|
||||
|
||||
logger.info("CSV results exported to %s (%d trials)", output_path, len(study.trials))
|
||||
|
||||
|
||||
def export_summary(study: optuna.Study, output_path: str) -> None:
|
||||
"""Export study summary as JSON.
|
||||
|
||||
Includes metadata, statistics, best feasible design, and
|
||||
constraint satisfaction rates.
|
||||
|
||||
Args:
|
||||
study: Completed Optuna study.
|
||||
output_path: Path to output JSON file.
|
||||
"""
|
||||
trials = study.trials
|
||||
n_total = len(trials)
|
||||
|
||||
# Count by status
|
||||
n_solved = sum(1 for t in trials if t.user_attrs.get("status") == "solved")
|
||||
n_geo_infeasible = sum(
|
||||
1 for t in trials if t.user_attrs.get("status") == "geo_infeasible"
|
||||
)
|
||||
n_failed = sum(
|
||||
1 for t in trials if t.user_attrs.get("status") == "solve_failed"
|
||||
)
|
||||
n_feasible = sum(
|
||||
1 for t in trials if t.user_attrs.get("fully_feasible", False)
|
||||
)
|
||||
|
||||
# Best feasible trial
|
||||
best_feasible = None
|
||||
best_mass = float("inf")
|
||||
for t in trials:
|
||||
if t.user_attrs.get("fully_feasible", False):
|
||||
mass = t.user_attrs.get("mass", float("inf"))
|
||||
if mass < best_mass:
|
||||
best_mass = mass
|
||||
best_feasible = t
|
||||
|
||||
best_info = None
|
||||
if best_feasible is not None:
|
||||
ua = best_feasible.user_attrs
|
||||
best_info = {
|
||||
"trial_number": best_feasible.number,
|
||||
"mass_kg": ua.get("mass"),
|
||||
"tip_displacement_mm": ua.get("tip_displacement"),
|
||||
"max_von_mises_MPa": ua.get("max_von_mises"),
|
||||
"design_variables": {
|
||||
"beam_half_core_thickness": ua.get("beam_half_core_thickness"),
|
||||
"beam_face_thickness": ua.get("beam_face_thickness"),
|
||||
"holes_diameter": ua.get("holes_diameter"),
|
||||
"hole_count": ua.get("hole_count"),
|
||||
},
|
||||
}
|
||||
|
||||
summary = {
|
||||
"study_name": study.study_name,
|
||||
"phase": "Phase 1 — LHS DoE",
|
||||
"project": "Hydrotech Beam Structural Optimization",
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
"configuration": {
|
||||
"n_lhs_samples": DEFAULT_N_SAMPLES,
|
||||
"seed": DEFAULT_SEED,
|
||||
"baseline_included": True,
|
||||
"algorithm": "Latin Hypercube Sampling (scipy.stats.qmc)",
|
||||
"constraint_handling": "Deb's feasibility rules",
|
||||
"displacement_limit_mm": DISPLACEMENT_LIMIT,
|
||||
"stress_limit_MPa": STRESS_LIMIT,
|
||||
},
|
||||
"design_variables": [
|
||||
{
|
||||
"name": dv.name,
|
||||
"nx_expression": dv.nx_expression,
|
||||
"lower": dv.lower,
|
||||
"upper": dv.upper,
|
||||
"baseline": dv.baseline,
|
||||
"type": "integer" if dv.is_integer else "continuous",
|
||||
}
|
||||
for dv in DV_DEFINITIONS
|
||||
],
|
||||
"results": {
|
||||
"total_trials": n_total,
|
||||
"solved": n_solved,
|
||||
"geo_infeasible": n_geo_infeasible,
|
||||
"solve_failed": n_failed,
|
||||
"fully_feasible": n_feasible,
|
||||
"solve_success_rate": round(n_solved / max(n_total, 1) * 100, 1),
|
||||
"feasibility_rate": round(n_feasible / max(n_solved, 1) * 100, 1),
|
||||
},
|
||||
"best_feasible": best_info,
|
||||
"phase1_gate_check": {
|
||||
"min_feasible_5": n_feasible >= 5,
|
||||
"solve_success_80pct": (n_solved / max(n_total, 1)) >= 0.80,
|
||||
"gate_passed": n_feasible >= 5 and (n_solved / max(n_total, 1)) >= 0.80,
|
||||
},
|
||||
}
|
||||
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(summary, f, indent=2)
|
||||
|
||||
logger.info("Summary exported to %s", output_path)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main study runner
|
||||
# ---------------------------------------------------------------------------
|
||||
def run_study(args: argparse.Namespace) -> None:
|
||||
"""Execute the Phase 1 LHS DoE study.
|
||||
|
||||
Steps:
|
||||
1. Generate LHS sample points + baseline
|
||||
2. Create/load Optuna study with SQLite storage
|
||||
3. Enqueue all trials (baseline first, then LHS)
|
||||
4. Run optimization (all trials evaluated via objective fn)
|
||||
5. Export results to CSV and JSON
|
||||
|
||||
Args:
|
||||
args: Parsed command-line arguments.
|
||||
"""
|
||||
results_dir = Path(args.results_dir)
|
||||
results_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# 1. Generate sample points
|
||||
# -----------------------------------------------------------------------
|
||||
logger.info("=" * 70)
|
||||
logger.info("HYDROTECH BEAM — Phase 1 LHS DoE")
|
||||
logger.info("=" * 70)
|
||||
|
||||
points = generate_lhs_samples(
|
||||
n_samples=args.n_samples,
|
||||
seed=args.seed,
|
||||
include_baseline=True,
|
||||
)
|
||||
n_trials = len(points)
|
||||
logger.info("Generated %d trial points (1 baseline + %d LHS)", n_trials, n_trials - 1)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# 2. Create Optuna study
|
||||
# -----------------------------------------------------------------------
|
||||
db_path = results_dir / "optuna_study.db"
|
||||
storage = f"sqlite:///{db_path}"
|
||||
|
||||
if args.resume:
|
||||
logger.info("Resuming existing study: %s", args.study_name)
|
||||
study = optuna.load_study(
|
||||
study_name=args.study_name,
|
||||
storage=storage,
|
||||
)
|
||||
logger.info("Loaded study with %d existing trials", len(study.trials))
|
||||
else:
|
||||
study = optuna.create_study(
|
||||
study_name=args.study_name,
|
||||
storage=storage,
|
||||
direction="minimize", # minimize mass
|
||||
load_if_exists=False,
|
||||
sampler=optuna.samplers.TPESampler(seed=args.seed),
|
||||
)
|
||||
logger.info("Created new study: %s (storage: %s)", args.study_name, db_path)
|
||||
|
||||
# Enqueue all trial points
|
||||
trial_dicts = points_to_dicts(points)
|
||||
for i, td in enumerate(trial_dicts):
|
||||
study.enqueue_trial(td)
|
||||
if i == 0:
|
||||
logger.info("Enqueued Trial 0 (baseline): %s", td)
|
||||
|
||||
logger.info("Enqueued %d trials (1 baseline + %d LHS)", n_trials, n_trials - 1)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# 3. Create solver
|
||||
# -----------------------------------------------------------------------
|
||||
solver = create_solver(
|
||||
backend=args.backend,
|
||||
model_dir=args.model_dir,
|
||||
)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# 4. Run all trials
|
||||
# -----------------------------------------------------------------------
|
||||
logger.info("-" * 70)
|
||||
logger.info("Starting trial evaluations...")
|
||||
logger.info("-" * 70)
|
||||
|
||||
t_study_start = time.monotonic()
|
||||
|
||||
# Suppress Optuna's verbose logging during trials
|
||||
optuna.logging.set_verbosity(optuna.logging.WARNING)
|
||||
|
||||
study.optimize(
|
||||
lambda trial: evaluate_trial(trial, solver),
|
||||
n_trials=n_trials,
|
||||
callbacks=[_progress_callback],
|
||||
)
|
||||
|
||||
t_study_elapsed = time.monotonic() - t_study_start
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# 5. Export results
|
||||
# -----------------------------------------------------------------------
|
||||
logger.info("-" * 70)
|
||||
logger.info("Study complete. Exporting results...")
|
||||
logger.info("-" * 70)
|
||||
|
||||
csv_path = str(results_dir / "doe_results.csv")
|
||||
json_path = str(results_dir / "doe_summary.json")
|
||||
|
||||
export_csv(study, csv_path)
|
||||
export_summary(study, json_path)
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# 6. Print summary
|
||||
# -----------------------------------------------------------------------
|
||||
_print_summary(study, t_study_elapsed)
|
||||
|
||||
# Cleanup
|
||||
solver.close()
|
||||
|
||||
|
||||
def _progress_callback(study: optuna.Study, trial: optuna.trial.FrozenTrial) -> None:
|
||||
"""Log progress after each trial."""
|
||||
n_complete = len(study.trials)
|
||||
status = trial.user_attrs.get("status", "unknown")
|
||||
mass = trial.user_attrs.get("mass", "N/A")
|
||||
feasible = trial.user_attrs.get("fully_feasible", False)
|
||||
logger.info(
|
||||
" [%d/%d] Trial %d: status=%s, mass=%s, feasible=%s",
|
||||
n_complete,
|
||||
DEFAULT_N_SAMPLES + 1,
|
||||
trial.number,
|
||||
status,
|
||||
f"{mass:.2f} kg" if isinstance(mass, (int, float)) else mass,
|
||||
feasible,
|
||||
)
|
||||
|
||||
|
||||
def _print_summary(study: optuna.Study, elapsed: float) -> None:
|
||||
"""Print a human-readable summary to stdout."""
|
||||
trials = study.trials
|
||||
n_total = len(trials)
|
||||
n_solved = sum(1 for t in trials if t.user_attrs.get("status") == "solved")
|
||||
n_geo_inf = sum(1 for t in trials if t.user_attrs.get("status") == "geo_infeasible")
|
||||
n_failed = sum(1 for t in trials if t.user_attrs.get("status") == "solve_failed")
|
||||
n_feasible = sum(1 for t in trials if t.user_attrs.get("fully_feasible", False))
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("PHASE 1 DoE — RESULTS SUMMARY")
|
||||
print("=" * 70)
|
||||
print(f" Total trials: {n_total}")
|
||||
print(f" Solved: {n_solved}")
|
||||
print(f" Geometrically infeasible: {n_geo_inf}")
|
||||
print(f" Solve failures: {n_failed}")
|
||||
print(f" Fully feasible: {n_feasible}")
|
||||
print(f" Solve success rate: {n_solved/max(n_total,1)*100:.1f}%")
|
||||
print(f" Feasibility rate: {n_feasible/max(n_solved,1)*100:.1f}%")
|
||||
print(f" Total time: {elapsed:.1f}s ({elapsed/60:.1f} min)")
|
||||
|
||||
# Phase 1 → Phase 2 gate check
|
||||
print("\n GATE CHECK (Phase 1 → Phase 2):")
|
||||
gate_feasible = n_feasible >= 5
|
||||
gate_solve = (n_solved / max(n_total, 1)) >= 0.80
|
||||
print(f" ≥5 feasible points: {'✓ PASS' if gate_feasible else '✗ FAIL'} ({n_feasible})")
|
||||
print(f" ≥80% solve success: {'✓ PASS' if gate_solve else '✗ FAIL'} ({n_solved/max(n_total,1)*100:.0f}%)")
|
||||
print(f" GATE: {'✓ PASSED' if gate_feasible and gate_solve else '✗ BLOCKED'}")
|
||||
|
||||
# Best feasible
|
||||
best_mass = float("inf")
|
||||
best_trial = None
|
||||
for t in trials:
|
||||
if t.user_attrs.get("fully_feasible", False):
|
||||
m = t.user_attrs.get("mass", float("inf"))
|
||||
if m < best_mass:
|
||||
best_mass = m
|
||||
best_trial = t
|
||||
|
||||
if best_trial is not None:
|
||||
ua = best_trial.user_attrs
|
||||
print(f"\n BEST FEASIBLE DESIGN (Trial {best_trial.number}):")
|
||||
print(f" Mass: {ua['mass']:.2f} kg")
|
||||
print(f" Displacement: {ua['tip_displacement']:.2f} mm (limit: {DISPLACEMENT_LIMIT} mm)")
|
||||
print(f" VM Stress: {ua['max_von_mises']:.1f} MPa (limit: {STRESS_LIMIT} MPa)")
|
||||
print(f" Core thickness: {ua['beam_half_core_thickness']:.3f} mm")
|
||||
print(f" Face thickness: {ua['beam_face_thickness']:.3f} mm")
|
||||
print(f" Hole diameter: {ua['holes_diameter']:.1f} mm")
|
||||
print(f" Hole count: {ua['hole_count']}")
|
||||
else:
|
||||
print("\n ⚠️ NO FEASIBLE DESIGN FOUND — see OPTIMIZATION_STRATEGY.md §7.1")
|
||||
|
||||
print("=" * 70)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CLI
|
||||
# ---------------------------------------------------------------------------
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command-line arguments."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Hydrotech Beam — Phase 1 LHS DoE Study",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog=(
|
||||
"Examples:\n"
|
||||
" # Development (stub solver):\n"
|
||||
" python run_doe.py --backend stub\n\n"
|
||||
" # Real NX evaluation:\n"
|
||||
" python run_doe.py --backend nxopen --model-dir /path/to/models\n\n"
|
||||
" # Resume interrupted study:\n"
|
||||
" python run_doe.py --backend stub --resume\n"
|
||||
),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--backend",
|
||||
choices=["stub", "nxopen"],
|
||||
default="stub",
|
||||
help="NX solver backend: 'stub' for testing, 'nxopen' for real (default: stub)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--model-dir",
|
||||
default="",
|
||||
help="Path to NX model directory (required for --backend nxopen)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--study-name",
|
||||
default=DEFAULT_STUDY_NAME,
|
||||
help=f"Optuna study name (default: {DEFAULT_STUDY_NAME})",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--n-samples",
|
||||
type=int,
|
||||
default=DEFAULT_N_SAMPLES,
|
||||
help=f"Number of LHS sample points (default: {DEFAULT_N_SAMPLES})",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
default=DEFAULT_SEED,
|
||||
help=f"Random seed for LHS (default: {DEFAULT_SEED})",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--results-dir",
|
||||
default=DEFAULT_RESULTS_DIR,
|
||||
help=f"Output directory for results (default: {DEFAULT_RESULTS_DIR})",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--resume",
|
||||
action="store_true",
|
||||
help="Resume an existing study instead of creating a new one",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verbose", "-v",
|
||||
action="store_true",
|
||||
help="Enable verbose (DEBUG) logging",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
"""Entry point."""
|
||||
args = parse_args()
|
||||
|
||||
# Configure logging
|
||||
log_level = logging.DEBUG if args.verbose else logging.INFO
|
||||
logging.basicConfig(
|
||||
level=log_level,
|
||||
format="%(asctime)s [%(levelname)-7s] %(name)s: %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
# Run
|
||||
try:
|
||||
run_study(args)
|
||||
except KeyboardInterrupt:
|
||||
logger.warning("Study interrupted by user. Progress saved to Optuna DB.")
|
||||
logger.info("Resume with: python run_doe.py --resume --study-name %s", args.study_name)
|
||||
sys.exit(1)
|
||||
except Exception:
|
||||
logger.exception("Study failed with unexpected error")
|
||||
sys.exit(2)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
264
projects/hydrotech-beam/studies/01_doe_landscape/sampling.py
Normal file
264
projects/hydrotech-beam/studies/01_doe_landscape/sampling.py
Normal file
@@ -0,0 +1,264 @@
|
||||
"""Latin Hypercube Sampling for Hydrotech Beam DoE.
|
||||
|
||||
Generates LHS sample points for the 4 design variables with:
|
||||
- Maximin LHS for space-filling coverage
|
||||
- Integer rounding for hole_count (DV4)
|
||||
- Stratified integer sampling to ensure all 11 hole_count levels are covered
|
||||
- Baseline (Trial 0) always included
|
||||
|
||||
References:
|
||||
OPTIMIZATION_STRATEGY.md §2.3 — Phase 1 configuration
|
||||
OPTIMIZATION_STRATEGY.md §1.4 — Integer handling
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
from typing import Any
|
||||
|
||||
import numpy as np
|
||||
from scipy.stats.qmc import LatinHypercube
|
||||
|
||||
from geometric_checks import DesignPoint
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Design variable bounds
|
||||
# ---------------------------------------------------------------------------
|
||||
@dataclass(frozen=True)
|
||||
class DVBounds:
|
||||
"""Design variable bounds and metadata."""
|
||||
|
||||
name: str
|
||||
nx_expression: str
|
||||
lower: float
|
||||
upper: float
|
||||
baseline: float
|
||||
is_integer: bool = False
|
||||
|
||||
|
||||
DV_DEFINITIONS: list[DVBounds] = [
|
||||
DVBounds("beam_half_core_thickness", "beam_half_core_thickness", 10.0, 40.0, 25.162),
|
||||
DVBounds("beam_face_thickness", "beam_face_thickness", 10.0, 40.0, 21.504),
|
||||
DVBounds("holes_diameter", "holes_diameter", 150.0, 450.0, 300.0),
|
||||
DVBounds("hole_count", "hole_count", 5.0, 15.0, 10.0, is_integer=True),
|
||||
]
|
||||
|
||||
N_DVS = len(DV_DEFINITIONS)
|
||||
BASELINE_VALUES = [dv.baseline for dv in DV_DEFINITIONS]
|
||||
|
||||
|
||||
def get_baseline_point() -> DesignPoint:
|
||||
"""Return the baseline design point (Trial 0).
|
||||
|
||||
Returns:
|
||||
DesignPoint with confirmed baseline values from NX introspection.
|
||||
"""
|
||||
return DesignPoint(
|
||||
beam_half_core_thickness=25.162,
|
||||
beam_face_thickness=21.504,
|
||||
holes_diameter=300.0,
|
||||
hole_count=10,
|
||||
)
|
||||
|
||||
|
||||
def generate_lhs_samples(
|
||||
n_samples: int = 50,
|
||||
seed: int = 42,
|
||||
include_baseline: bool = True,
|
||||
) -> list[DesignPoint]:
|
||||
"""Generate LHS sample points for the DoE study.
|
||||
|
||||
Strategy for integer coverage (hole_count = 5..15, 11 levels):
|
||||
1. Generate `n_samples` LHS points with continuous DV4
|
||||
2. Round DV4 to nearest integer
|
||||
3. Check coverage — if any of the 11 levels are missing, replace
|
||||
the closest duplicate trial with the missing level
|
||||
This ensures all integer levels are represented while maintaining
|
||||
the space-filling property of LHS for the continuous variables.
|
||||
|
||||
Args:
|
||||
n_samples: Number of LHS sample points (default: 50).
|
||||
seed: Random seed for reproducibility.
|
||||
include_baseline: If True, prepend baseline as Trial 0.
|
||||
|
||||
Returns:
|
||||
List of DesignPoint instances. If include_baseline is True,
|
||||
the first element is the baseline (Trial 0).
|
||||
"""
|
||||
logger.info(
|
||||
"Generating %d LHS samples (seed=%d, baseline=%s)",
|
||||
n_samples, seed, include_baseline,
|
||||
)
|
||||
|
||||
# Generate unit hypercube LHS samples
|
||||
sampler = LatinHypercube(d=N_DVS, seed=seed, optimization="random-cd")
|
||||
unit_samples = sampler.random(n=n_samples) # shape: (n_samples, 4)
|
||||
|
||||
# Scale to design variable bounds
|
||||
samples = _scale_samples(unit_samples)
|
||||
|
||||
# Round hole_count to nearest integer
|
||||
samples[:, 3] = np.round(samples[:, 3]).astype(int)
|
||||
samples[:, 3] = np.clip(samples[:, 3], 5, 15)
|
||||
|
||||
# Ensure full integer coverage for hole_count
|
||||
samples = _ensure_integer_coverage(samples, rng=np.random.default_rng(seed))
|
||||
|
||||
# Convert to DesignPoint list
|
||||
points: list[DesignPoint] = []
|
||||
if include_baseline:
|
||||
points.append(get_baseline_point())
|
||||
|
||||
for row in samples:
|
||||
points.append(
|
||||
DesignPoint(
|
||||
beam_half_core_thickness=float(row[0]),
|
||||
beam_face_thickness=float(row[1]),
|
||||
holes_diameter=float(row[2]),
|
||||
hole_count=int(row[3]),
|
||||
)
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"Generated %d total points (%d LHS + %s baseline)",
|
||||
len(points),
|
||||
n_samples,
|
||||
"1" if include_baseline else "0",
|
||||
)
|
||||
_log_coverage(points)
|
||||
|
||||
return points
|
||||
|
||||
|
||||
def _scale_samples(unit_samples: np.ndarray) -> np.ndarray:
|
||||
"""Scale unit hypercube [0,1]^d samples to design variable bounds.
|
||||
|
||||
Args:
|
||||
unit_samples: Array of shape (n, 4) with values in [0, 1].
|
||||
|
||||
Returns:
|
||||
Scaled array with values in [lower, upper] for each DV.
|
||||
"""
|
||||
lower = np.array([dv.lower for dv in DV_DEFINITIONS])
|
||||
upper = np.array([dv.upper for dv in DV_DEFINITIONS])
|
||||
return lower + unit_samples * (upper - lower)
|
||||
|
||||
|
||||
def _ensure_integer_coverage(
|
||||
samples: np.ndarray,
|
||||
rng: np.random.Generator,
|
||||
) -> np.ndarray:
|
||||
"""Ensure all 11 hole_count levels (5-15) are represented.
|
||||
|
||||
If any integer level is missing, replace a duplicate from the most
|
||||
over-represented level with a sample at the missing level.
|
||||
Continuous DVs for replacement samples are drawn randomly within bounds.
|
||||
|
||||
Args:
|
||||
samples: Array of shape (n, 4) with rounded hole_count in col 3.
|
||||
rng: NumPy random generator for reproducibility.
|
||||
|
||||
Returns:
|
||||
Modified samples array with full integer coverage.
|
||||
"""
|
||||
all_levels = set(range(5, 16)) # {5, 6, 7, ..., 15}
|
||||
present_levels = set(int(x) for x in samples[:, 3])
|
||||
missing_levels = all_levels - present_levels
|
||||
|
||||
if not missing_levels:
|
||||
logger.info("All 11 hole_count levels represented ✓")
|
||||
return samples
|
||||
|
||||
logger.warning(
|
||||
"Missing hole_count levels: %s — patching with replacements",
|
||||
sorted(missing_levels),
|
||||
)
|
||||
|
||||
for missing_level in sorted(missing_levels):
|
||||
# Find the most over-represented level
|
||||
unique, counts = np.unique(samples[:, 3].astype(int), return_counts=True)
|
||||
most_common_idx = np.argmax(counts)
|
||||
most_common_level = unique[most_common_idx]
|
||||
|
||||
# Find indices with the most common level
|
||||
candidates = np.where(samples[:, 3].astype(int) == most_common_level)[0]
|
||||
replace_idx = rng.choice(candidates)
|
||||
|
||||
# Replace: keep continuous DVs random within bounds, set hole_count
|
||||
for j, dv in enumerate(DV_DEFINITIONS):
|
||||
if dv.is_integer:
|
||||
samples[replace_idx, j] = missing_level
|
||||
else:
|
||||
samples[replace_idx, j] = rng.uniform(dv.lower, dv.upper)
|
||||
|
||||
logger.info(
|
||||
" Replaced trial at idx %d: hole_count %d → %d",
|
||||
replace_idx, most_common_level, missing_level,
|
||||
)
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
def _log_coverage(points: list[DesignPoint]) -> None:
|
||||
"""Log hole_count coverage statistics."""
|
||||
counts: dict[int, int] = {}
|
||||
for p in points:
|
||||
counts[p.hole_count] = counts.get(p.hole_count, 0) + 1
|
||||
|
||||
logger.info("Hole count coverage:")
|
||||
for level in range(5, 16):
|
||||
n = counts.get(level, 0)
|
||||
logger.info(" hole_count=%2d: %d trials", level, n)
|
||||
|
||||
|
||||
def points_to_dicts(points: list[DesignPoint]) -> list[dict[str, Any]]:
|
||||
"""Convert DesignPoint list to list of dicts (for Optuna enqueue).
|
||||
|
||||
Args:
|
||||
points: List of DesignPoint instances.
|
||||
|
||||
Returns:
|
||||
List of dicts with DV names as keys.
|
||||
"""
|
||||
return [
|
||||
{
|
||||
"beam_half_core_thickness": p.beam_half_core_thickness,
|
||||
"beam_face_thickness": p.beam_face_thickness,
|
||||
"holes_diameter": p.holes_diameter,
|
||||
"hole_count": p.hole_count,
|
||||
}
|
||||
for p in points
|
||||
]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Quick self-test
|
||||
# ---------------------------------------------------------------------------
|
||||
if __name__ == "__main__":
|
||||
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
|
||||
|
||||
points = generate_lhs_samples(n_samples=50, seed=42)
|
||||
print(f"\nGenerated {len(points)} total points")
|
||||
print(f" Trial 0 (baseline): {points[0]}")
|
||||
print(f" Trial 1 (first LHS): {points[1]}")
|
||||
print(f" Trial {len(points)-1} (last LHS): {points[-1]}")
|
||||
|
||||
# Verify coverage
|
||||
hole_counts = {p.hole_count for p in points}
|
||||
expected = set(range(5, 16))
|
||||
assert hole_counts == expected, (
|
||||
f"Missing hole_count levels: {expected - hole_counts}"
|
||||
)
|
||||
print("\nAll 11 hole_count levels covered ✓")
|
||||
|
||||
# Verify bounds
|
||||
for p in points[1:]: # skip baseline
|
||||
assert 10.0 <= p.beam_half_core_thickness <= 40.0
|
||||
assert 10.0 <= p.beam_face_thickness <= 40.0
|
||||
assert 150.0 <= p.holes_diameter <= 450.0
|
||||
assert 5 <= p.hole_count <= 15
|
||||
print("All samples within bounds ✓")
|
||||
Reference in New Issue
Block a user