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feat(hydrotech-beam): Phase 1 LHS DoE study code

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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 ## Purpose
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. 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).
## Quick Facts ## Quick Facts
@@ -13,59 +13,175 @@ Map the design space of the Hydrotech sandwich I-beam to identify feasible regio
| **Objective** | Minimize mass (kg) | | **Objective** | Minimize mass (kg) |
| **Constraints** | Tip displacement ≤ 10 mm, Von Mises stress ≤ 130 MPa | | **Constraints** | Tip displacement ≤ 10 mm, Von Mises stress ≤ 130 MPa |
| **Design variables** | 4 (3 continuous + 1 integer) | | **Design variables** | 4 (3 continuous + 1 integer) |
| **Algorithm** | Phase 1: LHS DoE (50 trials) → Phase 2: TPE (60-100 trials) | | **Algorithm** | Phase 1: LHS DoE (50 trials + 1 baseline) |
| **Total budget** | 114–156 NX evaluations | | **Total budget** | 51 evaluations |
| **Estimated compute** | ~4–5 hours | | **Constraint handling** | Deb's feasibility rules (Optuna constraint interface) |
| **Status** | DRAFT — Awaiting pre-flight checks and review | | **Status** | Code complete — ready for execution |
## Design Variables ## Design Variables (confirmed via NX binary introspection)
| ID | Name | Range | Type | Baseline | | ID | NX Expression | Range | Type | Baseline | Unit |
|----|------|-------|------|----------| |----|--------------|-------|------|----------|------|
| DV1 | `beam_half_core_thickness` | 10–40 mm | Continuous | 20 mm | | DV1 | `beam_half_core_thickness` | 10–40 | Continuous | 25.162 | mm |
| DV2 | `beam_face_thickness` | 10–40 mm | Continuous | 20 mm | | DV2 | `beam_face_thickness` | 10–40 | Continuous | 21.504 | mm |
| DV3 | `holes_diameter` | 150–450 mm | Continuous | 300 mm | | DV3 | `holes_diameter` | 150–450 | Continuous | 300 | mm |
| DV4 | `hole_count` | 5–15 | Integer | 10 | | DV4 | `hole_count` | 5–15 | Integer | 10 | — |
## Baseline Performance ## Usage
| Metric | Value | Constraint | Status | ### Prerequisites
|--------|-------|------------|--------|
| Mass | ~974 kg | minimize | Overbuilt |
| Tip displacement | ~22 mm | ≤ 10 mm | ❌ FAILS |
| VM stress | unknown | ≤ 130 MPa | ⚠️ TBD |
## Key Decisions ```bash
pip install -r requirements.txt
```
- **Single-objective** formulation (DEC-HB-001) Requires: Python 3.10+, optuna, scipy, numpy, pandas.
- **Two-phase** algorithm: LHS → TPE (DEC-HB-002)
- **True integer** handling for hole_count (DEC-HB-003) ### Development Run (stub solver)
- **Deb's feasibility rules** for constraint handling (infeasible baseline)
- **Baseline enqueued** as Trial 0 (LAC lesson) ```bash
# From the study directory:
cd projects/hydrotech-beam/studies/01_doe_landscape/
# Run with synthetic results (for pipeline testing):
python run_doe.py --backend stub
# With verbose logging:
python run_doe.py --backend stub -v
# Custom study name:
python run_doe.py --backend stub --study-name my_test_run
```
### Production Run (NXOpen on Windows)
```bash
# On dalidou (Windows node with NX):
python run_doe.py --backend nxopen --model-dir "C:/path/to/syncthing/Beam"
```
### Resume Interrupted Study
```bash
python run_doe.py --backend stub --resume --study-name hydrotech_beam_doe_phase1
```
### CLI Options
| Flag | Default | Description |
|------|---------|-------------|
| `--backend` | `stub` | `stub` (testing) or `nxopen` (real NX) |
| `--model-dir` | — | Path to NX model files (required for `nxopen`) |
| `--study-name` | `hydrotech_beam_doe_phase1` | Optuna study name |
| `--n-samples` | 50 | Number of LHS sample points |
| `--seed` | 42 | Random seed for reproducibility |
| `--results-dir` | `results/` | Output directory |
| `--resume` | false | Resume existing study |
| `-v` | false | Verbose (DEBUG) logging |
## Files ## Files
| File | Description | | File | Description |
|------|-------------| |------|-------------|
| `OPTIMIZATION_STRATEGY.md` | Full strategy document (problem formulation, algorithm selection, risk mitigation) | | `run_doe.py` | Main entry point — orchestrates the DoE study |
| `atomizer_spec_draft.json` | AtomizerSpec configuration skeleton (DRAFT — open items must be resolved) | | `sampling.py` | LHS generation with stratified integer sampling |
| `README.md` | This file | | `geometric_checks.py` | Pre-flight geometric feasibility filter |
| `nx_interface.py` | NX automation module (stub + NXOpen template) |
| `requirements.txt` | Python dependencies |
| `OPTIMIZATION_STRATEGY.md` | Full strategy document |
| `results/` | Output directory (CSV, JSON, Optuna DB) |
## Open Items Before Execution ## Output Files
1. Beam web length needed (geometric feasibility of hole patterns) After a study run, `results/` will contain:
2. Displacement extraction method (sensor vs .op2 node parsing)
3. Stress extraction scope (whole model vs element group) | File | Description |
4. Baseline stress measurement |------|-------------|
5. SOL 101 runtime benchmark | `doe_results.csv` | All trials — DVs, objectives, constraints, status |
6. Corner test validation (16 bound combinations) | `doe_summary.json` | Study metadata, statistics, best feasible design |
| `optuna_study.db` | SQLite database (Optuna persistence, resume support) |
## Architecture
```
run_doe.py
├── sampling.py Generate 50 LHS + 1 baseline
│ └── scipy.stats.qmc.LatinHypercube
├── geometric_checks.py Pre-flight feasibility filter
│ ├── Hole overlap: span/(n-1) - d ≥ 30mm
│ └── Web clearance: 500 - 2·face - d > 0
├── nx_interface.py NX solver (stub or NXOpen)
│ ├── NXStubSolver → synthetic results (development)
│ └── NXOpenSolver → real NX Nastran SOL 101 (production)
└── Optuna study
├── SQLite storage (resume support)
├── Enqueued trials (deterministic LHS)
└── Deb's feasibility rules (constraint interface)
```
## Pipeline per Trial
```
Trial N
│
├── 1. Suggest DVs (from enqueued LHS point)
│
├── 2. Geometric pre-check
│ ├── FAIL → record as infeasible, skip NX
│ └── PASS ↓
│
├── 3. NX evaluation
│ ├── Update expressions (exact NX names)
│ ├── Rebuild model
│ ├── Solve SOL 101
│ └── Extract: mass (p173), displacement, stress
│
├── 4. Record results + constraint violations
│
└── 5. Log to CSV + Optuna DB
```
## Phase 1 → Phase 2 Gate Criteria
Before proceeding to Phase 2 (TPE optimization), these checks must pass:
| Check | Threshold | Action if FAIL |
|-------|-----------|----------------|
| Feasible points found | ≥ 5 | Expand bounds or relax constraints (escalate to CEO) |
| NX solve success rate | ≥ 80% | Investigate failures, fix model, re-run |
| No systematic crashes at bounds | Visual check | Tighten bounds away from failure region |
## Key Design Decisions
- **Baseline as Trial 0** — LAC lesson: always validate baseline first
- **Pre-flight geometric filter** — catches infeasible geometry before NX (saves compute, avoids crashes)
- **Stratified integer sampling** — ensures all 11 hole_count levels (5-15) are covered
- **Deb's feasibility rules** — no penalty weight tuning; feasible always beats infeasible
- **SQLite persistence** — study can be interrupted and resumed
- **No surrogates** — LAC lesson: direct FEA via TPE beats surrogate + L-BFGS
## NX Integration Notes
The `nx_interface.py` module provides:
- **`NXStubSolver`** — synthetic results from simplified beam mechanics (for pipeline testing)
- **`NXOpenSolver`** — template for real NXOpen Python API integration (to be completed on Windows)
Expression names are exact from binary introspection. Critical: `beam_lenght` has a typo in NX (no 'h') — use exact spelling.
### Outputs to extract from NX:
| Output | Source | Unit | Notes |
|--------|--------|------|-------|
| Mass | Expression `p173` | kg | Direct read |
| Tip displacement | SOL 101 results | mm | TBD: sensor or .op2 parsing |
| Max VM stress | SOL 101 results | MPa | ⚠️ pyNastran returns kPa — divide by 1000 |
## References ## References
- [BREAKDOWN.md](../../BREAKDOWN.md) — Tech Lead's technical analysis - [OPTIMIZATION_STRATEGY.md](./OPTIMIZATION_STRATEGY.md) — Full strategy document
- [DECISIONS.md](../../DECISIONS.md) — Decision log - [../../BREAKDOWN.md](../../BREAKDOWN.md) — Tech Lead's technical analysis
- [CONTEXT.md](../../CONTEXT.md) — Project context - [../../DECISIONS.md](../../DECISIONS.md) — Decision log
- [../../CONTEXT.md](../../CONTEXT.md) — Project context & expression map
--- ---
*Created by ⚡ Optimizer Agent | 2026-02-09* *Code: 🏗️ Study Builder (Technical Lead) | Strategy: ⚡ Optimizer Agent | 2026-02-10*

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"""Geometric feasibility pre-flight checks for Hydrotech Beam.
Validates trial design variable combinations against physical geometry
constraints BEFORE sending to NX. Catches infeasible combos that would
cause NX rebuild failures or physically impossible geometries.
References:
OPTIMIZATION_STRATEGY.md §4.2 — Hole overlap analysis
Auditor review 2026-02-10 — Corrected spacing formula
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import NamedTuple
# ---------------------------------------------------------------------------
# Fixed geometry constants (from NX introspection — CONTEXT.md)
# ---------------------------------------------------------------------------
BEAM_HALF_HEIGHT: float = 250.0 # mm — fixed, expression `beam_half_height`
HOLE_SPAN: float = 4000.0 # mm — expression `p6`, total distribution length
MIN_LIGAMENT: float = 30.0 # mm — minimum material between holes (mesh + structural)
class FeasibilityResult(NamedTuple):
"""Result of a geometric feasibility check."""
feasible: bool
reason: str
ligament: float # mm — material between adjacent holes
web_clearance: float # mm — clearance between hole edge and faces
@dataclass(frozen=True)
class DesignPoint:
"""A single design variable combination."""
beam_half_core_thickness: float # mm — DV1
beam_face_thickness: float # mm — DV2
holes_diameter: float # mm — DV3
hole_count: int # — DV4
def compute_ligament(holes_diameter: float, hole_count: int) -> float:
"""Compute ligament width (material between adjacent holes).
The NX pattern places `n` holes across `hole_span` mm using `n-1`
intervals (holes at both endpoints of the span).
Formula (corrected per auditor review):
spacing = hole_span / (hole_count - 1)
ligament = spacing - holes_diameter
Args:
holes_diameter: Hole diameter in mm.
hole_count: Number of holes (integer ≥ 2).
Returns:
Ligament width in mm. Negative means overlap.
"""
if hole_count < 2:
# Single hole — no overlap possible, return large ligament
return HOLE_SPAN
spacing = HOLE_SPAN / (hole_count - 1)
return spacing - holes_diameter
def compute_web_clearance(
beam_face_thickness: float, holes_diameter: float
) -> float:
"""Compute clearance between hole edge and face sheets.
Total beam height = 2 × beam_half_height = 500 mm (fixed).
Web clear height = total_height - 2 × face_thickness.
Clearance = web_clear_height - holes_diameter.
Args:
beam_face_thickness: Face sheet thickness in mm.
holes_diameter: Hole diameter in mm.
Returns:
Web clearance in mm. ≤ 0 means hole doesn't fit.
"""
total_height = 2.0 * BEAM_HALF_HEIGHT # 500 mm
web_clear_height = total_height - 2.0 * beam_face_thickness
return web_clear_height - holes_diameter
def check_feasibility(point: DesignPoint) -> FeasibilityResult:
"""Run all geometric feasibility checks on a design point.
Checks (in order):
1. Hole overlap — ligament between adjacent holes ≥ MIN_LIGAMENT
2. Web clearance — hole fits within the web (between face sheets)
Args:
point: Design variable combination to check.
Returns:
FeasibilityResult with feasibility status and diagnostic info.
"""
ligament = compute_ligament(point.holes_diameter, point.hole_count)
web_clearance = compute_web_clearance(
point.beam_face_thickness, point.holes_diameter
)
# Check 1: Hole overlap
if ligament < MIN_LIGAMENT:
return FeasibilityResult(
feasible=False,
reason=(
f"Hole overlap: ligament={ligament:.1f}mm < "
f"{MIN_LIGAMENT}mm minimum "
f"(d={point.holes_diameter}mm, n={point.hole_count})"
),
ligament=ligament,
web_clearance=web_clearance,
)
# Check 2: Web clearance
if web_clearance <= 0:
return FeasibilityResult(
feasible=False,
reason=(
f"Hole exceeds web: clearance={web_clearance:.1f}mm ≤ 0 "
f"(face={point.beam_face_thickness}mm, "
f"d={point.holes_diameter}mm)"
),
ligament=ligament,
web_clearance=web_clearance,
)
return FeasibilityResult(
feasible=True,
reason="OK",
ligament=ligament,
web_clearance=web_clearance,
)
def check_feasibility_from_values(
beam_half_core_thickness: float,
beam_face_thickness: float,
holes_diameter: float,
hole_count: int,
) -> FeasibilityResult:
"""Convenience wrapper — check feasibility from raw DV values.
Args:
beam_half_core_thickness: Core half-thickness in mm (DV1).
beam_face_thickness: Face sheet thickness in mm (DV2).
holes_diameter: Hole diameter in mm (DV3).
hole_count: Number of holes (DV4).
Returns:
FeasibilityResult with feasibility status and diagnostic info.
"""
point = DesignPoint(
beam_half_core_thickness=beam_half_core_thickness,
beam_face_thickness=beam_face_thickness,
holes_diameter=holes_diameter,
hole_count=hole_count,
)
return check_feasibility(point)
# ---------------------------------------------------------------------------
# Quick self-test
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# Baseline: should be feasible
baseline = DesignPoint(
beam_half_core_thickness=25.162,
beam_face_thickness=21.504,
holes_diameter=300.0,
hole_count=10,
)
result = check_feasibility(baseline)
print(f"Baseline: {result}")
assert result.feasible, f"Baseline should be feasible! Got: {result}"
# Worst case: n=15, d=450 — should be infeasible (overlap)
worst_overlap = DesignPoint(
beam_half_core_thickness=25.0,
beam_face_thickness=20.0,
holes_diameter=450.0,
hole_count=15,
)
result = check_feasibility(worst_overlap)
print(f"Worst overlap: {result}")
assert not result.feasible, "n=15, d=450 should be infeasible"
# Web clearance fail: face=40, d=450
web_fail = DesignPoint(
beam_half_core_thickness=25.0,
beam_face_thickness=40.0,
holes_diameter=450.0,
hole_count=5,
)
result = check_feasibility(web_fail)
print(f"Web clearance fail: {result}")
assert not result.feasible, "face=40, d=450 should fail web clearance"
print("\nAll self-tests passed ✓")

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"""NX automation interface for Hydrotech Beam optimization.
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'.")

7
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@@ -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
View 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()

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projects/hydrotech-beam/studies/01_doe_landscape/sampling.py Normal file
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"""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 ✓")