Atomizer/projects/hydrotech-beam/studies/01_doe_landscape

Study: 01_doe_landscape — Hydrotech Beam

See ../../README.md for project overview.

Purpose

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

Item	Value
Objective	Minimize mass (kg)
Constraints	Tip displacement ≤ 10 mm, Von Mises stress ≤ 130 MPa
Design variables	4 (3 continuous + 1 integer)
Algorithm	Phase 1: LHS DoE (50 trials + 1 baseline)
Total budget	51 evaluations
Constraint handling	Deb's feasibility rules (Optuna constraint interface)
Status	Code complete — ready for execution

Design Variables (confirmed via NX binary introspection)

ID	NX Expression	Range	Type	Baseline	Unit
DV1	beam_half_core_thickness	10–40	Continuous	25.162	mm
DV2	beam_face_thickness	10–40	Continuous	21.504	mm
DV3	holes_diameter	150–450	Continuous	300	mm
DV4	hole_count	5–15	Integer	10	—

Usage

Prerequisites

pip install -r requirements.txt

Requires: Python 3.10+, optuna, scipy, numpy, pandas.

Development Run (stub solver)

# 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)

# On dalidou (Windows node with NX):
python run_doe.py --backend nxopen --model-dir "C:/path/to/syncthing/Beam"

Resume Interrupted Study

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

File	Description
run_doe.py	Main entry point — orchestrates the DoE study
sampling.py	LHS generation with stratified integer sampling
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)

Output Files

After a study run, results/ will contain:

File	Description
doe_results.csv	All trials — DVs, objectives, constraints, status
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

• OPTIMIZATION_STRATEGY.md — Full strategy document
• ../../BREAKDOWN.md — Tech Lead's technical analysis
• ../../DECISIONS.md — Decision log
• ../../CONTEXT.md — Project context & expression map

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Code: 🏗️ Study Builder (Technical Lead) | Strategy: ⚡ Optimizer Agent | 2026-02-10