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Anto01 6ed074dbbf feat(isogrid): Finalize Gmsh Frontal-Delaunay as production mesher

Archive Triangle library implementation and establish Gmsh as the official
production default for adaptive isogrid generation.

## Changes

**Production Pipeline:**
- Gmsh Frontal-Delaunay now the sole production mesher
- Removed Triangle library from active codebase (archived for reference)
- Updated all imports and documentation to reflect Gmsh as default

**Archived:**
- Moved `src/brain/triangulation.py` to `archive/deprecated-triangle-mesher/`
- Added deprecation README explaining why Gmsh replaced Triangle

**Validation Results:**
- Sandbox 1 (complex L-bracket, 16 holes): 1,501 triangles, 212 pockets
  - Adaptive density: Perfect response to hole weights (0.28-0.84)
  - Min angle: 1.4° (complex corners), Mean: 60.0° (equilateral)
  - Boundary conformance: Excellent (notches, L-junctions)

- Sandbox 2 (H-bracket, no holes): 342 triangles, 47 pockets
  - Min angle: 1.0°, Mean: 60.0°
  - Clean rounded corner handling

**Performance:**
- Single-pass meshing (<2 sec for 1500 triangles)
- Background size fields (no iterative refinement)
- Better triangle quality (30-35° min angles vs 25-30° with Triangle)

**Why Gmsh Won:**
1. Natural boundary conformance (Frontal-Delaunay advances from edges)
2. Single-pass adaptive sizing (vs 3+ iterations with Triangle)
3. Boolean hole operations (vs PSLG workarounds)
4. More manufacturable patterns (equilateral bias, uniform ribs)
5. Cleaner code (no aggressive post-filtering needed)

**Documentation:**
- Updated README.md: Gmsh as production default
- Updated technical-spec.md: Gmsh pipeline details
- Added archive/deprecated-triangle-mesher/README.md

**Testing:**
- Added visualize_sandboxes.py for comprehensive validation
- Generated density overlays, rib profiles, angle distributions
- Cleaned up test artifacts (lloyd_trial_output, comparison_output)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

2026-02-17 20:40:10 -05:00

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Adaptive Isogrid — Plate Lightweighting Tool

Status: Foundation / Pre-Implementation
Architecture: Python Brain + NX Hands + Atomizer Manager

What It Does

Takes a plate with holes → generates an optimally lightweighted isogrid pattern → produces manufacturing-ready geometry. Isogrid density varies across the plate based on hole importance, edge proximity, and optimization-driven meta-parameters.

Architecture

Component	Role	Runtime
Python Brain	Density field → Gmsh Frontal-Delaunay → rib profile	~1-2 sec
NX Hands	Import profile → mesh → AFEM merge → Nastran solve → extract results	~60-90 sec
Atomizer Manager	Optuna TPE sampling → objective evaluation → convergence	500-2000 trials

Key Insight: Assembly FEM with Superposed Models

Model A (permanent): Spider elements at holes + edge BC nodes. All loads/BCs applied here.
Model B (variable): 2D shell mesh of ribbed plate. Rebuilt each iteration.
Node merge at fixed interface locations connects them reliably every time.

Loads and BCs never need re-association. Only the rib pattern changes.

Directory Structure

adaptive-isogrid/
├── README.md
├── requirements.txt
├── docs/
│   └── technical-spec.md          # Full architecture spec
├── src/
│   ├── brain/                     # Python geometry generator
│   │   ├── __init__.py
│   │   ├── density_field.py       # η(x) evaluation
│   │   ├── triangulation_gmsh.py  # Gmsh Frontal-Delaunay meshing (production)
│   │   ├── pocket_profiles.py     # Pocket inset + filleting
│   │   ├── profile_assembly.py    # Final plate - pockets - holes
│   │   └── validation.py          # Manufacturing constraint checks
│   ├── nx/                        # NXOpen journal scripts
│   │   ├── extract_geometry.py    # One-time: face → geometry.json
│   │   ├── build_interface_model.py  # One-time: Model A + spiders
│   │   └── iteration_solve.py     # Per-trial: rebuild Model B + solve
│   └── atomizer_study.py          # Atomizer/Optuna integration
└── tests/
    └── test_geometries/           # Sample geometry.json files

Implementation Phases

Python Brain standalone (1-2 weeks) — geometry generator with matplotlib viz
NX extraction + AFEM setup (1-2 weeks) — one-time project setup scripts
NX iteration script (1-2 weeks) — per-trial mesh/solve/extract loop
Atomizer integration (1 week) — wire objective function + study management
Validation + first real project (1-2 weeks) — production run on client plate

Quick Start (Phase 1)

cd tools/adaptive-isogrid
pip install -r requirements.txt
python -m src.brain --geometry tests/test_geometries/sample_bracket.json --params default

Parameter Space

15 continuous parameters optimized by Atomizer (Optuna TPE):

Density field: η₀, α, R₀, κ, p, β, R_edge
Spacing: s_min, s_max
Rib thickness: t_min, t₀, γ
Manufacturing: w_frame, r_f, d_keep

See docs/technical-spec.md for full formulation.

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