Anto01
5c63d877f0
Replaces Triangle library with Gmsh as the default triangulation engine for adaptive isogrid generation. Gmsh's Frontal-Delaunay algorithm provides: - Better adaptive density response (concentric rings around holes) - Superior triangle quality (min angles 30-35° vs 25-30°) - Single-pass meshing with background size fields (vs iterative refinement) - More equilateral triangles → uniform rib widths, better manufacturability - Natural boundary conformance → cleaner frame edges Comparison results (mixed hole weights plate): - Min angle improvement: +5.1° (25.7° → 30.8°) - Density field accuracy: Excellent vs Poor - Visual quality: Concentric hole refinement vs random patterns Changes: - Updated src/brain/__main__.py to import triangulation_gmsh - Added gmsh>=4.11 to requirements.txt (Triangle kept as fallback) - Updated README and technical-spec.md - Added comparison script and test results Triangle library remains available as fallback option. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
76 lines
3.2 KiB
Markdown
76 lines
3.2 KiB
Markdown
# Adaptive Isogrid — Plate Lightweighting Tool
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**Status:** Foundation / Pre-Implementation
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**Architecture:** Python Brain + NX Hands + Atomizer Manager
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## What It Does
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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.
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## Architecture
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| Component | Role | Runtime |
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|-----------|------|---------|
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| **Python Brain** | Density field → Gmsh Frontal-Delaunay → rib profile | ~1-2 sec |
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| **NX Hands** | Import profile → mesh → AFEM merge → Nastran solve → extract results | ~60-90 sec |
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| **Atomizer Manager** | Optuna TPE sampling → objective evaluation → convergence | 500-2000 trials |
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### Key Insight: Assembly FEM with Superposed Models
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- **Model A** (permanent): Spider elements at holes + edge BC nodes. All loads/BCs applied here.
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- **Model B** (variable): 2D shell mesh of ribbed plate. Rebuilt each iteration.
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- **Node merge** at fixed interface locations connects them reliably every time.
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Loads and BCs never need re-association. Only the rib pattern changes.
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## Directory Structure
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```
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adaptive-isogrid/
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├── README.md
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├── requirements.txt
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├── docs/
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│ └── technical-spec.md # Full architecture spec
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├── src/
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│ ├── brain/ # Python geometry generator
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│ │ ├── __init__.py
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│ │ ├── density_field.py # η(x) evaluation
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│ │ ├── triangulation.py # Constrained Delaunay + refinement
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│ │ ├── pocket_profiles.py # Pocket inset + filleting
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│ │ ├── profile_assembly.py # Final plate - pockets - holes
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│ │ └── validation.py # Manufacturing constraint checks
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│ ├── nx/ # NXOpen journal scripts
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│ │ ├── extract_geometry.py # One-time: face → geometry.json
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│ │ ├── build_interface_model.py # One-time: Model A + spiders
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│ │ └── iteration_solve.py # Per-trial: rebuild Model B + solve
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│ └── atomizer_study.py # Atomizer/Optuna integration
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└── tests/
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└── test_geometries/ # Sample geometry.json files
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```
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## Implementation Phases
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1. **Python Brain standalone** (1-2 weeks) — geometry generator with matplotlib viz
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2. **NX extraction + AFEM setup** (1-2 weeks) — one-time project setup scripts
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3. **NX iteration script** (1-2 weeks) — per-trial mesh/solve/extract loop
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4. **Atomizer integration** (1 week) — wire objective function + study management
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5. **Validation + first real project** (1-2 weeks) — production run on client plate
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## Quick Start (Phase 1)
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```bash
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cd tools/adaptive-isogrid
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pip install -r requirements.txt
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python -m src.brain --geometry tests/test_geometries/sample_bracket.json --params default
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```
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## Parameter Space
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15 continuous parameters optimized by Atomizer (Optuna TPE):
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- Density field: η₀, α, R₀, κ, p, β, R_edge
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- Spacing: s_min, s_max
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- Rib thickness: t_min, t₀, γ
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- Manufacturing: w_frame, r_f, d_keep
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See `docs/technical-spec.md` for full formulation.
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