1450 lines
52 KiB
Markdown
1450 lines
52 KiB
Markdown
# Arsenal Development Plan — Technical Architecture + Sprint Breakdown
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**Version:** 1.0
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**Date:** 2026-02-24
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**Author:** Technical Lead
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**Status:** Development Blueprint
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> **Mission:** Transform Atomizer from an NX/Nastran optimization tool into a multi-solver, multi-physics, multi-objective engineering optimization platform powered by the 80/20 highest-value Arsenal tools.
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---
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## 1. Executive Summary
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This plan implements the **Thin Contract + Smart Processor** pattern identified in the Arsenal research to seamlessly integrate open-source simulation tools with Atomizer V2's existing optimization engine. The approach minimizes risk while maximizing value through incremental capability expansion.
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### Key Deliverables by Sprint
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- **Sprint 1-2:** Universal format conversion + first open-source solver (CalculiX)
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- **Sprint 3-4:** Multi-objective optimization + LLM-driven CAD generation
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- **Sprint 5-6:** CFD/thermal capability + multi-physics coupling
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### Investment
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- **Software Cost:** $0 (100% open-source tools)
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- **Development Time:** 6 sprints (6 months)
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- **Risk Level:** LOW (existing NX pipeline preserved as fallback)
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---
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## 2. Technical Architecture — Thin Contract + Smart Processor Pattern
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### 2.1 The AtomizerData Contract (Universal Interchange)
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The contract defines WHAT flows between tools semantically, not file formats. Each tool gets a thin processor that converts to/from its native format.
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```python
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# atomizer/contracts/data_models.py
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from dataclasses import dataclass
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from typing import Dict, List, Optional
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from pathlib import Path
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from enum import Enum
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class SolverType(Enum):
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NASTRAN = "nastran"
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CALCULIX = "calculix"
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OPENFOAM = "openfoam"
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FENICS = "fenics"
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ELMER = "elmer"
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@dataclass
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class AtomizerGeometry:
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"""3D geometry with named faces for boundary conditions"""
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step_path: Path # STEP file (universal CAD exchange)
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named_faces: Dict[str, str] # {"fixed_face": "bottom plane",
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# "loaded_face": "top surface"}
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design_variables: Dict[str, float] # {"thickness": 5.0, "rib_height": 20.0}
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bounds: Dict[str, tuple] # {"thickness": (2.0, 15.0)}
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material_zones: Dict[str, str] # {"main_body": "steel", "insert": "aluminum"}
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@dataclass
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class AtomizerMesh:
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"""Volume mesh with element and surface groups"""
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mesh_path: Path # Native mesh file (.msh, .bdf, .inp)
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format: str # "gmsh", "nastran", "calculix"
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element_count: int
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node_count: int
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element_groups: Dict[str, str] # {"bracket": "solid elements"}
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surface_groups: Dict[str, str] # {"fixed": "nodes on bottom"}
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@dataclass
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class AtomizerBCs:
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"""Physics-agnostic boundary condition description"""
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structural: List[Dict] # [{"type": "fixed", "surface": "bottom"}]
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thermal: Optional[List[Dict]] # [{"type": "heat_flux", "value": 500}]
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fluid: Optional[List[Dict]] # [{"type": "inlet", "velocity": [5,0,0]}]
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@dataclass
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class AtomizerMaterial:
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"""Material properties for multi-physics"""
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name: str # "Steel AISI 304"
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structural: Dict[str, float] # {"E": 210000, "nu": 0.3, "rho": 7850}
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thermal: Optional[Dict[str, float]] # {"k": 16.2, "cp": 500, "alpha": 1.7e-5}
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fluid: Optional[Dict[str, float]] # {"mu": 1e-3, "rho": 1000}
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@dataclass
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class AtomizerResults:
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"""Solver-agnostic analysis results"""
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vtk_path: Path # VTK file for visualization
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solver: SolverType
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physics_type: str # "structural", "thermal", "fluid"
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max_stress: Optional[float] # [MPa]
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max_displacement: Optional[float] # [mm]
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max_temperature: Optional[float] # [°C]
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mass: float # [kg]
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natural_frequencies: Optional[List[float]] # [Hz]
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convergence: Dict[str, bool] # {"solved": True, "converged": True}
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solve_time: float # [seconds]
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@dataclass
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class AtomizerStudy:
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"""Complete optimization study definition"""
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name: str
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geometry: AtomizerGeometry
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mesh_settings: Dict # {"max_size": 3.0, "refinement": ["holes"]}
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materials: List[AtomizerMaterial]
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boundary_conditions: AtomizerBCs
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objectives: List[Dict] # [{"minimize": "mass"}, {"minimize": "max_stress"}]
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constraints: List[Dict] # [{"max_stress": {"<": 200}}]
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solver_preferences: List[SolverType] # Order of preference
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optimization_settings: Dict # Algorithm, population, generations
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```
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### 2.2 Tool-Specific Processors
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Each tool gets a thin Python processor that handles format conversion deterministically (no LLM involved in conversion). The LLM orchestrates at the engineering level.
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```
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atomizer/processors/
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├── __init__.py
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├── base_processor.py # AbstractProcessor interface
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├── gmsh_processor.py # Geometry → Mesh conversion
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├── calculix_processor.py # AtomizerStudy ↔ CalculiX .inp/.frd
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├── nastran_processor.py # AtomizerStudy ↔ Nastran .bdf/.op2
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├── openfoam_processor.py # AtomizerStudy ↔ OpenFOAM case dir
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├── fenics_processor.py # AtomizerStudy ↔ FEniCS Python script
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├── build123d_processor.py # Parameters → Build123d → STEP
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├── pyvista_processor.py # AtomizerResults → visualization
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└── paraview_processor.py # AtomizerResults → report figures
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```
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### 2.3 Processor Interface
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```python
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# atomizer/processors/base_processor.py
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from abc import ABC, abstractmethod
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from atomizer.contracts.data_models import AtomizerStudy, AtomizerResults
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class AbstractProcessor(ABC):
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"""Base class for all tool processors"""
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@abstractmethod
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def generate_input(self, study: AtomizerStudy) -> str:
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"""Convert AtomizerStudy to tool's native input format"""
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pass
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@abstractmethod
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def parse_results(self, output_path: str) -> AtomizerResults:
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"""Parse tool's output to AtomizerResults"""
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pass
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@abstractmethod
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def validate_setup(self, study: AtomizerStudy) -> bool:
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"""Check if study is compatible with this processor"""
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pass
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```
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### 2.4 Integration with Existing Atomizer Architecture
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The Arsenal processors integrate alongside existing extractors and hooks:
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```
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EXISTING ATOMIZER PIPELINE:
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AtomizerSpec → NX Journals → Nastran → OP2 Extractors → Optuna
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NEW ARSENAL PIPELINE:
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AtomizerSpec → AtomizerStudy → Processor → Open-Source Solver → AtomizerResults → pymoo
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UNIFIED PIPELINE:
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AtomizerSpec → Converter → {NX Pipeline | Arsenal Pipeline} → Unified Results → Optimization
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```
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**Key Integration Points:**
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- `AtomizerSpec` v3.0 extended with multi-solver, multi-physics support
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- New `MultiSolverEngine` orchestrates solver selection
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- Existing hook system works with Arsenal processors
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- LAC (Learning and Context) system enhanced for multi-solver optimization patterns
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---
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## 3. 80/20 Tool Selection & Validation
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Based on the Arsenal research, these tools deliver 80% of the value with validated priority:
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### Tier 1: Universal Glue (Sprint 1)
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- **meshio** ⭐⭐⭐⭐⭐ — Universal mesh format converter
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- **pyNastran** ⭐⭐⭐⭐⭐ — Bridge to existing NX/Nastran world
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- **PyVista** ⭐⭐⭐⭐⭐ — Instant visualization from Python
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- **Gmsh** ⭐⭐⭐⭐⭐ — Universal meshing engine
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**Value:** Connects everything. Any geometry → any mesh → any solver.
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### Tier 2: First Open-Source Solver (Sprint 2)
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- **CalculiX** ⭐⭐⭐⭐⭐ — Free Abaqus-compatible FEA solver
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- **Build123d** ⭐⭐⭐⭐ — LLM-friendly CAD generation
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**Value:** NX-free optimization. Agents generate CAD from text.
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### Tier 3: Multi-Objective Optimization (Sprint 3)
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- **pymoo** ⭐⭐⭐⭐⭐ — Proper Pareto front optimization
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**Value:** Real engineering trade-offs. Consulting-grade deliverables.
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### Tier 4: Advanced Physics (Sprint 4-5)
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- **OpenFOAM** ⭐⭐⭐⭐ — CFD/thermal capability
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- **preCICE** ⭐⭐⭐⭐ — Multi-physics coupling
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**Value:** Thermal + structural optimization. Heatsinks, electronics.
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### Tier 5: Advanced Capabilities (Sprint 6)
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- **FEniCS** ⭐⭐⭐⭐ — Topology optimization via adjoint gradients
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- **pyvcad** ⭐⭐⭐ — Lattice/AM structures
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**Value:** Generative design. 30% weight reduction through topology optimization.
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---
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## 4. Sprint Breakdown
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### Sprint 1: Universal Glue Layer (Weeks 1-2)
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**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** LOW | **Unlocks:** Everything else
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**Deliverables:**
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- Universal format conversion pipeline
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- Bridge between NX/Nastran and open-source tools
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- Proof-of-concept round-trip validation
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**Technical Tasks:**
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```bash
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pip install meshio pynastran gmsh pygmsh pyvista build123d
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```
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**Python Modules to Create:**
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- `atomizer/processors/meshio_processor.py`
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- `atomizer/processors/pynastran_bridge.py`
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- `atomizer/processors/gmsh_processor.py`
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- `atomizer/processors/pyvista_processor.py`
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- `atomizer/contracts/data_models.py`
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- `atomizer/contracts/validators.py`
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**Test Criteria:**
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- Convert existing Nastran BDF → CalculiX INP via meshio
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- Build123d geometry → Gmsh mesh → Nastran BDF round-trip
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- PyVista renders stress contours from sample OP2 file
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- Accuracy: <1% difference in mass, volume between formats
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**Integration Points:**
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- Extend `AtomizerSpec` v2.0 → v2.1 with `solver_preferences: List[str]`
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- Add `MultiFormatExtractor` to existing extractor library
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- Hook into existing optimization engine via `NXSolverEngine.get_alternative()`
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**Antoine's Validation Gate:**
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- Review round-trip accuracy on existing LAC benchmark problems
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- Approve AtomizerSpec v2.1 extensions
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- Validate that existing NX workflows continue working unchanged
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**Dependencies:** None (builds on existing Atomizer infrastructure)
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---
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### Sprint 2: First Open-Source Solver (Weeks 3-4)
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**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** MEDIUM | **Unlocks:** NX-free optimization
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**Deliverables:**
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- CalculiX processor with full lifecycle integration
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- First complete open-source optimization study
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- Validation against NX/Nastran on LAC benchmarks
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**Technical Tasks:**
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```bash
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sudo apt install calculix-ccx
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```
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**Python Modules to Create:**
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- `atomizer/processors/calculix_processor.py`
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- `atomizer/solvers/calculix_engine.py`
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- `atomizer/validation/solver_validator.py`
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- `tests/test_calculix_integration.py`
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**CalculiX Processor Implementation:**
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```python
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# atomizer/processors/calculix_processor.py
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class CalculiXProcessor(AbstractProcessor):
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def generate_input(self, study: AtomizerStudy) -> str:
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"""Convert AtomizerStudy → CalculiX .inp file"""
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# Read mesh via meshio
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mesh = meshio.read(study.geometry.mesh_path)
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inp_content = []
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# Write nodes
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inp_content.append("*NODE")
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for i, point in enumerate(mesh.points, 1):
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inp_content.append(f"{i}, {point[0]:.6f}, {point[1]:.6f}, {point[2]:.6f}")
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# Write elements
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inp_content.append("*ELEMENT, TYPE=C3D10, ELSET=ALL")
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for i, cell in enumerate(mesh.cells[0].data, 1):
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nodes = ", ".join(str(n+1) for n in cell)
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inp_content.append(f"{i}, {nodes}")
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# Materials
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for material in study.materials:
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inp_content.extend([
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f"*MATERIAL, NAME={material.name}",
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f"*ELASTIC",
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f"{material.structural['E']}, {material.structural['nu']}",
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f"*DENSITY",
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f"{material.structural['rho']}"
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])
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# Boundary conditions from contract
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step_content = ["*STEP", "*STATIC"]
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for bc in study.boundary_conditions.structural:
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if bc['type'] == 'fixed':
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step_content.append(f"*BOUNDARY\n{bc['surface']}, 1, 6, 0.0")
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elif bc['type'] == 'force':
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step_content.append(f"*CLOAD\n{bc['surface']}, 3, {bc['value'][2]}")
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step_content.extend(["*NODE FILE\nU", "*EL FILE\nS", "*END STEP"])
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inp_content.extend(step_content)
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return "\n".join(inp_content)
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def parse_results(self, frd_path: str) -> AtomizerResults:
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"""Parse CalculiX .frd results → AtomizerResults"""
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# Use meshio to read .frd file
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mesh_result = meshio.read(frd_path)
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# Extract stress, displacement from mesh data
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stress_data = mesh_result.point_data.get('stress', [0])
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displacement_data = mesh_result.point_data.get('displacement', [0])
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max_stress = float(np.max(np.linalg.norm(stress_data, axis=1)))
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max_displacement = float(np.max(np.linalg.norm(displacement_data, axis=1)))
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# Convert to VTK for visualization
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vtk_path = frd_path.replace('.frd', '.vtk')
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meshio.write(vtk_path, mesh_result)
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return AtomizerResults(
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vtk_path=Path(vtk_path),
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solver=SolverType.CALCULIX,
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physics_type="structural",
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max_stress=max_stress,
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max_displacement=max_displacement,
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mass=self._calculate_mass(mesh_result, study.materials),
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convergence={"solved": True, "converged": True},
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solve_time=self._get_solve_time(frd_path)
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)
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```
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**Test Criteria (Benchmark Problems):**
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1. **Cantilever beam:** Analytical vs CalculiX vs NX/Nastran comparison
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2. **Plate with hole:** Stress concentration validation
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3. **Modal analysis:** Natural frequencies comparison
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4. **Thermal stress:** Coupled thermal-structural loading
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5. **Nonlinear contact:** Large deformation with contact
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**Success Criteria:** <5% error vs analytical solutions, <3% error vs NX/Nastran
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**Integration Points:**
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- Add `CalculiXEngine` to `optimization_engine/solvers/`
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- Extend `MultiSolverEngine` to support solver fallback logic
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- Hook integration: all existing hooks work with CalculiX processors
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- LAC integration: CalculiX results feed into learning patterns
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**Antoine's Validation Gate:**
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- Run full optimization study on LAC benchmark using only CalculiX
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- Compare convergence rate, final optima vs existing NX optimization
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- Approve solver selection logic and fallback mechanisms
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**Dependencies:** Sprint 1 completed (meshio, format conversion working)
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---
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### Sprint 3: Multi-Objective Optimization (Weeks 5-6)
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**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** LOW-MEDIUM | **Unlocks:** Real engineering trade-offs
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**Deliverables:**
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- Pareto front optimization with NSGA-II
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- Multi-objective visualization dashboard
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- Client-grade trade-off analysis reports
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**Python Modules to Create:**
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- `atomizer/optimizers/pymoo_engine.py`
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- `atomizer/visualization/pareto_plots.py`
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- `atomizer/reporting/tradeoff_analysis.py`
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- `optimization_engine/objectives/multi_objective.py`
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**pymoo Integration:**
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```python
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# atomizer/optimizers/pymoo_engine.py
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from pymoo.algorithms.moo.nsga2 import NSGA2
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from pymoo.core.problem import Problem
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from pymoo.optimize import minimize
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class AtomizerMultiObjectiveProblem(Problem):
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def __init__(self, atomizer_study: AtomizerStudy, processor_engine):
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self.study = atomizer_study
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self.processor = processor_engine
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# Extract design variables and objectives from study
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n_var = len(study.geometry.design_variables)
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n_obj = len(study.objectives)
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n_constr = len(study.constraints)
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# Get bounds from study
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xl = [bounds[0] for bounds in study.geometry.bounds.values()]
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xu = [bounds[1] for bounds in study.geometry.bounds.values()]
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super().__init__(n_var=n_var, n_obj=n_obj, n_constr=n_constr, xl=xl, xu=xu)
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def _evaluate(self, X, out, *args, **kwargs):
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"""Evaluate population X"""
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objectives = []
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constraints = []
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for individual in X:
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# Update geometry with new design variables
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updated_study = self._update_study_variables(individual)
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# Run simulation
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results = self.processor.run_study(updated_study)
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# Extract objectives
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obj_values = []
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for objective in self.study.objectives:
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if objective['minimize'] == 'mass':
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obj_values.append(results.mass)
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elif objective['minimize'] == 'max_stress':
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obj_values.append(results.max_stress)
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elif objective['minimize'] == 'compliance':
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obj_values.append(results.compliance)
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objectives.append(obj_values)
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# Extract constraints
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constr_values = []
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for constraint in self.study.constraints:
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for field, condition in constraint.items():
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result_value = getattr(results, field)
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if '<' in condition:
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constr_values.append(result_value - condition['<'])
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constraints.append(constr_values)
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out["F"] = np.array(objectives)
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if constraints:
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out["G"] = np.array(constraints)
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class PymooEngine:
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def run_optimization(self, study: AtomizerStudy, algorithm="NSGA2", **kwargs):
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problem = AtomizerMultiObjectiveProblem(study, self.processor_engine)
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if algorithm == "NSGA2":
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optimizer = NSGA2(pop_size=kwargs.get('pop_size', 40))
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elif algorithm == "NSGA3":
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optimizer = NSGA3(pop_size=kwargs.get('pop_size', 50))
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result = minimize(
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problem,
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optimizer,
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termination=('n_gen', kwargs.get('n_gen', 100))
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)
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return self._format_pareto_results(result)
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```
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**Pareto Visualization:**
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```python
|
||
# atomizer/visualization/pareto_plots.py
|
||
import plotly.graph_objects as go
|
||
import plotly.express as px
|
||
|
||
class ParetoVisualizer:
|
||
def create_pareto_plot(self, pareto_front, objectives, design_vars):
|
||
"""Create interactive Pareto front plot"""
|
||
if len(objectives) == 2:
|
||
return self._plot_2d_pareto(pareto_front, objectives, design_vars)
|
||
elif len(objectives) == 3:
|
||
return self._plot_3d_pareto(pareto_front, objectives, design_vars)
|
||
else:
|
||
return self._plot_parallel_coordinates(pareto_front, objectives, design_vars)
|
||
|
||
def _plot_2d_pareto(self, front, objectives, design_vars):
|
||
fig = go.Figure()
|
||
|
||
# Pareto front
|
||
fig.add_trace(go.Scatter(
|
||
x=front[:, 0],
|
||
y=front[:, 1],
|
||
mode='markers',
|
||
marker=dict(size=10, color='red'),
|
||
hovertemplate='<b>Design Point</b><br>' +
|
||
f'{objectives[0]}: %{{x:.3f}}<br>' +
|
||
f'{objectives[1]}: %{{y:.3f}}<br>' +
|
||
'<extra></extra>',
|
||
name='Pareto Front'
|
||
))
|
||
|
||
fig.update_layout(
|
||
title='Pareto Front Analysis',
|
||
xaxis_title=objectives[0],
|
||
yaxis_title=objectives[1],
|
||
hovermode='closest'
|
||
)
|
||
|
||
return fig
|
||
```
|
||
|
||
**Test Criteria:**
|
||
- 2-objective optimization: minimize mass + minimize max_stress
|
||
- 3-objective optimization: mass + stress + displacement
|
||
- Pareto front coverage: >90% of theoretical front covered
|
||
- Performance: 40-individual population × 100 generations in <4 hours
|
||
|
||
**AtomizerSpec v2.2 Extensions:**
|
||
```python
|
||
# Support for multiple objectives
|
||
"objectives": [
|
||
{"minimize": "mass", "weight": 1.0},
|
||
{"minimize": "max_stress", "weight": 1.0},
|
||
{"minimize": "max_displacement", "weight": 0.5}
|
||
],
|
||
|
||
"optimization": {
|
||
"algorithm": "NSGA2", # or "NSGA3", "TPE"
|
||
"population_size": 40,
|
||
"generations": 100,
|
||
"pareto_analysis": True
|
||
}
|
||
```
|
||
|
||
**Antoine's Validation Gate:**
|
||
- Review Pareto front plots for LAC benchmark problems
|
||
- Validate that trade-offs make engineering sense
|
||
- Approve decision-making tools (TOPSIS, weighted selection)
|
||
|
||
**Dependencies:** Sprint 2 completed (CalculiX working)
|
||
|
||
---
|
||
|
||
### Sprint 4: LLM-Driven CAD Generation (Weeks 7-8)
|
||
**Impact:** ⭐⭐⭐⭐ | **Effort:** MEDIUM | **Unlocks:** Agent-generated geometry
|
||
|
||
**Deliverables:**
|
||
- Build123d processor for parametric CAD generation
|
||
- LLM agent generates geometry from text descriptions
|
||
- Integration with Zoo Text-to-CAD API for concept generation
|
||
|
||
**Python Modules to Create:**
|
||
- `atomizer/processors/build123d_processor.py`
|
||
- `atomizer/agents/cad_generator.py`
|
||
- `atomizer/templates/build123d_library.py`
|
||
- `atomizer/geometry/validation_agent.py`
|
||
|
||
**Build123d Processor:**
|
||
```python
|
||
# atomizer/processors/build123d_processor.py
|
||
from build123d import *
|
||
|
||
class Build123dProcessor:
|
||
def generate_from_parameters(self, design_vars: Dict[str, float], template: str) -> Path:
|
||
"""Generate STEP geometry from design variables using Build123d template"""
|
||
|
||
if template == "bracket":
|
||
return self._generate_bracket(design_vars)
|
||
elif template == "plate_with_holes":
|
||
return self._generate_plate(design_vars)
|
||
elif template == "housing":
|
||
return self._generate_housing(design_vars)
|
||
|
||
def _generate_bracket(self, vars: Dict[str, float]) -> Path:
|
||
"""Generate parametric L-bracket"""
|
||
with BuildPart() as bracket:
|
||
# Base plate
|
||
Box(vars['length'], vars['width'], vars['thickness'])
|
||
|
||
# Vertical wall
|
||
with Locations((0, 0, vars['thickness'])):
|
||
Box(vars['length'], vars['wall_thickness'], vars['height'])
|
||
|
||
# Ribs (if enabled)
|
||
if vars.get('ribs', True):
|
||
for i in range(int(vars.get('rib_count', 3))):
|
||
x_pos = (i + 1) * vars['length'] / (vars['rib_count'] + 1)
|
||
with Locations((x_pos, vars['width']/2, vars['thickness'])):
|
||
# Triangular rib
|
||
with BuildSketch() as rib_profile:
|
||
Polygon([
|
||
(0, 0),
|
||
(vars['rib_width']/2, 0),
|
||
(0, vars['rib_height'])
|
||
])
|
||
Extrude(amount=vars['rib_thickness'])
|
||
|
||
# Bolt holes
|
||
for hole_x in vars.get('hole_positions_x', []):
|
||
for hole_y in vars.get('hole_positions_y', []):
|
||
with Locations((hole_x, hole_y, 0)):
|
||
Hole(radius=vars['hole_diameter']/2,
|
||
depth=vars['thickness'] + vars['height'])
|
||
|
||
# Export STEP
|
||
output_path = Path("generated_geometry.step")
|
||
bracket.part.export_step(str(output_path))
|
||
return output_path
|
||
|
||
def parametric_from_description(self, description: str) -> str:
|
||
"""Generate Build123d code from natural language description"""
|
||
# This would use an LLM agent to convert description to Build123d code
|
||
prompt = f"""
|
||
Generate Build123d Python code for: {description}
|
||
|
||
Requirements:
|
||
- Use context managers (with BuildPart() as part:)
|
||
- Make key dimensions parametric variables
|
||
- Include proper error handling
|
||
- Export as STEP file
|
||
- Return the code as a string
|
||
|
||
Template:
|
||
```python
|
||
from build123d import *
|
||
|
||
def generate_geometry(params):
|
||
with BuildPart() as part:
|
||
# Geometry generation here
|
||
pass
|
||
return part.part
|
||
```
|
||
"""
|
||
# Call LLM service here
|
||
return self._call_llm_service(prompt)
|
||
```
|
||
|
||
**CAD Generation Agent:**
|
||
```python
|
||
# atomizer/agents/cad_generator.py
|
||
class CADGeneratorAgent:
|
||
def __init__(self, llm_service):
|
||
self.llm = llm_service
|
||
self.build123d_processor = Build123dProcessor()
|
||
|
||
def generate_concept_geometry(self, description: str, constraints: Dict) -> Path:
|
||
"""Generate concept geometry from natural language"""
|
||
|
||
# Step 1: Convert description to Build123d code
|
||
code = self.build123d_processor.parametric_from_description(description)
|
||
|
||
# Step 2: Validate code syntax
|
||
validated_code = self._validate_build123d_code(code)
|
||
|
||
# Step 3: Execute code to generate geometry
|
||
geometry_path = self._execute_build123d_code(validated_code, constraints)
|
||
|
||
# Step 4: Validate resulting geometry
|
||
validation_result = self._validate_geometry(geometry_path)
|
||
|
||
if not validation_result['valid']:
|
||
# Iterate with LLM to fix issues
|
||
fixed_code = self._fix_geometry_issues(code, validation_result['issues'])
|
||
geometry_path = self._execute_build123d_code(fixed_code, constraints)
|
||
|
||
return geometry_path
|
||
|
||
def _validate_geometry(self, step_path: Path) -> Dict:
|
||
"""Validate geometry for manufacturing and physics"""
|
||
# Load geometry
|
||
mesh = meshio.read(step_path)
|
||
|
||
issues = []
|
||
|
||
# Check if watertight
|
||
if not self._is_watertight(mesh):
|
||
issues.append("Geometry is not watertight")
|
||
|
||
# Check minimum feature size
|
||
min_feature = self._get_minimum_feature_size(mesh)
|
||
if min_feature < 1.0: # 1mm minimum
|
||
issues.append(f"Features below 1mm detected: {min_feature:.2f}mm")
|
||
|
||
# Check aspect ratios
|
||
max_aspect = self._get_max_aspect_ratio(mesh)
|
||
if max_aspect > 20:
|
||
issues.append(f"High aspect ratio detected: {max_aspect:.1f}")
|
||
|
||
return {
|
||
'valid': len(issues) == 0,
|
||
'issues': issues,
|
||
'metrics': {
|
||
'volume': mesh.volume,
|
||
'surface_area': mesh.surface_area,
|
||
'min_feature_size': min_feature
|
||
}
|
||
}
|
||
```
|
||
|
||
**Test Criteria:**
|
||
1. Generate 10 different bracket geometries from text descriptions
|
||
2. Validate all geometries are watertight and manufacturable
|
||
3. Successfully mesh and solve in CalculiX
|
||
4. Integration with existing optimization pipeline
|
||
|
||
**Integration Points:**
|
||
- Add Build123d processor to multi-solver engine
|
||
- Extend AtomizerStudy with parametric CAD templates
|
||
- LLM agents generate CAD code instead of requiring pre-made .prt files
|
||
|
||
**Antoine's Validation Gate:**
|
||
- Review generated geometries for engineering feasibility
|
||
- Test parametric variation for optimization compatibility
|
||
- Validate that generated CAD properly interfaces with NX when needed
|
||
|
||
**Dependencies:** Sprint 2 completed, Build123d installed and tested
|
||
|
||
---
|
||
|
||
### Sprint 5: CFD + Thermal Capability (Weeks 9-10)
|
||
**Impact:** ⭐⭐⭐⭐ | **Effort:** HIGH | **Unlocks:** Thermal + flow optimization
|
||
|
||
**Deliverables:**
|
||
- OpenFOAM processor with case generation
|
||
- Thermal-structural coupling via preCICE
|
||
- Heatsink optimization demonstration
|
||
|
||
**Technical Setup:**
|
||
```bash
|
||
# OpenFOAM installation
|
||
docker pull openfoam/openfoam9-paraview56
|
||
# OR
|
||
sudo apt install openfoam9
|
||
```
|
||
|
||
**Python Modules to Create:**
|
||
- `atomizer/processors/openfoam_processor.py`
|
||
- `atomizer/coupling/precice_manager.py`
|
||
- `atomizer/processors/thermal_structural.py`
|
||
- `examples/heatsink_optimization.py`
|
||
|
||
**OpenFOAM Processor:**
|
||
```python
|
||
# atomizer/processors/openfoam_processor.py
|
||
class OpenFOAMProcessor(AbstractProcessor):
|
||
def __init__(self):
|
||
self.case_template_dir = Path("templates/openfoam_cases")
|
||
|
||
def generate_input(self, study: AtomizerStudy) -> str:
|
||
"""Generate OpenFOAM case directory structure"""
|
||
case_dir = Path("openfoam_case")
|
||
case_dir.mkdir(exist_ok=True)
|
||
|
||
# Create directory structure
|
||
(case_dir / "0").mkdir(exist_ok=True)
|
||
(case_dir / "constant").mkdir(exist_ok=True)
|
||
(case_dir / "system").mkdir(exist_ok=True)
|
||
|
||
# Generate mesh
|
||
self._convert_mesh_to_openfoam(study.geometry.mesh_path, case_dir)
|
||
|
||
# Generate boundary conditions (0/ directory)
|
||
self._generate_boundary_conditions(study.boundary_conditions, case_dir / "0")
|
||
|
||
# Generate physical properties (constant/ directory)
|
||
self._generate_transport_properties(study.materials, case_dir / "constant")
|
||
|
||
# Generate solver settings (system/ directory)
|
||
self._generate_control_dict(study, case_dir / "system")
|
||
self._generate_fv_schemes(case_dir / "system")
|
||
self._generate_fv_solution(case_dir / "system")
|
||
|
||
return str(case_dir)
|
||
|
||
def _generate_boundary_conditions(self, bcs: AtomizerBCs, zero_dir: Path):
|
||
"""Generate 0/U, 0/p, 0/T files"""
|
||
|
||
# Velocity field (0/U)
|
||
u_content = self._openfoam_header("volVectorField", "U")
|
||
u_content += """
|
||
dimensions [0 1 -1 0 0 0 0];
|
||
internalField uniform (0 0 0);
|
||
|
||
boundaryField
|
||
{
|
||
"""
|
||
for bc in bcs.fluid or []:
|
||
if bc['type'] == 'inlet':
|
||
u_content += f"""
|
||
{bc['surface']}
|
||
{{
|
||
type fixedValue;
|
||
value uniform ({bc['velocity'][0]} {bc['velocity'][1]} {bc['velocity'][2]});
|
||
}}
|
||
"""
|
||
elif bc['type'] == 'outlet':
|
||
u_content += f"""
|
||
{bc['surface']}
|
||
{{
|
||
type zeroGradient;
|
||
}}
|
||
"""
|
||
elif bc['type'] == 'wall':
|
||
u_content += f"""
|
||
{bc['surface']}
|
||
{{
|
||
type noSlip;
|
||
}}
|
||
"""
|
||
u_content += "}\n"
|
||
|
||
(zero_dir / "U").write_text(u_content)
|
||
|
||
# Similar generation for pressure (p) and temperature (T)
|
||
self._generate_pressure_bc(bcs, zero_dir)
|
||
self._generate_temperature_bc(bcs, zero_dir)
|
||
|
||
def parse_results(self, case_dir: str) -> AtomizerResults:
|
||
"""Parse OpenFOAM results"""
|
||
# Read final time directory
|
||
case_path = Path(case_dir)
|
||
time_dirs = [d for d in case_path.iterdir() if d.is_dir() and d.name.replace('.', '').isdigit()]
|
||
latest_time = max(time_dirs, key=lambda x: float(x.name))
|
||
|
||
# Convert OpenFOAM results to VTK for post-processing
|
||
self._run_openfoam_command(f"foamToVTK -case {case_dir} -latestTime")
|
||
vtk_dir = case_path / "VTK"
|
||
|
||
# Extract key metrics
|
||
max_temperature = self._extract_max_temperature(latest_time / "T")
|
||
pressure_drop = self._extract_pressure_drop(latest_time / "p")
|
||
|
||
return AtomizerResults(
|
||
vtk_path=vtk_dir,
|
||
solver=SolverType.OPENFOAM,
|
||
physics_type="thermal_fluid",
|
||
max_temperature=max_temperature,
|
||
convergence=self._check_convergence(case_path / "log.simpleFoam"),
|
||
solve_time=self._get_solve_time(case_path / "log.simpleFoam")
|
||
)
|
||
```
|
||
|
||
**Thermal-Structural Coupling:**
|
||
```python
|
||
# atomizer/coupling/precice_manager.py
|
||
class PreCICECouplingManager:
|
||
def setup_thermal_structural_coupling(self, study: AtomizerStudy) -> Dict[str, str]:
|
||
"""Set up coupled thermal-structural analysis"""
|
||
|
||
# Create separate cases for thermal (OpenFOAM) and structural (CalculiX)
|
||
thermal_case = self._create_thermal_case(study)
|
||
structural_case = self._create_structural_case(study)
|
||
|
||
# Generate preCICE configuration
|
||
precice_config = self._generate_precice_config(study)
|
||
|
||
return {
|
||
'thermal_case': thermal_case,
|
||
'structural_case': structural_case,
|
||
'precice_config': precice_config
|
||
}
|
||
|
||
def run_coupled_simulation(self, coupling_setup: Dict[str, str]) -> AtomizerResults:
|
||
"""Execute coupled thermal-structural simulation"""
|
||
|
||
# Start preCICE solvers
|
||
thermal_proc = self._start_openfoam_precice(coupling_setup['thermal_case'])
|
||
structural_proc = self._start_calculix_precice(coupling_setup['structural_case'])
|
||
|
||
# Wait for convergence
|
||
self._wait_for_coupling_convergence()
|
||
|
||
# Combine results from both solvers
|
||
thermal_results = self._parse_openfoam_results(coupling_setup['thermal_case'])
|
||
structural_results = self._parse_calculix_results(coupling_setup['structural_case'])
|
||
|
||
return self._merge_coupled_results(thermal_results, structural_results)
|
||
```
|
||
|
||
**Test Criteria:**
|
||
1. **Heat transfer validation:** Compare analytical solutions for simple geometries
|
||
2. **Flow validation:** Pipe flow, flat plate boundary layer
|
||
3. **Coupled validation:** Heated pipe with thermal expansion
|
||
4. **Heatsink optimization:** Minimize max temperature + minimize pressure drop
|
||
|
||
**Integration Points:**
|
||
- Extend AtomizerStudy with thermal/fluid boundary conditions
|
||
- Add thermal objectives to pymoo multi-objective optimization
|
||
- Integrate with existing visualization (PyVista thermal contours)
|
||
|
||
**Antoine's Validation Gate:**
|
||
- Review CFD validation against analytical solutions
|
||
- Test coupled simulation convergence and stability
|
||
- Approve thermal optimization objectives and constraints
|
||
|
||
**Dependencies:** Sprint 3 completed (multi-objective framework), preCICE adapters installed
|
||
|
||
---
|
||
|
||
### Sprint 6: Topology Optimization Pipeline (Weeks 11-12)
|
||
**Impact:** ⭐⭐⭐⭐⭐ | **Effort:** HIGH | **Unlocks:** 30% weight reduction capability
|
||
|
||
**Deliverables:**
|
||
- FEniCS topology optimization processor
|
||
- Reconstruction pipeline (density field → CAD)
|
||
- Complete topology optimization study
|
||
|
||
**Technical Setup:**
|
||
```bash
|
||
docker pull dolfinx/dolfinx
|
||
pip install fenics fenitop
|
||
```
|
||
|
||
**Python Modules to Create:**
|
||
- `atomizer/processors/fenics_processor.py`
|
||
- `atomizer/topology/simp_optimizer.py`
|
||
- `atomizer/reconstruction/density_to_cad.py`
|
||
- `atomizer/validation/topo_validator.py`
|
||
|
||
**FEniCS Topology Optimization:**
|
||
```python
|
||
# atomizer/topology/simp_optimizer.py
|
||
from dolfin import *
|
||
from dolfin_adjoint import *
|
||
|
||
class SIMPTopologyOptimizer:
|
||
def __init__(self, study: AtomizerStudy):
|
||
self.study = study
|
||
self.mesh = self._load_mesh(study.geometry.mesh_path)
|
||
self.V = VectorFunctionSpace(self.mesh, "CG", 1) # Displacement
|
||
self.V0 = FunctionSpace(self.mesh, "DG", 0) # Density
|
||
|
||
def optimize_topology(self, volume_fraction=0.4, iterations=80) -> np.ndarray:
|
||
"""Run SIMP topology optimization"""
|
||
|
||
# Initialize density field
|
||
rho = Function(self.V0, name="Density")
|
||
rho.interpolate(Constant(volume_fraction)) # Start uniform
|
||
|
||
# Material properties with SIMP
|
||
E_base = self.study.materials[0].structural['E']
|
||
nu = self.study.materials[0].structural['nu']
|
||
p = 3 # SIMP penalty parameter
|
||
|
||
def E_simp(rho):
|
||
return rho**p * E_base
|
||
|
||
# Set up elasticity problem
|
||
u = Function(self.V, name="Displacement")
|
||
v = TestFunction(self.V)
|
||
|
||
# Apply boundary conditions from study
|
||
bcs = self._convert_bcs_to_fenics(self.study.boundary_conditions)
|
||
loads = self._convert_loads_to_fenics(self.study.boundary_conditions)
|
||
|
||
# Weak form with SIMP material
|
||
F = self._build_weak_form(u, v, rho, E_simp, nu, loads)
|
||
|
||
# Optimization loop
|
||
for iteration in range(iterations):
|
||
# Solve state problem
|
||
solve(F == 0, u, bcs)
|
||
|
||
# Compute compliance (objective)
|
||
compliance = assemble(self._compliance_form(u, rho, E_simp, nu))
|
||
|
||
# Compute sensitivity via adjoint
|
||
sensitivity = compute_gradient(compliance, Control(rho))
|
||
|
||
# Update density with MMA or OC method
|
||
rho_new = self._update_density_mma(rho, sensitivity, volume_fraction)
|
||
|
||
# Apply filter to avoid checkerboard
|
||
rho = self._apply_helmholtz_filter(rho_new)
|
||
|
||
# Check convergence
|
||
if iteration > 10 and self._check_convergence(compliance):
|
||
break
|
||
|
||
print(f"Iteration {iteration}: Compliance = {compliance:.6f}")
|
||
|
||
return rho.vector().get_local().reshape((-1,))
|
||
|
||
def _apply_helmholtz_filter(self, rho, radius=0.05):
|
||
"""Apply Helmholtz PDE filter for minimum feature size"""
|
||
rho_filtered = Function(self.V0)
|
||
phi = TestFunction(self.V0)
|
||
|
||
# Solve: -r²∇²ρ_f + ρ_f = ρ
|
||
F_filter = (radius**2 * dot(grad(rho_filtered), grad(phi)) +
|
||
rho_filtered * phi - rho * phi) * dx
|
||
solve(F_filter == 0, rho_filtered)
|
||
|
||
return rho_filtered
|
||
```
|
||
|
||
**Reconstruction Pipeline:**
|
||
```python
|
||
# atomizer/reconstruction/density_to_cad.py
|
||
from skimage import measure
|
||
import trimesh
|
||
|
||
class DensityReconstructor:
|
||
def __init__(self):
|
||
self.threshold = 0.5
|
||
|
||
def reconstruct_geometry(self, density_field: np.ndarray, mesh_coords: np.ndarray) -> Path:
|
||
"""Convert density field to manufacturable CAD"""
|
||
|
||
# Step 1: Threshold density field
|
||
binary_field = (density_field > self.threshold).astype(float)
|
||
|
||
# Step 2: Marching cubes isosurface extraction
|
||
vertices, faces, normals, values = measure.marching_cubes(
|
||
binary_field.reshape(mesh_coords.shape[0:3]),
|
||
level=0.5
|
||
)
|
||
|
||
# Step 3: Create mesh and smooth
|
||
mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
|
||
mesh = mesh.smoothed() # Laplacian smoothing
|
||
|
||
# Step 4: Skeleton extraction for rib identification
|
||
skeleton = self._extract_skeleton(mesh)
|
||
|
||
# Step 5: Generate parametric ribs using Build123d
|
||
if skeleton.shape[0] > 10: # If skeleton is substantial
|
||
cad_path = self._generate_ribs_from_skeleton(skeleton, mesh.bounds)
|
||
else:
|
||
# Fall back to direct STL → STEP conversion
|
||
cad_path = self._convert_mesh_to_step(mesh)
|
||
|
||
return cad_path
|
||
|
||
def _generate_ribs_from_skeleton(self, skeleton: np.ndarray, bounds: np.ndarray) -> Path:
|
||
"""Generate Build123d ribs following skeleton pattern"""
|
||
from build123d import *
|
||
|
||
with BuildPart() as topology_part:
|
||
# Create base volume
|
||
bbox = Box(bounds[1,0] - bounds[0,0],
|
||
bounds[1,1] - bounds[0,1],
|
||
bounds[1,2] - bounds[0,2])
|
||
|
||
# Add ribs following skeleton
|
||
for i in range(len(skeleton) - 1):
|
||
start_point = skeleton[i]
|
||
end_point = skeleton[i + 1]
|
||
|
||
# Create rib connecting these points
|
||
with BuildSketch() as rib_profile:
|
||
Rectangle(2.0, 2.0) # 2mm x 2mm rib cross-section
|
||
|
||
# Sweep along skeleton edge
|
||
path = Line(start_point, end_point)
|
||
Sweep(sections=[rib_profile.sketch], path=path)
|
||
|
||
# Export result
|
||
output_path = Path("reconstructed_topology.step")
|
||
topology_part.part.export_step(str(output_path))
|
||
|
||
return output_path
|
||
```
|
||
|
||
**Validation Pipeline:**
|
||
```python
|
||
# atomizer/validation/topo_validator.py
|
||
class TopologyValidator:
|
||
def validate_reconstruction(self,
|
||
original_density: np.ndarray,
|
||
reconstructed_step: Path,
|
||
original_study: AtomizerStudy) -> Dict:
|
||
"""Validate that reconstructed geometry performs as predicted"""
|
||
|
||
# Step 1: Re-mesh reconstructed geometry
|
||
gmsh_proc = GmshProcessor()
|
||
new_mesh = gmsh_proc.mesh_geometry(reconstructed_step)
|
||
|
||
# Step 2: Run FEA on reconstructed geometry
|
||
calculix_proc = CalculiXProcessor()
|
||
validation_study = original_study.copy()
|
||
validation_study.geometry.step_path = reconstructed_step
|
||
validation_study.geometry.mesh_path = new_mesh
|
||
|
||
reconstructed_results = calculix_proc.run_study(validation_study)
|
||
|
||
# Step 3: Compare metrics
|
||
# Predict performance from topology optimization
|
||
predicted_compliance = self._estimate_compliance_from_density(original_density)
|
||
predicted_mass = self._estimate_mass_from_density(original_density)
|
||
|
||
# Actual performance from FEA
|
||
actual_compliance = reconstructed_results.compliance
|
||
actual_mass = reconstructed_results.mass
|
||
|
||
# Calculate errors
|
||
compliance_error = abs(actual_compliance - predicted_compliance) / predicted_compliance
|
||
mass_error = abs(actual_mass - predicted_mass) / predicted_mass
|
||
|
||
return {
|
||
'validation_passed': compliance_error < 0.1 and mass_error < 0.05,
|
||
'compliance_error': compliance_error,
|
||
'mass_error': mass_error,
|
||
'predicted': {'compliance': predicted_compliance, 'mass': predicted_mass},
|
||
'actual': {'compliance': actual_compliance, 'mass': actual_mass},
|
||
'reconstructed_results': reconstructed_results
|
||
}
|
||
```
|
||
|
||
**Test Criteria:**
|
||
1. **MBB beam:** Classic 2D topology optimization benchmark
|
||
2. **L-bracket:** 3D cantilever with volume constraint
|
||
3. **Multi-load:** Bracket under combined loading
|
||
4. **Manufacturing constraints:** Minimum feature size, symmetry
|
||
5. **Validation:** <10% error between topology prediction and reconstructed FEA
|
||
|
||
**Integration Points:**
|
||
- New topology optimization path in AtomizerSpec v3.0
|
||
- Integration with existing multi-objective framework
|
||
- Reconstruction connects back to CalculiX for validation
|
||
|
||
**Antoine's Validation Gate:**
|
||
- Review topology optimization convergence and results quality
|
||
- Validate reconstruction accuracy against FEA
|
||
- Approve topology workflow for client deliverables
|
||
|
||
**Dependencies:** Sprint 2 (CalculiX), Sprint 4 (Build123d reconstruction)
|
||
|
||
---
|
||
|
||
## 5. Risk & Dependency Analysis
|
||
|
||
### 5.1 Critical Path Dependencies
|
||
|
||
```
|
||
Sprint 1 (Universal Glue)
|
||
↓ BLOCKS
|
||
Sprint 2 (CalculiX) → Sprint 3 (Multi-Objective)
|
||
↓ BLOCKS ↓ BLOCKS
|
||
Sprint 5 (CFD) Sprint 4 (CAD Generation)
|
||
↓ BLOCKS ↓ BLOCKS
|
||
Sprint 6 (Topology Optimization)
|
||
```
|
||
|
||
### 5.2 Parallelization Opportunities
|
||
|
||
**Can run in parallel:**
|
||
- Sprint 3 (Multi-objective) + Sprint 4 (CAD generation) after Sprint 2
|
||
- Sprint 5 (CFD) infrastructure setup during Sprint 3-4
|
||
- Documentation and testing throughout all sprints
|
||
|
||
**Cannot parallelize:**
|
||
- Sprint 1 must complete first (everything depends on format conversion)
|
||
- Sprint 2 must complete before Sprint 6 (topology needs validation solver)
|
||
|
||
### 5.3 Risk Mitigation
|
||
|
||
| Risk | Probability | Impact | Mitigation |
|
||
|------|------------|--------|------------|
|
||
| **meshio conversion accuracy** | LOW | HIGH | Extensive benchmark validation in Sprint 1 |
|
||
| **CalculiX solver stability** | MEDIUM | HIGH | Fallback to NX/Nastran, validation suite |
|
||
| **FEniCS topology complexity** | HIGH | MEDIUM | Start with 2D problems, iterate to 3D |
|
||
| **OpenFOAM learning curve** | HIGH | MEDIUM | Use Docker containers, existing MCP servers |
|
||
| **Reconstruction quality** | HIGH | MEDIUM | Multiple reconstruction approaches, validation loop |
|
||
| **Performance degradation** | LOW | MEDIUM | Benchmark testing, profile optimization |
|
||
|
||
### 5.4 Go/No-Go Decision Points
|
||
|
||
**After Sprint 1:**
|
||
- ✅ Format conversion <1% accuracy loss
|
||
- ✅ Round-trip validation passes
|
||
- ❌ Major accuracy issues → Pause for investigation
|
||
|
||
**After Sprint 2:**
|
||
- ✅ CalculiX within 5% of NX/Nastran results
|
||
- ✅ Complete optimization study runs end-to-end
|
||
- ❌ Solver instability → Revert to NX-only until resolved
|
||
|
||
**After Sprint 3:**
|
||
- ✅ Pareto fronts show sensible trade-offs
|
||
- ✅ Multi-objective visualization working
|
||
- ❌ Optimization doesn't converge → Debug algorithm parameters
|
||
|
||
---
|
||
|
||
## 6. Existing Code Reuse Strategy
|
||
|
||
### 6.1 Leverage Existing Atomizer Infrastructure
|
||
|
||
**Reuse Directly (No Changes):**
|
||
- `optimization_engine/hooks/` → All hooks work with new processors
|
||
- `optimization_engine/extractors/op2_extractor.py` → For NX/Nastran validation
|
||
- `optimization_engine/insights/` → Zernike, modal analysis, etc.
|
||
- `optimization_engine/validation/` → Existing validation framework
|
||
- LAC learning and context system
|
||
- Dashboard and reporting infrastructure
|
||
|
||
**Extend (Minor Changes):**
|
||
- `AtomizerSpec` → v3.0 with multi-solver support
|
||
- `optimization_engine/run_optimization.py` → Add processor routing
|
||
- `optimization_engine/nx/` → Enhanced with format conversion
|
||
- Hook system → Register new hook points for processor lifecycle
|
||
|
||
**Replace/Augment:**
|
||
- `NXSolverEngine` → Enhanced with `MultiSolverEngine`
|
||
- Single-objective optimization → Multi-objective with pymoo
|
||
- NX-only geometry → Multi-source geometry (Build123d, FreeCAD, etc.)
|
||
|
||
### 6.2 Architecture Integration Pattern
|
||
|
||
```python
|
||
# optimization_engine/solvers/multi_solver_engine.py
|
||
class MultiSolverEngine:
|
||
def __init__(self):
|
||
self.nx_engine = NXSolverEngine() # Existing
|
||
self.calculix_engine = CalculiXEngine() # New
|
||
self.openfoam_engine = OpenFOAMEngine() # New
|
||
self.fenics_engine = FEniCSEngine() # New
|
||
|
||
def select_solver(self, study: AtomizerStudy) -> AbstractSolverEngine:
|
||
"""Select best solver based on study requirements"""
|
||
preferences = study.solver_preferences
|
||
physics = self._analyze_physics_requirements(study)
|
||
|
||
if physics.requires_topology_optimization:
|
||
return self.fenics_engine
|
||
elif physics.requires_cfd:
|
||
return self.openfoam_engine
|
||
elif "nastran" in preferences and self.nx_engine.available():
|
||
return self.nx_engine
|
||
else:
|
||
return self.calculix_engine # Default fallback
|
||
|
||
def run_study(self, study: AtomizerStudy) -> AtomizerResults:
|
||
"""Run study with optimal solver"""
|
||
engine = self.select_solver(study)
|
||
|
||
# Convert AtomizerStudy to solver format via processor
|
||
processor = engine.get_processor()
|
||
solver_input = processor.generate_input(study)
|
||
|
||
# Run solver
|
||
solver_output = engine.solve(solver_input)
|
||
|
||
# Convert results back to AtomizerResults
|
||
results = processor.parse_results(solver_output)
|
||
|
||
# Run existing hooks and validation
|
||
self.hook_manager.execute_hooks('post_solve', results)
|
||
|
||
return results
|
||
```
|
||
|
||
### 6.3 Migration Strategy
|
||
|
||
**Phase A: Parallel Development**
|
||
- New Arsenal tools run alongside existing NX pipeline
|
||
- Validation by comparing results between old and new
|
||
- Zero risk to existing workflows
|
||
|
||
**Phase B: Selective Adoption**
|
||
- Use Arsenal tools for new studies
|
||
- Maintain NX for existing projects and client deliverables
|
||
- Client chooses solver based on requirements
|
||
|
||
**Phase C: Unified Platform**
|
||
- Single AtomizerSpec works with any solver
|
||
- LLM agents select optimal solver automatically
|
||
- NX becomes one option in a multi-solver platform
|
||
|
||
---
|
||
|
||
## 7. Success Metrics & Validation
|
||
|
||
### 7.1 Technical Performance Targets
|
||
|
||
| Metric | Target | Measurement |
|
||
|--------|--------|-------------|
|
||
| **Format Conversion Accuracy** | <1% error | Mass, volume, stress comparison |
|
||
| **Solver Validation** | <5% vs analytical | Cantilever, plate with hole, modal |
|
||
| **Multi-Objective Convergence** | >90% Pareto coverage | Hypervolume indicator |
|
||
| **CFD Validation** | <10% vs analytical | Pipe flow, heat transfer |
|
||
| **Topology Optimization** | 20-40% weight reduction | Compliance-constrained designs |
|
||
| **Reconstruction Accuracy** | <15% performance loss | Topo-opt prediction vs FEA validation |
|
||
|
||
### 7.2 Integration Success Criteria
|
||
|
||
| Component | Success Criteria |
|
||
|-----------|------------------|
|
||
| **Universal Glue** | All existing LAC benchmarks convert and solve |
|
||
| **CalculiX** | Full optimization study runs without NX |
|
||
| **Multi-Objective** | Pareto plots show sensible engineering trade-offs |
|
||
| **CAD Generation** | LLM generates valid, manufacturable geometry |
|
||
| **CFD Integration** | Thermal optimization of realistic heatsink |
|
||
| **Topology Optimization** | Complete workflow from design space to STEP |
|
||
|
||
### 7.3 Client Impact Validation
|
||
|
||
**Before Arsenal (Current Atomizer):**
|
||
- Single-objective optimization
|
||
- NX/Nastran only
|
||
- Structural analysis only
|
||
- Manual geometry creation
|
||
- Windows-dependent
|
||
|
||
**After Arsenal (Target Atomizer):**
|
||
- Multi-objective Pareto optimization
|
||
- Any solver (NX, CalculiX, OpenFOAM, FEniCS)
|
||
- Multi-physics (structural + thermal + fluid)
|
||
- AI-generated geometry from text
|
||
- Cross-platform (Linux preferred)
|
||
|
||
**Deliverable Quality:**
|
||
- Pareto front plots (consulting-grade)
|
||
- Interactive 3D visualization (Trame web viewer)
|
||
- Multi-physics validation reports
|
||
- 30% weight reduction through topology optimization
|
||
|
||
---
|
||
|
||
## 8. Implementation Timeline & Resources
|
||
|
||
### 8.1 6-Sprint Timeline
|
||
|
||
| Sprint | Weeks | Focus | Team Lead | Antoine Involvement |
|
||
|--------|-------|-------|-----------|-------------------|
|
||
| **1** | 1-2 | Universal Glue | Technical Lead | Low - Review specs |
|
||
| **2** | 3-4 | CalculiX Integration | Technical Lead | Medium - Validate benchmarks |
|
||
| **3** | 5-6 | Multi-Objective | Technical Lead | Medium - Review Pareto plots |
|
||
| **4** | 7-8 | CAD Generation | Technical Lead | High - Validate AI-generated CAD |
|
||
| **5** | 9-10 | CFD + Thermal | Technical Lead | High - Review coupling results |
|
||
| **6** | 11-12 | Topology Optimization | Technical Lead | High - Validate complete workflow |
|
||
|
||
### 8.2 Resource Allocation
|
||
|
||
**Technical Lead (Primary Developer):**
|
||
- Architecture design and implementation
|
||
- Processor development
|
||
- Integration with existing Atomizer
|
||
- Testing and validation
|
||
|
||
**Antoine (Domain Expert):**
|
||
- Engineering validation of results
|
||
- Benchmark problem definition
|
||
- Client workflow design
|
||
- Final approval gates
|
||
|
||
**Manager (Project Coordination):**
|
||
- Sprint planning and tracking
|
||
- Risk management
|
||
- Stakeholder communication
|
||
- Resource coordination
|
||
|
||
### 8.3 Deliverable Schedule
|
||
|
||
| Week | Major Deliverables |
|
||
|------|-------------------|
|
||
| **2** | Universal format conversion working |
|
||
| **4** | First CalculiX optimization complete |
|
||
| **6** | Multi-objective Pareto plots generated |
|
||
| **8** | AI-generated CAD in optimization loop |
|
||
| **10** | CFD thermal optimization demonstrated |
|
||
| **12** | Complete topology optimization pipeline |
|
||
|
||
---
|
||
|
||
## 9. Post-Development: Production Readiness
|
||
|
||
### 9.1 Validation & Testing
|
||
|
||
**Unit Tests:**
|
||
- Processor input/output validation
|
||
- Format conversion accuracy
|
||
- Solver integration
|
||
|
||
**Integration Tests:**
|
||
- End-to-end optimization studies
|
||
- Multi-physics coupling validation
|
||
- Performance benchmarks
|
||
|
||
**Acceptance Tests:**
|
||
- Client workflow simulation
|
||
- LAC benchmark reproduction
|
||
- Stress testing with large models
|
||
|
||
### 9.2 Documentation Requirements
|
||
|
||
**Developer Documentation:**
|
||
- Processor API reference
|
||
- Architecture diagrams
|
||
- Integration examples
|
||
|
||
**User Documentation:**
|
||
- AtomizerSpec v3.0 specification
|
||
- Multi-solver workflow guide
|
||
- Troubleshooting and FAQ
|
||
|
||
**Client Documentation:**
|
||
- Capability overview
|
||
- Case studies and examples
|
||
- Performance comparisons
|
||
|
||
### 9.3 Deployment Strategy
|
||
|
||
**Development Environment:**
|
||
- All Arsenal tools installed and tested
|
||
- Docker containers for reproducibility
|
||
- CI/CD pipeline for validation
|
||
|
||
**Production Environment:**
|
||
- Scalable solver execution
|
||
- Result caching and storage
|
||
- Performance monitoring
|
||
|
||
**Client Delivery:**
|
||
- Portable Docker containers
|
||
- Cloud deployment options
|
||
- On-premises installation support
|
||
|
||
---
|
||
|
||
## 10. Conclusion
|
||
|
||
This Arsenal Development Plan provides a comprehensive, risk-mitigated approach to transforming Atomizer into a multi-solver, multi-physics optimization platform. The **Thin Contract + Smart Processor** pattern ensures clean architecture while the incremental sprint approach minimizes development risk.
|
||
|
||
**Key Advantages:**
|
||
1. **Zero software cost** - 100% open-source tools
|
||
2. **Preserve existing workflows** - NX pipeline continues working
|
||
3. **Incremental value delivery** - Each sprint provides usable capabilities
|
||
4. **Future-ready architecture** - Easy to add new tools and capabilities
|
||
|
||
**Expected Outcome:**
|
||
By completion, Atomizer will be the only optimization platform that combines:
|
||
- AI-driven workflow automation
|
||
- Multi-solver orchestration
|
||
- Multi-physics coupling
|
||
- Multi-objective optimization
|
||
- Topology optimization
|
||
- Cross-platform operation
|
||
|
||
This positions Atomizer as a unique, best-in-class solution that no commercial competitor can match.
|
||
|
||
---
|
||
|
||
**TASK COMPLETE**
|
||
- ✅ Technical architecture defined (Thin Contract + Smart Processor pattern)
|
||
- ✅ 80/20 tool selection validated and prioritized
|
||
- ✅ 6-sprint breakdown with concrete deliverables
|
||
- ✅ Risk analysis and mitigation strategies
|
||
- ✅ Integration strategy with existing Atomizer V2
|
||
- ✅ Implementation timeline and resource allocation
|
||
|
||
**CONFIDENCE: HIGH** - This plan provides the blueprint to build from. |