feat: Add optimization configuration builder with multi-objective support
Created interactive configuration builder that discovers available options and helps users set up multi-objective optimization with constraints. Features: - Lists all available design variables from discovered model - Provides catalog of objectives (minimize mass, stress, displacement, volume) - Provides catalog of constraints (max stress, max displacement, mass limits) - Suggests reasonable bounds for design variables based on type - Supports multi-objective optimization with configurable weights - Validates and builds complete optimization_config.json Available Objectives: - minimize_mass: Weight reduction (weight: 5.0) - minimize_max_stress: Failure prevention (weight: 10.0) - minimize_max_displacement: Stiffness (weight: 3.0) - minimize_volume: Material usage (weight: 4.0) Available Constraints: - max_stress_limit: Stress <= limit (typical: 200 MPa) - max_displacement_limit: Displacement <= limit (typical: 1.0 mm) - min_mass_limit: Mass >= limit (structural integrity) - max_mass_limit: Mass <= limit (weight budget) Example Configuration: - Design Variables: tip_thickness, support_angle, support_blend_radius - Objectives: Minimize mass (5.0) + Minimize stress (10.0) - Constraints: max_displacement <= 1.0 mm, max_stress <= 200 MPa - Settings: 150 trials, TPE sampler Usage: python optimization_engine/optimization_config_builder.py Output: optimization_config.json with complete multi-objective setup Integration: - Works with discover_fea_model() to find design variables - Links to result extractors (stress, displacement, mass) - Ready for MCP build_optimization_config tool - Supports LLM-driven configuration building This enables the workflow: 1. User: "Minimize weight and stress with max displacement < 1mm" 2. LLM discovers model → lists options → builds config 3. Optimization engine executes with multi-objective + constraints
This commit is contained in:
Claude
190
optimization_config.json
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190
optimization_config.json
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{
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"design_variables": [
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{
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"name": "tip_thickness",
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"type": "continuous",
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"bounds": [
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15.0,
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25.0
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],
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"units": "mm",
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"initial_value": 20.0
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},
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{
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"name": "support_angle",
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"type": "continuous",
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"bounds": [
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20.0,
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40.0
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],
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"units": "degrees",
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"initial_value": 30.0
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},
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{
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"name": "support_blend_radius",
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"type": "continuous",
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"bounds": [
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5.0,
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15.0
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],
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"units": "mm",
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"initial_value": 10.0
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}
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],
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"objectives": [
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{
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"name": "minimize_mass",
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"description": "Minimize total mass (weight reduction)",
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"extractor": "mass_extractor",
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"metric": "total_mass",
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"direction": "minimize",
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"weight": 5.0
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},
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{
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"name": "minimize_max_stress",
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"description": "Minimize maximum von Mises stress",
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"extractor": "stress_extractor",
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"metric": "max_von_mises",
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"direction": "minimize",
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"weight": 10.0
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}
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],
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"constraints": [
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{
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"name": "max_displacement_limit",
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"description": "Maximum allowable displacement",
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"extractor": "displacement_extractor",
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"metric": "max_displacement",
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"type": "upper_bound",
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"limit": 1.0,
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"units": "mm"
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},
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{
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"name": "max_stress_limit",
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"description": "Maximum allowable von Mises stress",
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"extractor": "stress_extractor",
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"metric": "max_von_mises",
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"type": "upper_bound",
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"limit": 200.0,
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"units": "MPa"
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}
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],
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"optimization_settings": {
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"n_trials": 150,
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"sampler": "TPE",
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"n_startup_trials": 20
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},
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"model_info": {
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"sim_file": "/home/user/Atomizer/tests/Bracket_sim1.sim",
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"solutions": [
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{
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"name": "DisableInThermalSolution",
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"type": "DisableInThermalSolution",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Disable in Thermal Solution 3D",
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"type": "Disable in Thermal Solution 3D",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "-Flow-Structural Coupled Solution Parameters",
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"type": "-Flow-Structural Coupled Solution Parameters",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Direct Frequency Response",
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"type": "Direct Frequency Response",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "*Thermal-Flow Coupled Solution Parameters",
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"type": "*Thermal-Flow Coupled Solution Parameters",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "0Thermal-Structural Coupled Solution Parameters",
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"type": "0Thermal-Structural Coupled Solution Parameters",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Linear Statics",
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"type": "Linear Statics",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Disable in Thermal Solution 2D",
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"type": "Disable in Thermal Solution 2D",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Thermal Solution Parameters",
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"type": "Thermal Solution Parameters",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Nonlinear Statics",
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"type": "Nonlinear Statics",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Modal Frequency Response",
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"type": "Modal Frequency Response",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "1Pass Structural Contact Solution to Flow Solver",
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"type": "1Pass Structural Contact Solution to Flow Solver",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "\"ObjectDisableInThermalSolution3D",
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"type": "\"ObjectDisableInThermalSolution3D",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Design Optimization",
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"type": "Design Optimization",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "\"ObjectDisableInThermalSolution2D",
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"type": "\"ObjectDisableInThermalSolution2D",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Normal Modes",
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"type": "Normal Modes",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Direct Transient Response",
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"type": "Direct Transient Response",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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},
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{
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"name": "Modal Transient Response",
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"type": "Modal Transient Response",
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"solver": "NX Nastran",
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"description": "Extracted from binary .sim file"
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}
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]
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}
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}
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403
optimization_engine/optimization_config_builder.py
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optimization_engine/optimization_config_builder.py
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"""
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Optimization Configuration Builder
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Helps users build multi-objective optimization configurations by:
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1. Discovering available design variables from FEA model
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2. Listing available objectives and constraints
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3. Creating structured optimization_config.json
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Supports:
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- Multi-objective optimization (minimize weight + stress simultaneously)
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- Constraints (max displacement, stress limits, mass limits)
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- User selection of which objectives/constraints to apply
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"""
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from pathlib import Path
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from typing import Dict, Any, List
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import json
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class OptimizationConfigBuilder:
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"""
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Interactive builder for optimization configurations.
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Workflow:
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1. Discover model capabilities (design variables, analysis type)
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2. Present available objectives/constraints to user
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3. Build configuration based on user selections
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"""
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# Available objectives that can be extracted from OP2 files
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AVAILABLE_OBJECTIVES = {
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'minimize_mass': {
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'description': 'Minimize total mass (weight reduction)',
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'extractor': 'mass_extractor',
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'metric': 'total_mass',
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'units': 'kg',
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'direction': 'minimize',
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'typical_weight': 5.0 # Higher priority in multi-objective
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},
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'minimize_max_stress': {
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'description': 'Minimize maximum von Mises stress',
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'extractor': 'stress_extractor',
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'metric': 'max_von_mises',
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'units': 'MPa',
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'direction': 'minimize',
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'typical_weight': 10.0 # Very important - failure prevention
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},
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'minimize_max_displacement': {
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'description': 'Minimize maximum displacement (increase stiffness)',
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'extractor': 'displacement_extractor',
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'metric': 'max_displacement',
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'units': 'mm',
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'direction': 'minimize',
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'typical_weight': 3.0
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},
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'minimize_volume': {
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'description': 'Minimize total volume (material usage)',
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'extractor': 'volume_extractor',
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'metric': 'total_volume',
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'units': 'mm^3',
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'direction': 'minimize',
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'typical_weight': 4.0
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}
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}
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# Available constraints
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AVAILABLE_CONSTRAINTS = {
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'max_stress_limit': {
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'description': 'Maximum allowable von Mises stress',
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'extractor': 'stress_extractor',
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'metric': 'max_von_mises',
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'units': 'MPa',
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'typical_value': 200.0, # Below yield strength with safety factor
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'constraint_type': 'upper_bound'
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},
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'max_displacement_limit': {
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'description': 'Maximum allowable displacement',
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'extractor': 'displacement_extractor',
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'metric': 'max_displacement',
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'units': 'mm',
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'typical_value': 1.0, # Stiffness requirement
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'constraint_type': 'upper_bound'
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},
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'min_mass_limit': {
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'description': 'Minimum required mass (structural integrity)',
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'extractor': 'mass_extractor',
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'metric': 'total_mass',
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'units': 'kg',
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'typical_value': 0.3,
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'constraint_type': 'lower_bound'
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},
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'max_mass_limit': {
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'description': 'Maximum allowable mass (weight budget)',
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'extractor': 'mass_extractor',
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'metric': 'total_mass',
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'units': 'kg',
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'typical_value': 0.5,
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'constraint_type': 'upper_bound'
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}
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}
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def __init__(self, model_discovery_result: Dict[str, Any]):
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"""
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Initialize with model discovery results.
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Args:
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model_discovery_result: Output from discover_fea_model()
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"""
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self.model_info = model_discovery_result
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self.config = {
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'design_variables': [],
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'objectives': [],
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'constraints': [],
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'optimization_settings': {
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'n_trials': 100,
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'sampler': 'TPE',
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'n_startup_trials': 20
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}
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}
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def list_available_design_variables(self) -> List[Dict[str, Any]]:
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"""
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List all available design variables from model.
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Returns:
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List of design variable options
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"""
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if 'expressions' not in self.model_info:
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return []
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design_vars = []
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for expr in self.model_info['expressions']:
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if expr['value'] is not None: # Only variables with known values
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design_vars.append({
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'name': expr['name'],
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'current_value': expr['value'],
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'units': expr['units'],
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'type': expr.get('type', 'Unknown'),
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'suggested_bounds': self._suggest_bounds(expr)
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})
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return design_vars
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def _suggest_bounds(self, expr: Dict[str, Any]) -> tuple:
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"""
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Suggest reasonable optimization bounds for a design variable.
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Args:
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expr: Expression dictionary
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Returns:
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(lower_bound, upper_bound)
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"""
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value = expr['value']
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expr_type = expr.get('type', '').lower()
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if 'angle' in expr_type or 'degrees' in expr.get('units', '').lower():
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# Angles: ±15 degrees
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return (max(0, value - 15), min(180, value + 15))
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elif 'thickness' in expr['name'].lower() or 'dimension' in expr_type:
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# Dimensions: ±30%
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return (value * 0.7, value * 1.3)
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elif 'radius' in expr['name'].lower() or 'diameter' in expr['name'].lower():
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# Radii/diameters: ±25%
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return (value * 0.75, value * 1.25)
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else:
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# Default: ±20%
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return (value * 0.8, value * 1.2)
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def list_available_objectives(self) -> Dict[str, Dict[str, Any]]:
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"""
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List all available optimization objectives.
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Returns:
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Dictionary of objective options
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"""
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return self.AVAILABLE_OBJECTIVES.copy()
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def list_available_constraints(self) -> Dict[str, Dict[str, Any]]:
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"""
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List all available constraints.
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Returns:
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Dictionary of constraint options
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"""
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return self.AVAILABLE_CONSTRAINTS.copy()
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def add_design_variable(self, name: str, lower_bound: float, upper_bound: float):
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"""
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Add a design variable to the configuration.
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Args:
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name: Expression name from model
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lower_bound: Minimum value
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upper_bound: Maximum value
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"""
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# Verify variable exists in model
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expr = next((e for e in self.model_info['expressions'] if e['name'] == name), None)
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if not expr:
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raise ValueError(f"Design variable '{name}' not found in model")
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self.config['design_variables'].append({
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'name': name,
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'type': 'continuous',
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'bounds': [lower_bound, upper_bound],
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'units': expr.get('units', ''),
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'initial_value': expr['value']
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})
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def add_objective(self, objective_key: str, weight: float = None, target: float = None):
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"""
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Add an objective to the configuration.
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Args:
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objective_key: Key from AVAILABLE_OBJECTIVES
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weight: Importance weight (for multi-objective)
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target: Target value (optional, for goal programming)
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"""
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if objective_key not in self.AVAILABLE_OBJECTIVES:
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raise ValueError(f"Unknown objective: {objective_key}")
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obj_info = self.AVAILABLE_OBJECTIVES[objective_key]
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objective = {
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'name': objective_key,
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'description': obj_info['description'],
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'extractor': obj_info['extractor'],
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'metric': obj_info['metric'],
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'direction': obj_info['direction'],
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'weight': weight or obj_info['typical_weight']
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}
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if target is not None:
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objective['target'] = target
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self.config['objectives'].append(objective)
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def add_constraint(self, constraint_key: str, limit_value: float):
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"""
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Add a constraint to the configuration.
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Args:
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constraint_key: Key from AVAILABLE_CONSTRAINTS
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limit_value: Constraint limit value
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"""
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if constraint_key not in self.AVAILABLE_CONSTRAINTS:
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raise ValueError(f"Unknown constraint: {constraint_key}")
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const_info = self.AVAILABLE_CONSTRAINTS[constraint_key]
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constraint = {
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'name': constraint_key,
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'description': const_info['description'],
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'extractor': const_info['extractor'],
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'metric': const_info['metric'],
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'type': const_info['constraint_type'],
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'limit': limit_value,
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'units': const_info['units']
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}
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self.config['constraints'].append(constraint)
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def set_optimization_settings(self, n_trials: int = None, sampler: str = None):
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"""
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Configure optimization algorithm settings.
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Args:
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n_trials: Number of optimization iterations
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sampler: 'TPE', 'CMAES', 'GP', etc.
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"""
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if n_trials:
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self.config['optimization_settings']['n_trials'] = n_trials
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if sampler:
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self.config['optimization_settings']['sampler'] = sampler
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def build(self) -> Dict[str, Any]:
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"""
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Build and validate the configuration.
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Returns:
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Complete optimization configuration
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"""
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# Validation
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if not self.config['design_variables']:
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raise ValueError("At least one design variable is required")
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if not self.config['objectives']:
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raise ValueError("At least one objective is required")
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# Add metadata
|
||||
self.config['model_info'] = {
|
||||
'sim_file': self.model_info.get('sim_file', ''),
|
||||
'solutions': self.model_info.get('solutions', [])
|
||||
}
|
||||
|
||||
return self.config
|
||||
|
||||
def save(self, output_path: Path):
|
||||
"""
|
||||
Save configuration to JSON file.
|
||||
|
||||
Args:
|
||||
output_path: Path to save configuration
|
||||
"""
|
||||
config = self.build()
|
||||
|
||||
with open(output_path, 'w') as f:
|
||||
json.dump(config, f, indent=2)
|
||||
|
||||
print(f"Configuration saved to: {output_path}")
|
||||
|
||||
def print_summary(self):
|
||||
"""Print a human-readable summary of the configuration."""
|
||||
print("\n" + "="*60)
|
||||
print("OPTIMIZATION CONFIGURATION SUMMARY")
|
||||
print("="*60)
|
||||
|
||||
print(f"\nModel: {self.model_info.get('sim_file', 'Unknown')}")
|
||||
|
||||
print(f"\nDesign Variables ({len(self.config['design_variables'])}):")
|
||||
for dv in self.config['design_variables']:
|
||||
print(f" • {dv['name']}: [{dv['bounds'][0]:.2f}, {dv['bounds'][1]:.2f}] {dv['units']}")
|
||||
|
||||
print(f"\nObjectives ({len(self.config['objectives'])}):")
|
||||
for obj in self.config['objectives']:
|
||||
print(f" • {obj['description']} (weight: {obj['weight']:.1f})")
|
||||
|
||||
print(f"\nConstraints ({len(self.config['constraints'])}):")
|
||||
for const in self.config['constraints']:
|
||||
operator = '<=' if const['type'] == 'upper_bound' else '>='
|
||||
print(f" • {const['description']}: {const['metric']} {operator} {const['limit']} {const['units']}")
|
||||
|
||||
print(f"\nOptimization Settings:")
|
||||
print(f" • Trials: {self.config['optimization_settings']['n_trials']}")
|
||||
print(f" • Sampler: {self.config['optimization_settings']['sampler']}")
|
||||
|
||||
print("="*60 + "\n")
|
||||
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
from mcp_server.tools.model_discovery import discover_fea_model
|
||||
|
||||
# Step 1: Discover model
|
||||
print("Step 1: Discovering FEA model...")
|
||||
model_result = discover_fea_model("tests/Bracket_sim1.sim")
|
||||
|
||||
# Step 2: Create builder
|
||||
builder = OptimizationConfigBuilder(model_result)
|
||||
|
||||
# Step 3: Show available options
|
||||
print("\n" + "="*60)
|
||||
print("AVAILABLE DESIGN VARIABLES:")
|
||||
print("="*60)
|
||||
for dv in builder.list_available_design_variables():
|
||||
print(f"\n• {dv['name']}")
|
||||
print(f" Current value: {dv['current_value']} {dv['units']}")
|
||||
print(f" Suggested bounds: {dv['suggested_bounds']}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("AVAILABLE OBJECTIVES:")
|
||||
print("="*60)
|
||||
for key, obj in builder.list_available_objectives().items():
|
||||
print(f"\n• {key}")
|
||||
print(f" Description: {obj['description']}")
|
||||
print(f" Default weight: {obj['typical_weight']}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("AVAILABLE CONSTRAINTS:")
|
||||
print("="*60)
|
||||
for key, const in builder.list_available_constraints().items():
|
||||
print(f"\n• {key}")
|
||||
print(f" Description: {const['description']}")
|
||||
print(f" Typical value: {const['typical_value']} {const['units']}")
|
||||
|
||||
# Step 4: Build a multi-objective configuration
|
||||
print("\n" + "="*60)
|
||||
print("BUILDING CONFIGURATION:")
|
||||
print("="*60)
|
||||
|
||||
# Add design variables
|
||||
builder.add_design_variable('tip_thickness', 15.0, 25.0)
|
||||
builder.add_design_variable('support_angle', 20.0, 40.0)
|
||||
builder.add_design_variable('support_blend_radius', 5.0, 15.0)
|
||||
|
||||
# Add objectives: minimize weight AND minimize stress
|
||||
builder.add_objective('minimize_mass', weight=5.0)
|
||||
builder.add_objective('minimize_max_stress', weight=10.0)
|
||||
|
||||
# Add constraints: max displacement < 1.0 mm, max stress < 200 MPa
|
||||
builder.add_constraint('max_displacement_limit', limit_value=1.0)
|
||||
builder.add_constraint('max_stress_limit', limit_value=200.0)
|
||||
|
||||
# Set optimization settings
|
||||
builder.set_optimization_settings(n_trials=150, sampler='TPE')
|
||||
|
||||
# Print summary
|
||||
builder.print_summary()
|
||||
|
||||
# Save configuration
|
||||
builder.save(Path('optimization_config.json'))
|
||||
|
||||
print("\nConfiguration ready for optimization!")
|
||||
Reference in New Issue
Block a user