Anto01
0a7cca9c6a
This commit implements three major architectural improvements to transform Atomizer from static pattern matching to intelligent AI-powered analysis. ## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅ Created intelligent system that understands existing capabilities before requesting examples: **New Files:** - optimization_engine/codebase_analyzer.py (379 lines) Scans Atomizer codebase for existing FEA/CAE capabilities - optimization_engine/workflow_decomposer.py (507 lines, v0.2.0) Breaks user requests into atomic workflow steps Complete rewrite with multi-objective, constraints, subcase targeting - optimization_engine/capability_matcher.py (312 lines) Matches workflow steps to existing code implementations - optimization_engine/targeted_research_planner.py (259 lines) Creates focused research plans for only missing capabilities **Results:** - 80-90% coverage on complex optimization requests - 87-93% confidence in capability matching - Fixed expression reading misclassification (geometry vs result_extraction) ## Phase 2.6: Intelligent Step Classification ✅ Distinguishes engineering features from simple math operations: **New Files:** - optimization_engine/step_classifier.py (335 lines) **Classification Types:** 1. Engineering Features - Complex FEA/CAE needing research 2. Inline Calculations - Simple math to auto-generate 3. Post-Processing Hooks - Middleware between FEA steps ## Phase 2.7: LLM-Powered Workflow Intelligence ✅ Replaces static regex patterns with Claude AI analysis: **New Files:** - optimization_engine/llm_workflow_analyzer.py (395 lines) Uses Claude API for intelligent request analysis Supports both Claude Code (dev) and API (production) modes - .claude/skills/analyze-workflow.md Skill template for LLM workflow analysis integration **Key Breakthrough:** - Detects ALL intermediate steps (avg, min, normalization, etc.) - Understands engineering context (CBUSH vs CBAR, directions, metrics) - Distinguishes OP2 extraction from part expression reading - Expected 95%+ accuracy with full nuance detection ## Test Coverage **New Test Files:** - tests/test_phase_2_5_intelligent_gap_detection.py (335 lines) - tests/test_complex_multiobj_request.py (130 lines) - tests/test_cbush_optimization.py (130 lines) - tests/test_cbar_genetic_algorithm.py (150 lines) - tests/test_step_classifier.py (140 lines) - tests/test_llm_complex_request.py (387 lines) All tests include: - UTF-8 encoding for Windows console - atomizer environment (not test_env) - Comprehensive validation checks ## Documentation **New Documentation:** - docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines) - docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines) - docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines) **Updated:** - README.md - Added Phase 2.5-2.7 completion status - DEVELOPMENT_ROADMAP.md - Updated phase progress ## Critical Fixes 1. **Expression Reading Misclassification** (lines cited in session summary) - Updated codebase_analyzer.py pattern detection - Fixed workflow_decomposer.py domain classification - Added capability_matcher.py read_expression mapping 2. **Environment Standardization** - All code now uses 'atomizer' conda environment - Removed test_env references throughout 3. **Multi-Objective Support** - WorkflowDecomposer v0.2.0 handles multiple objectives - Constraint extraction and validation - Subcase and direction targeting ## Architecture Evolution **Before (Static & Dumb):** User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌ **After (LLM-Powered & Intelligent):** User Request → Claude AI Analysis → Structured JSON → ├─ Engineering (research needed) ├─ Inline (auto-generate Python) ├─ Hooks (middleware scripts) └─ Optimization (config) ✅ ## LLM Integration Strategy **Development Mode (Current):** - Use Claude Code directly for interactive analysis - No API consumption or costs - Perfect for iterative development **Production Mode (Future):** - Optional Anthropic API integration - Falls back to heuristics if no API key - For standalone batch processing ## Next Steps - Phase 2.8: Inline Code Generation - Phase 2.9: Post-Processing Hook Generation - Phase 3: MCP Integration for automated documentation research 🚀 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
87 lines
2.4 KiB
Markdown
87 lines
2.4 KiB
Markdown
# Bracket Stress Minimization Study
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## Overview
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This study optimizes a structural bracket to minimize maximum von Mises stress while maintaining displacement constraints.
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## Objective
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Minimize maximum von Mises stress in the bracket under applied loading conditions.
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## Design Variables
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- **tip_thickness**: 15.0 - 25.0 mm
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- Controls the thickness of the bracket tip
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- Directly affects stress distribution and structural rigidity
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- **support_angle**: 20.0 - 40.0 degrees
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- Controls the angle of the support structure
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- Affects load path and stress concentration
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## Constraints
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- **Maximum displacement** ≤ 1.0 mm
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- Ensures the bracket maintains acceptable deformation under load
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- Prevents excessive deflection that could affect functionality
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## Model Information
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All FEA files are located in [model/](model/):
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- **Part**: [Bracket.prt](model/Bracket.prt)
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- **Simulation**: [Bracket_sim1.sim](model/Bracket_sim1.sim)
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- **FEM**: [Bracket_fem1.fem](model/Bracket_fem1.fem)
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## Optimization Settings
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- **Sampler**: TPE (Tree-structured Parzen Estimator)
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- **Total trials**: 50
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- **Startup trials**: 20 (random sampling for initial exploration)
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- **TPE candidates**: 24
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- **Multivariate**: Enabled
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## Running the Optimization
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From the project root:
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```bash
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python run_5trial_test.py # Quick 5-trial test
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```
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Or for the full optimization:
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```python
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from pathlib import Path
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from optimization_engine.runner import OptimizationRunner
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config_path = Path("studies/bracket_stress_minimization/optimization_config_stress_displacement.json")
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runner = OptimizationRunner(
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config_path=config_path,
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model_updater=bracket_model_updater,
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simulation_runner=bracket_simulation_runner,
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result_extractors={...}
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)
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study = runner.run(study_name="bracket_study", n_trials=50)
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```
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## Results
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Results are stored in [optimization_results/](optimization_results/):
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- **trial_logs/**: Detailed logs for each trial iteration
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- **history.json**: Complete trial-by-trial results
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- **history.csv**: Results in CSV format for analysis
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- **optimization_summary.json**: Best parameters and final results
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- **study_*.db**: Optuna database for resuming optimizations
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## Notes
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- Uses NX Simcenter 2412 for FEA simulation
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- Journal-based solver execution for automation
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- Results extracted from OP2 files using pyNastran
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- Stress values in MPa, displacement in mm
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## Analysis
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Post-optimization analysis plots and reports will be stored in [analysis/](analysis/).
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