Anto01
2f3afc3813
Major Features: - Hierarchical substudy system (like NX Solutions/Subcases) * Shared model files across all substudies * Independent configuration per substudy * Continuation support from previous substudies * Real-time incremental history updates - Live history tracking with optimization_history_incremental.json - Complete bracket_displacement_maximizing study with substudy examples Core Fixes: - Fixed expression update workflow to pass design_vars through simulation_runner * Restored working NX journal expression update mechanism * OP2 timestamp verification instead of file deletion * Resolved issue where all trials returned identical objective values - Fixed LLMOptimizationRunner to pass design variables to simulation runner - Enhanced NXSolver with timestamp-based file regeneration verification New Components: - optimization_engine/llm_optimization_runner.py - LLM-driven optimization runner - optimization_engine/optimization_setup_wizard.py - Phase 3.3 setup wizard - studies/bracket_displacement_maximizing/ - Complete substudy example * run_substudy.py - Substudy runner with continuation * run_optimization.py - Standalone optimization runner * config/substudy_template.json - Template for new substudies * substudies/coarse_exploration/ - 20-trial coarse search * substudies/fine_tuning/ - 50-trial refinement (continuation example) * SUBSTUDIES_README.md - Complete substudy documentation Technical Improvements: - Incremental history saving after each trial (optimization_history_incremental.json) - Expression update workflow: .prt update → NX journal receives values → geometry update → FEM update → solve - Trial indexing fix in substudy result saving - Updated README with substudy system documentation Testing: - Successfully ran 20-trial coarse_exploration substudy - Verified different objective values across trials (workflow fix validated) - Confirmed live history updates in real-time - Tested shared model file usage across substudies 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
"""
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Safety Factor Constraint Hook - Manual Implementation
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This hook enforces a minimum safety factor constraint on stress.
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If safety_factor < minimum required, the objective is heavily penalized.
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Safety Factor = Yield Strength / Max Stress
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For Aluminum 6061-T6:
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- Yield Strength: 276 MPa
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- Required Safety Factor: 4.0
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- Allowable Stress: 69 MPa
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Author: Atomizer Development Team
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Version: 1.0
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"""
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from optimization_engine.plugins.hooks import Hook, HookPoint
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def safety_factor_constraint_hook(context: dict) -> dict:
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"""
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Enforce safety factor constraint on optimization.
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This hook checks if the calculated safety factor meets the minimum requirement.
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If violated, it adds a large penalty to the objective to guide optimization
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away from unsafe designs.
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Args:
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context: Dict containing:
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- calculations: Dict with 'safety_factor' value
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- results: Dict with stress results
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Returns:
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Dict with:
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- safety_factor_satisfied: bool
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- safety_factor_violation: float (0 if satisfied, penalty otherwise)
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- constrained_objective: float (original or penalized objective)
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"""
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calculations = context.get('calculations', {})
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# Get safety factor from calculations
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safety_factor = calculations.get('safety_factor', 0.0)
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# Get objective (negative displacement to maximize)
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neg_displacement = calculations.get('neg_displacement', 0.0)
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# Required minimum safety factor
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min_safety_factor = 4.0
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# Check constraint
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satisfied = safety_factor >= min_safety_factor
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# Calculate violation (how much we're under the limit)
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violation = max(0.0, min_safety_factor - safety_factor)
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# Apply penalty if constraint violated
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if not satisfied:
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# Heavy penalty: add large value to objective (we're minimizing)
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# Penalty scales with violation severity
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penalty = 1000.0 * violation
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constrained_objective = neg_displacement + penalty
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print(f" [CONSTRAINT VIOLATED] Safety factor {safety_factor:.2f} < {min_safety_factor}")
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print(f" [PENALTY APPLIED] Adding {penalty:.2f} to objective")
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else:
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constrained_objective = neg_displacement
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print(f" [CONSTRAINT SATISFIED] Safety factor {safety_factor:.2f} >= {min_safety_factor}")
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return {
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'safety_factor_satisfied': satisfied,
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'safety_factor_violation': violation,
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'constrained_objective': constrained_objective,
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'objective': constrained_objective # This becomes the final objective
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}
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# Register hook with plugin system
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hook = Hook(
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name="safety_factor_constraint",
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hook_point=HookPoint.POST_CALCULATION,
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function=safety_factor_constraint_hook,
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enabled=True,
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description="Enforce minimum safety factor constraint with penalty"
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)
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def register_hooks(hook_manager):
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"""Register hooks with the plugin system."""
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return [hook]
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