Phase 3.3: Multi-objective optimization fix, updated docs & Claude skill
- Fixed drone gimbal optimization to use proper semantic directions - Changed from ['minimize', 'minimize'] to ['minimize', 'maximize'] - Updated Claude skill (v2.0) with Phase 3.3 integration - Added centralized extractor library documentation - Added multi-objective optimization (Protocol 11) section - Added NX multi-solution protocol documentation - Added dashboard integration documentation - Fixed Pareto front degenerate issue with proper NSGA-II configuration 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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docs/NX_MULTI_SOLUTION_PROTOCOL.md
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docs/NX_MULTI_SOLUTION_PROTOCOL.md
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# NX Multi-Solution Solve Protocol
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## Critical Finding: SolveAllSolutions API Required for Multi-Solution Models
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**Date**: November 23, 2025
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**Last Updated**: November 23, 2025
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**Protocol**: Multi-Solution Nastran Solve
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**Affected Models**: Any NX simulation with multiple solutions (e.g., static + modal, thermal + structural)
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---
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## Problem Statement
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When an NX simulation contains multiple solutions (e.g., Solution 1 = Static Analysis, Solution 2 = Modal Analysis), using `SolveChainOfSolutions()` with Background mode **does not wait for all solutions to complete** before returning control to Python. This causes:
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1. **Missing OP2 Files**: Only the first solution's OP2 file is generated
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2. **Stale Data**: Subsequent trials read old OP2 files from previous runs
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3. **Identical Results**: All trials show the same values for results from missing solutions
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4. **Silent Failures**: No error is raised - the solve completes but files are not written
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### Example Scenario
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**Drone Gimbal Arm Optimization**:
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- Solution 1: Static analysis (stress, displacement)
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- Solution 2: Modal analysis (frequency)
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**Symptoms**:
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- All 100 trials showed **identical frequency** (27.476 Hz)
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- Only `beam_sim1-solution_1.op2` was created
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- `beam_sim1-solution_2.op2` was never regenerated after Trial 0
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- Both `.dat` files were written correctly, but solve didn't wait for completion
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---
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## Root Cause
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```python
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# WRONG APPROACH (doesn't wait for completion)
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psolutions1 = []
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solution_idx = 1
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while True:
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solution_obj_name = f"Solution[Solution {solution_idx}]"
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simSolution = simSimulation1.FindObject(solution_obj_name)
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if simSolution:
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psolutions1.append(simSolution)
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solution_idx += 1
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else:
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break
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theCAESimSolveManager.SolveChainOfSolutions(
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psolutions1,
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NXOpen.CAE.SimSolution.SolveOption.Solve,
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NXOpen.CAE.SimSolution.SetupCheckOption.CompleteDeepCheckAndOutputErrors,
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NXOpen.CAE.SimSolution.SolveMode.Background # ❌ Returns immediately!
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)
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```
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**Issue**: Background mode runs asynchronously and returns control to Python before all solutions finish solving.
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---
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## Correct Solution
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### For Solving All Solutions
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Use `SolveAllSolutions()` API with **Foreground mode**:
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```python
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# CORRECT APPROACH (waits for completion)
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if solution_name:
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# Solve specific solution in background mode
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solution_obj_name = f"Solution[{solution_name}]"
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simSolution1 = simSimulation1.FindObject(solution_obj_name)
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psolutions1 = [simSolution1]
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numsolutionssolved1, numsolutionsfailed1, numsolutionsskipped1 = theCAESimSolveManager.SolveChainOfSolutions(
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psolutions1,
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NXOpen.CAE.SimSolution.SolveOption.Solve,
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NXOpen.CAE.SimSolution.SetupCheckOption.CompleteDeepCheckAndOutputErrors,
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NXOpen.CAE.SimSolution.SolveMode.Background
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)
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else:
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# Solve ALL solutions using SolveAllSolutions API (Foreground mode)
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# This ensures all solutions (static + modal, etc.) complete before returning
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print(f"[JOURNAL] Solving all solutions using SolveAllSolutions API (Foreground mode)...")
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numsolutionssolved1, numsolutionsfailed1, numsolutionsskipped1 = theCAESimSolveManager.SolveAllSolutions(
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NXOpen.CAE.SimSolution.SolveOption.Solve,
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NXOpen.CAE.SimSolution.SetupCheckOption.CompleteCheckAndOutputErrors,
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NXOpen.CAE.SimSolution.SolveMode.Foreground, # ✅ Blocks until complete
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False
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)
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```
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### Key Differences
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| Aspect | SolveChainOfSolutions | SolveAllSolutions |
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|--------|----------------------|-------------------|
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| **Manual enumeration** | Required (loop through solutions) | Automatic (handles all solutions) |
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| **Background mode behavior** | Returns immediately, async | N/A (Foreground recommended) |
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| **Foreground mode behavior** | Blocks until complete | Blocks until complete ✅ |
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| **Use case** | Specific solution selection | Solve all solutions |
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---
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## Implementation Location
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**File**: `optimization_engine/solve_simulation.py`
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**Lines**: 271-295
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**When to use this protocol**:
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- When `solution_name=None` is passed to `NXSolver.run_simulation()`
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- Any simulation with multiple solutions that must all complete
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- Multi-objective optimization requiring results from different analysis types
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---
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## Verification Steps
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After implementing the fix, verify:
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1. **Both .dat files are written** (one per solution)
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```
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beam_sim1-solution_1.dat # Static analysis
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beam_sim1-solution_2.dat # Modal analysis
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```
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2. **Both .op2 files are created** with updated timestamps
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```
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beam_sim1-solution_1.op2 # Contains stress, displacement
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beam_sim1-solution_2.op2 # Contains eigenvalues, mode shapes
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```
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3. **Results are unique per trial** - check that frequency values vary across trials
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4. **Journal log shows**:
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```
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[JOURNAL] Solving all solutions using SolveAllSolutions API (Foreground mode)...
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[JOURNAL] Solve completed!
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[JOURNAL] Solutions solved: 2
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```
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---
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## Related Issues Fixed
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1. **All trials showing identical frequency**: Fixed by ensuring modal solution runs
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2. **Only one data point in dashboard**: Fixed by all trials succeeding
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3. **Parallel coordinates with NaN**: Fixed by having complete data from all solutions
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---
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## References
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- **User's Example**: `nx_journals/user_generated_journals/journal_solve_all_solution.py` (line 27)
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- **NX Open Documentation**: SimSolveManager.SolveAllSolutions() method
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- **Implementation**: `optimization_engine/solve_simulation.py`
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---
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## Best Practices
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1. **Always use Foreground mode** when solving all solutions
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2. **Verify OP2 timestamp changes** to ensure fresh solves
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3. **Check solve counts** in journal output to confirm both solutions ran
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4. **Test with 5 trials** before running large optimizations
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5. **Monitor unique frequency values** as a smoke test for multi-solution models
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---
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## Example Use Cases
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### ✅ Correct Usage
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```python
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# Multi-objective optimization with static + modal
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result = nx_solver.run_simulation(
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sim_file=sim_file,
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working_dir=model_dir,
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expression_updates=design_vars,
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solution_name=None # Solve ALL solutions
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)
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```
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### ❌ Incorrect Usage (Don't Do This)
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```python
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# Running modal separately - inefficient and error-prone
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result1 = nx_solver.run_simulation(..., solution_name="Solution 1") # Static
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result2 = nx_solver.run_simulation(..., solution_name="Solution 2") # Modal
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# This doubles the solve time and requires managing two result objects
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```
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---
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**Status**: ✅ Implemented and Verified
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**Impact**: Critical for all multi-solution optimization workflows
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