feat: Add substudy system with live history tracking and workflow fixes

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>

optimization_engine/pynastran_research_agent.py

@@ -118,15 +118,21 @@ class PyNastranResearchAgent:
     model.read_op2(str(op2_file))
 
     # Get stress object for element type
+    # In pyNastran, stress is stored in model.op2_results.stress
     stress_attr = f"{element_type}_stress"
-    if not hasattr(model, stress_attr):
+
+    if not hasattr(model, 'op2_results') or not hasattr(model.op2_results, 'stress'):
+        raise ValueError(f"No stress results in OP2")
+
+    stress_obj = model.op2_results.stress
+    if not hasattr(stress_obj, stress_attr):
         raise ValueError(f"No {element_type} stress results in OP2")
 
-    stress = getattr(model, stress_attr)[subcase]
+    stress = getattr(stress_obj, stress_attr)[subcase]
     itime = 0
 
     # Extract von Mises if available
-    if stress.is_von_mises():
+    if stress.is_von_mises:  # Property, not method
         von_mises = stress.data[itime, :, 9]  # Column 9 is von Mises
         max_stress = float(np.max(von_mises))