feat: Add AtomizerField training data export and intelligent model discovery

Major additions:
- Training data export system for AtomizerField neural network training
- Bracket stiffness optimization study with 50+ training samples
- Intelligent NX model discovery (auto-detect solutions, expressions, mesh)
- Result extractors module for displacement, stress, frequency, mass
- User-generated NX journals for advanced workflows
- Archive structure for legacy scripts and test outputs
- Protocol documentation and dashboard launcher

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

archive/scripts/create_circular_plate_study_v2.py

@@ -0,0 +1,89 @@
+"""
+Create circular_plate_frequency_tuning_V2 study with ALL fixes applied.
+
+Improvements:
+- Proper study naming
+- Reports go to 3_reports/ folder
+- Reports discuss actual goal (115 Hz target)
+- Fixed objective function
+"""
+
+from pathlib import Path
+import sys
+import argparse
+sys.path.insert(0, str(Path(__file__).parent))
+
+from optimization_engine.hybrid_study_creator import HybridStudyCreator
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Create circular plate frequency tuning study')
+    parser.add_argument('--study-name', default='circular_plate_frequency_tuning_V2',
+                       help='Name of the study folder')
+    args = parser.parse_args()
+
+    study_name = args.study_name
+
+    creator = HybridStudyCreator()
+
+    # Create workflow JSON
+    import json
+    import tempfile
+    workflow = {
+        "study_name": study_name,
+        "optimization_request": "Tune the first natural frequency mode to exactly 115 Hz (within 0.1 Hz tolerance)",
+        "design_variables": [
+            {"parameter": "inner_diameter", "bounds": [50, 150]},
+            {"parameter": "plate_thickness", "bounds": [2, 10]}
+        ],
+        "objectives": [{
+            "name": "frequency_error",
+            "goal": "minimize",
+            "extraction": {
+                "action": "extract_first_natural_frequency",
+                "params": {"mode_number": 1, "target_frequency": 115.0}
+            }
+        }],
+        "constraints": [{
+            "name": "frequency_tolerance",
+            "type": "less_than",
+            "threshold": 0.1
+        }]
+    }
+
+    # Write to temp file
+    temp_workflow = Path(tempfile.gettempdir()) / f"{study_name}_workflow.json"
+    with open(temp_workflow, 'w') as f:
+        json.dump(workflow, f, indent=2)
+
+    # Create study
+    study_dir = creator.create_from_workflow(
+        workflow_json_path=temp_workflow,
+        model_files={
+            'prt': Path("examples/Models/Circular Plate/Circular_Plate.prt"),
+            'sim': Path("examples/Models/Circular Plate/Circular_Plate_sim1.sim"),
+            'fem': Path("examples/Models/Circular Plate/Circular_Plate_fem1.fem"),
+            'fem_i': Path("examples/Models/Circular Plate/Circular_Plate_fem1_i.prt")
+        },
+        study_name=study_name
+    )
+
+    print()
+    print("=" * 80)
+    print(f"[OK] Study created: {study_name}")
+    print("=" * 80)
+    print()
+    print(f"Location: {study_dir}")
+    print()
+    print("Structure:")
+    print("  - 1_setup/: Model files and configuration")
+    print("  - 2_results/: Optimization history and database")
+    print("  - 3_reports/: Human-readable reports with graphs")
+    print()
+    print("To run optimization:")
+    print(f"  python {study_dir}/run_optimization.py")
+    print()
+
+
+if __name__ == "__main__":
+    main()