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_v2_study.py

@@ -0,0 +1,89 @@
+"""
+Create circular_plate_frequency_tuning_V2 study with all fixes.
+"""
+from pathlib import Path
+import shutil
+import json
+
+# Study configuration
+study_name = "circular_plate_frequency_tuning_V2"
+study_dir = Path("studies") / study_name
+
+# Create study structure
+print(f"Creating study: {study_name}")
+print("=" * 80)
+
+# 1. Create directory structure
+(study_dir / "1_setup" / "model").mkdir(parents=True, exist_ok=True)
+(study_dir / "2_results").mkdir(parents=True, exist_ok=True)
+(study_dir / "3_reports").mkdir(parents=True, exist_ok=True)
+
+# 2. Copy model files
+source_dir = Path("examples/Models/Circular Plate")
+model_files = [
+    "Circular_Plate.prt",
+    "Circular_Plate_sim1.sim",
+    "Circular_Plate_fem1.fem",
+    "Circular_Plate_fem1_i.prt"
+]
+
+print("\n[1/5] Copying model files...")
+for file in model_files:
+    src = source_dir / file
+    dst = study_dir / "1_setup" / "model" / file
+    if src.exists():
+        shutil.copy2(src, dst)
+        print(f"  ✓ {file}")
+
+# 3. Create workflow config
+print("\n[2/5] Creating workflow configuration...")
+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
+        }
+    ]
+}
+
+config_file = study_dir / "1_setup" / "workflow_config.json"
+with open(config_file, 'w') as f:
+    json.dump(workflow, f, indent=2)
+print(f"  ✓ Configuration saved")
+
+print("\n[3/5] Study structure created")
+print(f"  Location: {study_dir}")
+print(f"  - 1_setup/model: Model files")
+print(f"  - 2_results: Optimization results")
+print(f"  - 3_reports: Human-readable reports")
+
+print("\n[4/5] Next: Run intelligent setup to generate optimization runner")
+print(f"  Command: python create_circular_plate_study.py --study-name {study_name}")
+
+print("\nDone!")