feat: Add post-optimization tools and mandatory best design archiving

New Tools (tools/):
- analyze_study.py: Generate comprehensive optimization reports
- find_best_iteration.py: Find best iteration folder, optionally copy it
- archive_best_design.py: Archive best design to 3_results/best_design_archive/<timestamp>/

Protocol Updates:
- OP_02_RUN_OPTIMIZATION.md v1.1: Add mandatory archive_best_design step
  in Post-Run Actions. This MUST be done after every optimization run.

V14 Updates:
- run_optimization.py: Auto-archive best design at end of optimization
- optimization_config.json: Expand bounds for V14 continuation
  - lateral_outer_angle: min 13->11 deg (was at 4.7%)
  - lateral_inner_pivot: min 7->5 mm (was at 8.1%)
  - lateral_middle_pivot: max 23->27 mm (was at 99.4%)
  - whiffle_min: max 60->72 mm (was at 96.3%)

Usage:
  python tools/analyze_study.py m1_mirror_adaptive_V14
  python tools/find_best_iteration.py m1_mirror_adaptive_V14
  python tools/archive_best_design.py m1_mirror_adaptive_V14

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

studies/m1_mirror_adaptive_V14/1_setup/optimization_config.json

@@ -1,7 +1,7 @@
 {
   "$schema": "Atomizer M1 Mirror TPE Optimization V14",
   "study_name": "m1_mirror_adaptive_V14",
-  "description": "V14 - TPE single-objective optimization seeded from V11+V12+V13 FEA trials. Weighted-sum objective for efficient convergence.",
+  "description": "V14 continuation - TPE with expanded bounds based on V14 analysis. 4 params were at bounds: lateral_middle_pivot (99.4%), whiffle_min (96.3%), lateral_outer_angle (4.7%), lateral_inner_pivot (8.1%).",
 
   "source_studies": {
     "v11": {
@@ -31,11 +31,12 @@
     {
       "name": "lateral_outer_angle",
       "expression_name": "lateral_outer_angle",
-      "min": 13.0,
+      "min": 11.0,
       "max": 17.0,
       "baseline": 14.64,
       "units": "degrees",
-      "enabled": true
+      "enabled": true,
+      "_note": "Expanded min from 13 to 11 (was at 4.7% of range)"
     },
     {
       "name": "lateral_outer_pivot",
@@ -49,20 +50,22 @@
     {
       "name": "lateral_inner_pivot",
       "expression_name": "lateral_inner_pivot",
-      "min": 7.0,
+      "min": 5.0,
       "max": 12.0,
       "baseline": 10.07,
       "units": "mm",
-      "enabled": true
+      "enabled": true,
+      "_note": "Expanded min from 7 to 5 (was at 8.1% of range)"
     },
     {
       "name": "lateral_middle_pivot",
       "expression_name": "lateral_middle_pivot",
       "min": 15.0,
-      "max": 23.0,
+      "max": 27.0,
       "baseline": 20.73,
       "units": "mm",
-      "enabled": true
+      "enabled": true,
+      "_note": "Expanded max from 23 to 27 (was at 99.4% of range)"
     },
     {
       "name": "lateral_closeness",
@@ -77,10 +80,11 @@
       "name": "whiffle_min",
       "expression_name": "whiffle_min",
       "min": 30.0,
-      "max": 60.0,
+      "max": 72.0,
       "baseline": 40.55,
       "units": "mm",
-      "enabled": true
+      "enabled": true,
+      "_note": "Expanded max from 60 to 72 (was at 96.3% of range)"
     },
     {
       "name": "whiffle_outer_to_vertical",

studies/m1_mirror_adaptive_V14/run_optimization.py

@@ -600,6 +600,32 @@ class TPEOptimizer:
 
         logger.info(f"\nResults saved to {RESULTS_DIR / 'final_results.json'}")
 
+        # Archive best design
+        self._archive_best_design()
+
+    def _archive_best_design(self):
+        """Archive the best design iteration folder."""
+        try:
+            # Import archive tool
+            tools_dir = Path(__file__).parent.parent.parent / "tools"
+            sys.path.insert(0, str(tools_dir))
+            from archive_best_design import archive_best_design
+
+            logger.info("\n" + "-" * 70)
+            logger.info("ARCHIVING BEST DESIGN")
+            logger.info("-" * 70)
+
+            result = archive_best_design(str(Path(__file__).parent))
+
+            if result.get('success'):
+                logger.info(f"[OK] Best design archived to: {result['archive_path']}")
+                logger.info(f"     Trial #{result['trial_number']}, WS={result['weighted_sum']:.2f}")
+            else:
+                logger.warning(f"[WARN] Archive skipped: {result.get('reason', 'Unknown')}")
+
+        except Exception as e:
+            logger.error(f"[ERROR] Failed to archive best design: {e}")
+
 
 # ============================================================================
 # Main