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>

studies/bracket_displacement_maximizing/substudies/coarse_exploration/config.json

@@ -0,0 +1,31 @@
+{
+  "substudy_name": "coarse_exploration",
+  "description": "Fast coarse exploration with 20 trials across wide parameter space",
+  "parent_study": "bracket_displacement_maximizing",
+  "created_date": "2025-11-16",
+
+  "optimization": {
+    "algorithm": "TPE",
+    "direction": "minimize",
+    "n_trials": 20,
+    "n_startup_trials": 10,
+    "design_variables": [
+      {
+        "parameter": "tip_thickness",
+        "min": 15.0,
+        "max": 25.0,
+        "units": "mm"
+      },
+      {
+        "parameter": "support_angle",
+        "min": 20.0,
+        "max": 40.0,
+        "units": "degrees"
+      }
+    ]
+  },
+
+  "continuation": {
+    "enabled": false
+  }
+}

studies/bracket_displacement_maximizing/substudies/coarse_exploration/generated_extractors/extract_displacement.py

@@ -0,0 +1,56 @@
+"""
+
+Auto-generated by Atomizer Phase 3 - pyNastran Research Agent
+
+Pattern: displacement
+Element Type: General
+Result Type: displacement
+API: model.displacements[subcase]
+"""
+
+from pathlib import Path
+from typing import Dict, Any
+import numpy as np
+from pyNastran.op2.op2 import OP2
+
+
+def extract_displacement(op2_file: Path, subcase: int = 1):
+    """Extract displacement results from OP2 file."""
+    from pyNastran.op2.op2 import OP2
+    import numpy as np
+
+    model = OP2()
+    model.read_op2(str(op2_file))
+
+    disp = model.displacements[subcase]
+    itime = 0  # static case
+
+    # Extract translation components
+    txyz = disp.data[itime, :, :3]  # [tx, ty, tz]
+
+    # Calculate total displacement
+    total_disp = np.linalg.norm(txyz, axis=1)
+    max_disp = np.max(total_disp)
+
+    # Get node info
+    node_ids = [nid for (nid, grid_type) in disp.node_gridtype]
+    max_disp_node = node_ids[np.argmax(total_disp)]
+
+    return {
+        'max_displacement': float(max_disp),
+        'max_disp_node': int(max_disp_node),
+        'max_disp_x': float(np.max(np.abs(txyz[:, 0]))),
+        'max_disp_y': float(np.max(np.abs(txyz[:, 1]))),
+        'max_disp_z': float(np.max(np.abs(txyz[:, 2])))
+    }
+
+
+if __name__ == '__main__':
+    # Example usage
+    import sys
+    if len(sys.argv) > 1:
+        op2_file = Path(sys.argv[1])
+        result = extract_displacement(op2_file)
+        print(f"Extraction result: {result}")
+    else:
+        print("Usage: python {sys.argv[0]} <op2_file>")

studies/bracket_displacement_maximizing/substudies/coarse_exploration/generated_extractors/extract_solid_stress.py

@@ -0,0 +1,64 @@
+"""
+
+Auto-generated by Atomizer Phase 3 - pyNastran Research Agent
+
+Pattern: solid_stress
+Element Type: CTETRA
+Result Type: stress
+API: model.ctetra_stress[subcase] or model.chexa_stress[subcase]
+"""
+
+from pathlib import Path
+from typing import Dict, Any
+import numpy as np
+from pyNastran.op2.op2 import OP2
+
+
+def extract_solid_stress(op2_file: Path, subcase: int = 1, element_type: str = 'ctetra'):
+    """Extract stress from solid elements."""
+    from pyNastran.op2.op2 import OP2
+    import numpy as np
+
+    model = OP2()
+    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, '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(stress_obj, stress_attr)[subcase]
+    itime = 0
+
+    # Extract von Mises if available
+    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))
+
+        # Get element info
+        element_ids = [eid for (eid, node) in stress.element_node]
+        max_stress_elem = element_ids[np.argmax(von_mises)]
+
+        return {
+            'max_von_mises': max_stress,
+            'max_stress_element': int(max_stress_elem)
+        }
+    else:
+        raise ValueError("von Mises stress not available")
+
+
+if __name__ == '__main__':
+    # Example usage
+    import sys
+    if len(sys.argv) > 1:
+        op2_file = Path(sys.argv[1])
+        result = extract_solid_stress(op2_file)
+        print(f"Extraction result: {result}")
+    else:
+        print("Usage: python {sys.argv[0]} <op2_file>")

studies/bracket_displacement_maximizing/substudies/coarse_exploration/optimization_history.json

@@ -0,0 +1,362 @@
+[
+  {
+    "trial_number": 1,
+    "design_variables": {
+      "tip_thickness": 17.389878779619163,
+      "support_angle": 20.866887194604573
+    },
+    "results": {
+      "max_displacement": 0.6116114854812622,
+      "max_disp_node": 55,
+      "max_disp_x": 0.0039984011091291904,
+      "max_disp_y": 0.10416693240404129,
+      "max_disp_z": 0.6026755571365356
+    },
+    "calculations": {
+      "neg_displacement": -0.6116114854812622
+    },
+    "objective": 0.6116114854812622
+  },
+  {
+    "trial_number": 2,
+    "design_variables": {
+      "tip_thickness": 22.220441224956033,
+      "support_angle": 30.840923860420357
+    },
+    "results": {
+      "max_displacement": 0.28716832399368286,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0024955333210527897,
+      "max_disp_y": 0.06367127597332001,
+      "max_disp_z": 0.28002074360847473
+    },
+    "calculations": {
+      "neg_displacement": -0.28716832399368286
+    },
+    "objective": 0.28716832399368286
+  },
+  {
+    "trial_number": 3,
+    "design_variables": {
+      "tip_thickness": 16.556374034848037,
+      "support_angle": 22.45231282549564
+    },
+    "results": {
+      "max_displacement": 0.6623445749282837,
+      "max_disp_node": 58,
+      "max_disp_x": 0.004246150143444538,
+      "max_disp_y": 0.1103561595082283,
+      "max_disp_z": 0.6530864238739014
+    },
+    "calculations": {
+      "neg_displacement": -0.6623445749282837
+    },
+    "objective": 0.6623445749282837
+  },
+  {
+    "trial_number": 4,
+    "design_variables": {
+      "tip_thickness": 22.88337112412688,
+      "support_angle": 34.142850054848
+    },
+    "results": {
+      "max_displacement": 0.2482926845550537,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002143946709111333,
+      "max_disp_y": 0.05768103152513504,
+      "max_disp_z": 0.24149981141090393
+    },
+    "calculations": {
+      "neg_displacement": -0.2482926845550537
+    },
+    "objective": 0.2482926845550537
+  },
+  {
+    "trial_number": 5,
+    "design_variables": {
+      "tip_thickness": 20.338667724550465,
+      "support_angle": 29.92278029095064
+    },
+    "results": {
+      "max_displacement": 0.34881672263145447,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0029699159786105156,
+      "max_disp_y": 0.07262033224105835,
+      "max_disp_z": 0.34117355942726135
+    },
+    "calculations": {
+      "neg_displacement": -0.34881672263145447
+    },
+    "objective": 0.34881672263145447
+  },
+  {
+    "trial_number": 6,
+    "design_variables": {
+      "tip_thickness": 24.50967137117151,
+      "support_angle": 21.697159236156473
+    },
+    "results": {
+      "max_displacement": 0.2758632004261017,
+      "max_disp_node": 58,
+      "max_disp_x": 0.00244400673545897,
+      "max_disp_y": 0.06081655994057655,
+      "max_disp_z": 0.2690759301185608
+    },
+    "calculations": {
+      "neg_displacement": -0.2758632004261017
+    },
+    "objective": 0.2758632004261017
+  },
+  {
+    "trial_number": 7,
+    "design_variables": {
+      "tip_thickness": 22.377093973916722,
+      "support_angle": 31.532495067510975
+    },
+    "results": {
+      "max_displacement": 0.27826353907585144,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0023749535903334618,
+      "max_disp_y": 0.06228335201740265,
+      "max_disp_z": 0.27120357751846313
+    },
+    "calculations": {
+      "neg_displacement": -0.27826353907585144
+    },
+    "objective": 0.27826353907585144
+  },
+  {
+    "trial_number": 8,
+    "design_variables": {
+      "tip_thickness": 16.397088760589114,
+      "support_angle": 39.6907686972716
+    },
+    "results": {
+      "max_displacement": 0.38282549381256104,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0032198228873312473,
+      "max_disp_y": 0.08009155839681625,
+      "max_disp_z": 0.37435370683670044
+    },
+    "calculations": {
+      "neg_displacement": -0.38282549381256104
+    },
+    "objective": 0.38282549381256104
+  },
+  {
+    "trial_number": 9,
+    "design_variables": {
+      "tip_thickness": 15.779109046050685,
+      "support_angle": 37.544844028080135
+    },
+    "results": {
+      "max_displacement": 0.4436984956264496,
+      "max_disp_node": 58,
+      "max_disp_x": 0.003604060271754861,
+      "max_disp_y": 0.08796636015176773,
+      "max_disp_z": 0.43489110469818115
+    },
+    "calculations": {
+      "neg_displacement": -0.4436984956264496
+    },
+    "objective": 0.4436984956264496
+  },
+  {
+    "trial_number": 10,
+    "design_variables": {
+      "tip_thickness": 21.072632312643005,
+      "support_angle": 38.313469876751704
+    },
+    "results": {
+      "max_displacement": 0.2564539313316345,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002298053354024887,
+      "max_disp_y": 0.05997171252965927,
+      "max_disp_z": 0.2493431717157364
+    },
+    "calculations": {
+      "neg_displacement": -0.2564539313316345
+    },
+    "objective": 0.2564539313316345
+  },
+  {
+    "trial_number": 11,
+    "design_variables": {
+      "tip_thickness": 24.95032894956337,
+      "support_angle": 34.20965061068569
+    },
+    "results": {
+      "max_displacement": 0.21304214000701904,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002044219756498933,
+      "max_disp_y": 0.052074797451496124,
+      "max_disp_z": 0.20657968521118164
+    },
+    "calculations": {
+      "neg_displacement": -0.21304214000701904
+    },
+    "objective": 0.21304214000701904
+  },
+  {
+    "trial_number": 12,
+    "design_variables": {
+      "tip_thickness": 24.894489548087346,
+      "support_angle": 34.551063779949075
+    },
+    "results": {
+      "max_displacement": 0.21274541318416595,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002061901381239295,
+      "max_disp_y": 0.052174802869558334,
+      "max_disp_z": 0.20624838769435883
+    },
+    "calculations": {
+      "neg_displacement": -0.21274541318416595
+    },
+    "objective": 0.21274541318416595
+  },
+  {
+    "trial_number": 13,
+    "design_variables": {
+      "tip_thickness": 24.952883393809497,
+      "support_angle": 34.77451710881955
+    },
+    "results": {
+      "max_displacement": 0.21082018315792084,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0020510517060756683,
+      "max_disp_y": 0.05187264829874039,
+      "max_disp_z": 0.20433887839317322
+    },
+    "calculations": {
+      "neg_displacement": -0.21082018315792084
+    },
+    "objective": 0.21082018315792084
+  },
+  {
+    "trial_number": 14,
+    "design_variables": {
+      "tip_thickness": 18.70443149555537,
+      "support_angle": 26.11221806912443
+    },
+    "results": {
+      "max_displacement": 0.46135807037353516,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0034599562641233206,
+      "max_disp_y": 0.08689243346452713,
+      "max_disp_z": 0.4531014859676361
+    },
+    "calculations": {
+      "neg_displacement": -0.46135807037353516
+    },
+    "objective": 0.46135807037353516
+  },
+  {
+    "trial_number": 15,
+    "design_variables": {
+      "tip_thickness": 23.84954584574589,
+      "support_angle": 34.69889355903453
+    },
+    "results": {
+      "max_displacement": 0.22793325781822205,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0021433867514133453,
+      "max_disp_y": 0.05458956956863403,
+      "max_disp_z": 0.2212996780872345
+    },
+    "calculations": {
+      "neg_displacement": -0.22793325781822205
+    },
+    "objective": 0.22793325781822205
+  },
+  {
+    "trial_number": 16,
+    "design_variables": {
+      "tip_thickness": 23.545082857154053,
+      "support_angle": 27.33776442268342
+    },
+    "results": {
+      "max_displacement": 0.27459517121315,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002392930444329977,
+      "max_disp_y": 0.06125558167695999,
+      "max_disp_z": 0.2676756680011749
+    },
+    "calculations": {
+      "neg_displacement": -0.27459517121315
+    },
+    "objective": 0.27459517121315
+  },
+  {
+    "trial_number": 17,
+    "design_variables": {
+      "tip_thickness": 19.395958700724172,
+      "support_angle": 35.874156921626124
+    },
+    "results": {
+      "max_displacement": 0.32070934772491455,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002739877672865987,
+      "max_disp_y": 0.06964103132486343,
+      "max_disp_z": 0.3130568861961365
+    },
+    "calculations": {
+      "neg_displacement": -0.32070934772491455
+    },
+    "objective": 0.32070934772491455
+  },
+  {
+    "trial_number": 18,
+    "design_variables": {
+      "tip_thickness": 21.536592042850017,
+      "support_angle": 32.171364735552
+    },
+    "results": {
+      "max_displacement": 0.29555052518844604,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0025494550354778767,
+      "max_disp_y": 0.06503863632678986,
+      "max_disp_z": 0.2883055508136749
+    },
+    "calculations": {
+      "neg_displacement": -0.29555052518844604
+    },
+    "objective": 0.29555052518844604
+  },
+  {
+    "trial_number": 19,
+    "design_variables": {
+      "tip_thickness": 23.75885172872223,
+      "support_angle": 28.17043178772967
+    },
+    "results": {
+      "max_displacement": 0.2622140347957611,
+      "max_disp_node": 58,
+      "max_disp_x": 0.002298196544870734,
+      "max_disp_y": 0.05939820408821106,
+      "max_disp_z": 0.25539782643318176
+    },
+    "calculations": {
+      "neg_displacement": -0.2622140347957611
+    },
+    "objective": 0.2622140347957611
+  },
+  {
+    "trial_number": 20,
+    "design_variables": {
+      "tip_thickness": 24.946257551293495,
+      "support_angle": 36.54595394544202
+    },
+    "results": {
+      "max_displacement": 0.20071247220039368,
+      "max_disp_node": 58,
+      "max_disp_x": 0.0018801360856741667,
+      "max_disp_y": 0.05026965215802193,
+      "max_disp_z": 0.1943153589963913
+    },
+    "calculations": {
+      "neg_displacement": -0.20071247220039368
+    },
+    "objective": 0.20071247220039368
+  }
+]

studies/bracket_displacement_maximizing/substudies/coarse_exploration/optimization_history_incremental.json

@@ -0,0 +1,362 @@
+[
+  {
+    "trial_number": 1,
+    "design_variables": {
+      "tip_thickness": 17.389878779619163,
+      "support_angle": 20.866887194604573
+    },
+    "results": {
+      "max_displacement": 0.6116114854812622,
+      "max_disp_node": 55.0,
+      "max_disp_x": 0.0039984011091291904,
+      "max_disp_y": 0.10416693240404129,
+      "max_disp_z": 0.6026755571365356
+    },
+    "calculations": {
+      "neg_displacement": -0.6116114854812622
+    },
+    "objective": 0.6116114854812622
+  },
+  {
+    "trial_number": 2,
+    "design_variables": {
+      "tip_thickness": 22.220441224956033,
+      "support_angle": 30.840923860420357
+    },
+    "results": {
+      "max_displacement": 0.28716832399368286,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0024955333210527897,
+      "max_disp_y": 0.06367127597332001,
+      "max_disp_z": 0.28002074360847473
+    },
+    "calculations": {
+      "neg_displacement": -0.28716832399368286
+    },
+    "objective": 0.28716832399368286
+  },
+  {
+    "trial_number": 3,
+    "design_variables": {
+      "tip_thickness": 16.556374034848037,
+      "support_angle": 22.45231282549564
+    },
+    "results": {
+      "max_displacement": 0.6623445749282837,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.004246150143444538,
+      "max_disp_y": 0.1103561595082283,
+      "max_disp_z": 0.6530864238739014
+    },
+    "calculations": {
+      "neg_displacement": -0.6623445749282837
+    },
+    "objective": 0.6623445749282837
+  },
+  {
+    "trial_number": 4,
+    "design_variables": {
+      "tip_thickness": 22.88337112412688,
+      "support_angle": 34.142850054848
+    },
+    "results": {
+      "max_displacement": 0.2482926845550537,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002143946709111333,
+      "max_disp_y": 0.05768103152513504,
+      "max_disp_z": 0.24149981141090393
+    },
+    "calculations": {
+      "neg_displacement": -0.2482926845550537
+    },
+    "objective": 0.2482926845550537
+  },
+  {
+    "trial_number": 5,
+    "design_variables": {
+      "tip_thickness": 20.338667724550465,
+      "support_angle": 29.92278029095064
+    },
+    "results": {
+      "max_displacement": 0.34881672263145447,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0029699159786105156,
+      "max_disp_y": 0.07262033224105835,
+      "max_disp_z": 0.34117355942726135
+    },
+    "calculations": {
+      "neg_displacement": -0.34881672263145447
+    },
+    "objective": 0.34881672263145447
+  },
+  {
+    "trial_number": 6,
+    "design_variables": {
+      "tip_thickness": 24.50967137117151,
+      "support_angle": 21.697159236156473
+    },
+    "results": {
+      "max_displacement": 0.2758632004261017,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.00244400673545897,
+      "max_disp_y": 0.06081655994057655,
+      "max_disp_z": 0.2690759301185608
+    },
+    "calculations": {
+      "neg_displacement": -0.2758632004261017
+    },
+    "objective": 0.2758632004261017
+  },
+  {
+    "trial_number": 7,
+    "design_variables": {
+      "tip_thickness": 22.377093973916722,
+      "support_angle": 31.532495067510975
+    },
+    "results": {
+      "max_displacement": 0.27826353907585144,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0023749535903334618,
+      "max_disp_y": 0.06228335201740265,
+      "max_disp_z": 0.27120357751846313
+    },
+    "calculations": {
+      "neg_displacement": -0.27826353907585144
+    },
+    "objective": 0.27826353907585144
+  },
+  {
+    "trial_number": 8,
+    "design_variables": {
+      "tip_thickness": 16.397088760589114,
+      "support_angle": 39.6907686972716
+    },
+    "results": {
+      "max_displacement": 0.38282549381256104,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0032198228873312473,
+      "max_disp_y": 0.08009155839681625,
+      "max_disp_z": 0.37435370683670044
+    },
+    "calculations": {
+      "neg_displacement": -0.38282549381256104
+    },
+    "objective": 0.38282549381256104
+  },
+  {
+    "trial_number": 9,
+    "design_variables": {
+      "tip_thickness": 15.779109046050685,
+      "support_angle": 37.544844028080135
+    },
+    "results": {
+      "max_displacement": 0.4436984956264496,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.003604060271754861,
+      "max_disp_y": 0.08796636015176773,
+      "max_disp_z": 0.43489110469818115
+    },
+    "calculations": {
+      "neg_displacement": -0.4436984956264496
+    },
+    "objective": 0.4436984956264496
+  },
+  {
+    "trial_number": 10,
+    "design_variables": {
+      "tip_thickness": 21.072632312643005,
+      "support_angle": 38.313469876751704
+    },
+    "results": {
+      "max_displacement": 0.2564539313316345,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002298053354024887,
+      "max_disp_y": 0.05997171252965927,
+      "max_disp_z": 0.2493431717157364
+    },
+    "calculations": {
+      "neg_displacement": -0.2564539313316345
+    },
+    "objective": 0.2564539313316345
+  },
+  {
+    "trial_number": 11,
+    "design_variables": {
+      "tip_thickness": 24.95032894956337,
+      "support_angle": 34.20965061068569
+    },
+    "results": {
+      "max_displacement": 0.21304214000701904,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002044219756498933,
+      "max_disp_y": 0.052074797451496124,
+      "max_disp_z": 0.20657968521118164
+    },
+    "calculations": {
+      "neg_displacement": -0.21304214000701904
+    },
+    "objective": 0.21304214000701904
+  },
+  {
+    "trial_number": 12,
+    "design_variables": {
+      "tip_thickness": 24.894489548087346,
+      "support_angle": 34.551063779949075
+    },
+    "results": {
+      "max_displacement": 0.21274541318416595,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002061901381239295,
+      "max_disp_y": 0.052174802869558334,
+      "max_disp_z": 0.20624838769435883
+    },
+    "calculations": {
+      "neg_displacement": -0.21274541318416595
+    },
+    "objective": 0.21274541318416595
+  },
+  {
+    "trial_number": 13,
+    "design_variables": {
+      "tip_thickness": 24.952883393809497,
+      "support_angle": 34.77451710881955
+    },
+    "results": {
+      "max_displacement": 0.21082018315792084,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0020510517060756683,
+      "max_disp_y": 0.05187264829874039,
+      "max_disp_z": 0.20433887839317322
+    },
+    "calculations": {
+      "neg_displacement": -0.21082018315792084
+    },
+    "objective": 0.21082018315792084
+  },
+  {
+    "trial_number": 14,
+    "design_variables": {
+      "tip_thickness": 18.70443149555537,
+      "support_angle": 26.11221806912443
+    },
+    "results": {
+      "max_displacement": 0.46135807037353516,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0034599562641233206,
+      "max_disp_y": 0.08689243346452713,
+      "max_disp_z": 0.4531014859676361
+    },
+    "calculations": {
+      "neg_displacement": -0.46135807037353516
+    },
+    "objective": 0.46135807037353516
+  },
+  {
+    "trial_number": 15,
+    "design_variables": {
+      "tip_thickness": 23.84954584574589,
+      "support_angle": 34.69889355903453
+    },
+    "results": {
+      "max_displacement": 0.22793325781822205,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0021433867514133453,
+      "max_disp_y": 0.05458956956863403,
+      "max_disp_z": 0.2212996780872345
+    },
+    "calculations": {
+      "neg_displacement": -0.22793325781822205
+    },
+    "objective": 0.22793325781822205
+  },
+  {
+    "trial_number": 16,
+    "design_variables": {
+      "tip_thickness": 23.545082857154053,
+      "support_angle": 27.33776442268342
+    },
+    "results": {
+      "max_displacement": 0.27459517121315,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002392930444329977,
+      "max_disp_y": 0.06125558167695999,
+      "max_disp_z": 0.2676756680011749
+    },
+    "calculations": {
+      "neg_displacement": -0.27459517121315
+    },
+    "objective": 0.27459517121315
+  },
+  {
+    "trial_number": 17,
+    "design_variables": {
+      "tip_thickness": 19.395958700724172,
+      "support_angle": 35.874156921626124
+    },
+    "results": {
+      "max_displacement": 0.32070934772491455,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002739877672865987,
+      "max_disp_y": 0.06964103132486343,
+      "max_disp_z": 0.3130568861961365
+    },
+    "calculations": {
+      "neg_displacement": -0.32070934772491455
+    },
+    "objective": 0.32070934772491455
+  },
+  {
+    "trial_number": 18,
+    "design_variables": {
+      "tip_thickness": 21.536592042850017,
+      "support_angle": 32.171364735552
+    },
+    "results": {
+      "max_displacement": 0.29555052518844604,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0025494550354778767,
+      "max_disp_y": 0.06503863632678986,
+      "max_disp_z": 0.2883055508136749
+    },
+    "calculations": {
+      "neg_displacement": -0.29555052518844604
+    },
+    "objective": 0.29555052518844604
+  },
+  {
+    "trial_number": 19,
+    "design_variables": {
+      "tip_thickness": 23.75885172872223,
+      "support_angle": 28.17043178772967
+    },
+    "results": {
+      "max_displacement": 0.2622140347957611,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.002298196544870734,
+      "max_disp_y": 0.05939820408821106,
+      "max_disp_z": 0.25539782643318176
+    },
+    "calculations": {
+      "neg_displacement": -0.2622140347957611
+    },
+    "objective": 0.2622140347957611
+  },
+  {
+    "trial_number": 20,
+    "design_variables": {
+      "tip_thickness": 24.946257551293495,
+      "support_angle": 36.54595394544202
+    },
+    "results": {
+      "max_displacement": 0.20071247220039368,
+      "max_disp_node": 58.0,
+      "max_disp_x": 0.0018801360856741667,
+      "max_disp_y": 0.05026965215802193,
+      "max_disp_z": 0.1943153589963913
+    },
+    "calculations": {
+      "neg_displacement": -0.20071247220039368
+    },
+    "objective": 0.20071247220039368
+  }
+]

studies/bracket_displacement_maximizing/substudies/coarse_exploration/optimization_results.json

@@ -0,0 +1,11 @@
+{
+  "best_params": {
+    "tip_thickness": 24.946257551293495,
+    "support_angle": 36.54595394544202
+  },
+  "best_value": 0.20071247220039368,
+  "best_trial_number": 20,
+  "timestamp": "2025-11-16T21:21:37.211289",
+  "study_name": "coarse_exploration",
+  "n_trials": 20
+}