Anto01
2b3573ec42
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>
90 lines
2.4 KiB
Python
90 lines
2.4 KiB
Python
"""
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Create circular_plate_frequency_tuning_V2 study with all fixes.
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"""
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from pathlib import Path
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import shutil
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import json
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# Study configuration
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study_name = "circular_plate_frequency_tuning_V2"
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study_dir = Path("studies") / study_name
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# Create study structure
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print(f"Creating study: {study_name}")
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print("=" * 80)
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# 1. Create directory structure
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(study_dir / "1_setup" / "model").mkdir(parents=True, exist_ok=True)
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(study_dir / "2_results").mkdir(parents=True, exist_ok=True)
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(study_dir / "3_reports").mkdir(parents=True, exist_ok=True)
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# 2. Copy model files
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source_dir = Path("examples/Models/Circular Plate")
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model_files = [
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"Circular_Plate.prt",
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"Circular_Plate_sim1.sim",
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"Circular_Plate_fem1.fem",
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"Circular_Plate_fem1_i.prt"
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]
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print("\n[1/5] Copying model files...")
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for file in model_files:
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src = source_dir / file
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dst = study_dir / "1_setup" / "model" / file
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if src.exists():
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shutil.copy2(src, dst)
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print(f" ✓ {file}")
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# 3. Create workflow config
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print("\n[2/5] Creating workflow configuration...")
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workflow = {
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"study_name": study_name,
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"optimization_request": "Tune the first natural frequency mode to exactly 115 Hz (within 0.1 Hz tolerance)",
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"design_variables": [
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{
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"parameter": "inner_diameter",
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"bounds": [50, 150]
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},
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{
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"parameter": "plate_thickness",
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"bounds": [2, 10]
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}
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],
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"objectives": [
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{
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"name": "frequency_error",
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"goal": "minimize",
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"extraction": {
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"action": "extract_first_natural_frequency",
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"params": {
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"mode_number": 1,
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"target_frequency": 115.0
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}
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}
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}
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],
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"constraints": [
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{
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"name": "frequency_tolerance",
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"type": "less_than",
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"threshold": 0.1
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}
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]
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}
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config_file = study_dir / "1_setup" / "workflow_config.json"
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with open(config_file, 'w') as f:
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json.dump(workflow, f, indent=2)
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print(f" ✓ Configuration saved")
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print("\n[3/5] Study structure created")
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print(f" Location: {study_dir}")
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print(f" - 1_setup/model: Model files")
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print(f" - 2_results: Optimization results")
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print(f" - 3_reports: Human-readable reports")
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print("\n[4/5] Next: Run intelligent setup to generate optimization runner")
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print(f" Command: python create_circular_plate_study.py --study-name {study_name}")
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print("\nDone!")
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