Atomizer/docs/archive/historical/STUDY_ORGANIZATION.md

Study Organization Guide

Date: 2025-11-17 Purpose: Document recommended study directory structure and organization principles

---

Current Organization Analysis

Study Directory: studies/simple_beam_optimization/

Current Structure:

studies/simple_beam_optimization/
├── model/                           # Base CAD/FEM model (reference)
│   ├── Beam.prt
│   ├── Beam_sim1.sim
│   ├── beam_sim1-solution_1.op2
│   ├── beam_sim1-solution_1.f06
│   └── comprehensive_results_analysis.json
│
├── substudies/                      # All optimization runs
│   ├── benchmarking/
│   │   ├── benchmark_results.json
│   │   └── BENCHMARK_REPORT.md
│   ├── initial_exploration/
│   │   ├── config.json
│   │   └── optimization_config.json
│   ├── validation_3trials/
│   │   ├── trial_000/
│   │   ├── trial_001/
│   │   ├── trial_002/
│   │   ├── best_trial.json
│   │   └── optuna_study.pkl
│   ├── validation_4d_3trials/
│   │   └── [similar structure]
│   └── full_optimization_50trials/
│       ├── trial_000/
│       ├── ... trial_049/
│       ├── plots/                   # NEW: Auto-generated plots
│       ├── history.json
│       ├── best_trial.json
│       └── optuna_study.pkl
│
├── README.md                        # Study overview
├── study_metadata.json              # Study metadata
├── beam_optimization_config.json    # Main configuration
├── baseline_validation.json         # Baseline results
├── COMPREHENSIVE_BENCHMARK_RESULTS.md
├── OPTIMIZATION_RESULTS_50TRIALS.md
└── run_optimization.py              # Study-specific runner

---

Assessment

✅ What's Working Well

1. Substudy Isolation: Each optimization run (substudy) is self-contained with its own trial directories, making it easy to compare different optimization strategies.

2. Centralized Model: The model/ directory serves as a reference CAD/FEM model, which all substudies copy from.

3. Configuration at Study Level: beam_optimization_config.json provides the main configuration that substudies inherit from.

4. Study-Level Documentation: README.md and results markdown files at the study level provide high-level overviews.

5. Clear Hierarchy:

   • Study = Overall project (e.g., "optimize this beam")
   • Substudy = Specific optimization run (e.g., "50 trials with TPE sampler")
   • Trial = Individual design evaluation

⚠️ Issues Found

1. Documentation Scattered: Results documentation is at the study level (OPTIMIZATION_RESULTS_50TRIALS.md) but describes a specific substudy (full_optimization_50trials).

2. Benchmarking Placement: substudies/benchmarking/ is not really a "substudy" - it's a validation step that should happen before optimization.

3. Missing Substudy Metadata: Some substudies lack their own README or summary files to explain what they tested.

4. Inconsistent Naming: validation_3trials vs validation_4d_3trials - unclear what distinguishes them without investigation.

5. Study Metadata Incomplete: study_metadata.json lists only "initial_exploration" substudy, but there are 5 substudies present.

---

Recommended Organization

Proposed Structure

studies/simple_beam_optimization/
│
├── 1_setup/                         # NEW: Pre-optimization setup
│   ├── model/                       # Reference CAD/FEM model
│   │   ├── Beam.prt
│   │   ├── Beam_sim1.sim
│   │   └── ...
│   ├── benchmarking/                # Baseline validation
│   │   ├── benchmark_results.json
│   │   └── BENCHMARK_REPORT.md
│   └── baseline_validation.json
│
├── 2_substudies/                    # Optimization runs
│   ├── 01_initial_exploration/
│   │   ├── README.md                # What was tested, why
│   │   ├── config.json
│   │   ├── trial_000/
│   │   ├── ...
│   │   └── results_summary.md       # Substudy-specific results
│   ├── 02_validation_3d_3trials/
│   │   └── [similar structure]
│   ├── 03_validation_4d_3trials/
│   │   └── [similar structure]
│   └── 04_full_optimization_50trials/
│       ├── README.md
│       ├── trial_000/
│       ├── ... trial_049/
│       ├── plots/
│       ├── history.json
│       ├── best_trial.json
│       ├── OPTIMIZATION_RESULTS.md  # Moved from study level
│       └── cleanup_log.json
│
├── 3_reports/                       # NEW: Study-level analysis
│   ├── COMPREHENSIVE_BENCHMARK_RESULTS.md
│   ├── COMPARISON_ALL_SUBSTUDIES.md # NEW: Compare substudies
│   └── final_recommendations.md     # NEW: Engineering insights
│
├── README.md                        # Study overview
├── study_metadata.json              # Updated with all substudies
├── beam_optimization_config.json    # Main configuration
└── run_optimization.py              # Study-specific runner

Key Changes

1. Numbered Directories: Indicate workflow sequence (setup → substudies → reports)

2. Numbered Substudies: Chronological naming (01_, 02_, 03_) makes progression clear

3. Moved Benchmarking: From substudies/ to 1_setup/ (it's pre-optimization)

4. Substudy-Level Documentation: Each substudy has:

   • README.md - What was tested, parameters, hypothesis
   • OPTIMIZATION_RESULTS.md - Results and analysis

5. Centralized Reports: All comparative analysis and final recommendations in 3_reports/

6. Updated Metadata: study_metadata.json tracks all substudies with status

---

Comparison: Current vs Proposed

Aspect	Current	Proposed	Benefit
Substudy naming	Descriptive only	Numbered + descriptive	Chronological clarity
Documentation	Mixed levels	Clear hierarchy	Easier to find results
Benchmarking	In substudies/	In 1_setup/	Reflects true purpose
Model location	study root	1_setup/model/	Grouped with setup
Reports	Study root	3_reports/	Centralized analysis
Substudy docs	Minimal	README + results	Self-documenting
Metadata	Incomplete	All substudies tracked	Accurate status

---

Migration Guide

Option 1: Reorganize Existing Study (Recommended)

Steps:

1. Create new directory structure
2. Move files to new locations
3. Update study_metadata.json
4. Update file references in documentation
5. Create missing substudy READMEs

Commands:

# Create new structure
mkdir -p studies/simple_beam_optimization/1_setup/model
mkdir -p studies/simple_beam_optimization/1_setup/benchmarking
mkdir -p studies/simple_beam_optimization/2_substudies
mkdir -p studies/simple_beam_optimization/3_reports

# Move model
mv studies/simple_beam_optimization/model/* studies/simple_beam_optimization/1_setup/model/

# Move benchmarking
mv studies/simple_beam_optimization/substudies/benchmarking/* studies/simple_beam_optimization/1_setup/benchmarking/

# Rename and move substudies
mv studies/simple_beam_optimization/substudies/initial_exploration studies/simple_beam_optimization/2_substudies/01_initial_exploration
mv studies/simple_beam_optimization/substudies/validation_3trials studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials
mv studies/simple_beam_optimization/substudies/validation_4d_3trials studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials
mv studies/simple_beam_optimization/substudies/full_optimization_50trials studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials

# Move reports
mv studies/simple_beam_optimization/COMPREHENSIVE_BENCHMARK_RESULTS.md studies/simple_beam_optimization/3_reports/
mv studies/simple_beam_optimization/OPTIMIZATION_RESULTS_50TRIALS.md studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/

# Clean up
rm -rf studies/simple_beam_optimization/substudies/
rm -rf studies/simple_beam_optimization/model/

Option 2: Apply to Future Studies Only

Keep existing study as-is, apply new organization to future studies.

When to Use:

• Current study is complete and well-understood
• Reorganization would break existing scripts/references
• Want to test new organization before migrating

---

Best Practices

Study-Level Files

Required:

• README.md - High-level overview, purpose, design variables, objectives
• study_metadata.json - Metadata, status, substudy registry
• beam_optimization_config.json - Main configuration (inheritable)
• run_optimization.py - Study-specific runner script

Optional:

• CHANGELOG.md - Track configuration changes across substudies
• LESSONS_LEARNED.md - Engineering insights, dead ends avoided

Substudy-Level Files

Required (Generated by Runner):

• trial_XXX/ - Trial directories with CAD/FEM files and results.json
• history.json - Full optimization history
• best_trial.json - Best trial metadata
• optuna_study.pkl - Optuna study object
• config.json - Substudy-specific configuration

Required (User-Created):

• README.md - Purpose, hypothesis, parameter choices

Optional (Auto-Generated):

• plots/ - Visualization plots (if post_processing.generate_plots = true)
• cleanup_log.json - Model cleanup statistics (if post_processing.cleanup_models = true)

Optional (User-Created):

• OPTIMIZATION_RESULTS.md - Detailed analysis and interpretation

Trial-Level Files

Always Kept (Small, Critical):

• results.json - Extracted objectives, constraints, design variables

Kept for Top-N Trials (Large, Useful):

• Beam.prt - CAD model
• Beam_sim1.sim - Simulation setup
• beam_sim1-solution_1.op2 - FEA results (binary)
• beam_sim1-solution_1.f06 - FEA results (text)

Cleaned for Poor Trials (Large, Less Useful):

• All .prt, .sim, .fem, .op2, .f06 files deleted
• Only results.json preserved

---

Naming Conventions

Substudy Names

Format: NN_descriptive_name

Examples:

• 01_initial_exploration - First exploration of design space
• 02_validation_3d_3trials - Validate 3 design variables work
• 03_validation_4d_3trials - Validate 4 design variables work
• 04_full_optimization_50trials - Full optimization run
• 05_refined_search_30trials - Refined search in promising region
• 06_sensitivity_analysis - Parameter sensitivity study

Guidelines:

• Start with two-digit number (01, 02, ..., 99)
• Use underscores for spaces
• Be concise but descriptive
• Include trial count if relevant

Study Names

Format: descriptive_name (no numbering)

Examples:

• simple_beam_optimization - Optimize simple beam
• bracket_displacement_maximizing - Maximize bracket displacement
• engine_mount_fatigue - Engine mount fatigue optimization

Guidelines:

• Use underscores for spaces
• Include part name and optimization goal
• Avoid dates (use substudy numbering for chronology)

---

Metadata Format

study_metadata.json

Recommended Format:

{
  "study_name": "simple_beam_optimization",
  "description": "Minimize displacement and weight of beam with existing loadcases",
  "created": "2025-11-17T10:24:09.613688",
  "status": "active",
  "design_variables": ["beam_half_core_thickness", "beam_face_thickness", "holes_diameter", "hole_count"],
  "objectives": ["minimize_displacement", "minimize_stress", "minimize_mass"],
  "constraints": ["displacement_limit"],
  "substudies": [
    {
      "name": "01_initial_exploration",
      "created": "2025-11-17T10:30:00",
      "status": "completed",
      "trials": 10,
      "purpose": "Explore design space boundaries"
    },
    {
      "name": "02_validation_3d_3trials",
      "created": "2025-11-17T11:00:00",
      "status": "completed",
      "trials": 3,
      "purpose": "Validate 3D parameter updates (without hole_count)"
    },
    {
      "name": "03_validation_4d_3trials",
      "created": "2025-11-17T12:00:00",
      "status": "completed",
      "trials": 3,
      "purpose": "Validate 4D parameter updates (with hole_count)"
    },
    {
      "name": "04_full_optimization_50trials",
      "created": "2025-11-17T13:00:00",
      "status": "completed",
      "trials": 50,
      "purpose": "Full optimization with all 4 design variables"
    }
  ],
  "last_modified": "2025-11-17T15:30:00"
}

Substudy README.md Template

# [Substudy Name]

**Date**: YYYY-MM-DD
**Status**: [planned | running | completed | failed]
**Trials**: N

## Purpose

[Why this substudy was created, what hypothesis is being tested]

## Configuration Changes

[Compared to previous substudy or baseline config, what changed?]

- Design variable bounds: [if changed]
- Objective weights: [if changed]
- Sampler settings: [if changed]

## Expected Outcome

[What do you hope to learn or achieve?]

## Actual Results

[Fill in after completion]

- Best objective: X.XX
- Feasible designs: N / N_total
- Key findings: [summary]

## Next Steps

[What substudy should follow based on these results?]

---

Workflow Integration

Creating a New Substudy

Steps:

1. Determine substudy number (next in sequence)
2. Create substudy README.md with purpose and changes
3. Update configuration if needed
4. Run optimization:

   python run_optimization.py --substudy-name "05_refined_search_30trials"
   

5. After completion:
   • Review results
   • Update substudy README.md with findings
   • Create OPTIMIZATION_RESULTS.md if significant
   • Update study_metadata.json

Comparing Substudies

Create Comparison Report:

# Substudy Comparison

| Substudy | Trials | Best Obj | Feasible | Key Finding |
|----------|--------|----------|----------|-------------|
| 01_initial_exploration | 10 | 1250.3 | 0/10 | Design space too large |
| 02_validation_3d_3trials | 3 | 1180.5 | 0/3 | 3D updates work |
| 03_validation_4d_3trials | 3 | 1120.2 | 0/3 | hole_count updates work |
| 04_full_optimization_50trials | 50 | 842.6 | 0/50 | No feasible designs found |

**Conclusion**: Constraint appears infeasible. Recommend relaxing displacement limit.

---

Benefits of Proposed Organization

For Users

1. Clarity: Numbered substudies show chronological progression
2. Self-Documenting: Each substudy explains its purpose
3. Easy Comparison: All results in one place (3_reports/)
4. Less Clutter: Study root only has essential files

For Developers

1. Predictable Structure: Scripts can rely on consistent paths
2. Automated Discovery: Easy to find all substudies programmatically
3. Version Control: Clear history through numbered substudies
4. Scalability: Works for 5 substudies or 50

For Collaboration

1. Onboarding: New team members can understand study progression quickly
2. Documentation: Substudy READMEs explain decisions made
3. Reproducibility: Clear configuration history
4. Communication: Easy to reference specific substudies in discussions

---

FAQ

Q: Should I reorganize my existing study?

A: Only if:

• Study is still active (more substudies planned)
• Current organization is causing confusion
• You have time to update documentation references

Otherwise, apply to future studies only.

Q: What if my substudy doesn't have a fixed trial count?

A: Use descriptive name instead:

• 05_refined_search_until_feasible
• 06_sensitivity_sweep
• 07_validation_run

Q: Can I delete old substudies?

A: Generally no. Keep for:

• Historical record
• Lessons learned
• Reproducibility

If disk space is critical:

• Use model cleanup to delete CAD/FEM files
• Archive old substudies to external storage
• Keep metadata and results.json files

Q: Should benchmarking be a substudy?

A: No. Benchmarking validates the baseline model before optimization. It belongs in 1_setup/benchmarking/.

Q: How do I handle multi-stage optimizations?

A: Create separate substudies:

• 05_stage1_meet_constraint_20trials
• 06_stage2_minimize_mass_30trials

Document the relationship in substudy READMEs.

---

Summary

Current Organization: Functional but has room for improvement

• ✅ Substudy isolation works well
• ⚠️ Documentation scattered across levels
• ⚠️ Chronology unclear from names alone

Proposed Organization: Clearer hierarchy and progression

• 📁 1_setup/ - Pre-optimization (model, benchmarking)
• 📁 2_substudies/ - Numbered optimization runs
• 📁 3_reports/ - Comparative analysis

Next Steps:

1. Decide: Reorganize existing study or apply to future only
2. If reorganizing: Follow migration guide
3. Update study_metadata.json with all substudies
4. Create substudy README templates
5. Document lessons learned in study-level docs

Bottom Line: The proposed organization makes it easier to understand what was done, why it was done, and what was learned.