README.md

---
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: file_name
    dtype: string
  - name: source_file
    dtype: string
  - name: question
    dtype: string
  - name: question_type
    dtype: string
  - name: question_id
    dtype: int32
  - name: answer
    dtype: string
  - name: answer_choices
    list: string
  - name: correct_choice_idx
    dtype: int32
  - name: image
    dtype: image
  - name: video
    dtype: video
  - name: media_type
    dtype: string
  splits:
  - name: test
    num_bytes: 1132139207159
    num_examples: 98326
  download_size: 1123845484193
  dataset_size: 1132139207159
license: mit
task_categories:
- visual-question-answering
language:
- en
size_categories:
- 10K<n<100K
---

# OpenSeeSimE-Fluid: Engineering Simulation Visual Question Answering Benchmark

## Dataset Summary

OpenSeeSimE-Fluid is a large-scale benchmark dataset for evaluating vision-language models on computational fluid dynamics (CFD) simulation interpretation tasks. It contains approximately 98,000 question-answer pairs across parametrically-varied fluid simulations including turbulent flow, heat transfer, and complex flow patterns.

## Purpose

While vision-language models (VLMs) have shown promise in general visual reasoning, their effectiveness for specialized engineering simulation interpretation remains unknown. This benchmark enables:

- Statistically robust evaluation of VLM performance on CFD visualizations
- Assessment across multiple reasoning capabilities (captioning, reasoning, grounding, relationship understanding)
- Evaluation using different question formats (binary classification, multiple-choice, spatial grounding)

## Dataset Composition

### Statistics
- **Total instances**: ~98,000 question-answer pairs
- **Simulation types**: 5 fluid models (Bent Pipe, Converging Nozzle, Mixing Pipe, Heat Sink, Heat Exchanger)
- **Parametric variations**: 1,024 unique instances per base model (4^5 parameter combinations)
- **Question categories**: Captioning, Reasoning, Grounding, Relationship Understanding
- **Question types**: Binary, Multiple-choice, Spatial grounding
- **Media formats**: Both static images (1920×1440 PNG) and videos (Originally Extracted at: 200 frames, 40 fps, 5 seconds (some exceptions apply for longer fluid flow development))

### Simulation Parameters

Each base model varies across 5 parameters with 4 values each:

**Bent Pipe**: Bend Angle, Turn Radius, Pipe Diameter, Fluid Viscosity, Fluid Velocity  
**Converging Nozzle**: Pipe Diameter, Front Chamfer Length, Back Chamfer Length, Inner Fillet Radius, Fluid Velocity  
**Mixing Pipe**: Pipe 1 Diameter, Pipe 2 Diameter, Fillet Radius, Fluid 1 Velocity, Fluid 2 Velocity  
**Heat Sink**: Fin Thickness, Sink Length, Fin Spacing, Fin Number, Fluid Velocity  
**Heat Exchanger**: Tube Diameter, Fin Diameter, Fin Thickness, Fin Spacing, Fluid Velocity

### Question Distribution

- **Binary Classification**: 40% (yes/no questions about dead zones, symmetry, flow direction, etc.)
- **Multiple-Choice**: 30% (4-option questions about flow regime, axis of symmetry, magnitude ranges, etc.)
- **Spatial Grounding**: 30% (location-based questions with labeled regions A/B/C/D)

## Data Collection Process

### Simulation Generation
1. Base models sourced from Ansys Fluent tutorial files
2. Parametric automation via PyFluent and PyGeometry interfaces
3. Systematic variation across 5 parameters with 4 linearly-spaced values
4. All simulations solved with validated turbulence models and convergence criteria

### Ground Truth Extraction
Automated extraction eliminates human annotation costs and ensures consistency:

- **Statistical Analysis**: Direct queries on velocity, pressure, and temperature fields
- **Distribution Analysis**: Dead zone detection via velocity magnitude thresholds (1% of maximum)
- **Physics-Based Classification**: Mach number calculations for flow regime classification
- **Spatial Localization**: Color-based region generation with computer vision algorithms

All ground truth derived from numerical simulation results rather than visual interpretation.

## Preprocessing and Data Format

### Image Processing
- Resolution: 1920×1440 pixels
- Format: PNG with lossless compression
- Standardized viewing orientations: front, back, left, right, top, bottom, isometric
- Consistent color mapping: rainbow gradients (red=maximum, blue=minimum)
- Visualization types: contour plots (pressure, velocity magnitude) and vector plots (velocity vectors, streamlines)

### Video Processing
- 200 frames at 40 fps (5 seconds duration); some exceptions apply for longer fluid flow development
- Pathlines showing steady-state flow solution
- H.264 compression at 1920×1440 resolution

### Data Fields
```python
{
    'file_name': str,           # Unique identifier
    'source_file': str,         # Base simulation model
    'question': str,            # Question text
    'question_type': str,       # 'Binary', 'Multiple Choice', or 'Spatial'
    'question_id': int,         # Question identifier (1-20)
    'answer': str,              # Ground truth answer
    'answer_choices': List[str], # Available options
    'correct_choice_idx': int,  # Index of correct answer
    'image': Image,             # PIL Image object (1920×1440)
    'video': Video,             # Video frames
    'media_type': str          # 'image' or 'video'
}
```

## Labels

All labels are automatically generated from simulation numerical results:

- **Binary questions**: "Yes" or "No"
- **Multiple-choice**: Single letter (A/B/C/D) or descriptive option
- **Spatial grounding**: Region label (A/B/C/D) corresponding to labeled visualization locations

## Dataset Splits

- **Test split only**: ~98K instances
- No train/validation splits provided (evaluation benchmark, not for model training)
- Representative sampling across all simulation types and question categories

## Intended Use

### Primary Use Cases
1. **Benchmark evaluation** of vision-language models on CFD simulation interpretation
2. **Capability assessment** across visual reasoning dimensions (captioning, spatial grounding, relationship understanding)
3. **Transfer learning analysis** from general-domain to specialized technical visual reasoning

### Out-of-Scope Use
- Real-time engineering decision-making without expert validation
- Safety-critical applications without human oversight
- Generalization to simulation types beyond fluid dynamics

## Limitations

### Technical Limitations
- **Objective tasks only**: Excludes subjective engineering judgments requiring domain expertise
- **Single physics domain**: Fluid dynamics only (see OpenSeeSimE-Structural for structural mechanics)
- **Ansys-specific**: Visualizations generated using Ansys Fluent rendering conventions
- **Steady-state focus**: Videos show pathlines of steady-state solutions, not transient phenomena
- **2D visualizations**: All inputs are 2D projections of 3D simulations (or 2D Cross Sectional Planes)

### Known Biases
- **Color scheme dependency**: Questions exploit default color gradient conventions
- **Geometry bias**: Selected simulation types may not represent full diversity of CFD applications
- **Flow regime bias**: Limited supersonic cases due to parameter range constraints
- **View orientation bias**: Standardized camera positions may not capture all critical flow features

## Ethical Considerations

### Responsible Use
- Models evaluated on this benchmark should NOT be deployed for safety-critical engineering decisions without expert validation
- Automated interpretation should augment, not replace, human engineering expertise
- Users should verify that benchmark performance translates to their specific simulation contexts

### Data Privacy
- All simulations have no proprietary or confidential engineering data
- No personal information collected
- Publicly available tutorial files used as base models

### Environmental Impact
- Dataset generation required significant CPU computational resources with parallel processing
- Consider environmental cost of large-scale model evaluation on this benchmark
- CFD simulations are computationally intensive (hours per case on multi-core workstations)

## License

MIT License - Free for academic and commercial use with attribution

## Citation

If you use this dataset, please cite:

```bibtex
@article{ezemba2024opensesime,
  title={OpenSeeSimE: A Large-Scale Benchmark to Assess Vision-Language Model Question Answering Capabilities in Engineering Simulations},
  author={Ezemba, Jessica and Pohl, Jason and Tucker, Conrad and McComb, Christopher},
  year={2025}
}
```

## AI Usage Disclosure

### Dataset Generation
- **Simulation automation**: Python scripts with Ansys PyFluent interface
- **Ground truth extraction**: Automated computational protocols (no AI involvement)
- **Quality validation**: Expert oversight of automated extraction procedures
- **No generative AI** used in dataset creation, labeling, or curation

### Visualization Generation
- Ansys Fluent rendering engine (deterministic, physics-based)
- Standardized color mapping and camera controls
- No AI-based image generation or enhancement

## Contact

**Authors**: Jessica Ezemba (jezemba@andrew.cmu.edu), Jason Pohl, Conrad Tucker, Christopher McComb  
**Institution**: Department of Mechanical Engineering, Carnegie Mellon University  

## Acknowledgments

- Ansys for providing simulation software and tutorial files
- Carnegie Mellon University for computational resources
- Reviewers and domain experts who validated the automated extraction protocols

---

**Version**: 1.0  
**Last Updated**: December 2025  
**Status**: Complete and stable