OpenSeeSimE-Structural: Engineering Simulation Visual Question Answering Benchmark

Dataset Summary

OpenSeeSimE-Structural is a large-scale benchmark dataset for evaluating vision-language models on structural analysis simulation interpretation tasks. It contains over 100,000 question-answer pairs across parametrically-varied structural simulations including stress analysis, and deformation patterns.

Purpose

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

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

Dataset Composition

Statistics

• Total instances: 102,678 question-answer pairs
• Simulation types: 5 structural models (Dog Bone, Hip Implant, Pressure Vessel, Beams, Wall Bracket)
• 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, 29 fps, 7 seconds)

Simulation Parameters

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

Dog Bone: Length, Thickness, Diameter, Axial Load, Bending Load
Hip Implant: Beam Length, Beam Diameter, Ball Diameter, Axial Load, Bending Load
Pressure Vessel: Length, Thickness, Diameter, Material, Pressure
Thermal Beam: Thickness, Bending Load, Young's Modulus, Tensile Yield Strength, Cross Section Shape
Wall Bracket: Length, Width, Height, Thickness, Bending Force

Question Distribution

• Binary Classification: 40% (yes/no questions about symmetry, stress types, uniformity, etc.)
• Multiple-Choice: 30% (4-option questions about deformation direction, stress dominance, 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 Mechanical tutorial files
2. Parametric automation via PyMechanical and PyGeometry interfaces
3. Systematic variation across 5 parameters with 4 linearly-spaced values
4. All simulations solved using finite element analysis with validated convergence settings

Ground Truth Extraction

Automated extraction eliminates human annotation costs and ensures consistency:

• Statistical Analysis: Direct queries on result arrays (max, min, mean, std)
• Distribution Analysis: Threshold-based classification using coefficient of variation
• Physics-Based Classification: Stress tensor analysis and mechanics principles
• 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)
• Automatic deformation scaling (1.5× relative to maximum dimension)

Video Processing

• 200 frames at 29 fps (7 seconds duration)
• Maximum deformation at frame 100 (temporal midpoint)
• H.264 compression at 1920×1440 resolution
• Uniform frame sampling for model input (32 frames)

Data Fields

{
    '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

Label generation employs domain-specific thresholds:

• Uniformity: CV ≤ 0.2 (20%)
• Symmetry: 60% of node pairs within 10% tolerance (structural)
• Spatial matching: 50-pixel separation for region placement

Dataset Splits

• Test split only: 102,678 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 engineering 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 structural mechanics

Limitations

Technical Limitations

• Objective tasks only: Excludes subjective engineering judgments requiring domain expertise
• Single physics domain: Structural mechanics only (see OpenSeeSimE-Fluid for fluid dynamics)
• Ansys-specific: Visualizations generated using Ansys Mechanical rendering conventions
• Static parameters: Fixed material properties and boundary conditions per instance
• 2D visualizations: All inputs are 2D projections of 3D simulations

Known Biases

• Color scheme dependency: Questions exploit default rainbow gradient conventions
• Geometry bias: Selected simulation types may not represent full diversity of structural analysis applications
• View orientation bias: Standardized camera positions may not capture all critical simulation 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 contain no proprietary or confidential engineering data
• No personal information collected
• Publicly available tutorial files used as base models

Environmental Impact

• Dataset generation required significant computational CPU resources
• Consider environmental cost of large-scale model evaluation on this benchmark

License

MIT License - Free for academic and commercial use with attribution

Citation

If you use this dataset, please cite:

@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 PyMechanical 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 Mechanical rendering engine (deterministic, physics-based)
• Standardized color mapping and camera controls
• No AI-based image generation or enhancement

Contact

Authors: Jessica Ezemba ([email protected]), 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

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