Cooldata - A Large-Scale Electronics Cooling 3D Flow Field Dataset

Cooldata is a large-scale electronics cooling dataset, containing over 60k stationary 3D flow fields for a diverse set of geometries, simulated with the commercial solver Simcenter STAR-CCM+. This library can be used to acccess the dataset and streamline its application in machine learning tasks.

A sample case from the Cooldata dataset

Features

  • Data Storage: Organized in folders containing .cgns files for compatibility with computational fluid dynamics tools.

  • PyVista Integration: Access to dataset samples as PyVista objects for easy 3D visualization and manipulation.

  • Graph Neural Network Support:

  • DGL Support: Surface and volume data in mesh format, 3D visualization of samples and predictions, L2 loss computation and aggregate force evaluation for model training.

  • PyG Support: Implementing functionalities similar to DGL.

  • Hugging Face Integration: Direct dataset loading from [Hugging Face](https://huggingface.co/).

  • Voxelized Flow Field Support: Facilitates image processing-based ML approaches.

  • Comprehensive Metadata Accessibility: All metadata is accessible through the library.

Installation

Run

pip install cooldata

If you want to use the DGL support, you also need to install the [DGL](https://www.dgl.ai/) library, as documented [here](https://www.dgl.ai/pages/start.html).

Contents:

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). You can view the full license [here](https://creativecommons.org/licenses/by-nc/4.0/).

Contributing

We welcome contributions to the Cooldata library! If you have suggestions, bug reports, or feature requests, please open an issue on our [GitHub repository](https://github.com/peteole/flow_field_dataset/issues).