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Published June 7, 2023 | Version 2.0
Dataset Open

BLASTNet Simulation Dataset

Creators

  • 1. Stanford University
  • 2. Sandia National Labs
  • 3. University of Melbourne
  • 4. Polytechnique Montréal
  • 5. University of Connecticut

Description

Go to https://blastnet.github.io/ to access and download this reacting and non-reacting flow physics simulations.

Mission

BLASTNet 2.0 was developed to provide the researchers in  reacting and non-reacting flow physics communities with high-fidelity simulation datasets in a convenient format for ML applications. With 2.2 TB, 744 full-domain samples, and 34 configurations, BLASTNet can effectively address these gaps and aid in fostering open/fair ML development within reacting and non-reacting flow physics communities.

Application

This data is useful for fluid flows in a wide range of ML applications tied to automotive, propulsion, energy, and the environment. Specifically, scientific engineering tasks related to these domains may include turbulent closure modeling, spatio-temporal modeling, and inverse modeling.
    

Notes

URL: https://blastnet.github.io/

Files

Files (2.4 kB)

Name Size Download all
md5:7c6fed023b8aaf6be7d3d84193346fe7
2.2 kB Download
md5:fcd21741a4aa4a210405a42935a133d4
184 Bytes Download

Additional details

Related works

Is cited by
Journal article: 10.1016/j.jaecs.2022.100087 (DOI)
Conference paper: 10.48550/ARXIV.2207.12546 (DOI)
Is supplement to
Journal article: 10.1016/j.jaecs.2022.100087 (DOI)
Conference paper: 10.48550/ARXIV.2207.12546 (DOI)

References

  • Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme (2022), BLASTNet: A call for community-involved big data in combustion machine learning, Applications in Energy and Combustion Science 12 pp. 100087.
  • Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme (2022), The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning, arXiv 2207.12546