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.

{"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"]}

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