Open-CK: A Large Multi-Physics Fields Coupling benchmarks in Combustion Kinetics

In this paper, we use the Fire Dynamics Simulator (FDS) combined with the lmttsupercomputer support to create a Combustion Kinetics (CK) dataset for machine learning and scientific research. This dataset captures the development of fires in industrial parks with high-precision Computational Fluid Dynamics (CFD) simulations. It includes various physical fields such as temperature and pressure, and covers multiple environmental combinations for exploring multi-physics field coupling phenomena. Additionally, we evaluate several advanced machine learning architectures across our lmttOpen-CK benchmark using a substantial computational setup of 64 NVIDIA A100 GPUs: 182 vision backbone; 183 spatio-temporal predictive models; 184 operator learning frameworks. These architectures uniquely excel at handling complex physical field data. We also introduce three benchmarks to demonstrate their potential in enhancing the exploration of downstream tasks: (a) capturing continuous changes in combustion kinetics; (b) a neural partial differential equation solver for learning temperature fields and turbulence; (c) reconstruction of sparse physical observations. The Open-CK dataset and benchmarks aim to advance research in combustion kinetics driven by machine learning, providing a reliable baseline for developing and comparing cutting-edge technologies and models. We hope to further promote the application of deep learning in earth sciences. Our project is available at https://github.com/whscience/Open-CK.