MACHINE LEARNING-BASED THERMAL AND FLOW SIMULATION ON HETEROGENEOUS PLATFORM FOR SIGNATURE PREDICTION

The data-driven machine learning (ML) method is developed to rapidly evaluate the thermal and flow fields of a ground vehicle and its neighboring environment at various conditions. The artificial neural network (ANN) is implemented as the ML model to evaluate the fields, while achieving equivalent accuracy as the CFD simulations. In order for ANN to precisely map a relationship between the simulation parameters and the solution field, the proper orthogonal decomposition (POD) technique is applied to reduce the dimension of the field variables. Consequently, the compressed data (i.e. modal coefficients) is selected as the target for the ANN. Once trained, POD reconstruction is performed on the ANN predicted modal coefficients to recover the CFD solution. The developed framework is tested at diverse sample sites, and the maximum mean absolute errors are found to be 0.41 K and 0.019 m/s for thermal and flow simulations, respectively, verifying the outstanding prediction performance.

Citation: S.H. Hong, A. House, A.L. Kaminsky, N. Tison, Y. Ruan, V. Korivi, Y. Wang, K. Pant, “Machine Learning-based Thermal and Flow Simulation on Heterogeneous Platform for Signature Prediction”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 10-12, 2021.