Research on Fast CFD Simulation of Automobile Flow Field Based on Artificial Intelligence

Abstract

Abstract Fast automotive aerodynamic evaluation can extremely reduce the design cycles of automotive. This paper establishes a rapid simulation model of the automobile flow field based on the artificial intelligence surrogate model. The end-to-end network is used to map the geometric model, incoming flow conditions, and result. The decoder is used to splice high-dimensional and low-dimensional features to achieve feature sharing. The average MAE error of the optimal model is 5.249%. The average calculation time of a single example is 1.2968s, which is about 0.62% of CFD solver. The simulation result demonstrate that the deep learning method can not only accelerate the calculation, but also can improve the design efficiency of automobile aerodynamic profile with high accuracy.