Deep-Learning Strategy Based on Convolutional Neural Network for Wall Heat Flux Prediction
Gang Dai, Wenwen Zhao, Shaobo Yao, Weifang Chen
Abstract
Aerodynamic thermal prediction plays an important role in the design of hypersonic aircraft, especially in the design of the aircraft’s thermal protection system. The main challenges of the aerothermal prediction lie in the slow converging speed and the strict requirements of the computational grid. In this paper, a convolutional-neural-network-based hybrid-features deep-learning strategy is constructed to efficiently predict aerodynamic heating, which is named the convolutional neural network/hybrid-feature method. The hybrid features of this strategy consist of the normal distribution of physical quantities from the wall and the flow parameters at the extreme temperature point. The strategy, which extends through the multilayer perceptron regression layer method, constructs the relationship between the hybrid features and the wall heat flux to obtain a high-precision model trained by the flowfield data without gradient convergence. It is demonstrated that the model has a better inflow generalization ability to predict wall heat flux with different inflow conditions and angles of attack by zero-angle-of-attack training data, which has great potential in aircraft thermal protection system design and shape optimization.