Prediction of Aerothermal Heating: From Numerical Simulations to Machine Learning Models

High-speed aircraft experiences severe aerodynamic heating at high Mach numbers, requiring accurate prediction of aerothermal heating effects before designing thermal protection systems. With the rise of artificial intelligence and the potential of neural networks, data-driven methods for aerothermal heating prediction have gained significant attention. This study focuses on numerical simulations of aerothermal heating phenomena and explores machine learning applications in heat prediction. First, a two-dimensional cylinder case was simulated using the finite volume method with chemical non-equilibrium flow to understand flow characteristics and heat distribution. Subsequently, Two aerothermal heating datasets were established: the first varies Mach number from 7.0 to 11.9 under fixed freestream conditions, while the second combines Mach numbers (8.5–9.5) with varying temperatures (890 K, 901 K, 910 K) and pressures (460 Pa, 470 Pa, 476 Pa). And the influence of incoming flow conditions on shock waves, temperature fields, wall heat flux was analyzed. Finally, machine learning methods were applied to predict aerothermal heating properties. A multilayer perceptron (MLP) prediction model was established to predict wall heat flux, the reverse line from the stagnation point along the flow direction pressure and temperature, as well as the temperature and pressure fields. Additionally, a convolutional neural network (CNN) model was developed to accurately predict the temperature and pressure fields. While the MLP model demonstrates strong predictive accuracy for physical quantities along the cylinder surface and the reverse line from the stagnation point along the flow direction, the CNN model exhibits superior performance in predicting the entire flow field. Compared to the numerical simulation methods used, the established model can quickly predict the aerothermal environment of a two-dimensional cylinder, helping to shorten the design cycle of thermal protection systems.