Machine Learning Strategy for Wall Heat Flux Prediction in Aerodynamic Heating

Abstract

The efficient and accurate prediction of the aeroheating performance of hypersonic vehicles is a challenging task in the thermal protection system structure design process, which is greatly affected by grid distribution, numerical schemes, and iterative steps. From the inspiration of the theoretical analysis and machine learning strategy, a new wall heat flux prediction framework is proposed first by establishing the relationship between the wall heat flux and the flow variables at an extreme temperature point (ETP) in the normal direction of the corresponding wall grid cell, which is named the machine learning (ML)-ETP method. In the training phase, the flow properties and their gradients at the ETP and the distance from the ETP normal to the wall are employed as feature values, and the accurate wall heat flux predicted by the converged fine grid is regarded as the tag value. With the assistance of the trained regression model, the heat flux of the same configuration with a coarse grid in the wall-normal direction could be predicted accurately and efficiently. Moreover, test cases of different configurations and inflow conditions with a coarse grid are also carried out to assess the model’s generalization performance. All comparison results demonstrate that the ML-ETP strategy could predict wall heat flux more rapidly and accurately than the traditional numerical method due to its nonstrict grid distribution requirements. The improvement of the predictive capability of the coarse-graining model could make the ML-ETP method an effective tool in hypersonic engineering applications, especially for unsteady ablation simulations or aerothermal optimizations.