A data-driven aeroheating prediction model

Aeroheating prediction with high efficiency and high accuracy is crucial for the design of hypersonic vehicles. However, the increasing shape complexity and tight design period of hypersonic vehicles make it difficult for existing methods to meet the requirements of efficient and accurate aeroheating prediction. In this study, a localized data-driven modeling method for rapid aeroheating prediction is developed based on the boundary layer theory and the support vector machine. Firstly, the outer edge boundary layer information is obtained by solving the Euler equations, and the RANS method is used to generate samples of heat flux distributions. Then, a feature selection approach is developed to acquire the outer edge boundary layer features. Finally, the support vector machine is used to construct the aeroheating prediction model to achieve the mapping between the outer edge boundary layer features and the heat flux on the wall. Results of the aeroheating prediction for a double ellipsoid and a two-stage compression surface show that the model considers local boundary conditions such as the non-uniform wall temperature, and has high accuracy as well as good extrapolation and generalization capability. The relative errors of heat flux between the model prediction and the RANS calculation are less than 5%. Moreover, for the aeroheating flux prediction along the center line on the upper surface of the double ellipsoid, the prediction ability of the present model is better than the traditional proper orthogonal decomposition (POD) reduction method, especially the prediction accuracy of the present model in the extrapolation regime is more than four times higher than that of the POD reduction model.