| Citation: | YANG F，LIN M Y，HU Z M，et al. Prediction method of aero-heating of hypersonic vehicle bi-curvature leading edge based on machine learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2826-2834 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0746
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The prediction technology of hypersonic aero-heating is one of the key technologies for the development of high-speed vehicles. Creating an efficient method for predicting the hypersonic thermal conditions is highly important for designing thermal protection systems and optimizing aerodynamics. In order to obtain the heat flux distribution on the surface of hypersonic vehicles quickly and to shorten the vehicle design cycle, a fast prediction method for the aerothermal environment of the bi-curvature leading edge of hypersonic vehicles is proposed based on the multi-level block building (MBB) algorithm. The MBB algorithm is distinguished by its generalized separability, which enables it to efficiently represent highly nonlinear data. First, numerical simulations are conducted to obtain the database composed of the aero-heating data of the bi-curvature leading edges of the vehicles in the training set. Based on the MBB algorithm, an explicit expression for predicting the distributions of heat flux is given. The statistical analysis results demonstrate that the discrepancy between the estimated value and the observed value is below 2%, suggesting that the formula given in this study exhibits a high level of predictive precision. Further, an extrapolation of the formula is performed to verify its applicability for different geometric shapes. At the stage of thermal design and aerodynamic optimization of the bi-curvature leading edge configuration, the formulation proposed in this paper enables accurate and rapid prediction of the aerodynamic thermal environment.
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