空气动力学学报2025,Vol.43Issue(7):93-107,15.DOI:10.7638/kqdlxxb-2025.0109
数据驱动的升力体飞行器表面热流快速预测
Data-driven rapid prediction of surface heat flux for lifting-body aircraft
摘要
Abstract
This study developed a rapid method for aerodynamic heating prediction in high-speed lifting bodies.A dataset comprising 540 simulation cases was constructed through parametric modeling,encompassing 15 three-dimensional configurations and 36 flight conditions.To facilitate efficient neural network training,a geometry-aware preprocessing method based on surface slicing and interpolation was proposed,converting complex surface and geometry information into matrix representations.Subsequently,a D-TMU model based on deep learning was introduced.The model employed an encoder-decoder architecture integrating transformer modules,multilayer perceptrons,and UNet structures,with depthwise over-parameterized convolutions replacing standard convolution layers.D-TMU directly predicted surface heat flux distributions without iterative computations from input geometry,pressure distribution,and flight conditions.Validation results demonstrated the predictive performance of the model,with mean prediction errors of 1.21%on the test set,1.19%in high heat flux regions,and 0.97%at key points.The average inference time per case was 0.03 seconds,representing computational acceleration a speedup of approximately six orders of magnitude compared over traditional CFD methods.These results indicated that D-TMU effectively captured global geometric characteristics and local feature interactions,maintaining achieving high predictive accuracy and computational efficiency.In addition,the model exhibited promising generalization capability for lifting body configurations beyond the training set.关键词
气动热/数据驱动/神经网络/快速预测/升力体Key words
aerodynamic heating/data-driven/neural network/fast prediction/lifting body分类
航空航天引用本文复制引用
杜文聪,陈智..数据驱动的升力体飞行器表面热流快速预测[J].空气动力学学报,2025,43(7):93-107,15.基金项目
稳定支持项目(800009000199C1652442) (800009000199C1652442)