航空学报2025，Vol.46Issue(20)：67-87,21.DOI:10.7527/S1000-6893.2025.31936
基于多保真深度神经网络的超声速客机声爆预测
Sonic boom prediction of supersonic passenger aircraft based on multi-fidelity deep neural network
王雨桐 1罗骁 1刘红阳 1宋超 1赵莹 2周铸1
作者信息
- 1. 中国空气动力研究与发展中心 计算空气动力研究所,绵阳 621000||空天飞行空气动力科学与技术全国重点实验室,绵阳 621000
- 2. 中国空气动力研究与发展中心 计算空气动力研究所,绵阳 621000||西北工业大学 航空学院,西安 710072
- 折叠
摘要
Abstract
Sonic boom is a core issue restricting the development of supersonic passenger aircraft.Accurate prediction of sonic boom signatures is a prerequisite for the noise reduction design of supersonic passenger aircraft.In engineer-ing,the sonic boom prediction method mainly combines Computational Fluid Dynamics(CFD)and acoustic propaga-tion theory,in which the calculation accuracy of the near-field overpressure distribution is crucial,but the acquisition cost of high-precision solutions is high.To alleviate the contradiction between the cost and accuracy of sonic boom pre-diction,this paper uses a multi-fidelity deep neural network to construct the mapping relationship between the aerody-namic shape and the near-field overpressure distribution,and combines the acoustic propagation theory to achieve fast and accurate sonic boom prediction.Based on the analysis of the correlation characteristics between the high and low fidelity near-field overpressure data,this study explores the construction methods and prediction performance of deep neural networks under two different multi-fidelity modeling strategies.The experimental results show that by adding an adaptive search method,the MF-DNN based on the linear/nonlinear comprehensive correction strategy has both robust-ness and performance advantages,and by carefully designing the normalization method and regularization coefficient,the TF-DNN based on transfer learning achieves the smallest prediction error.Compared with the single-fidelity deep neural network,both models can significantly improve the prediction accuracy of the near-field overpressure distribution and sonic boom value by combining low-fidelity data under the condition of less high-fidelity data.Relevant research re-sults provide support for the high-efficiency and low-sonic-boom design of supersonic passenger aircraft.关键词
超声速客机/声爆/多保真/迁移学习/深度神经网络Key words
supersonic passenger aircraft/sonic boom/multi-fidelity/transfer learning/deep neural network分类
航空航天引用本文复制引用
王雨桐,罗骁,刘红阳,宋超,赵莹,周铸..基于多保真深度神经网络的超声速客机声爆预测[J].航空学报,2025,46(20):67-87,21.
