声爆作为制约超声速客机发展的核心问题,对其进行精确预测是开展超声速客机降噪设计的先决条件。在工程中主要采用计算流体力学(CFD)和声学传播理论相结合的声爆预测方法,其中近场过压分布的计算精度至关重要,但高精度解的获取成本高昂。为缓解声爆预测成本与精度之间的矛盾,本文采用多保真深度神经网络构建气动外形和近场过压分布之间的映射关系,并结合增广Burgers方程实现快速准确的声爆预测。在剖析高低保真近场过压数据之间关联特性的基础上,本研究探讨了两种不同的多保真建模策略下深度神经网络的构建方法与预测性能。实验结果显示,通过增加自适应搜索方法,基于线性/非线性综合矫正策略的MF-DNN的兼顾鲁棒性与性能优势,而通过精细设计归一化方法和正则化系数,基于迁移学习的TF-DNN实现了最小的预测误差,相比于单保真深度神经网络,两个模型都可以在高保真数据较少的条件下,通过联合低保真度数据来显著提升近场过压分布和声爆值的预测精度,相关研究结果为超声速客机高效率低声爆设计提供支撑。
Sonic boom is a core issue restricting the development of supersonic passenger aircraft, and accurate prediction of it is a prerequisite for the noise reduction design of supersonic passenger aircraft. In engineering, the sonic boom prediction method mainly combines computational fluid dynamics (CFD) and acoustic propagation 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 prediction, this paper uses a multi-fidelity deep neural network to construct the mapping relationship between the aerodynamic shape and the near-field overpressure distribution, and combines the augmented Burgers equation 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 robustness 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, and the relevant research results provide support for the high-efficiency and low-sonic-boom design of supersonic passenger aircraft.