空气动力学学报2024，Vol.42Issue(3)：30-46,17.DOI:10.7638/kqdlxxb-2023.0073
深度学习在边界层流动稳定性分析中的应用
Application of deep learning in boundary layer flow instability analysis
摘要
Abstract
The eN method based on linear stability theory(LST)is one of the more reliable methods in the prediction of boundary layer transition.In order to greatly simplify and automate the solution process of the traditional LST eigenvalue problem,the convolutional neural network(CNN)is trained on the LST analysis sample set of the boundary layer similarity solution.For the streamwise and crossflow instabilities,the local growth rate,N factor and transition location are predicted by CNN on a naturally laminar airfoil and an infinite swept-back wing respectively,which are in good agreement with the results of standard LST.It is verified that CNN can encode the velocity derivative information of the boundary layer profile into a scalar feature that satisfies the Galilean invariance,and plays a role in characterizing the pressure gradient in the boundary layer of an airfoil or the crossflow intensity in the boundary layer of a swept-back wing.Based on the prediction of LST eigenvalues by CNN,the total loss function is constructed by the governing equations of LST,the boundary conditions and the trivial solution penalty term to train the physics-informed neural network(PINN),which realizes an accurate prediction of LST eigenfunctions without relying on samples.The results show that the PINN model can provide an effective modeling method for the eigenfunction problem of LST.关键词
线性稳定性理论/eN方法/卷积神经网络/内嵌物理信息神经网络/流向不稳定性/横流不稳定性Key words
linear stability theory/eN method/convolutional neural network/physics-informed neural network/streamwise instability/crossflow instability分类
航空航天引用本文复制引用
樊佳坤,姚方舟,黄江涛,徐家宽,乔磊,白俊强..深度学习在边界层流动稳定性分析中的应用[J].空气动力学学报,2024,42(3):30-46,17.基金项目
国家自然科学基金(12102361) （12102361）
中央高校基本科研业务费(G2021KY05101) （G2021KY05101）
