深度学习方法在流场重建中的应用综述OA

High resolution flow field data has the characteristics of nonlinearity and large data volume,which makes it difficult to obtain through both experimental and simulation methods.Flow field reconstruction technology can fully utilize the observable information of the flow field to mine unobservable information,and recover high-resolution flow field data from sparse or low resolution flow field data.Deep learning methods have been widely applied in fluid mech-anics problems due to their powerful feature extraction and nonlinear fitting capabilities.Among them,flow field recon-struction methods based on deep learning have high research potential.This article investigates deep learning based flow field reconstruction methods and categorizes modeling approaches for flow field reconstruction problems from different perspectives.This paper provides a detailed summary of the research progress and achievements in flow field recon-struction methods for modal recombination,local global prediction,and element solver,and discusses the advantages and disadvantages of each method.Finally,the challenges faced by deep learning based flow field reconstruction tech-nology were summarized and analyzed,and future research directions were discussed.