Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence

作     者：Chenyue Xie Jianchun Wang Hui Li Minping Wan Shiyi Chen Chenyue Xie;Jianchun Wang;Hui Li;Minping Wan;Shiyi Chen

作者机构：Shenzhen Key Laboratory of Complex Aerospace FlowsCenter for Complex Flows and Soft Matter ResearchDepartment of Mechanics and Aerospace EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Power and Mechanical EngineeringWuhan UniversityWuhan 430072China State Key Laboratory of Turbulence and Complex SystemsPeking UniversityBeijing 100871China 

出 版 物：《Theoretical & Applied Mechanics Letters》 (力学快报（英文版）)

年 卷 期：2020年第10卷第1期

页      面：27-32页

核心收录：

学科分类：08[工学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 081104[工学-模式识别与智能系统] 080103[工学-流体力学] 0707[理学-海洋科学] 0815[工学-水利工程] 0805[工学-材料科学与工程（可授工学、理学学位）] 0813[工学-建筑学] 0802[工学-机械工程] 0824[工学-船舶与海洋工程] 0814[工学-土木工程] 0825[工学-航空宇航科学与技术] 0836[工学-生物工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：This work was supported by the National Natural Science Foundation of China(Grants 91952104,11702127,and 91752201) the Technology and Innovation Commission of Shenzhen Municipality(Grants KQTD20180411143441009,JCYJ20170412151759222,and ZDSYS201802081843517).This work was also supported by Center for Computational Science and Engineering of Southern University of Science and Technology.J.Wang acknowledges the support from Young Elite Scientist Sponsorship Program by CAST(Grant 2016QNRC001). 

主　　题：Compressible turbulence Large eddy simulation Artificial neural network 

摘      要：The subgrid-scale(SGS)stress and SGS heat flux are modeled by using an artificial neural network(ANN)for large eddy simulation(LES)of compressible turbulence.The input features of ANN model are based on the first-order and second-order derivatives of filtered velocity and temperature at different spatial locations.The proposed spatial artificial neural network(SANN)model gives much larger correlation coefficients and much smaller relative errors than the gradient model in an a priori analysis.In an a posteriori analysis,the SANN model performs better than the dynamic mixed model(DMM)in the prediction of spectra and statistical properties of velocity and temperature,and the instantaneous flow structures.