Computing mean fields with known Reynolds stresses at steady state

作     者：Xianwen Guo Zhenhua Xia Shiyi Chen Xianwen Guo;Zhenhua Xia;Shiyi Chen

作者机构：State Key Laboratory for Turbulence and Complex SystemsCollege of EngineeringPeking UniversityBeijing 100871China Department of Engineering MechanicsZhejiang UniversityHangzhou 310027China Department of Mechanics and Aerospace EngineeringSouthern University of Science and TechnologyShenzhen 518055China 

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

年 卷 期：2021年第11卷第3期

页      面：137-145页

核心收录：

学科分类：080704[工学-流体机械及工程] 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）] 0707[理学-海洋科学] 080103[工学-流体力学] 0807[工学-动力工程及工程热物理] 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] 0824[工学-船舶与海洋工程] 0802[工学-机械工程] 0836[工学-生物工程] 0814[工学-土木工程] 0825[工学-航空宇航科学与技术] 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 11822208,11988102,11772297,91852205 and 91752202) Shenzhen Science and Technology Program(Grant KQTD20180411143441009) Xia was also supported from the Fun-damental Research Funds for the Central Universities 

主　　题：RANS Propagation error Nonlinear Reynolds stresses 

摘      要：With the rising of modern data science,data-driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising *** approaches are able to achieve better Reynolds stress prediction,with much lower modeling error(∈_(M)),than traditional Reynolds-averaged Navier-Stokes(RANS)models,but they still suffer from numerical error and stability issues when the mean velocity fields are estimated by solving RANS equations with the predicted Reynolds *** fact illustrates that the error of solving the RANS equations(∈_(P))is also very important for a RANS *** the present work,the error∈_(P)is studied separately by using the Reynolds stresses obtained from direct numerical simulation(DNS)/highly resolved large-eddy simulation to minimize the modeling error∈_(M),and the sources of∈_(P)are derived *** the implementations with known Reynolds stresses solely,we suggest to run an auxiliary RANS simulation to make a first guess onν_(t)^(*)and S_(ij)^(0).With around 10 iterations,the error of the streamwise velocity component could be reduced by about one-order of magnitude in flow over periodic *** present work is not to develop a new RANS model,but to clarify the facts that obtaining mean field with known Reynolds stresses is nontrivial and that the nonlinear part of the Reynolds stresses is very important in flow problems with *** proposed approach to reduce∈_(P)may be very useful for the a posteriori applications of the data-driven turbulence models.