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Computing mean fields with known Reynolds stresses at steady state

Theoretical & Applied Mechanics Letters 2021年第3期11卷 137-145页

作者： Xianwen Guo Zhenhua Xia Shiyi Chen State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China Department of Engineering Mechanics Zhejiang UniversityHangzhou 310027China Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China

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.

关键词： RANS propagation error Nonlinear Reynolds stresses

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