Applying extended intrinsic mean spin tensor in evolution algorithm for RANS modelling of turbulent rotating channel flow  

作　　者：Wei Zhang Bing Zhu Hui Xu Yong Wang 

机构地区：[1]Science and Technology on Water Jet Propulsion Laboratory,Marine Design and Research Institute of China,Shanghai 200011,China [2]School of energy and power engineering,University of Shanghai for Science and Technology,Shanghai 210098,China [3]School of Aeronautic and Astronautic,Shanghai Jiao Tong University,Shanghai 200011,China [4]Max Planck Institute for Dynamics and Self-Organization,Gottingen 37077,Germany

出　　处：《Journal of Hydrodynamics》2019年第6期1255-1258,共4页水动力学研究与进展B辑（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.91852117,91852106),the MOE Key Laboratory of Hydrodynamics,Shanghai Jiao Tong University.

摘　　要：We present a machine learning based method for RANS modeling in the rotating frame of reference(RFR).The extended intrinsic mean spin tensor(EIMST)is adopted in a novel expansion of the evolution algorithm,named multi-dimensional gene expression programming(MGEP).Based on DNS data,a constrain free model for Reynolds stress is created by considering system rotating.The anisotropy behavior of Reynolds stress is considered in the model,which is then for the first time applied for modeling turbulent flow inside a rotating channel.Compared with the traditional RANS model,the new model can predict the non-symmetric profile of Reynolds stress.Meanwhile,the Taylor-Gortler vortex is captured in our simulations with the new model.It is demonstrated that the application of EIMST in MGEP can be successfully adopted for RANS modeling in the RFR.

关 键 词：Extended intrinsic mean spin tensor RANS modeling rotating frame of reference gene expression programming 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]