We investigate the role of extended intrinsic mean spin tensor introduced in this work for turbulence modelling in a non-inertial frame of reference. It is described by the Euclidean group of transformations and, in p...We investigate the role of extended intrinsic mean spin tensor introduced in this work for turbulence modelling in a non-inertial frame of reference. It is described by the Euclidean group of transformations and, in particular, its significance and importance in the approach of the algebraic Reynolds stress modelling, such as in a nonlinear K-ε model. To this end and for illustration of the effect of extended intrinsic spin tensor on turbulence modelling, we examine several recently developed nonlinear K-ε models and compare their performance in predicting the homogeneous turbulent shear flow in a rotating frame of reference with LES data. Our results and analysis indicate that, only if the deficiencies of these models and the like be well understood and properly corrected, may in the near future, more sophisticated nonlinear K-ε models be developed to better predict complex turbulent flows in a non-inertial frame of reference.展开更多
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...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.展开更多
文摘We investigate the role of extended intrinsic mean spin tensor introduced in this work for turbulence modelling in a non-inertial frame of reference. It is described by the Euclidean group of transformations and, in particular, its significance and importance in the approach of the algebraic Reynolds stress modelling, such as in a nonlinear K-ε model. To this end and for illustration of the effect of extended intrinsic spin tensor on turbulence modelling, we examine several recently developed nonlinear K-ε models and compare their performance in predicting the homogeneous turbulent shear flow in a rotating frame of reference with LES data. Our results and analysis indicate that, only if the deficiencies of these models and the like be well understood and properly corrected, may in the near future, more sophisticated nonlinear K-ε models be developed to better predict complex turbulent flows in a non-inertial frame of reference.
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.
文摘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.
基金Supported in part by SF for Pure Research of Natural Sciences of the Education Department of Hunan Province (No.2000c315)NNSF (No.10271071) and specialized Research Fund for Doctoral Program of Higher Education of China
