LAUSR

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-Görtler 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.