Enhancement of RANS models by means of the tensor basis random forest for turbulent flows in two-dimensional channels with bumps

DNS and RANS computation results for flows in two-dimensional channels with bumps are processed to generate input and output data for a machine learning method aimed to enhance the Reynolds stress anisotropy model and, thus, improve the RANS approach accuracy. The tensor basis random forest method is chosen as a machine learning tool. The prediction of the new model for the Reynolds stress anisotropy tensor is in better agreement with DNS data for two channel flow geometries than those obtained by the conventional linear eddy viscosity model.