Enhancement of RANS Models by Means of the Tensor Basis Random Forest for Turbulent Flows in Two-Dimensional Channels with Bumps
A. Bernard1, S. N. Yakovenko2
1Novosibirsk State University, Novosibirsk, Russia
2Khristianovich Institute of Theoretical and Applied Mechanics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia
Keywords: turbulence modeling, Reynolds stress, machine learning, random forest
Abstract
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.
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