Year: 2025
Type: article
Abstract: Machine-learning methods to enhance an approximation for the Reynolds-stress anisotropy tensor are presented. The approach of tensor basis random forest is applied for this. The set of input features and tensors in the basis are discussed. Different ways to propagate the Reynoldsstress anisotropy tensor into the Reynolds-averaged Navier–Stokes equation solver are explored. It is demonstrated that the conventional expression for Reynolds-stress anisotropy based on the linear eddy-viscosity model is not able to reproduce a secondary flow in the square duct cross-section, whereas the machine-learning modifications can fix such a disadvantage and have potentials for further improvements of turbulence models.
Authors: A Bernard, S Yakovenko
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Funder: Russian Science Foundation
Award: G2676292179
OpenAlex ID: W4412368100