Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector

For developing a reliable data-driven Reynold stress tensor (RST) model, successful reconstruction of the mean velocity field based on high-fidelity information (i.e., direct numerical simulations or large-eddy simulations) is crucial and challenging, considering ill-conditioning problem Reynolds-averaged Navier–Stokes (RANS) equations. It shown that frozen treatment Reynolds force vector (RFV) reduced even for cases with very high number; therefore, it has better potential to be used in development RANS models. In this study, we compare algebraic RST correction models are trained both RFV aforementioned potential. We derive vector-based framework similar tensor-based RST. Regarding complexity models, sparse regression set candidate functions multi-layer perceptron network. The training process applied data three cases, including square-duct secondary flow, roughness-induced periodic hills flow. results showed using discrepancy values, instead generally does not improve despite fact propagation shows lower errors all cases. complexity, improves prediction flows, but performance case hills.