Data-Driven Turbulence Modeling for Separated Flows Considering Nonlocal Effect

This study aims to enhance the generalizability of Reynolds-averaged Navier–Stokes (RANS) turbulence models, which are crucial for engineering applications. Classic RANS turbulence models often struggle to predict separated flows accurately. Recently, data-driven machine-learning approaches for turbulence modeling have been explored to address this issue. However, these models are often criticized for their limited generalizability. In this study, we address this issue by incorporating nonlocal effects into data-driven turbulence modeling. Specifically, we introduce a transport equation for the correction term [Formula: see text] of the shear stress transport model to encode nonlocal information along the mean streamline. The coefficients of the equation are calibrated using high-fidelity data from the NASA hump and periodic hills. The resulting model, termed the [Formula: see text]-transport model, demonstrates high accuracy across various separated flows outside the training set, including periodic hills with different geometries and Reynolds numbers, the curved backward-facing step, a two-dimensional bump, and a three-dimensional simplified car body. In all tested cases, the [Formula: see text]-transport model yields smaller or equal prediction errors compared to those from classic local data-driven turbulence models using algebraic relations for [Formula: see text]. These results indicate improved generalizability for separated flows. Furthermore, the [Formula: see text]-transport model shows similar accuracy to the baseline model in basic flows including the mixing layer and the channel flow. Therefore, the nonlocal modeling approach presented here offers a promising pathway for developing more generalizable data-driven turbulence models.