Reynolds number dependence of length scales governing turbulent flow separation with application to wall-modeled large-eddy simulations

Abstract: This article proposes a Reynolds number scaling of the required grid points to perform wall-modeled LES of turbulent flows encountering separation off a solid surface. Based on comparisons between the various time scales in a non-equilibrium (due to the action of an external pressure gradient) turbulent boundary layer, a simple definition of the near-wall under-equilibrium" andout-of-equilibrium" scales is put forward (where under-equilibrium" refers to scales governed by a quasi-balance between the viscous and the pressure gradient terms). It is shown that the former length scale varies with Reynolds number as lp Re^(-2/3). The same scaling is obtained from a simplified Green's function solution of the Poisson equation in the vicinity of the separation point. A-priori analysis demonstrates that the resolution required to reasonably predict the wall-shear stress (for example, errors lower than approximately 10-15% in the entire domain) in several nonequilibrium flows is at least O(10) lp irrespective of the Reynolds number and the Clauser parameter. Further, a series of a-posteriori validation studies are performed to determine the accuracy of this scaling including the flow over the Boeing speed bump, Song-Eaton diffuser, Notre-Dame Ramp, and the backward-facing step. The results suggest that for these flows, scaling the computational grids () such that / lp is independent of the Reynolds number results in accurate predictions of flow separation at the samenominal" grid resolution across different Reynolds numbers. Finally, it is suggested that in the vicinity of the separation and reattachment points, the grid-point requirements for wall-modeled large eddy simulations may scale as Re^4/3, which is more restrictive than the previously proposed flat-plate boundary layer-based estimates (Re1) of Choi and Moin (Phys. Fluids, 2012) and Yang and Griffin (Phys. Fluids, 2021).