Effect of grid resolution on large eddy simulation of wall-bounded turbulence

TL;DR

This study systematically examines how grid resolution affects large eddy simulation accuracy of wall-bounded turbulence, using uncertainty quantification techniques to optimize and quantify predictive errors.

Contribution

It introduces a methodology combining LES with polynomial chaos expansion to efficiently assess grid resolution impacts on simulation accuracy.

Findings

Velocity and Reynolds stress profiles are most sensitive to spanwise grid resolution.

Turbulent kinetic energy errors are mainly influenced by streamwise grid resolution.

Recommendations for grid resolution to achieve desired accuracy are provided.

Abstract

The effect of grid resolution on large eddy simulation (LES) of wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving systematic variation of the streamwise ($\Delta x$) and spanwise ($\Delta z$) grid resolution is used for this purpose. The main friction-velocity based Reynolds number investigated is 300. Near the walls, the grid cell size is determined by the frictional scaling, $\Delta x^+$ and $\Delta z^+$, and strongly anisotropic cells, with first $\Delta y^+ \sim 1$, thus aiming for wall-resolving LES. Results are compared to direct numerical simulations (DNS) and several quality measures are investigated, including the error in the predicted mean friction velocity and the error in cross-channel profiles of flow statistics. To reduce the total number of channel flow simulations, techniques from the framework of uncertainty quantification (UQ) are employed. In particular, generalized polynomial chaos expansion (gPCE) is used to create meta models for the errors over the allowed parameter ranges. The differing behavior of the different quality measures is demonstrated and analyzed. It is shown that friction velocity, and profiles of velocity and the Reynolds stress tensor, are most sensitive to $\Delta z^+$, while the error in the turbulent kinetic energy is mostly influenced by $\Delta x^+$. Recommendations for grid resolution requirements are given, together with quantification of the resulting predictive accuracy. The sensitivity of the results to subgrid-scale (SGS) model and varying Reynolds number is also investigated. All simulations are carried out with second-order accurate finite-volume based solver. The choice of numerical methods and SGS model is expected to influence the conclusions, but it is emphasized that the proposed methodology, involving gPCE, can be applied to other modeling approaches as well.