Near-Wall Scaling and Separation Prediction of a Rotation-Based Subgrid-Scale Stress Model

TL;DR

This paper analyzes a novel rotation-based subgrid-scale stress model, demonstrating its accurate near-wall behavior, separation prediction, and improved flow simulation capabilities over traditional models in canonical turbulent flows.

Contribution

The study provides the first detailed analysis of the near-wall asymptotics and separation prediction of this new rotation-based SGS model, highlighting its advantages over existing models.

Findings

Eddy viscosity correlates well with vortices and shows O(y) near-wall behavior.

Model predicts reattachment points with less error than Smagorinsky models.

Outperforms WALE in Reynolds stress prediction in separated flows.

Abstract

This paper presents an in-depth analysis of a novel subgrid-scale stress model proposed in 2022, which utilizes the rotational part of the velocity gradient as the velocity scale for computing eddy viscosity. This study investigates the near-wall asymptotic behavior and separation prediction capability of this model for the first time. Two canonical flows--fully-developed turbulent channel flow and periodic hill flow--are selected for analysis. The eddy viscosity predicted by this model correlates well with the visualized vortices and exhibits an asymptotic behavior of O(y) near the walls. The dimensionless eddy viscosity, like that of the Wall-Adapting Local Eddy Viscosity (WALE) subgrid model, remains within a small numerical range of 10^-2 to 10^-4. The power spectral density results reveal the asymptotic behavior of the velocity scale in the dissipation range, following a -10/3 scaling law. Additionally, this model predicts velocity profiles more accurately than the Smagorinsky model, even when using Van Driest damping. For the periodic hill case, this model predicts the reattachment point with only a 6.9% error, compared to 14.0% for the Smagorinsky model and 16.4% for the Smagorinsky model with Van Driest damping. In near-wall regions with separation, this model achieves even greater accuracy in Reynolds stress prediction than the WALE model, demonstrating its superior potential for separated flow simulations.