A local dynamic gradient Smagorinsky model for large-eddy simulation

TL;DR

This paper introduces a local dynamic gradient Smagorinsky model for LES that removes singularities without averaging, improving stability and computational efficiency in turbulent flow simulations.

Contribution

The paper presents a novel dynamic gradient Smagorinsky model that eliminates singularities without averaging, enhancing stability and ease of implementation in LES.

Findings

The DGSM improves stability over the local DSM.

DGSM achieves comparable accuracy to DSM.

DGSM has lower computational complexity.

Abstract

This paper proposes a local dynamic model for large-eddy simulation (LES) without averaging in homogeneous directions. It is demonstrated that the widely-used dynamic Smagorinsky model (DSM) has a singular dynamic model constant if it is used without averaging. The singularity can cause exceedingly large local values of the dynamic model constant. If these large values are not mitigated by application of averaging, they can amplify discretization errors and impair the stability of simulations. To improve the local applicability of the DSM, the singularity is removed by replacing the resolved rate-of-strain tensors in the Smagorinsky model with the resolved velocity gradient tensor. These replacements result in the new dynamic gradient Smagorinsky model (DGSM). Results of simulations of three canonical turbulent flows (decaying homogeneous isotropic turbulence, a temporal mixing layer, and turbulent channel flow) are presented to demonstrate the potential of this model. The DGSM provides improved stability compared to the local DSM, and does not require averaging for stability at time step sizes that are typically used for a static locally consistent LES model. Results obtained with the DGSM are generally as accurate as results obtained with the DSM, while the DGSM has lower computational complexity. Moreover, the DGSM is easy to implement and does not require any homogeneous direction in space or time. It is thus anticipated that the model has a high potential for the simulation of complex flows.