A dynamic subgrid-scale modeling framework for large eddy simulation using approximate deconvolution

TL;DR

This paper introduces a dynamic subgrid-scale modeling framework for large eddy simulation that adaptively computes the Smagorinsky constant using approximate deconvolution, improving turbulence modeling flexibility.

Contribution

It presents a novel adaptive modeling approach for LES that self-adjusts the Smagorinsky constant based on resolved flow data.

Findings

The framework effectively models turbulence in Burgers turbulence simulations.

It demonstrates flexibility and viability for turbulence closure.

The method adapts the subgrid-scale parameters dynamically.

Abstract

We put forth a dynamic modeling framework for sub-grid parametrization of large eddy simulation of turbulent flows based upon the use of the approximate deconvolution procedure to compute the Smagorinsky constant self-adaptively from the resolved flow quantities. Our numerical assessments for solving the Burgers turbulence problem shows that the proposed approach could be used as a viable tool to address the turbulence closure problem due to its flexibility.