A Small-Eddy-Dissipation Mechanism for Developing Turbulence Models

TL;DR

This paper introduces a small-eddy-dissipation mechanism for turbulence modeling that improves accuracy over RANS and reduces computational cost compared to LES, through a new SED-ML model and analysis.

Contribution

The paper develops a novel turbulence model based on the SED mechanism, providing deeper understanding and better performance than traditional models.

Findings

SED-ML model aligns with the SED mechanism

Model achieves higher accuracy than RANS

Requires lower computational cost than LES

Abstract

Jin (Phys. Fluids, vol. 31, 2019, p. 125102) proposed a new turbulence simulation method which shows better performance than other classic turbulence models. It is composed of a small-eddy-dissipation mixing length (SED-ML) model for calculating the reference solution and a parameter extension method for correcting the solution. The mechanism of this method is more deeply analyzed in this study to find out how to develop a turbulence model with a high accuracy and a low computational cost. The turbulent channel flows with Re_tau=821 and 2003 and decaying homogenous and isotropic turbulence are simulated to demonstrate how the new turbulence simulation method works. The small-eddy-dissipation (SED) mechanism for developing turbulence models has been proposed through our analysis. According to this mechanism, the model solution is an asymptotic approximation of the exact solution of the Navier-Stokes equations. The modeling term introduces an artificial dissipation which dissipates small eddies. The purpose of turbulence modeling is to dissipate more small eddies without changing the statistical solution qualitatively. We expect more small eddies can be dissipated where the turbulence is stronger. This mechanism is different from RANS which approximates the Reynolds stresses and LES which approximates the sub-grid-scale (SGS) motions, while it interprets the physics of turbulence modeling more precisely. A modified mixing length with two damping functions is developed to identify the characteristic length of turbulence, leading to the SED-ML model. The simulation accuracy can be further improved using a linear extension. Our numerical results show that the SED-ML model is in accordance with the SED mechanism. This might explain why the new method is more accurate than RANS while it requires a lower computational cost than LES.