English

Mixed modeling for large-eddy simulation: the minimum-dissipation-bardina model

Fluid Dynamics 2018-07-02 v1

Abstract

The Navier-Stokes equations describe the motion of viscous fluids. In order to predict turbulent flows with reasonable computational time and accuracy, these equations are spatially filtered according to the large-eddy simulation (LES) approach. The current work applies a volume filtering procedure according to Schumann (1975). To demonstrate the procedure the Schumann filter is first applied to a convection-diffusion equation. The Schumann filter results in volume-averaged equations, which are not closed. To close these equations a model is introduced, which represents the interaction between the resolved scales and the small subgrid scales. Here, the anisotropic minimum-dissipation model of Rozema et al. (2015) is considered. The interpolation scheme necessary to evaluate the convective flux at the cell faces can be viewed as a second filter. Thus, the convection term of the filtered convection-diffusion equation can be interpreted as a double-filtered term. This term is approximated by the scale similarity model of Bardina et al. (1983). Thus, a mixed minimum-dissipation-Bardina model is obtained. Secondly, the mathematical methodology is extended to the Navier-Stokes equations. Here, the pressure term is analyzed separately and added to the convection-diffusion equation as a sink term. Finally, spatially filtered Navier-Stokes equations that depend on both the anisotropic minimum-dissipation (AMD) model proposed by Rozema et al. (2015) and the scale similarity model of Bardina et al. (1983) are obtained. Hence, a mathematically consistent method of mixing the AMD model and the Bardina model is achieved.

Keywords

Cite

@article{arxiv.1806.11317,
  title  = {Mixed modeling for large-eddy simulation: the minimum-dissipation-bardina model},
  author = {Larissa B. Streher and Maurits H. Silvis and Roel Verstappen},
  journal= {arXiv preprint arXiv:1806.11317},
  year   = {2018}
}

Comments

Conference Paper - ECFD 7

R2 v1 2026-06-23T02:45:47.544Z