GAMM Workshop
Numerical Methods

in Fluid Mechanics

Sept. 1999, Kirchzarten

Comparison of LES and RANS for Bluff-Body Wake Flows

Stefan Schmidt, Holger Lübcke
Technische Universität Berlin
Hermann-Föttinger-Institut für Ströungsmechanik
Fachgebiet: CFD
Prof. Dr.-Ing. F. Thiele

Diesen Forschungsbericht gibt es derzeit nur auf Englisch.

Introduction

In the last decade CFD has become a major tool in engineering. Due to the progress in computer technology CFD seems now able to deal with industrial applications at moderate costs and turnaround times. The future relevance of CFD will therefore depend on how accurate complex flows can be calculated. Since many flows of engineering interest are turbulent, the appropriate treatment of turbulence will be crucial to the success of CFD.

Here we compare the two principal techniques for the numerical simulation of turbulent flows - LES and RANS. This comparison focuses on bluff-body flows, that often occur in engineering application, i.e. the flow around a car, Fig. 1. This flow involves strong streamline curvature, separation and often vortex shedding. Such phenomena are directly influenced by turbulence and therefore any method which does not account for turbulence in a correct manner is most likely to fail.

votices in flow around a car, 16k download
Fig. 1 votices in flow around a car (v=100 km/h, Re=7*106)

Numerical Methods

The flow field of a newtonian fluid is fully described by the Navier-Stokes equation. However, turbulent flows contain small fluctuations. The resolution of such small motions requires fine grids and time steps, such that a direct simulation becomes unfeasible for high Reynolds numbers.

Using RANS, the computational costs can be reduced by solving the statistically averaged equation system, which requires closure assumptions for the higher moments.

LES aims to reduce the dependence on the turbulence model, hence the major portion of the flow is simulated without any models, and must be resolved by the grid. Only scales smaller than the resolution of the grid need a model. Since the large scales captured without contraints, a LES will be unsteady and 3D and consequently computationally more demanding than RANS.

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