A Rapid Switch from RANS to WMLES for Spatially Developing Boundary Layers

  • R. LaraufieEmail author
  • S. Deck
  • P. Sagaut
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 117)


The present paper aims to provide an efficient and flexible solution to carry out RANS to WMLES transitions, when near wall turbulent flows are involved, in a Zonal Detached Eddy Simulation (ZDES) context. Indeed, WMLES generally suffer from very long transient states. The main purpose is to broaden DES-type method range of applications to cases where the overall flow physics is driven by the near wall turbulence, while being affordable in an industrial context. Among other, shock/boundary layer interactions and shallow recirculation bubles are the main targeted applications.

The solution proposed in this paper consists in resorting to the dynamic forcing method recently proposed by the authors (R. Laraufie, S. Deck, P. Sagaut. A dynamic forcing method for unsteady turbulent inflow conditions. Journal of Computational Physics 230(23), 8647-8663 (2011)), combined with a ZDES resolution method and a synthetic turbulence generation approach. The dynamic forcing method, firstly employed with the Synthetic Eddy Method (SEM) achieves dramatic transition distance shortening (~65 % in the present case).

Furthermore, the ability of the dynamic forcing method to regenerate a turbulent boundary layer from one of the most simple turbulence generation method, namely random noise, is also demonstrated. It is then shown that results similar to those obtained when the SEM turbulent inflow is used, can be achieved with a simple white noise at the inlet, when the dynamic forcing method is employed. Such flexibility is expected to make one able to use the dynamic forcing method with whatever synthetic turbulent generation method.


Turbulent Boundary Layer Reynolds Shear Stress Momentum Thickness Wall Turbulence Turbulence Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.The French Aerospace LabONERAMeudonFrance
  2. 2.Institut Jean Le Rond d’Alembert, UMR 7190Université Pierre et Marie Curie - Paris 6Paris Cedex 5France

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