A Low Reynolds Number Partially-Averaged Navier-Stokes Model for Turbulence
Journal article, 2011

A low Reynolds number (LRN) formulation based on the Partially Averaged Navier-Stokes (PANS) modelling method is presented, which incorporates improved asymptotic representation in near-wall turbulence modelling. The effect of near-wall viscous damping can thus be better accounted for in simulations of wall-bounded turbulent flows. The proposed LRN PANS model uses an LRN k-epsilon model as the base model and introduces directly its model functions into the PANS formulation. As a result, the inappropriate wall-limiting behavior inherent in the original PANS model is corrected. An interesting feature of the PANS model is that the turbulent Prandtl numbers in the k and epsilon equations are modified compared to the base model. It is found that this modification has a significant effect on the modelled turbulence. The proposed LRN PANS model is scrutinized in computations of decaying grid turbulence, turbulent channel flow and periodic hill flow, of which the latter has been computed at two different Reynolds numbers of Re = 10,600 and 37,000. In comparison with available DNS, LES or experimental data, the LRN PANS model produces improved predictions over the standard PANS model, particularly in the near-wall region and for resolved turbulence statistics. Furthermore, the LRN PANS model gives similar or better results - at a reduced CPU time - as compared to the Dynamic Smagorinsky model.

Near-wall behavior

Turbulent flow

Low Reynolds number model

Author

Jiamei Ma

Chalmers, Applied Mechanics, Fluid Dynamics

Peng Shia-Hui

Chalmers, Applied Mechanics, Fluid Dynamics

Lars Davidson

Chalmers, Applied Mechanics, Fluid Dynamics

F. Wang

International Journal of Heat and Fluid Flow

0142-727X (ISSN)

Vol. 32 3 652-669

Driving Forces

Sustainable development

Areas of Advance

Transport

Roots

Basic sciences

Subject Categories

Fluid Mechanics and Acoustics

DOI

10.1016/j.ijheatfluidflow.2011.02.001

More information

Created

10/7/2017