Improved wall-layer model for forced-convection environmental LES

  • V. Stocca
  • V. Armenio
  • K. R. Sreenivasan
Part of the ERCOFTAC Series book series (ERCO, volume 15)


In environmental flows where the Reynolds numbers are very high it is not possible to solve directly the near-wall region because of the still-limited computer power. Furthermore, the presence of irregular wall-roughness in practical high Reynolds flows would make the direct resolution of the near wall viscous layer somewhat useless. When using Large-eddy simulations (LES), many efforts have been made in the last decades to develop LES wall-layer models (LWM) designed to skip the resolution of the near-wall layer (Piomelli, 2008). One of the common approaches used both in channel flows and planetary boundary layers, is to derive the stress to be used as boundary condition by the assumption that the instantaneous near-wall velocity belongs to a logarithmic profile (see among the others (Mason et al., 1986), (Temmerman et al., 2003)). We propose a modification to this kind of approach in which an analytically derived correction to the evaluation of the near wall Smagorinsky eddy viscosity and diffusivity is made. This correction, which is a simplified version of the two part model first proposed by (Schumann, 1975) and then adapted by (Sullivan et al., 1994) for planetary boundary layers, is computationally simple to implement and does not require homogeneity, thus being a good candidate to simulate complex-terrain configurations typical of environmental flows of practical interest. The quality of the resulting velocity prediction as well as of the temperature profiles is significantly improved. The proposed LWM is validated reproducing with a very coarse mesh a plane channel flow at two Reynolds numbers, Re =4000 and Re =20000 for a case of forced convection.


Planetary Boundary Layer Coarse Grid Eddy Viscosity Smagorinsky Model Passive Scalar Transport 
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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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.University of TriesteTriesteItaly

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