Penalized Wall Function Method for Turbulent Flow Modeling

Authors

DOI:

https://doi.org/10.14529/jsfi220406

Keywords:

turbulence modeling, wall function, wall-bounded compressible turbulent flow, volume penalization

Abstract

A novel penalized wall function method for simulations of wall-bounded compressible turbulent flows is proposed. The new approach is based on the Reynolds-averaged Navier–Stokes (RANS) equations to model the outer region of the turbulent boundary layer, while the inner part is approximated by the equilibrium wall function model. The differential formulation to match the external and the wall function solutions is reformulated in a form of the generalized characteristic-based volume penalization method to model the transfer of the shear stress from the outer region of the boundary layer to the wall and to impose the wall-stress boundary conditions on the RANS solution. The exchange location is specified implicitly through a localized source term in the boundary layer equation, written as a function of the normalized distance from the wall. The wall-stress condition is determined by solving an auxiliary equation for the wall-stress, ensuring the correct matching of the RANS and the wall function solutions at the exchange layer. The proposed method noticeably reduces the near-wall mesh resolution requirements without significant modification of the RANS solver and removes the ill-defined explicit matching procedure, commonly used by traditional wall function-based methods. The penalized wall function approach is implemented using the vertex-centered control volume method on unstructured computational grids. The effectiveness of the developed penalized wall function method is demonstrated for twodimensional bump-in-channel flow for the Spalart–Allmaras turbulence model.

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Published

2022-12-30

How to Cite

Zhdanova, N. S., & Vasilyev, O. V. (2022). Penalized Wall Function Method for Turbulent Flow Modeling. Supercomputing Frontiers and Innovations, 9(4), 55–68. https://doi.org/10.14529/jsfi220406

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