Wall-modeled large eddy simulation in the immersed boundary-lattice Boltzmann method

Author:

Wang Li1ORCID,Liu Zhengliang1ORCID,Jin Bruce Ruishu1ORCID,Huang Qiuxiang1ORCID,Young John1ORCID,Tian Fang-Bao1ORCID

Affiliation:

1. School of Engineering and Technology, University of New South Wales , Canberra ACT 2600, Australia

Abstract

This work presents the wall-modeled large eddy simulation (WMLES) in the immersed boundary-lattice Boltzmann method (IB-LBM). Here, the wall model with both diffusive- and sharp-interface immersed boundary methods (IBMs) is incorporated into the IB-LBM to handle the turbulent boundary layer in high Reynolds number turbulent flows. To maintain the numerical stability, two collision models, i.e., multiple-relaxation-time (MRT) and recursive regularized (RR), are implemented. The performance of these models in the WMLES is examined and compared in the simulation of internal and external flows by considering four benchmarks, i.e., turbulent flow in a channel, flow around a hull of submarine, flow around an Ahmed car model, and flow around a circular cylinder. It is found that a diffusive-interface IBM with wall model is capable to achieve excellent results for the simulation of external flows around bluff objects but fails in the simulation of internal flows of underestimating the wall shear stress due to its extra dissipation. The sharp-interface IBM with the wall model predicts the internal flow very well but fails in some simulations of external flow around bluff bodies due to the failure in the separation flow modeling. It is also found that the MRT-LBM is less dissipative than the RR-LBM, but it generates spurious nonphysical noise in the turbulent flows and tends to be unstable at high Reynolds numbers. Therefore, the diffusive-interface IBM with the wall model is more suitable for the external turbulent flow modeling, while its sharp-interface counterpart is more suitable for the internal turbulent flow modeling. The RR-LBM outperforms the MRT-LBM for the better stability and less nonphysical noise.

Funder

National Computational Infrastructure

Australian Research Council

Publisher

AIP Publishing

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