Brändle de Motta, Jorge César and Costa, Pedro and Derksen, Jos J. and Peng, Cheng and Wang, Lian-Ping and Breugem, Wim-Paul and Estivalèzes, Jean-Luc
and Vincent, Stéphane and Climent, Éric
and Fede, Pascal
and Barbaresco, Pierrette and Renon, Nicolas
Assessment of numerical methods for fully resolved simulations of particle-laden turbulent flows.
(2019)
Computers and Fluids, 179. 1-14. ISSN 0045-7930
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(Document in English)
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Official URL: https://doi.org/10.1016/j.compfluid.2018.10.016
Abstract
During the last decade, many approaches for resolved-particle simulation (RPS) have been developed for numerical studies of finite-size particle-laden turbulent flows. In this paper, three RPS approaches are compared for a particle-laden decaying turbulence case. These methods are, the Volume-of-Fluid La- grangian method, based on the viscosity penalty method (VoF-Lag); a direct forcing Immersed Bound- ary Method, based on a regularized delta function approach for the fluid/solid coupling (IBM); and the Bounce Back scheme developed for Lattice Boltzmann method (LBM-BB). The physics and the numerical performances of the methods are analyzed. Modulation of turbulence is observed for all the methods, with a faster decay of turbulent kinetic energy compared to the single-phase case. Lagrangian particle statistics, such as the velocity probability density function and the velocity autocorrelation function, show minor differences among the three methods. However, major differences between the codes are observed in the evolution of the particle kinetic energy. These differences are related to the treatment of the ini- tial condition when the particles are inserted in an initially single-phase turbulence. The averaged par- ticle/fluid slip velocity is also analyzed, showing similar behavior as compared to the results referred in the literature. The computational performances of the different methods differ significantly. The VoF-Lag method appears to be computationally most expensive. Indeed, this method is not adapted to turbulent cases. The IBM and LBM-BB implementations show very good scaling.
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