Large-eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid-scale model
We apply a machine-learned subgrid-scale model to large-eddy simulations (LES) of heavy particles in isotropic turbulence with different Stokes numbers. The data-driven model, originally developed for high Reynolds number isotropic turbulent flows based on the gene expression programming (GEP) method, has explicit model equations and is for the first time tested in multiphase problems. The performance of the GEP model has been investigated in detail, focusing on the particle statistics including particle acceleration, velocity, and clustering. Compared with the commonly used dynamic Smagorinsky model, the GEP model provides significantly improved predictions on the particle statistics with Stokes numbers varying from 0.01 to 20, showing satisfactory agreement with the results from direct numerical simulations. The reasons for the enhanced predictions of the GEP model are further discussed. As the GEP model is less dissipative and it introduces high-order terms closely related to vorticity distribution, the fine-scale structures usually missing in LES simulations can be better recovered, which are believed to be closely related to the intermittency of particle motion and also particle clustering.
National Natural Science Foundation of China[11988102,92152102,91752202] ; Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)[2022QNLM010201]
|WOS Research Area|
Mechanics ; Physics
Mechanics ; Physics, Fluids & Plasmas
|WOS Accession No|
|EI Accession Number|
Gene expression ; Reynolds number ; Turbulence
|ESI Classification Code|
Biology:461.9 ; Fluid Flow:631 ; Fluid Flow, General:631.1 ; Mathematics:921
|ESI Research Field|
Cited Times [WOS]:6
|Document Type||Journal Article|
|Department||Department of Mechanics and Aerospace Engineering|
1.State Key Laboratory for Turbulence and Complex Systems,College of Engineering,Peking University,Beijing,100871,China
2.HEDPS,Center for Applied Physics and Technology,College of Engineering,Peking University,Beijing,100871,China
3.Joint Laboratory of Marine Hydrodynamics and Ocean Engineering,Pilot National Laboratory for Marine Science and Technology,Shandong,Qingdao,266237,China
4.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Guangdong,Shenzhen,518055,China
Wu，Qi,Zhao，Yaomin,Shi，Yipeng,et al. Large-eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid-scale model[J]. PHYSICS OF FLUIDS,2022,34(6).
Wu，Qi,Zhao，Yaomin,Shi，Yipeng,&Chen，Shiyi.(2022).Large-eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid-scale model.PHYSICS OF FLUIDS,34(6).
Wu，Qi,et al."Large-eddy simulation of particle-laden isotropic turbulence using machine-learned subgrid-scale model".PHYSICS OF FLUIDS 34.6(2022).
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