Ensemble data assimilation-based mixed subgrid-scale model for large-eddy simulations

Wang，Yunpeng1,2,3; Yuan，Zelong1,2,3; Wang，Jianchun1,2,3

2023-08-01

10.1063/5.0160482

Physics of Fluids   Impact Factor & Quartile

1070-6631

1089-7666

An ensemble Kalman filter (EnKF)-based mixed model (EnKF-MM) is proposed for the subgrid-scale (SGS) closure in the large-eddy simulation (LES) of turbulence. The model coefficients are determined through the EnKF-based data assimilation technique. The direct numerical simulation (DNS) results are filtered to obtain the benchmark data for the LES. Reconstructing the correct kinetic energy spectrum of the filtered DNS (fDNS) data has been adopted as the target for the EnKF to optimize the coefficient of the functional part in the mixed model. The proposed EnKF-MM framework is subsequently tested in the LES of both the incompressible homogeneous isotropic turbulence and turbulent mixing layer. The performance of the LES is comprehensively examined through the predictions of the flow statistics including the velocity spectrum, the probability density functions (PDFs) of the SGS stress, the PDF of the strain rate, and the PDF of the SGS energy flux. The structure functions, the evolution of turbulent kinetic energy, the mean flow, the Reynolds stress profile, and the iso-surface of the Q-criterion are also examined to evaluate the spatial-temporal predictions by different SGS models. The results of the EnKF-MM framework are consistently more satisfying compared to the traditional SGS models, including the dynamic Smagorinsky model, the dynamic mixed model, and the velocity gradient model, demonstrating its great potential in the optimization of SGS models for the LES of turbulence.

SCI

English

First ; Corresponding

National Natural Science Foundation of China["91952104","92052301","12172161","91752201"] ; NSFC Basic Science Center Program[11988102] ; Shenzhen Science and Technology Program[KQTD20180411143441009] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0103] ; Department of Science and Technology of Guangdong Province[2020B1212030001]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:001074432500008

AIP Publishing

PHYSICS

2-s2.0-85167362803

Scopus

1.National Center for Applied Mathematics Shenzhen (NCAMS),Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications,Southern University of Science and Technology,Shenzhen,518055,China

Wang，Yunpeng,Yuan，Zelong,Wang，Jianchun. Ensemble data assimilation-based mixed subgrid-scale model for large-eddy simulations[J]. Physics of Fluids,2023,35(8).

Wang，Yunpeng,Yuan，Zelong,&Wang，Jianchun.(2023).Ensemble data assimilation-based mixed subgrid-scale model for large-eddy simulations.Physics of Fluids,35(8).

Wang，Yunpeng,et al."Ensemble data assimilation-based mixed subgrid-scale model for large-eddy simulations".Physics of Fluids 35.8(2023).