Data-driven model development for large-eddy simulation of turbulence using gene-expression programing

Li, Haochen1,2; Zhao, Yaomin1,2,3; Wang, Jianchun4; Sandberg, Richard D.5

2021-12-01

10.1063/5.0076693

PHYSICS OF FLUIDS   影响因子和分区

1070-6631

1089-7666

We apply the gene-expression programing (GEP) method to develop subgrid-scale models for large-eddy simulations (LESs) of turbulence. The GEP model is trained based on Galilean invariants and tensor basis functions, and the training data are from direct numerical simulation (DNS) of incompressible isotropic turbulence. The model trained with GEP has been explicitly tested, showing that the GEP model can not only provide high correlation coefficients in a priori tests but also show great agreement with filtered DNS data when applied to LES. Compared to commonly used models like the dynamic Smagorinsky model and the dynamic mixed model, the GEP model provides significantly improved results on turbulence statistics and flow structures. Based on an analysis of the explicitly given model equation, the enhanced predictions are related to the fact that the GEP model is less dissipative and that it introduces high-order terms closely related to vorticity distribution. Furthermore, the GEP model with the explicit equation is straightforward to be applied in LESs, and its additional computational cost is substantially smaller than that of models trained with artificial neural networks with similar levels of predictive accuracies in a posteriori tests.

SCI ; EI

英语

其他

National Key R&D Program of China[2020YFE0204200] ; National Natural Science Foundation of China[92152102]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:000731946900010

AIP Publishing

20220111431233

Internet protocols ; Large eddy simulation ; Neural networks ; Turbulence

Biology:461.9 ; Fluid Flow:631 ; Data Communication, Equipment and Techniques:722.3 ; Computer Software, Data Handling and Applications:723 ; Mathematics:921

PHYSICS

Web of Science

1.Peking Univ, Ctr Appl Phys & Technol, HEDPS, Beijing 100871, Peoples R China
2.Peking Univ, Coll Engn, Beijing 100871, Peoples R China
3.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China
4.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
5.Univ Melbourne, Dept Mech Engn, Melbourne, Vic 3010, Australia

Li, Haochen,Zhao, Yaomin,Wang, Jianchun,et al. Data-driven model development for large-eddy simulation of turbulence using gene-expression programing[J]. PHYSICS OF FLUIDS,2021,33(12).

Li, Haochen,Zhao, Yaomin,Wang, Jianchun,&Sandberg, Richard D..(2021).Data-driven model development for large-eddy simulation of turbulence using gene-expression programing.PHYSICS OF FLUIDS,33(12).

Li, Haochen,et al."Data-driven model development for large-eddy simulation of turbulence using gene-expression programing".PHYSICS OF FLUIDS 33.12(2021).