Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence

Yu, Changping1,2; Yuan, Zelong3; Qi, Han1,2; Wang, Jianchun3; Li, Xinliang1,2; Chen, Shiyi3,4

2021-12-03

10.1017/jfm.2021.1012

JOURNAL OF FLUID MECHANICS   影响因子和分区

0022-1120

1469-7645

Kinetic energy flux (KEF) is an important physical quantity that characterizes cascades of kinetic energy in turbulent flows. In large-eddy simulation (LES), it is crucial for the subgrid-scale (SGS) model to accurately predict the KEF in turbulence. In this paper, we propose a new eddy-viscosity SGS model constrained by the properly modelled KEF for LES of compressible wall-bounded turbulence. The new methodology has the advantages of both accurate prediction of the KEF and strong numerical stability in LES. We can obtain an approximate KEF by the tensor-diffusivity model, which has a high correlation with the real value. Then, using the artificial neural network method, the local ratios between the real KEF and the approximate KEF are accurately modelled. Consequently, the SGS model can be improved by the product of that ratio and the approximate KEF. In LES of compressible turbulent channel flow, the new model can accurately predict mean velocity profile, turbulence intensities, Reynolds stress, temperature-velocity correlation, etc. Additionally, for the case of a compressible flat-plate boundary layer, the new model can accurately predict some key quantities, including the onset of transitions and transition peaks, the skin-friction coefficient, the mean velocity in the turbulence region, etc., and it can also predict the energy backscatters in turbulence. Furthermore, the proposed model also shows more advantages for coarser grids.

compressible turbulence turbulence modelling

SCI ; EI

英语

通讯

National Key Research and Development Program of China["2020YFA0711800","2019YFA0405302"] ; NSFC[12072349,91852203,91952104]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:000725678300001

CAMBRIDGE UNIV PRESS

20215111355359

Atmospheric thermodynamics ; Boundary layer flow ; Boundary layers ; Channel flow ; Forecasting ; Friction ; Kinetics ; Large eddy simulation ; Neural networks ; Reynolds number ; Turbulence models ; Turbulent flow

Atmospheric Properties:443.1 ; Fluid Flow:631 ; Fluid Flow, General:631.1 ; Thermodynamics:641.1 ; Mathematics:921 ; Classical Physics; Quantum Theory; Relativity:931

ENGINEERING

2-s2.0-85121205797

Web of Science

1.Chinese Acad Sci, Inst Mech, ILHD, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
3.Southern Univ Sci & Technol, Dept Mech & Aerosp Engn, Shenzhen 518055, Peoples R China
4.Peking Univ, Coll Engn, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China

Yu, Changping,Yuan, Zelong,Qi, Han,et al. Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence[J]. JOURNAL OF FLUID MECHANICS,2021,932.

Yu, Changping,Yuan, Zelong,Qi, Han,Wang, Jianchun,Li, Xinliang,&Chen, Shiyi.(2021).Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence.JOURNAL OF FLUID MECHANICS,932.

Yu, Changping,et al."Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence".JOURNAL OF FLUID MECHANICS 932(2021).