Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence

Xu，Dehao1; Wang，Jianchun2; Yu，Changping3; Chen，Shiyi1,2,4

2023-04-10

10.1017/jfm.2023.179

Journal of Fluid Mechanics   Impact Factor & Quartile

0022-1120

1469-7645

In this paper, we propose artificial-neural-network-based (ANN-based) nonlinear algebraic models for the large-eddy simulation (LES) of compressible wall-bounded turbulence. An innovative modification is applied to the invariants and the tensor bases of the nonlinear algebraic models through using the local grid widths along each direction to normalise the corresponding gradients of the flow variables. Furthermore, the dimensionless model coefficients are determined by the ANN method. The modified ANN-based nonlinear algebraic model (MANA model) has much higher correlation coefficients and much lower relative errors than the dynamic Smagorinsky model (DSM), Vreman model and wall-adapting local eddy-viscosity model in the a priori test. The significantly more accurate estimations of the mean subgrid-scale (SGS) fluxes of the kinetic energy and temperature variance are also obtained by the MANA models in the a priori test. Furthermore, in the a posteriori test, the MANA model can give much more accurate predictions of the flow statistics and the mean SGS fluxes of the kinetic energy and the temperature variance than other traditional eddy-viscosity models in compressible turbulent channel flows with untrained Reynolds numbers, Mach numbers and grid resolutions. The MANA model has a better performance in predicting the flow statistics in supersonic turbulent boundary layer. The MANA model can well predict both direct and inverse transfer of the kinetic energy and temperature variance, which overcomes the inherent shortcoming that the traditional eddy-viscosity models cannot predict the inverse energy transfer. Moreover, the MANA model is computationally more efficient than the DSM.

machine learning turbulence modelling

SCI

English

Corresponding

NSFC Basic Science Center Program[11988102] ; National Natural Science Foundation of China (NSFC)["91952104","92052301","12172161","91752201"] ; Technology and Innovation Commission of Shenzhen Municipality["KQTD20180411143441009","JCYJ20170412151759222"] ; Department of Science and Technology of Guangdong Province[2019B21203001]

Mechanics ; Physics

Mechanics ; Physics, Fluids & Plasmas

WOS:000960160100001

CAMBRIDGE UNIV PRESS

ENGINEERING

2-s2.0-85151509921

Scopus

1.State Key Laboratory of Turbulence and Complex Systems,College of Engineering,Peking University,Beijing,100871,China
2.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Laboratory of High Temperature Gas Dynamics,Institute of Mechanics,Chinese Academy of Sciences,Beijing,100190,China
4.Eastern Institute for Advanced Study,Ningbo,315200,China

Xu，Dehao,Wang，Jianchun,Yu，Changping,et al. Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence[J]. Journal of Fluid Mechanics,2023,960.

Xu，Dehao,Wang，Jianchun,Yu，Changping,&Chen，Shiyi.(2023).Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence.Journal of Fluid Mechanics,960.

Xu，Dehao,et al."Artificial-neural-network-based nonlinear algebraic models for large-eddy simulation of compressible wall-bounded turbulence".Journal of Fluid Mechanics 960(2023).