Subgrid-scale modelling using deconvolutional artificial neural networks in large eddy simulations of chemically reacting compressible turbulence

Teng，Jian1,2,3; Yuan，Zelong1,2,3; Wang，Jianchun1,2,3

2022-08-01

10.1016/j.ijheatfluidflow.2022.109000

INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW   影响因子和分区

0142-727X

1879-2278

Based on the framework of deconvolutional artificial neural network (DANN) proposed by Yuan et al.[DOI:https://doi.org/10.1063/5.0027146], we extend the DANN approach to model subgrid-scale (SGS) terms in large eddy simulation (LES) of chemically reacting compressible turbulent flow with evident heat release. In constructing the DANN, the normalized density-weighted filtered variables in the neighbouring stencils are taken as the inputs while the outputs are unfiltered density-weighted variables. The SGS stress, SGS heat flux, SGS scalar flux and chemical reaction source terms are modelled using those unfiltered variables which are recovered by the DANN framework to close the governing equations. The DANN framework is evaluated by two chemically reacting compressible isotropic turbulent flow cases adopting a simple one-step irreversible chemical reaction mechanism at turbulent Mach numbers 0.4 and 0.8. In the a priori study, the DANN method shows better performance compared to the classical approximate-deconvolution method (ADM) and the velocity-gradient model (VGM). In the a posteriori test, the DANN method outperforms the dynamic-Smagorinsky model (DSM), and the dynamic-mixed model (DMM). In addition, the DANN framework can predict flow variables with high accuracy by using limited training samples which are constructed from any single instantaneous flow data during the reaction process.

Artificial neural network Chemically reacting turbulence Compressible turbulent combustion Large-eddy simulation Subgrid-scale model

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China (NSFC)[91952104,92052301,12172161,12161141017,91752201] ; 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] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515110845] ; National Numerical Wind tunnel Project[NNW2019ZT1-A04]

Thermodynamics ; Engineering ; Mechanics

Thermodynamics ; Engineering, Mechanical ; Mechanics

WOS:000810178800003

ELSEVIER SCIENCE INC

20222312190188

Combustion ; Heat flux ; Large eddy simulation ; Turbulence ; Turbulent flow

Fluid Flow:631 ; Fluid Flow, General:631.1 ; Heat Transfer:641.2 ; Mathematics:921

ENGINEERING

2-s2.0-85131137423

Scopus

1.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou),Guangzhou,511458,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

Teng，Jian,Yuan，Zelong,Wang，Jianchun. Subgrid-scale modelling using deconvolutional artificial neural networks in large eddy simulations of chemically reacting compressible turbulence[J]. INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW,2022,96.

Teng，Jian,Yuan，Zelong,&Wang，Jianchun.(2022).Subgrid-scale modelling using deconvolutional artificial neural networks in large eddy simulations of chemically reacting compressible turbulence.INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW,96.

Teng，Jian,et al."Subgrid-scale modelling using deconvolutional artificial neural networks in large eddy simulations of chemically reacting compressible turbulence".INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW 96(2022).