This study is concerned with accurately predicting the subgrid-scale (SGS) stress using an artificial neural network (ANN) with a linear eddy-viscosity term and a nonlinear term as the input variables. A priori and a posteriori tests are conducted to examine the prediction performance of the ANN-based SGS stress model in decaying homogeneous isotropic turbulence. In a priori test, the present ANN-based SGS model shows high correlation coefficients between the true and predicted SGS stresses, and excellent predictions of the SGS stress and dissipation. In a posteriori test, it is found that the ANN-based SGS model can predict the turbulence statistics more accurately than the traditional dynamic SGS models. The generalization capabilities of the model to untrained flow conditions and unstrained types of turbulent flow have been evaluated. It is found that the proposed ANN-based model can provide an accurate prediction of the SGS stress under different Reynolds numbers and flow types. A comparison among several existing ANN-based models with different input variables is presented, demonstrating a significant advantage of the present model.
Skip Nav Destination
,
,
,
,
Article navigation
July 2024
Research Article|
July 02 2024
Artificial neural-network-based subgrid-scale model for large-eddy simulation of isotropic turbulence
Lei Yang (杨磊)
;
Lei Yang (杨磊)
(Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Department of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology
, Beijing 100081, China
Search for other works by this author on:
Dong Li (李栋)
;
Dong Li (李栋)
a)
(Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing)
1
Department of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology
, Beijing 100081, China
2
Tangshan Research Institute, Beijing Institute of Technology
, Tangshan 063099, China
Search for other works by this author on:
Kai Zhang (张凯)
;
Kai Zhang (张凯)
(Funding acquisition, Supervision)
1
Department of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology
, Beijing 100081, China
2
Tangshan Research Institute, Beijing Institute of Technology
, Tangshan 063099, China
Search for other works by this author on:
Kun Luo (罗坤)
;
Kun Luo (罗坤)
(Supervision)
3
State Key Laboratory of Clean Energy Utilization, Zhejiang University
, Hangzhou 310027, China
Search for other works by this author on:
Jianren Fan (樊建人)
Jianren Fan (樊建人)
a)
(Supervision)
3
State Key Laboratory of Clean Energy Utilization, Zhejiang University
, Hangzhou 310027, China
Search for other works by this author on:
1
Department of Mechanics, School of Aerospace Engineering, Beijing Institute of Technology
, Beijing 100081, China
2
Tangshan Research Institute, Beijing Institute of Technology
, Tangshan 063099, China
3
State Key Laboratory of Clean Energy Utilization, Zhejiang University
, Hangzhou 310027, China
Physics of Fluids 36, 075113 (2024)
Article history
Received:
April 03 2024
Accepted:
June 12 2024
Citation
Lei Yang, Dong Li, Kai Zhang, Kun Luo, Jianren Fan; Artificial neural-network-based subgrid-scale model for large-eddy simulation of isotropic turbulence. Physics of Fluids 1 July 2024; 36 (7): 075113. https://doi.org/10.1063/5.0212096
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Pour-over coffee: Mixing by a water jet impinging on a granular bed with avalanche dynamics
Ernest Park, Margot Young, et al.
Foie gras pâté without force-feeding
Mathias Baechle, Arlete M. L. Marques, et al.
Chinese Academy of Science Journal Ranking System (2015–2023)
Cruz Y. Li (李雨桐), 李雨桐, et al.
Related Content
A neural-network-based mixed model of the subgrid-scale stress for large-eddy simulation of forced isotropic turbulence
Physics of Fluids (February 2025)
A stochastic extension of the explicit algebraic subgrid-scale models
Physics of Fluids (May 2014)
Physical consistency of subgrid-scale models for large-eddy simulation of incompressible turbulent flows
Physics of Fluids (January 2017)
Exploration of robust machine learning strategy for subgrid scale stress modeling
Physics of Fluids (January 2023)
Subgrid-scale model for large-eddy simulation of transition and turbulence in compressible flows
Physics of Fluids (December 2019)