Subgrid-scale stress model for large-eddy simulation of turbulence using an artificial neural network

Lei Yang; Dong Li; Kai Zhang

doi:10.1117/12.3051348

18 November 2024 Subgrid-scale stress model for large-eddy simulation of turbulence using an artificial neural network

Lei Yang, Dong Li, Kai Zhang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 1340320 (2024) https://doi.org/10.1117/12.3051348
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Subgrid-scale (SGS) stress modeling based on filtered variables is one of the crucial scientific challenges in large-eddy simulation. With the rapid development of machine learning technologies in recent years, data-driven turbulence modeling methods have gained its popularity. In this study, an SGS stress model based on artificial neural network (ANN), with strain-rate tensor and modified Leonard tensor as inputs, is developed for incompressible isotropic homogeneous turbulence. The proposed ANN model demonstrates a substantial enhancement in the prediction of the SGS stress. Also, the ANN model could provide better predictions of turbulence statistics, as compared to the traditional models. It is suggested that the ANN methods exhibit obvious advantages and considerable potentials for the development of turbulence models with high accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lei Yang, Dong Li, and Kai Zhang "Subgrid-scale stress model for large-eddy simulation of turbulence using an artificial neural network", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 1340320 (18 November 2024); https://doi.org/10.1117/12.3051348

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KEYWORDS

Seaborgium

Artificial neural networks

Tunable filters

Turbulence

Data modeling

Backscatter

Education and training

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