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A Priori Sub-grid Modelling Using Artificial Neural Networks
International Journal of Computational Fluid Dynamics ( IF 1.3 ) Pub Date : 2020-07-02 , DOI: 10.1080/10618562.2020.1789116
Alvaro Prat 1 , Theophile Sautory 1 , S. Navarro-Martinez 1
Affiliation  

This paper presents results of Artificial Neural Networks (ANN) applications to sub-grid Large Eddy Simulation (LES) model. The training data for the ANN is provided by simulation of Homogeneous Isotropic Turbulence at different Reynolds numbers. The results show that the correlation coefficients are superior to other sub-grid models, using a similar set of input variables. As the ANN model extrapolates to larger Reynolds, the correlation coefficient decreases. However, it remains higher than other sub-grid approaches, and suggest that the combined LES-ANN methodology can potentially be used as a sub-grid model at realistic Reynolds numbers. Models derived from Homogeneous Isotropic Turbulence can also be used in different simple flows and provide relatively good agreement.

中文翻译:

使用人工神经网络的先验子网格建模

本文介绍了人工神经网络 (ANN) 应用于子网格大涡模拟 (LES) 模型的结果。ANN 的训练数据是通过模拟不同雷诺数下的均匀各向同性湍流提供的。结果表明,相关系数优于其他子网格模型,使用类似的输入变量集。随着 ANN 模型外推到更大的雷诺数,相关系数会降低。然而,它仍然高于其他子网格方法,并表明组合 LES-ANN 方法可以潜在地用作现实雷诺数下的子网格模型。从均质各向同性湍流导出的模型也可用于不同的简单流动并提供相对较好的一致性。
更新日期:2020-07-02
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