A Priori Sub-grid Modelling Using Artificial Neural Networks. Prat, A., Sautory, T., & Navarro-Martinez, S. International Journal of Computational Fluid Dynamics, 34(6):397-417, Taylor and Francis Ltd., 4, 2020. ![A Priori Sub-grid Modelling Using Artificial Neural Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 6 downloads 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.
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title = {A Priori Sub-grid Modelling Using Artificial Neural Networks},
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abstract = {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.},
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author = {Prat, Alvaro and Sautory, Theophile and Navarro-Martinez, S},
doi = {10.1080/10618562.2020.1789116},
journal = {International Journal of Computational Fluid Dynamics},
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