Lapeyre, Corentin J. and Misdariis, Antony and Cazard, Nicolas and Veynante, Denis and Poinsot, Thierry
Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates.
(2019)
Combustion and Flame, 203. 255-264. ISSN 0010-2180
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(Document in English)
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Official URL: https://doi.org/10.1016/j.combustflame.2019.02.019
Abstract
This work presents a new approach for premixed turbulent combustion modeling based on convolutional neural networks (CNN).1 We first propose a framework to reformulate the problem of subgrid flame surface density estimation as a machine learning task. Data needed to train the CNN is produced by direct numerical simulations (DNS) of a premixed turbulent flame stabilized in a slot-burner configuration. A CNN inspired from a U-Net architecture is designed and trained on the DNS fields to estimate subgrid-scale wrinkling. It is then tested on an unsteady turbulent flame where the mean inlet velocity is increased for a short time and the flame must react to a varying turbulent incoming flow. The CNN is found to efficiently extract the topological nature of the flame and predict subgrid-scale wrinkling, outperforming classical algebraic models.
Item Type: | Article |
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HAL Id: | hal-02072920 |
Audience (journal): | International peer-reviewed journal |
Uncontrolled Keywords: | |
Institution: | French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) Other partners > Université Paris-Saclay (FRANCE) Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE) Other partners > CentraleSupélec (FRANCE) Other partners > Centre Européen de Recherche et Formation Avancées en Calcul Scientifique - CERFACS (FRANCE) |
Laboratory name: | |
Statistics: | download |
Deposited On: | 19 Mar 2019 14:08 |
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