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Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates

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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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
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)
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Deposited On:19 Mar 2019 14:08

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