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Article

Generalization Capability of Convolutional Neural Networks for Progress Variable Variance and Reaction Rate Subgrid-Scale Modeling

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CERFACS, 42 Avenue Gaspard Coriolis, 31057 Toulouse, France
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IMFT, Allée du Professeur Camille Soula, 31400 Toulouse, France
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Author to whom correspondence should be addressed.
Academic Editor: Pinaki Pal
Energies 2021, 14(16), 5096; https://doi.org/10.3390/en14165096
Received: 10 July 2021 / Revised: 6 August 2021 / Accepted: 10 August 2021 / Published: 18 August 2021
Deep learning has recently emerged as a successful approach to produce accurate subgrid-scale (SGS) models for Large Eddy Simulations (LES) in combustion. However, the ability of these models to generalize to configurations far from their training distribution is still mainly unexplored, thus impeding their application to practical configurations. In this work, a convolutional neural network (CNN) model for the progress-variable SGS variance field is trained on a canonical premixed turbulent flame and evaluated a priori on a significantly more complex slot burner jet flame. Despite the extensive differences between the two configurations, the CNN generalizes well and outperforms existing algebraic models. Conditions for this successful generalization are discussed, including the effect of the filter size and flame–turbulence interaction parameters. The CNN is then integrated into an analytical reaction rate closure relying on a single-step chemical source term formulation and a presumed beta PDF (probability density function) approach. The proposed closure is able to accurately recover filtered reaction rate values on both training and generalization flames. View Full-Text
Keywords: large eddy simulation; turbulent combustion; deep learning; convolutional neural network; progress variable variance; generalization large eddy simulation; turbulent combustion; deep learning; convolutional neural network; progress variable variance; generalization
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MDPI and ACS Style

Xing, V.; Lapeyre, C.; Jaravel, T.; Poinsot, T. Generalization Capability of Convolutional Neural Networks for Progress Variable Variance and Reaction Rate Subgrid-Scale Modeling. Energies 2021, 14, 5096. https://doi.org/10.3390/en14165096

AMA Style

Xing V, Lapeyre C, Jaravel T, Poinsot T. Generalization Capability of Convolutional Neural Networks for Progress Variable Variance and Reaction Rate Subgrid-Scale Modeling. Energies. 2021; 14(16):5096. https://doi.org/10.3390/en14165096

Chicago/Turabian Style

Xing, Victor, Corentin Lapeyre, Thomas Jaravel, and Thierry Poinsot. 2021. "Generalization Capability of Convolutional Neural Networks for Progress Variable Variance and Reaction Rate Subgrid-Scale Modeling" Energies 14, no. 16: 5096. https://doi.org/10.3390/en14165096

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