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From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling

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Data Analysis for Direct Numerical Simulations of Turbulent Combustion

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Abstract

Following the rapid and continuous progress of computing power, allowing for increasing the mesh resolution in large eddy simulation (LES), new modeling strategies appear which are based on a direct treatment of the now well resolved, but still not fully resolved scalar signals. Along this line, deconvolution or inverse filtering, either based on discrete or iterative operators, is first discussed. Recent results obtained from a direct numerical simulation (DNS) database and LES of a premixed turbulent jet flame are presented. The analysis confirms the potential of deconvolution to approximate the unclosed non-linear terms and the SGS fluxes. Then, the introduction of machine learning in turbulent combustion modeling is illustrated in the context of convolutional neural networks.

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Notes

  1. 1.

    A resolution of 50 \(\upmu \)m would be necessary to fully resolve the flame (i.e., DNS) with tabulated chemistry and between 10 \(\upmu \)m and 80 \(\upmu \)m to resolve the Kolmogorov scale.

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Authors and Affiliations

  1. CORIA – CNRS, Normandie Université INSA de Rouen, 76801, Saint-Etienne-du-Rouvray, France

    P. Domingo, A. Seltz & L. Vervisch

  2. Safran Aircraft Engines Site de Villaroche Rond-Point René Ravaud-Réau, 77550, Moissy Cramayel, France

    A. Seltz

  3. Computation-based Science and Technology Research Centre (CaSToRC), The Cyprus Institute, 2121, Nicosia, Cyprus

    Z. Nikolaou

Authors
  1. P. Domingo
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  2. Z. Nikolaou
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  3. A. Seltz
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  4. L. Vervisch
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Correspondence to L. Vervisch .

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Editors and Affiliations

  1. Institute for Combustion Technology, RWTH Aachen University, Aachen, Germany

    Heinz Pitsch

  2. Institute for Combustion Technology, RWTH Aachen University, Aachen, Germany

    Antonio Attili

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Domingo, P., Nikolaou, Z., Seltz, A., Vervisch, L. (2020). From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling. In: Pitsch, H., Attili, A. (eds) Data Analysis for Direct Numerical Simulations of Turbulent Combustion. Springer, Cham. https://doi.org/10.1007/978-3-030-44718-2_11

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