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

P. Domingo (), Z. Nikolaou (), A. Seltz () and L. Vervisch ()
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P. Domingo: Normandie Université INSA de Rouen, CORIA – CNRS
Z. Nikolaou: The Cyprus Institute, Computation-based Science and Technology Research Centre (CaSToRC)
A. Seltz: Normandie Université INSA de Rouen, CORIA – CNRS
L. Vervisch: Normandie Université INSA de Rouen, CORIA – CNRS

Chapter Chapter 11 in Data Analysis for Direct Numerical Simulations of Turbulent Combustion, 2020, pp 215-232 from Springer

Abstract: 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.

Date: 2020
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DOI: 10.1007/978-3-030-44718-2_11

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