Journal ArticleDOI
Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods
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TLDR
A purely data-driven modelling approach using deep convolutional neural networks is discussed in the context of Large Eddy Simulation (LES) of turbulent premixed flames, demonstrating with success for both the sub-grid scale progress variable variance and the filtered reaction rate.Abstract:
A purely data-driven modelling approach using deep convolutional neural networks is discussed in the context of Large Eddy Simulation (LES) of turbulent premixed flames. The assessment of the method is conducted a priori using direct numerical simulation data. The network has been trained to perform deconvolution on the filtered density and the filtered density-progress variable product, and by doing so obtain estimates of the un-filtered progress variable field. A filtered function of the progress variable can then be approximated on the LES mesh using the deconvoluted field. This new strategy for tackling turbulent combustion modelling is demonstrated with success for both the sub-grid scale progress variable variance and the filtered reaction rate, using flamelet methods, two fundamental ingredients of premixed turbulent combustion modelling.read more
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Journal ArticleDOI
Application of dense neural networks for manifold-based modeling of flame-wall interactions
Julian Bissantz,Jeremy Karpowski,M. Steinhausen,Yujuan Luo,F. Ferraro,Arne Scholtissek,Christian Hasse,Luc Vervisch +7 more
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Criteria to switch from tabulation to neural networks in computational combustion
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Book ChapterDOI
From discrete and iterative deconvolution operators to machine learning for premixed turbulent combustion modeling.
TL;DR: The analysis confirms the potential of deconvolution to approximate the unclosed non-linear terms and the SGS fluxes and the introduction of machine learning in turbulent combustion modeling is illustrated in the context of convolutional neural networks.
Book ChapterDOI
Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling
TL;DR: In this paper , a deep convolutional neural network called a U-Net is trained to predict the total flame surface density from the resolved progress variable, and the network outperforms classical dynamic models.
Journal ArticleDOI
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
TL;DR: In this article, the authors investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings, which utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses.
References
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