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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Species reaction rate modelling based on physics-guided machine learning
TL;DR: In this paper , deep neural networks are applied to mean reaction rate modeling and two DNN structures, species-dependent (SD) and species-independent (SI), are considered, which can be used for any set of species appearing in the combustion.
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Modelling Subgrid-scale Scalar Dissipation Rate in Turbulent Premixed Flames using Gene Expression Programming and Deep Artificial Neural Networks
TL;DR: In this article , Gene Expression Programming (GEP) was used for training a model for subgrid scale (SGS) scalar dissipation rate (SDR) for a large range of filter widths, using a database of statistically planar turbulent premixed flames, featuring different turbulence intensities and heat release parameters.
Journal ArticleDOI
Generalization Capability of Convolutional Neural Networks for Progress Variable Variance and Reaction Rate Subgrid-Scale Modeling
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References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.