Journal ArticleDOI
Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Reaction-Diffusion Manifolds including differential diffusion applied to methane/air combustion in strong extinction regimes
TL;DR: In this paper , a 3D-REDIM with differential diffusion was used to capture the global behavior of flame F. The results showed that the upstream sections are well captured by the REDIM built for detailed transport, while the downstream sections are better captured by simplified transport database.
Journal ArticleDOI
Predictive Data-Driven Model Based on Generative Adversarial Network for Premixed Turbulence-Combustion Regimes
Temistocle Grenga,Ludovico Nista,Christoph Schumann,Amirhossein Karimi,Gandolfo Scialabba,Antonio Attili,Heinz Pitsch +6 more
TL;DR: The Generative Adversarial Network gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included, and cap-turing the interaction between heat release and turbulence closely to the DNS.
Proceedings ArticleDOI
Application of Artificial Neural Networks for the Simulation of a Perfectly Premixed Chemical Reactor
TL;DR: Performance benchmarks confirmed that the artificial neural network approaches are superior to direct source term integration in terms of computational costs and a priori comparison with direct integration proved the ability of both methods to give accurate results even for minor species.
Journal ArticleDOI
SGS Reaction rate modelling for MILD combustion based on machine-learning combustion mode classification: Development and a priori study
TL;DR: In this article , a neural network aided model is proposed for the filtered reaction rate in moderate or intense low-oxygen dilution (MILD) combustion, which is based on the partially stirred reactor (PaSR) approach, and the fraction of the reactive structure appearing in the PaSR is predicted using different NNs.
Journal ArticleDOI
Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based Tabulation
TL;DR: In this paper , the authors describe the use of machine learning algorithms with the linear-eddy mixing (LEM) based tabulation for modeling of subgrid turbulence-chemistry interaction.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Deep learning
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.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI
Deep learning in neural networks
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.