Machine Learning and Its Application to Reacting Flows ML and Combustion /

Dades bibliogràfiques
Autor corporatiu:	SpringerLink (Online service)
Altres autors:	Swaminathan, Nedunchezhian (Editor), Parente, Alessandro (Editor)
Sumari:	XI, 346 p. 127 illus., 98 illus. in color. text
Idioma:	anglès
Publicat:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Edició:	1st ed. 2023.
Col·lecció:	Lecture Notes in Energy, 44
Matèries:	Cogeneration of electric power and heat. Fossil fuels. Thermodynamics. Heat engineering. Heat transfer. Mass transfer. Machine learning. Fossil Fuel. Engineering Thermodynamics, Heat and Mass Transfer. Machine Learning.
Accés en línia:	https://doi.org/10.1007/978-3-031-16248-0
Format:	Electrònic Llibre

Taula de continguts:

Introduction
ML Algorithms, Techniques and their Application to Reactive Molecular Dynamics Simulations
Big Data Analysis, Analytics & ML role
ML for SGS Turbulence (including scalar flux) Closures
ML for Combustion Chemistry
Applying CNNs to model SGS flame wrinkling in thickened flame LES (TFLES)
Machine Learning Strategy for Subgrid Modelling of Turbulent Combustion using Linear Eddy Mixing based Tabulation
MILD Combustion–Joint SGS FDF
Machine Learning for Principal Component Analysis & Transport
Super Resolution Neural Network for Turbulent non-premixed Combustion
ML in Thermoacoustics
Concluding Remarks & Outlook.

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