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

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή:	SpringerLink (Online service)
Άλλοι συγγραφείς:	Swaminathan, Nedunchezhian (Επιμελητής έκδοσης), Parente, Alessandro (Επιμελητής έκδοσης)
Περίληψη:	XI, 346 p. 127 illus., 98 illus. in color. text
Γλώσσα:	Αγγλικά
Έκδοση:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Έκδοση:	1st ed. 2023.
Σειρά:	Lecture Notes in Energy, 44
Θέματα:	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.
Διαθέσιμο Online:	https://doi.org/10.1007/978-3-031-16248-0
Μορφή:	Ηλεκτρονική πηγή Βιβλίο

Πίνακας περιεχομένων:

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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