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

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

Bibliografske podrobnosti
Korporativna značnica: SpringerLink (Online service)
Drugi avtorji: Swaminathan, Nedunchezhian (Editor), Parente, Alessandro (Editor)
Izvleček:XI, 346 p. 127 illus., 98 illus. in color.
text
Jezik:angleščina
Izdano: Cham : Springer International Publishing : Imprint: Springer, 2023.
Izdaja:1st ed. 2023.
Serija:Lecture Notes in Energy, 44
Teme:
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.
Online dostop:https://doi.org/10.1007/978-3-031-16248-0
Format: Elektronski Knjiga
Kazalo:
  • 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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