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

Machine Learning and Its Application to Reacting Flows ML and Combustion

This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large bo...

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Detalles Bibliográficos
Autor principal: Swaminathan, Nedunchezhian (-)
Otros Autores: Parente, Alessandro
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cham : Springer International Publishing 2023.
Edición:1st ed. 2023.
Colección:Lecture Notes in Energy, 44
Materias:
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.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009720260106719
Tabla de Contenidos:
  • 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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