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

Machine Learning and Its Application to Reacting Flows : : ML and Combustion / / edited by Nedunchezhian Swaminathan, Alessandro Parente.

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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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Bibliographic Details
Superior document:Lecture Notes in Energy, 44
:
Swaminathan, Nedunchezhian.
TeilnehmendeR:
Parente, Alessandro.
Place / Publishing House:Cham : : Springer International Publishing :, Imprint: Springer,, 2023.
Year of Publication:2023
Edition:1st ed. 2023.
Language:English
Series:Lecture Notes in Energy, 44
Physical Description:1 electronic resource (346 p.)
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Table of Contents:
  • 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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