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Title
Machine learning and its application to reacting flows : ML and combustion / Nedunchezhian Swaminathan, Alessandro Parente, editors.
ISBN
9783031162480 (electronic bk.)
303116248X (electronic bk.)
9783031162473
3031162471
Published
Cham : Springer, 2023.
Language
English
Description
1 online resource (xi, 346 pages) : illustrations (some color).
Item Number
10.1007/978-3-031-16248-0 doi
Call Number
TJ254.5
Dewey Decimal Classification
541/.3610285631
Summary
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 body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world's total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and "greener" combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation. .
Note
Includes index.
Access Note
Open access.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 12, 2023).
Series
Lecture notes in energy ; 44. 2195-1292
Available in Other Form
Print version: 9783031162473
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.
