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Machine learning and its application...

Swaminathan, Nedunchezhian.

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Machine learning and its application to reacting flows = ml and combustion /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and its application to reacting flows/ edited by Nedunchezhian Swaminathan, Alessandro Parente.
Reminder of title:	ml and combustion /
other author:	Swaminathan, Nedunchezhian.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xi, 346 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Combustion - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-16248-0
ISBN:	9783031162480

Machine learning and its application to reacting flows = ml and combustion /
Machine learning and its application to reacting flowsml and combustion /[electronic resource] :edited by Nedunchezhian Swaminathan, Alessandro Parente. - Cham :Springer International Publishing :2023. - xi, 346 p. :ill. (some col.), digital ;24 cm. - Lecture notes in energy,v. 442195-1292 ;. - Lecture notes in energy ;v. 44..

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.

Open access.

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.

ISBN: 9783031162480

Standard No.: 10.1007/978-3-031-16248-0doiSubjects--Topical Terms:

3625656
Combustion
--Data processing.

LC Class. No.: TJ254.5 / .M33 2023

Dewey Class. No.: 541.3610285631

Machine learning and its application to reacting flows = ml and combustion /
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245 0 0 $a Machine learning and its application to reacting flows $h [electronic resource] : $b ml and combustion / $c edited by Nedunchezhian Swaminathan, Alessandro Parente.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xi, 346 p. : $b ill. (some col.), digital ; $c 24 cm.
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505 0 $a 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.
506 $a Open access.
520 $a 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.
650 0 $a Combustion $x Data processing. $3 3625656
650 0 $a Machine learning. $3 533906
650 0 $a Turbulence $x Data processing. $3 818242
650 1 4 $a Fossil Fuel. $3 3538641
650 2 4 $a Engineering Thermodynamics, Heat and Mass Transfer. $3 1002079
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Thermodynamics. $3 517304
700 1 $a Swaminathan, Nedunchezhian. $3 3625653
700 1 $a Parente, Alessandro. $3 3625654
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830 0 $a Lecture notes in energy ; $v v. 44. $3 3625655
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950 $a Energy (SpringerNature-40367)