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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 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.

1142044297
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 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.

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

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

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

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

Overview

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.

Product Details

ISBN-13: 9783031162503
Publisher: Springer International Publishing
Publication date: 01/02/2023
Series: Lecture Notes in Energy , #44
Edition description: 1st ed. 2023
Pages: 346
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Nedunchezhian Swaminathan is a Professor of Mechanical Engineering in Cambridge University, UK, and Fellow and Director of Studies in Robinson College, Cambridge. He is a Fellow of The Combustion Institute since 2018. Swaminathan holds visiting Professorships in many overseas Universities and consults to a number of industries in Transport and Energy Sectors. He has 25 years of research and teaching experiences in the fields of Combustion, Turbulence, Combustion Noise and Instabilities, and Simulations of Flows with Multi-physics occurring in engineering applications and geophysics.

Alessandro Parente is Professor of Thermodynamics, Fluid Mechanics and Combustion at the Aero-Thermo-Mechanical Department of Université Libre de Bruxelles, as well as director of the Combustion and Robust Optimisation research center (BURN, burn-research.be). In this capacity, he also serves as vice-president of the Belgian Section of the Combustion Institute. The research interests of Dr. Parente are in the field of turbulent/chemistry interaction in turbulent combustion and reduced-order models, non-conventional fuels and pollutant formation in combustion systems, novel combustion technologies, numerical simulation of atmospheric boundary layer flows, and validation and uncertainty quantification.

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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