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  • Open Access
  • © 2023

Machine Learning and Its Application to Reacting Flows

ML and Combustion

  • Contains the latest developments in machine learning methods (ML) for reacting flow applications
  • Includes machine learning algorithms
  • Points the way to future application of ML in new technologies
  • This book is open access, which means that you have free and unlimited access

Part of the book series: Lecture Notes in Energy (LNEN, volume 44)

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Softcover Book USD 49.99
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Hardcover Book USD 59.99
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  • Durable hardcover edition
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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xi
  2. Introduction

    • N. Swaminathan, A. Parente
    Pages 1-14Open Access
  3. Machine Learning Techniques in Reactive Atomistic Simulations

    • H. Aktulga, V. Ravindra, A. Grama, S. Pandit
    Pages 15-52Open Access
  4. A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event Detection

    • T. M. Shead, I. K. Tezaur, W. L. Davis IV, M. L. Carlson, D. M. Dunlavy, E. J. Parish et al.
    Pages 53-87Open Access
  5. Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation

    • Z. M. Nikolaou, Y. Minamoto, C. Chrysostomou, L. Vervisch
    Pages 89-116Open Access
  6. Machine Learning for Combustion Chemistry

    • T. Echekki, A. Farooq, M. Ihme, S. M. Sarathy
    Pages 117-147Open Access
  7. Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches

    • K. Zdybał, M. R. Malik, A. Coussement, J. C. Sutherland, A. Parente
    Pages 245-278Open Access
  8. Machine Learning for Thermoacoustics

    • Matthew P. Juniper
    Pages 307-337Open Access
  9. Back Matter

    Pages 339-346

About this book

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.  

Editors and Affiliations

  • Department of Engineering, University of Cambridge, Cambridge, UK

    Nedunchezhian Swaminathan

  • Aero-Thermo-Mechanics Laboratory, École polytechnique de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium

    Alessandro Parente

About the editors

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.  



Bibliographic Information

  • Book Title: Machine Learning and Its Application to Reacting Flows

  • Book Subtitle: ML and Combustion

  • Editors: Nedunchezhian Swaminathan, Alessandro Parente

  • Series Title: Lecture Notes in Energy

  • DOI: https://doi.org/10.1007/978-3-031-16248-0

  • Publisher: Springer Cham

  • eBook Packages: Energy, Energy (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2023

  • Hardcover ISBN: 978-3-031-16247-3Published: 02 January 2023

  • Softcover ISBN: 978-3-031-16250-3Published: 02 January 2023

  • eBook ISBN: 978-3-031-16248-0Published: 01 January 2023

  • Series ISSN: 2195-1284

  • Series E-ISSN: 2195-1292

  • Edition Number: 1

  • Number of Pages: XI, 346

  • Number of Illustrations: 29 b/w illustrations, 98 illustrations in colour

  • Topics: Fossil Fuels (incl. Carbon Capture), Engineering Thermodynamics, Heat and Mass Transfer, Machine Learning, Thermodynamics

Buy it now

Buying options

Softcover Book USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access