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

Aug 4, 2026

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Machine learning techniques are overviewed with a specific focus on combustion science and technology, linking statistical learning and data-driven inference to real engineering applications. The work frames ML as learned from collected data and prior targeted studies, with performance shaped by algorithm sophistication and available computational power. It motivates attention to combustion systems, noting combustion remains a dominant share of primary energy supply and requires updated analytical and predictive capabilities as renewable adoption grows.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages353

Reading Time14 h 50 min

AccessFree to download

Preface

Motivation and scope of machine learning

Combustion systems and energy relevance

Need for ML techniques in combustion technology

Lecture Notes in Energy 44 Nedunchezhian Swaminathan Alessandro Parente Editors Machine Learning and Its Application to Reacting Flows ML and Combustion Lecture Notes in Energy Volume 44 Lecture Notes in Energy (LNE) is a series that reports on new developments in the study of energy: from science and engineering to the analysis of energy policy. The series’scope includes but isnot limited to, renewable and green energy, nuclear, fossil fuels and carbon capture, energy systems, energy storage and harvesting, batteries and fuel cells, power systems, energy efﬁciency, energy in buildings, energy policy, as well as energy-related topics in economics, management and transportation. Books published in LNE are original and timely and bridge between advanced textbooks and the forefront ofresearch. Readers ofLNE include postgraduate students and nonspecialist researchers wishing to gain an accessible introduction to a ﬁeld of research as well as professionals and researchers with a need for an up-to-date reference book on a well-deﬁned topic. The series publishes single-and multi-authored volumes as well as advanced textbooks. **Indexed in Scopus and EI Compendex** The Springer Energy board welcomes your book proposal. Please get in touch with the series via Anthony Doyle, Executive Editor, Springer ([anthony.doyle@springer.com](anthony.doyle@springer.com)) Nedunchezhian Swaminathan · Alessandro Parente Editors Machine Learning and Its Application to Reacting Flows ML and Combustion Editors Nedunchezhian Swaminathan Department of Engineering University of Cambridge Cambridge, UK Alessandro Parente Aero-Thermo-Mechanics Laboratory École polytechnique de Bruxelles Université Libre de Bruxelles Brussels, Belgium Brussels Institute for Thermal-ﬂuid Systems, Brussels (BRITE) Université Libre de Bruxelles and Vrije Universiteit Brussel Brussels, Belgium ISSN 2195-1284 ISSN 2195-1292 (electronic) Lecture Notes in Energy ISBN 978-3-031-16247-3 ISBN 978-3-031-16248-0 (eBook) [https://doi.org/10.1007/978-3-031-16248-0](https://doi.org/10.1007/978-3-031-16248-0) © The Editor(s) (if applicable) and The Author(s) 2023 . This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors orthe editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11

FAQs

What is the document’s main focus?

It focuses on machine learning techniques and their application to reacting flows, with a particular emphasis on ML methods for combustion science and technology.

How does the document characterize machine learning?

Machine learning is described as statistical inference using data collected and knowledge gained through past targeted studies or real-life experiences.

Why is combustion technology highlighted as an important application area?

Combustion systems and technologies account for the majority of the world’s primary energy supply, and replacing them would require major paradigm change, making updated analysis and predictive approaches timely.

Aug 4, 2026

Upload by Jiven