Overview
- 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)
Buy print copy
Tax calculation will be finalised at checkout
Table of contents (11 chapters)
-
Front Matter
-
Back Matter
Keywords
- Machine Learning
- Combustion Simulations
- Combustion Modelling
- Big Data Analysis
- Dimensionality reduction
- Reduced-order modelling
- Neural Networks
- Turbulent Combustion
- Physics-based modelling
- Data-driven modelling
- Deep learning
- Thermoacoustics and its modelling
- Reactive molecular dynamics
- Simulations of reacting flows
- Open Access
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
About the editors
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