Machine Learning and Its Application to Reacting Flows: ML and Combustion: 44 (Lecture Notes in Energy) - Tapa blanda
Libro 79 de 81: Lecture Notes in Energy
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
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.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
Otras ediciones populares con el mismo título
Resultados de la búsqueda para Machine Learning and Its Application to Reacting Flows:...
Imagen de archivo
Machine Learning and Its Application to Reacting Flows : ML and Combustion
Librería: Chiron Media, Wallingford, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
PF. Condición: New. Nº de ref. del artículo: 6666-IUK-9783031162503
Comprar nuevo
EUR 23,01
Envío por EUR 17,79
Se envía de Reino Unido a Estados Unidos de America
Se envía de Reino Unido a Estados Unidos de America
Cantidad disponible: 10 disponibles
Imagen del vendedor
Machine Learning and Its Application to Reacting Flows
Publicado por
Springer International Publishing AG, CH, 2023
ISBN 10: 3031162501
ISBN 13: 9783031162503
Nuevo
Paperback
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 1st ed. 2023. 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. Nº de ref. del artículo: LU-9783031162503
Comprar nuevo
EUR 41,84
Gastos de envío gratis
Se envía de Reino Unido a Estados Unidos de America
Se envía de Reino Unido a Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Machine Learning and its Application to (eng)
Impresión bajo demandaLibrería: Brook Bookstore On Demand, Napoli, NA, Italia
Calificación del vendedor: 4 de 5 estrellas
Condición: new. Questo è un articolo print on demand. Nº de ref. del artículo: BIEMWKKOGS
Comprar nuevo
EUR 38,22
Envío por EUR 6,80
Se envía de Italia a Estados Unidos de America
Se envía de Italia a Estados Unidos de America
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Machine Learning and Its Application to Reacting Flows: ML and Combustion (Lecture Notes in Energy)
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. 1st ed. 2023 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26396296083
Comprar nuevo
EUR 53,96
Envío por EUR 3,39
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Machine Learning and Its Application to Reacting Flows: ML and Combustion (Lecture Notes in Energy)
Librería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Nº de ref. del artículo: 401162316
Comprar nuevo
EUR 51,55
Envío por EUR 7,47
Se envía de Reino Unido a Estados Unidos de America
Se envía de Reino Unido a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen de archivo
Machine Learning and Its Application to Reacting Flows: ML and Combustion (Lecture Notes in Energy)
Librería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Nº de ref. del artículo: 18396296089
Comprar nuevo
EUR 52,51
Envío por EUR 9,95
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: 1 disponibles
Imagen del vendedor
Machine Learning and Its Application to Reacting Flows
Publicado por
Springer International Publishing Jan 2023, 2023
ISBN 10: 3031162501
ISBN 13: 9783031162503
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 360 pp. Englisch. Nº de ref. del artículo: 9783031162503
Comprar nuevo
EUR 42,79
Envío por EUR 23,00
Se envía de Alemania a Estados Unidos de America
Se envía de Alemania a Estados Unidos de America
Cantidad disponible: 2 disponibles
Imagen de archivo
Machine Learning And Its Application To
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: V9783031162503
Comprar nuevo
EUR 64,20
Envío por EUR 10,50
Se envía de Irlanda a Estados Unidos de America
Se envía de Irlanda a Estados Unidos de America
Cantidad disponible: 15 disponibles
Imagen de archivo
Machine Learning and Its Application to Reacting Flows: Ml and Combustion
Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Brand New. 357 pages. 9.25x6.10x0.75 inches. In Stock. Nº de ref. del artículo: x-3031162501
Comprar nuevo
EUR 72,20
Envío por EUR 11,49
Se envía de Reino Unido a Estados Unidos de America
Se envía de Reino Unido a Estados Unidos de America
Cantidad disponible: 2 disponibles
Imagen de archivo
Machine Learning And Its Application To
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: V9783031162503
Comprar nuevo
EUR 78,63
Envío por EUR 8,92
Se envía dentro de Estados Unidos de America
Se envía dentro de Estados Unidos de America
Cantidad disponible: 15 disponibles