Machine learning for combustion

Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc...

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Autores principales:	Lei Zhou, Yuntong Song, Weiqi Ji, Haiqiao Wei
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Machine learning Data-driven Combustion modeling Combustion diagnostic Fuel Electrical engineering. Electronics. Nuclear engineering TK1-9971 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/e64eb739a6f146a4831b25162e95aa80
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id	oai:doaj.org-article:e64eb739a6f146a4831b25162e95aa80
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spelling	oai:doaj.org-article:e64eb739a6f146a4831b25162e95aa802021-12-04T04:36:05ZMachine learning for combustion2666-546810.1016/j.egyai.2021.100128https://doaj.org/article/e64eb739a6f146a4831b25162e95aa802022-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666546821000756https://doaj.org/toc/2666-5468Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc. The key to study combustion is to improve the combustion efficiency with minimum emission of pollutants. Machine learning facilitates data-driven techniques for handling large amounts of combustion data, either obtained through experiments or simulations under multiple spatiotemporal scales, thereby finding the hidden patterns underlying these data and promoting combustion research. This work presents an overview of studies on the applications of machine learning in combustion science fields over the past several decades. We introduce the fundamentals of machine learning and its usage in aiding chemical reactions, combustion modeling, combustion measurement, engine performance prediction and optimization, and fuel design. The opportunities and limitations of using machine learning in combustion studies are also discussed. This paper aims to provide readers with a portrait of what and how machine learning can be used in combustion research and to inspire researchers in their ongoing studies. Machine learning techniques are rapidly advancing in this era of big data, and there is high potential for exploring the combination between machine learning and combustion research and achieving remarkable results.Lei ZhouYuntong SongWeiqi JiHaiqiao WeiElsevierarticleMachine learningData-drivenCombustion modelingCombustion diagnosticFuelElectrical engineering. Electronics. Nuclear engineeringTK1-9971Computer softwareQA76.75-76.765ENEnergy and AI, Vol 7, Iss , Pp 100128- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Data-driven Combustion modeling Combustion diagnostic Fuel Electrical engineering. Electronics. Nuclear engineering TK1-9971 Computer software QA76.75-76.765
spellingShingle	Machine learning Data-driven Combustion modeling Combustion diagnostic Fuel Electrical engineering. Electronics. Nuclear engineering TK1-9971 Computer software QA76.75-76.765 Lei Zhou Yuntong Song Weiqi Ji Haiqiao Wei Machine learning for combustion
description	Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicles, heating houses, generating electricity, cooking food, etc. The key to study combustion is to improve the combustion efficiency with minimum emission of pollutants. Machine learning facilitates data-driven techniques for handling large amounts of combustion data, either obtained through experiments or simulations under multiple spatiotemporal scales, thereby finding the hidden patterns underlying these data and promoting combustion research. This work presents an overview of studies on the applications of machine learning in combustion science fields over the past several decades. We introduce the fundamentals of machine learning and its usage in aiding chemical reactions, combustion modeling, combustion measurement, engine performance prediction and optimization, and fuel design. The opportunities and limitations of using machine learning in combustion studies are also discussed. This paper aims to provide readers with a portrait of what and how machine learning can be used in combustion research and to inspire researchers in their ongoing studies. Machine learning techniques are rapidly advancing in this era of big data, and there is high potential for exploring the combination between machine learning and combustion research and achieving remarkable results.
format	article
author	Lei Zhou Yuntong Song Weiqi Ji Haiqiao Wei
author_facet	Lei Zhou Yuntong Song Weiqi Ji Haiqiao Wei
author_sort	Lei Zhou
title	Machine learning for combustion
title_short	Machine learning for combustion
title_full	Machine learning for combustion
title_fullStr	Machine learning for combustion
title_full_unstemmed	Machine learning for combustion
title_sort	machine learning for combustion
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/e64eb739a6f146a4831b25162e95aa80
work_keys_str_mv	AT leizhou machinelearningforcombustion AT yuntongsong machinelearningforcombustion AT weiqiji machinelearningforcombustion AT haiqiaowei machinelearningforcombustion
_version_	1718372945262280704

Machine learning for combustion

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