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Titlebook: Machine Learning and Its Application to Reacting Flows; ML and Combustion Nedunchezhian Swaminathan,Alessandro Parente Book‘‘‘‘‘‘‘‘ 2023 Th

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書目名稱Machine Learning and Its Application to Reacting Flows
副標(biāo)題ML and Combustion
編輯Nedunchezhian Swaminathan,Alessandro Parente
視頻videohttp://file.papertrans.cn/621/620485/620485.mp4
概述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.Thi
叢書名稱Lecture Notes in Energy
圖書封面Titlebook: Machine Learning and Its Application to Reacting Flows; ML and Combustion Nedunchezhian Swaminathan,Alessandro Parente Book‘‘‘‘‘‘‘‘ 2023 Th
描述.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 tech
出版日期Book‘‘‘‘‘‘‘‘ 2023
關(guān)鍵詞Machine Learning; Combustion Simulations; Combustion Modelling; Big Data Analysis; Dimensionality reduct
版次1
doihttps://doi.org/10.1007/978-3-031-16248-0
isbn_softcover978-3-031-16250-3
isbn_ebook978-3-031-16248-0Series ISSN 2195-1284 Series E-ISSN 2195-1292
issn_series 2195-1284
copyrightThe Editor(s) (if applicable) and The Author(s) 2023
The information of publication is updating

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