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Titlebook: New Paradigms in Flow Battery Modelling; Akeel A. Shah,Puiki Leung,Wei Xing Book 2023 The Editor(s) (if applicable) and The Author(s), und

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發(fā)表于 2025-3-21 18:26:51 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱New Paradigms in Flow Battery Modelling
編輯Akeel A. Shah,Puiki Leung,Wei Xing
視頻videohttp://file.papertrans.cn/666/665541/665541.mp4
概述Covers modern techniques in battery and fuel cell modeling.Explains methods in detail for beginners to grasp more easily with codes available.Introduces modern machine learning methods including deep
叢書名稱Engineering Applications of Computational Methods
圖書封面Titlebook: New Paradigms in Flow Battery Modelling;  Akeel A. Shah,Puiki Leung,Wei Xing Book 2023 The Editor(s) (if applicable) and The Author(s), und
描述.This book provides a comprehensive review of the latest modelling developments in flow batteries, as well as some new results and insights. Flow batteries have long been considered the most flexible answer to grid scale energy storage, and modelling is a key component in their development. Recent modelling has moved beyond macroscopic methods, towards mesoscopic and smaller scales to select materials and design components. This is important for both fundamental understanding and the design of new electrode, catalyst and electrolyte materials. There has also been a recent explosion in interest in machine learning for electrochemical energy technologies. The scope of the book includes these latest developments and is focused on advanced techniques, rather than traditional modelling paradigms. The aim of this book is to introduce these concepts and methods to flow battery researcher, but the book would have a much broader appeal since these methods also employed in other battery and fuel cell systems and far beyond. The methods will be described in detail (necessary fundamental material in Appendices). The book appeals to graduate students and researchers in academia/industry working
出版日期Book 2023
關(guān)鍵詞Machine Learning; Energy Storage; Modeling and Simulation; Flow Battery; Pore Scale; Ab Initio; Electroche
版次1
doihttps://doi.org/10.1007/978-981-99-2524-7
isbn_softcover978-981-99-2526-1
isbn_ebook978-981-99-2524-7Series ISSN 2662-3366 Series E-ISSN 2662-3374
issn_series 2662-3366
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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發(fā)表于 2025-3-21 23:19:28 | 只看該作者
Engineering Applications of Computational Methodshttp://image.papertrans.cn/n/image/665541.jpg
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Machine Learning for Flow Battery Systems,We refer to section 3.7 for an introduction to machine learning and the main terminology, as well as an outline of the basic approach to devising a machine learning model. Machine learning can be used in a variety of ways for the design, analysis, optimisation and control of flow batteries, their components and their materials.
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Introduction,tems. With renewable energy generation taking on an increasing share of electrical power production, the role of RFBs is expected to grow in importance. Indeed, they may well become critical to maintaining a continual supply of electricity to homes and businesses.
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978-981-99-2526-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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New Paradigms in Flow Battery Modelling978-981-99-2524-7Series ISSN 2662-3366 Series E-ISSN 2662-3374
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發(fā)表于 2025-3-22 21:55:54 | 只看該作者
rsive algorithms in the diverse forms in which they arise in applications. There are analogous continuous time algorithms, but the conditions and proofs are generally very close to those for the discrete time case. The original work was motivated by the problem of ?nding a root of a continuous funct
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