固体氧化物燃料电池建模综述:从大系统到精细电极

IF 51.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Zhen Wu, Pengfei Zhu, Yakun Huang, Jing Yao, Fusheng Yang, Zaoxiao Zhang and Meng Ni*, 
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引用次数: 0

摘要

固体氧化物燃料电池(SOFC)系统的商业化需要提高SOFC的性能和耐久性,这在很大程度上取决于SOFC堆与其他辅助组件、SOFC堆配置和电极微观结构的耦合。通过实验在系统/堆栈/电池/电极尺度上优化SOFC系统是昂贵且具有挑战性的,而数值模拟可以快速且具有成本效益。虽然已经发表了许多关于sofc的优秀评论,但之前的文章缺乏实用的问题导向的文献分类,并且没有涵盖新兴的模型,如人工智能(AI)辅助模型、异构模型等。这些模型对于加速求解大尺度多物理场模型和描述介观电极行为具有重要意义。在这篇综述中,采用了一种自上而下的方法,可以真正指导SOFC系统/堆栈/电池/电极设计,以满足目标应用。这篇综述的另一个显著特点是包含了SOFC建模的最新发展。本综述对SOFC模拟的大量研究进行了全面的总结和深入的分析,根据不同的尺度将模型分为不同的类别,并作为一个有价值的工具,帮助研究人员为不同的研究对象选择最合适的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Review of Modeling of Solid Oxide Fuel Cells: From Large Systems to Fine Electrodes

Commercialization of solid oxide fuel cell (SOFC) systems requires improved SOFC performance and durability, which is highly dependent on the coupling of the SOFC stack with other auxiliary components, SOFC stack configuration, and electrode microstructure. Optimization of SOFC systems at the system/stack/cell/electrode scale via experimentation is expensive and challenging, whereas numerical modeling can be fast and cost-effective. Although many excellent reviews on SOFCs have been published, the previous articles lack practical problem-oriented literature classification and do not cover new emerging models, such as artificial intelligence (AI) assisted models, heterogeneous models, and so on. These models are important for accelerating the solution of large-scale multiphysics models and describing mesoscopic electrode behaviors. In this review, a top-down approach is adopted that can truly guide SOFC system/stack/cell/electrode design to meet targeted applications. Another distinct feature of this review is the inclusion of the latest developments in SOFC modeling. This review offers a thorough summary and in-depth analysis of an extensive collection of research on SOFC simulations, classifying the models into distinct categories based on their varying scales, and serves as a valuable tool to assist researchers in selecting the most suitable models for diverse research objects.

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来源期刊
Chemical Reviews
Chemical Reviews 化学-化学综合
CiteScore
106.00
自引率
1.10%
发文量
278
审稿时长
4.3 months
期刊介绍: Chemical Reviews is a highly regarded and highest-ranked journal covering the general topic of chemistry. Its mission is to provide comprehensive, authoritative, critical, and readable reviews of important recent research in organic, inorganic, physical, analytical, theoretical, and biological chemistry. Since 1985, Chemical Reviews has also published periodic thematic issues that focus on a single theme or direction of emerging research.
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