Machine learning-assisted design of flow fields for redox flow batteries

Wan Shuaibin; Jiang Haoran; Guo ZixiaoHe ChangxiangLiang XiongweiDjilali NedZhao Tianshou

首页> 外文期刊>Energy & environmental science: EES >Machine learning-assisted design of flow fields for redox flow batteries

【24h】

Machine learning-assisted design of flow fields for redox flow batteries

机译：Machine learning-assisted design of flow fields for redox flow batteries

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Flow fields are a crucial component of redox flow batteries (RFBs). Conventional flow fields, designed by trial-and-error approaches and limited human intuition, are difficult to optimize, thus limiting the performance of RFBs. Here, we develop an end-to-end approach to the design of flow fields by combining machine learning and experimental methods. A library of 11 564 flow fields is generated using a custom-made path generation algorithm, in which flow fields are elegantly encoded by two-dimensional binary images. To accelerate the discovery process, we train convolutional neural networks with low test errors for predicting the uniformity factor and pressure drop of flow fields (0.59 and 1.37, respectively). Through a collaborative screening process, eight promising candidates are successfully identified. Experimental validation shows that the battery with the flow fields designed with this approach yields higher electrolyte utilization and exhibits about a 22 increase in limiting current density and up to 11 improvement in energy efficiency compared to the conventional serpentine flow field. Furthermore, statistical analysis suggests that the promising candidates have a saved channel length of 1490 +/- 100 and a torque integral of 20.1 +/- 1.8, revealing the quantitative design rules of flow fields for the first time.

著录项

来源
《Energy & environmental science: EES》 |2022年第7期|2874-2888|共15页
作者
Wan Shuaibin; Jiang Haoran; Guo ZixiaoHe ChangxiangLiang XiongweiDjilali NedZhao Tianshou;
展开▼
作者单位

Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China;

Tianjin Univ, Key Lab Efficient Utilizat Low & Medium Grade Ene, Tianjin, Peoples R China|Hong Kong Univ Sci & Technol, Inst Adv Study, Kowloon, Clear Water Bay, Hong Kong, Peoples R China;

Univ Victoria, Dept Mech Engn, Victoria, BC, Canada|Univ Victoria, Inst Integrated Energy Syst, Victoria, BC, Canada;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词

Machine learning-assisted design of flow fields for redox flow batteries

摘要

著录项

相关主题

期刊订阅