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

Author:

Wan Shuaibin1ORCID,Jiang Haoran23ORCID,Guo Zixiao1,He Changxiang1,Liang Xiongwei1,Djilali Ned45,Zhao Tianshou1ORCID

Affiliation:

1. Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China

2. Key Laboratory of Efficient Utilization of Low and Medium Grade Energy (MOE), Tianjin University, Tianjin, China

3. Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China

4. Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada

5. Institute for Integrated Energy Systems, University of Victoria, Victoria, BC, Canada

Abstract

An end-to-end approach is developed to design flow fields for redox flow batteries, and the quantitative design rules of flow fields are revealed for the first time.

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry

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