Data-driven topology optimization for designing of flow fields in redox flow batteries

2022 Fiscal Year Final Research Report

• Project/Area Number: 20H02054
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Basic Section 18030:Design engineering-related
• Research Institution: Osaka University
• Principal Investigator: Yaji Kentaro 大阪大学, 大学院工学研究科, 助教 (90779373)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: トポロジー最適化 / 深層学習 / レドックスフロー電池 / 深層生成モデル

Outline of Final Research Achievements

The next-generation large-scale energy storage system for renewable energies such as wind and solar power is attracting attention, and the Redox Flow Battery (RFB) is being considered as a potential solution. However, further improvements in charge and discharge performance are required for its practical application. In this study, considering that the flow channel structure inside the RFB strongly affects its charge and discharge performance, our aim was to create an innovative flow channel structure that could lead to the ultra-high performance of the RFB by employing mathematical optimization methods. As a specific approach, we developed a data-driven topology optimization incorporating an electrochemical reaction model based on realistic assumptions and conducted numerical experiments to validate the effectiveness of the proposed design solutions.

Free Research Field

最適設計

Academic Significance and Societal Importance of the Research Achievements

本研究では、従来のトポロジー最適化に代わる新しい最適設計法を開発し、それをRFBの流動場設計に展開した点に学術的新規性がある。特に今回構築したデータ駆動型トポロジー最適化については、従来のトポロジー最適化が主に勾配法に基づく最適化法であるのに対し、勾配を一切用いることなく高次元の最適化を実現している点は注目に値する。実社会において価値のある最適化問題は往々にして複雑であり有効な勾配を取り出しにくいことが少なくないことから、RFBに限らず実際の設計問題への展開も今後期待できる。