Multi-objective optimization of the drainage performance of dual-flow channel proton exchange membrane fuel cells driven by machine learning surrogate model

被引:0
|
作者
Liu, Qingshan [1 ]
Wang, Junfeng [2 ]
Li, Shixin [3 ]
Huang, Rong [1 ]
Wang, Xiaojing [4 ]
Yu, Binyan [1 ]
Fu, Pei [1 ]
Zhang, Yong [5 ]
Chen, Yisong [1 ]
机构
[1] Changan Univ, Sch Automobile, Xian 710064, Peoples R China
[2] South China Univ Technol, Sch Mech & Automot Engn, Guangzhou 510641, Peoples R China
[3] Shaanxi Automobile Grp Co Ltd, Automot Engn Res Inst, Xian 710200, Peoples R China
[4] Changan Univ, Sch Energy & Elect Engn, Xian 710064, Peoples R China
[5] Shanghai Polytech Univ, Sch Intelligent Mfg & Control Engn, Shanghai 201209, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
Proton exchange membrane fuel cell; Volume of fluid; Dynamic contact angle; Multiphase flow evolution; Helical baffle flow field; Structural optimization; REMOVAL;
D O I
10.1016/j.ijhydene.2024.12.326
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
To enhance the drainage performance of fuel cells under high load conditions, this study proposed a helical baffle flow field (FF). This FF is based on a semi-circular cross-section design and generated along a helical trajectory, with four structural parameters (pitch, radius, gap, and shear angle). To optimize the drainage performance of the novel FF, a multi-objective optimization framework is established with four structural parameters as the design variables and pressure drop in the gas flow channel and water coverage ratio on the gas diffusion layer surface as the optimization objectives. The optimization process is divided into three steps: First, initial sample data is collected through experimental design, and the simulation model considered the coupled effects of multichannels, dynamic contact angle effects of liquid droplets in high-speed motion, and linked the inlet gas and water velocities with the operating current density to enhance the simulation accuracy. Second, artificial neural network surrogate models are trained based on the obtained simulation data to establish a high-precision mapping relationship between the performance indicators and the design variables and ensure good generalization ability. Finally, based on the surrogate models, the multi-objective optimization is carried out using genetic algorithms to generate the Pareto front, and the optimal FF structure parameter combination is obtained. The research results show that the optimized FF structure improves the comprehensive drainage performance by 7.8%. Among them, the diameter of the baffle has the greatest impact on the drainage performance, while the shear angle has the least impact. This study provides a new perspective for optimizing the drainage performance of fuel cells under high load conditions.
引用
收藏
页码:617 / 634
页数:18
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