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Design optimization and thermal management of the PEMFC using artificial neural networks

Author

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  • Pourrahmani, Hossein
  • Siavashi, Majid
  • Moghimi, Mahdi

Abstract

The subject of this study is to analyze the heat transfer inside the gas flow channel (GFC) of the proton exchange membrane fuel cell (PEMFC) numerically. Increasing the fluid-solid contact area, as well as, changing the cooling fluid velocity and temperature profiles can improve the heat transfer. Consequently, trapezoid porous ribs with three different geometrical parameters are placed in the GFC to meet this goal. These geometrical parameters are the base and the tip widths of the ribs in addition to their distance from each other. Numerical simulations arose the respective Nu numbers and friction factors of the system to evaluate the influence of these ribs. Afterward, these simulations data are used to train an artificial neural network (ANN) to model the system and produce data to perform the sensitivity analysis and sketch the corresponding three-dimensional diagrams. Finally, the optimum values of the mentioned geometrical parameters are calculated to maximize the heat transfer rate with minimum friction losses. Results indicate that although the lower distance between porous ribs has led to the higher Nu, this also causes the higher friction factor. Therefore, it is better to utilize higher distances to meet the higher performance evaluation criterion.

Suggested Citation

  • Pourrahmani, Hossein & Siavashi, Majid & Moghimi, Mahdi, 2019. "Design optimization and thermal management of the PEMFC using artificial neural networks," Energy, Elsevier, vol. 182(C), pages 443-459.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:443-459
    DOI: 10.1016/j.energy.2019.06.019
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    Citations

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    Cited by:

    1. Han, Chaoling & Chen, Zhenqian, 2021. "Study on the synergism of thermal transport and electrochemical of PEMFC based on N, P co-doped graphene substrate electrode," Energy, Elsevier, vol. 214(C).
    2. Lan, Shunbo & Lin, Rui & Dong, Mengcheng & Lu, Kai & Lou, Mingyu, 2023. "Image recognition of cracks and the effect in the microporous layer of proton exchange membrane fuel cells on performance," Energy, Elsevier, vol. 266(C).
    3. Hossein Pourrahmani & Hamed Shakeri & Jan Van herle, 2022. "Thermoelectric Generator as the Waste Heat Recovery Unit of Proton Exchange Membrane Fuel Cell: A Numerical Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
    4. Alaa A. Zaky & Rania M. Ghoniem & F. Selim, 2023. "Precise Modeling of Proton Exchange Membrane Fuel Cell Using the Modified Bald Eagle Optimization Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    5. Yang, Zirong & Jiao, Kui & Wu, Kangcheng & Shi, Weilong & Jiang, Shangfeng & Zhang, Longhai & Du, Qing, 2021. "Numerical investigations of assisted heating cold start strategies for proton exchange membrane fuel cell systems," Energy, Elsevier, vol. 222(C).
    6. Rezk, Hegazy & Ferahtia, Seydali & Djeroui, Ali & Chouder, Aissa & Houari, Azeddine & Machmoum, Mohamed & Abdelkareem, Mohammad Ali, 2022. "Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer," Energy, Elsevier, vol. 239(PC).
    7. Cai, Yonghua & Wu, Di & Sun, Jingming & Chen, Ben, 2021. "The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC," Energy, Elsevier, vol. 222(C).
    8. Jongbin Woo & Younghyeon Kim & Sangseok Yu, 2023. "Cooling-System Configurations of a Dual-Stack Fuel-Cell System for Medium-Duty Trucks," Energies, MDPI, vol. 16(5), pages 1-19, February.
    9. Pourrahmani, Hossein & Van herle, Jan, 2022. "Water management of the proton exchange membrane fuel cells: Optimizing the effect of microstructural properties on the gas diffusion layer liquid removal," Energy, Elsevier, vol. 256(C).
    10. Liu, Zhao & Chen, Huicui & Peng, Lian & Ye, Xichen & Xu, Sichen & Zhang, Tong, 2022. "Feedforward-decoupled closed-loop fuzzy proportion-integral-derivative control of air supply system of proton exchange membrane fuel cell," Energy, Elsevier, vol. 240(C).
    11. Li, Yi & Yuan, Fang & Weng, Rengang & Xi, Fang & Liu, Wei, 2021. "Variational-principle-optimized porosity distribution in gas diffusion layer of high-temperature PEM fuel cells," Energy, Elsevier, vol. 235(C).
    12. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    13. Danqi Su & Jiayang Zheng & Junjie Ma & Zizhe Dong & Zhangjie Chen & Yanzhou Qin, 2023. "Application of Machine Learning in Fuel Cell Research," Energies, MDPI, vol. 16(11), pages 1-32, May.

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