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Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

Author

Listed:
  • Ying Da Wang

    (University of New South Wales)

  • Quentin Meyer

    (University of New South Wales)

  • Kunning Tang

    (University of New South Wales)

  • James E. McClure

    (Virginia Tech)

  • Robin T. White

    (Carl Zeiss X-ray Microscopy, ZEISS Innovation Center California)

  • Stephen T. Kelly

    (Carl Zeiss X-ray Microscopy, ZEISS Innovation Center California)

  • Matthew M. Crawford

    (Fuel Cell Store)

  • Francesco Iacoviello

    (University College London)

  • Dan J. L. Brett

    (University College London)

  • Paul R. Shearing

    (University College London)

  • Peyman Mostaghimi

    (University of New South Wales)

  • Chuan Zhao

    (University of New South Wales)

  • Ryan T. Armstrong

    (University of New South Wales)

Abstract

Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (

Suggested Citation

  • Ying Da Wang & Quentin Meyer & Kunning Tang & James E. McClure & Robin T. White & Stephen T. Kelly & Matthew M. Crawford & Francesco Iacoviello & Dan J. L. Brett & Paul R. Shearing & Peyman Mostaghimi, 2023. "Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-35973-8
    DOI: 10.1038/s41467-023-35973-8
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    References listed on IDEAS

    as
    1. Xuekun Lu & Antonio Bertei & Donal P. Finegan & Chun Tan & Sohrab R. Daemi & Julia S. Weaving & Kieran B. O’Regan & Thomas M. M. Heenan & Gareth Hinds & Emma Kendrick & Dan J. L. Brett & Paul R. Shear, 2020. "3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. Liu, Jiawen & Shin, Seungho & Um, Sukkee, 2019. "Comprehensive statistical analysis of heterogeneous transport characteristics in multifunctional porous gas diffusion layers using lattice Boltzmann method for fuel cell applications," Renewable Energy, Elsevier, vol. 139(C), pages 279-291.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Liu, Zhongyong & Sun, Yuning & Tang, Xiawei & Mao, Lei, 2024. "Enabling unsupervised fault diagnosis of proton exchange membrane fuel cell stack: Knowledge transfer from single-cell to stack," Applied Energy, Elsevier, vol. 360(C).
    2. Zhu, Huichao & Zhang, Houcheng, 2024. "Integration of proton exchange membrane fuel cell with air gap membrane distillation for sustainable electricity and freshwater cogeneration: Performance, influential mechanism, multi-objective optimi," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    3. Zhu, Huichao & Xiao, Liusheng & Kuang, Min & Wang, Jiatang & Zhang, Houcheng, 2024. "Innovative use of air gap membrane distillation to harvest waste heat from alkaline fuel cell for efficient freshwater production: A comprehensive 4E study," Renewable Energy, Elsevier, vol. 225(C).

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