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

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, Chuan Zhao () and Ryan T. Armstrong ()
Additional contact information
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

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: 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 (

Date: 2023
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DOI: 10.1038/s41467-023-35973-8

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