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; Ryan T Armstrong

doi:10.1038/s41467-023-35973-8

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

Nat Commun. 2023 Feb 14;14(1):745. doi: 10.1038/s41467-023-35973-8.

Authors

Affiliations

¹ School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW, 2052, Australia.
² School of Chemistry, University of New South Wales, Sydney, NSW, 2052, Australia. q.meyer@unsw.edu.au.
³ National Security Institute, Virginia Tech, Blacksburg, VA, 24061, USA.
⁴ Carl Zeiss X-ray Microscopy, ZEISS Innovation Center California, Dublin, CA, 94568, USA.
⁵ Fuel Cell Store, Bryan, TX, 77807, USA.
⁶ Electrochemical Innovation Lab, Department of Chemical Engineering, University College London, London, WC1E 7JE, UK.
⁷ School of Chemistry, University of New South Wales, Sydney, NSW, 2052, Australia. chuan.zhao@unsw.edu.au.
⁸ School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW, 2052, Australia. ryan.armstrong@unsw.edu.au.

^# Contributed equally.

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 (<1 mm²) or simplified architectures. Herein, an advancement in water modelling is achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multi-phase simulation. The resulting image is the most resolved domain (16 mm² with 700 nm voxel resolution) and the largest direct multi-phase flow simulation of a fuel cell. This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow fields, paving the way for next generation proton exchange membrane fuel cells with optimised structures and wettabilities.