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

Accurate liquid water modelling is challenging. Here the authors use X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multiphase simulation to simulate fuel cell and guide fuel cell design. 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 mm(2)) 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 mm(2) 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.

Automated summary

The authors utilize X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multiphase simulation to simulate fuel cells and guide their design, addressing the challenge of accurate liquid water modelling.