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

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 simulatio

SUBMITTER: Wang YD 

PROVIDER: S-EPMC9929041 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature