Predicting gas diffusion layer flow information in proton exchange membrane fuel cells from cross-sectional data using deep learning methods

Obtaining transient flow field information of gas diffusion layers (GDLs) is a crucial issue for improving the performance of proton exchange membrane fuel cells (PEMFCs). While physically-based methods, such as effective medium theory and solving partial differential equations, can be utilized to calculate fluid flow in GDL, the computational cost associated with such methods remains substantial. In this study, a 3D multiphase flow dataset of GDLs is obtained using fluid of volume (VOF) calculation, and the 3D data is sliced. The resulting cross-sectional data are used to establish a convolutional neural network (CNN) model based on two-dimensional (2D) data for predicting fluid flow in GDLs. The reliability of predicting the saturation information of 3D GDLs using 2D cross-sectional data is verified and discussed. Subsequently, a comparison is made between the accuracy and computational cost of predicting the saturation of 3D GDLs using 2D and 3D CNN models. The results indicate that using 25 cross-sectional images provide accurate predictions of GDL saturation in 3D. When using cross-sectional images as inputs, 100 × 100 images are found to be more representative than 50 × 50 images, resulting in an average increase of 1.86% and 13.36% in R2 and RMSE, respectively. While the 3D CNN model outperforms the 2D CNN model in predicting GDL saturation in 3D by only 0.62% in terms of R2 score, its computational cost is two orders of magnitude higher. These findings suggest that 2D GDL cross-sectional images can also be used for predicting 3D GDL flow information and effectively reducing computational costs. The findings of this study provide a profound insight into the GDL flow phenomena and contribute to the development of more efficient and accurate fuel cell models.