Water spatial distribution in polymer electrolyte membrane fuel cell: Convolutional neural network analysis of neutron radiography

Abstract

Polymer electrolyte membrane (PEM) fuel cells produce water as byproduct, which may cause electrode “flooding” and reduce cell performance. In operation, water usually builds up downstream in the gas flow channel due to the water production by the oxygen reduction reaction (ORR), leading to a water spatial distribution. In this study, a convolutional neural network (CNN) is presented to analyze neutron radiography images to obtain water spatial variation under various operating conditions. 5 and 10 segments of a fuel cell are analyzed for spatial variations. Image pre-processing treatments are carried out to improve the convolutional neural network accuracy to 96.6%. The results show that liquid water emerges at a position around 55% downstream for 50% relative humidity while the entire cell is subject to two-phase flow for 100% relative humidity under a co-flow configuration. Large water content is present in most of the segments and the near-outlet segment for the counter-flow and co-flow configurations, respectively. In addition, the quad-serpentine cell exhibits more water accumulation than the single serpentine one in most downstream segments. The convolutional neural network results agree well with the data obtained from a pixelation image processing method with an accuracy of 91.8%. Compared with conventional pixelation methods, the convolutional neural network method performs better in speed for high-resolution images. It also shows that the current CNN tool fails to predict local water for small spatial scales, such as 10 segments, which leads to a large error (>27%) in prediction.