Water transport is an essential process in proton exchange membrane fuel cells (PEMFC). For the novel metal foam flow fields, water transport is difficult to be visualized and still remains inadequately understood. In this work, for the first time, the two-phase flows in the metal foam flow fields of a transparent PEMFC was visualized by using an artificial intelligence (AI) image recognition method. In order to distinguish water from the metal foam ligaments, a scoring system was developed, which effectively solved the problem of inconsistent reflectance of liquid water between the ligament and the gas diffusion layer. According to the state of liquid water, the scoring system categorized the local images into three labels of waterless state, wet state, and slug state for AI image recognition network training. The trained AI image recognition model was then used to visualize the liquid water state in the metal foam flow fields under different operating conditions of relative humidity (RH), temperature, and inlet flow rate. Results show that, since the porous structure of the metal foam provides flexible transport paths, the PEMFC can operate effectively in a wide range of wet states. Even a 25% difference in the wet-state percentage between different operating conditions yields an almost consistent voltage. The increased slugs in the flow fields are the primary reason for the performance degradation of the PEMFC. At a high current density of 2 A/cm, the voltage is as low as 0.03 V when the slug state occupies 35% of the flow fields (100% RH, 60 °C, 1.0 SLPM cathode inlet flow rate), and as high as 0.56 V when the slug state is absent in the flow fields (100% RH, 60 °C, 2.0 SLPM cathode inlet flow rate). The present work provides a novel approach for the studies of water transport in PEMFC.

中文翻译：

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金属泡沫流场质子交换膜燃料电池中的水传输：基于AI图像识别的可视化

水传输是质子交换膜燃料电池（PEMFC）中的一个重要过程。对于新型金属泡沫流场，水的传输很难被可视化，并且仍然没有得到充分的理解。在这项工作中，首次利用人工智能（AI）图像识别方法对透明PEMFC的金属泡沫流场中的两相流进行可视化。为了区分水和泡沫金属韧带，开发了一种评分系统，有效解决了韧带和气体扩散层之间液态水反射率不一致的问题。评分系统根据液态水的状态，将局部图像分为无水状态、潮湿状态和slug状态三个标签，用于AI图像识别网络训练。然后使用经过训练的人工智能图像识别模型来可视化在相对湿度（RH）、温度和入口流量的不同操作条件下泡沫金属流场中的液态水状态。结果表明，由于泡沫金属的多孔结构提供了灵活的传输路径，质子交换膜燃料电池可以在各种潮湿状态下有效运行。即使不同操作条件之间的湿态百分比差异为 25%，也能产生几乎一致的电压。流场中的段塞增加是PEMFC性能下降的主要原因。在2 A/cm的高电流密度下，当段塞态占据流场的35%时（100% RH、60 °C、1.0 SLPM阴极入口流量），电压低至0.03 V，当流场中不存在段塞状态时（100% RH、60 °C、2.0 SLPM 阴极入口流速），电压为 0.56 V。目前的工作为 PEMFC 中水传输的研究提供了一种新方法。