Operation of proton-exchange membrane fuel cells is highly deteriorated by mass transfer loss, which is a result of spatial and temporal interaction between airflow, water flow, channel geometry, and its wettability. Prediction of two-phase flow dynamics in gas channels is essential for the optimization of the design and operating of fuel cells. We propose a mechanistic discrete particle model (DPM) to delineate dynamic water distribution in fuel cell gas channels and optimize the operating conditions. Similar to the experimental observations, the model predicts seven types of flow regimes from isolated, side wall, corner, slug, film, and plug flow droplets for industrial temporal and spatial scales. Consequently, two-phase flow regime maps are proposed. The results suggest that an increase in water accumulation in the channel is related to the increase in the water cluster density emerging from the gas diffusion layer rather than the increased water flow rate through constant water pathways. From a modeling perspective, the DPM replicated well volume-of-fluid channel simulation results in terms of saturation, water coverage ratio, and interface locations with an estimated 5 orders of magnitude increase in calculation speed.

中文翻译：

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优化质子交换膜燃料电池气体通道运行条件的离散粒子模型

质子交换膜燃料电池的运行会因传质损失而严重恶化，这是气流、水流、通道几何形状及其润湿性之间空间和时间相互作用的结果。预测气体通道中的两相流动力学对于优化燃料电池的设计和运行至关重要。我们提出了一种机械离散粒子模型（DPM）来描绘燃料电池气体通道中的动态水分布并优化操作条件。与实验观察类似，该模型从工业时空尺度的孤立、侧壁、角、段塞、薄膜和活塞流液滴中预测了七种类型的流态。因此，提出了两相流态图。结果表明，通道中积水的增加与从气体扩散层出现的水团簇密度的增加有关，而不是通过恒定水通道的水流速增加。从建模的角度来看，DPM 在饱和度、水覆盖率和界面位置方面复制了井体积流体通道模拟结果，计算速度估计提高了 5 个数量级。