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用于优化质子交换膜燃料电池气体通道运行条件的离散颗粒模型

Discrete-Particle Model to Optimize Operational Conditions of Proton-Exchange Membrane Fuel-Cell Gas Channels.

作者信息

Niblett Daniel, Holmes Stuart Martin, Niasar Vahid

机构信息

Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester M13 9PL, U.K.

出版信息

ACS Appl Energy Mater. 2021 Oct 25;4(10):10514-10533. doi: 10.1021/acsaem.1c01391. Epub 2021 Sep 22.

Abstract

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.

摘要

质子交换膜燃料电池的运行会因传质损失而严重恶化,传质损失是气流、水流、通道几何形状及其润湿性之间时空相互作用的结果。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。我们提出了一种机理离散颗粒模型(DPM)来描述燃料电池气体通道中的动态水分布并优化运行条件。与实验观察结果类似,该模型针对工业时空尺度预测了七种流动状态,从孤立流、侧壁流、角部流、弹状流、膜状流到塞状流液滴。因此,提出了两相流型图。结果表明,通道中积水的增加与气体扩散层中出现的水团簇密度的增加有关,而不是通过恒定水流路径的水流速率增加。从建模角度来看,DPM在饱和度、水覆盖率和界面位置方面很好地复制了流体体积通道模拟结果,计算速度估计提高了5个数量级。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/219c/8552217/62d3300441c8/ae1c01391_0002.jpg

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