气体扩散层亲疏水特性对流体分布的预测
作者:
作者单位:

同济大学 汽车学院,上海201804

作者简介:

高 源(1983—),女,副教授,博士生导师,工学博士,主要研究方向为燃料电池仿真、微孔隙流仿真。 E-mail:yuangao@tongji.edu.cn

通讯作者:

中图分类号:

TM911.4

基金项目:

上海市自然科学基金(19ZR1460300)


Prediction of Hydrophilic and Hydrophobic Characteristics of Gas Diffusion Layer on Fluid Distribution
Author:
Affiliation:

School of Automotive Studies, Tongji University, Shanghai 201804, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    采用伪势两相格子玻尔兹曼方法(LBM)研究了非增湿条件下碳纸和碳布气体扩散层(GDL)的流体流动状态。通过随机方法和X射线扫描法构建了气体扩散层样本。为确保模型中表面张力和接触角计算的准确性,采用玻璃微珠模型进行验证,随后通过调整气体扩散层的亲疏水特性,分析流体在气体扩散层中流动的实时状态,得到了亲疏水特性对孔隙结构内水饱和度的影响规律。结果表明:疏水性气体扩散层中的水分布与亲水性气体扩散层中的水分布明显不同,较大的疏水性更有利于气体扩散层内水的排出;疏水性更强的气体扩散层显著提高了液态水进入气体扩散层的入口压力,导致催化剂层(CL)更容易受到水渗透的影响。

    Abstract:

    The pseudo potential two-phase lattice Boltzmann method (LBM) was used to study the fluid flow state of gas diffusion layers (GDL) for carbon paper and carbon cloth under non-humidification conditions. GDL samples were constructed by random method and X-ray scanning method. In order to verify the accuracy of surface tension and contact angle calculation in the modeling process, the glass bead model was adopted. By adjusting the hydrophobic and hydrophilic characteristics of GDL, the real-time state of fluid flow in pore structure was analyzed, and the effect of hydrophobic and hydrophilic characteristics on the distribution of the water saturation in the pore structure was obtained. The results show that the water distribution in hydrophobic GDL is significantly different from that in hydrophilic GDL. Better hydrophobicity is more conducive to the discharge of water in GDL. In addition, GDL with stronger hydrophobicity significantly increases the inlet pressure of liquid water entering GDL, which may make catalyst layer (CL) more vulnerable to water penetration.

    参考文献
    相似文献
    引证文献
引用本文

高源,丁兆丰.气体扩散层亲疏水特性对流体分布的预测[J].同济大学学报(自然科学版),2023,51(8):1288~1295

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-02-23
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2023-08-28
  • 出版日期:
文章二维码