首页> 外文期刊>International journal of hydrogen energy >A constitutive law to predict the compression of gas diffusion layers
【24h】

A constitutive law to predict the compression of gas diffusion layers

机译:预测气体扩散层压缩的本构定律

获取原文
获取原文并翻译 | 示例
           

摘要

The mechanical behavior of gas diffusion layers (GDLs) is predicted via existing models developed for fibrous materials. For this purpose, a new representation of the behavior is required, function of the material relative density rather than the classical mechanical strain. This allows to shed light on the evolution of the GDLs mechanical properties according to the different levels of mechanical stresses encountered during its lifetime. Compression tests are performed in order to differentiate the experimental mechanical properties of two types of GDLs. Different levels of mechanical stress are applied on the samples to simulate a mechanical history similar to the one encountered in real use. Two different behaviors are observed; the compression is at first governed by the mechanical history of the samples before recovering the GDL original behavior. Accordingly, two sets of parameters are required to fit these different behaviors. The GDLs mechanical properties can be then predicted regardless of the samples state, i.e. pristine or used. Excellent correlations are found between predicted and experimental data. This model brings a better understanding of the mechanisms implied during the GDL compression which plays a major role in the performance of proton exchange membrane fuel cells. (C) 2018 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
机译:气体扩散层(GDL)的机械行为是通过为纤维材料开发的现有模型预测的。为此,需要对行为进行新的表示,即材料相对密度的函数,而不是经典的机械应变。根据其生命周期中遇到的不同水平的机械应力,这可以阐明GDL机械性能的演变。进行压缩测试是为了区分两种GDL的实验机械性能。在样品上施加不同水平的机械应力,以模拟与实际使用中相似的机械历史。观察到两种不同的行为。在恢复GDL原始行为之前,压缩首先由样品的机械历史决定。因此,需要两组参数来适应这些不同的行为。然后可以预测GDL的机械性能,而与样品状态(即原始状态或使用状态)无关。在预测和实验数据之间发现极好的相关性。该模型更好地理解了在GDL压缩过程中隐含的机理,该机理在质子交换膜燃料电池的性能中起着重要作用。 (C)2018氢能出版物有限公司。由Elsevier Ltd.出版。保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号