A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells
A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells
复制标题
建立聚合物电解质燃料电池多孔传输层微观结构-性能关系的数据驱动方法
DOI：
10.1016/j.jpowsour.2013.06.100
复制
发表时间：
2014
影响因子：
9.2
通讯作者：
S. Kalidindi
中科院分区：
文献类型：
--
作者：
A. Çeçen;T. Fast;E. C. Kumbur;S. Kalidindi
扩散介质（DM）是影响聚合物电解质燃料电池（pefc）性能的重要组成部分。DM具有双层结构，由称为气体扩散层（GDL）的宏观衬底和涂覆微孔层（MPL）组成。从其内部结构有效预测DM的有效传输特性对于优化这一关键组分的多功能特性至关重要。在这项工作中，介绍了一种独特的数据驱动方法来建立结构-性质相关性，并将其应用于GDL和MPL中气体扩散的情况。这种新方法提供了一个自动化的过程来产生无偏的微观结构方差估计，与许多过程相关的（因此是有偏的）参数相比，这些参数在该领域中被突出的相关性所采用。本方法首先以n点统计的形式对微观结构进行严格的量化。其次是通过使用主成分分析来确定内部结构的关键方面。通过多元线性回归，将主成分与有效扩散系数联系起来，建立数据驱动的相关性。将这种数据驱动的方法与传统的相关性进行了比较，并证明了在捕获被测PEFC组件中的扩散输运方面实现了非常高的准确性。
The diffusion media (DM) has been shown to be a vital component for performance of polymer electrolyte fuel cells (PEFCs). The DM has a dual-layer structure composed of a macro-substrate referred to as the gas diffusion layer (GDL) coated with a micro-porous layer (MPL). Efficient prediction of the effective transport properties of the DM from its internal structure is essential to optimizing the multifunctional characteristics of this critical component. In this work, a unique data-driven approach to establishing structure–property correlations is introduced and applied to the case of gas diffusion in the GDL and MPL. This new approach provides an automated process to produce unbiased estimators to microstructural variance, in contrast to many process-related (hence biased) parameters employed by prominent correlations in the field. The present approach starts with a rigorous quantification of microstructure in the form ofn-point statistics. It is followed by the identification of the key aspects of the internal structure through the use of principle component analysis. A data-driven correlation is established when the principal components are related to effective diffusivity by multivariate linear regression. This data-driven approach is compared to the conventional correlations and shown to achieve a very high accuracy for capturing the diffusive transport in the tested PEFC components.