Developing a Coupled Statistical and Monte Carlo Approach for Geometric Modeling and Optimizing of Infiltrated Solid Oxide Fuel Cell Electrode

Abstract

AbstractThis study proposes a novel geometric simulation methodology to estimate the performance of solid oxide fuel cells (SFOCs) electrodes using Monte‐Carlo approach coupled with a statistical algorithm to add infiltrated particles in the form of discrete voxels. Electrochemical performance of these infiltrated electrodes is then characterized by direct evaluation of triple phase boundary (TPB) density and active surface density of the particles. Study results, in agreement with experiment and analytical models, illustrate that there is an optimum point for particle loading, which is strongly depended on the backbone properties and particle aggregation behavior. Study also indicates that the reduction rate of the gas transport factor after adding particles strongly depends on the backbone porosity and the distribution of the particles. Further, it is shown that the average shape of aggregated particles alters the TPB density and gas transport behavior, but particle agglomeration always increases the surface density of particles. Finally, the geometric results are compared with electrochemical tests qualitatively and proposed theories in literature quantitatively. A case study is also performed to compare geometric properties of a realized model with a real reconstructed infiltrated electrode.