Abstract
The study of the microstructure of random heterogeneous materials, related to an electrochemical device, is relevant because their effective macroscopic properties, e.g., electrical or proton conductivity, are a function of their effective transport coefficients (ETC). The magnitude of ETC depends on the distribution and properties of the material phase. In this work, an algorithm is developed to generate stochastic two-phase (binary) image configurations with multiple geometries and polydispersed particle sizes. The recognizable geometry in the images is represented by the white phase dispersed and characterized by statistical descriptors (two-point and line-path correlation functions). Percolation is obtained for the geometries by identifying an infinite cluster to guarantee the connection between the edges of the microstructures. Finally, the finite volume method is used to determine the ETC. Agglomerate phase results show that the geometry with the highest local current distribution is the triangular geometry. In the matrix phase, the most significant results are obtained by circular geometry, while the lowest is obtained by the 3-sided polygon. The proposed methodology allows to establish criteria based on percolation and surface fraction to assure effective electrical conduction according to their geometric distribution; results provide an insight for the microstructure development with high projection to be used to improve the electrode of a Membrane Electrode Assembly (MEA).
Highlights
Due to its fluctuating and intermittent nature, the storage of renewable energy is a challenge
From random heterogeneous materials (RHM), different types of arrangement of two or more phases can be distinguished at the microstructural level, in which phenomena of mass and energy transport can occur, resulting in a valuable effect such as an electric charge based on its effective transport coefficients (ETC)
The present work presented the relationship between the geometry of a polygonal synthetic agglomerate with respect to effective transport coefficient, considering the percolation effect and surface fraction of both phases
Summary
Due to its fluctuating and intermittent nature, the storage of renewable energy is a challenge. There is a lack of investigations examining the geometry influence in conduction transport problems For this reason, numerical analyses are implemented using synthetic images to determine the behavior of different polygonal configurations and their repercussions on the effective electrical conductivity, considering percolation and tortuosity parameters. This approach can provide a new insight in achieving high conduction values which can be applied to scanning electron microscope images
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