A general method is proposed to model 3D microstructures representative of three-phases anode layers used in fuel cells. The models are based on SEM images of cells with varying morphologies. The materials are first characterized using three morphological measurements: (cross-)covariances, granulometry and linear erosion. They are measured on segmented SEM images, for each of the three phases. Second, a generic model for three-phases materials is proposed. The model is based on two independent underlying random sets which are otherwise arbitrary. The validity of this model is verified using the cross-covariance functions of the various phases. In a third step, several types of Boolean random sets and plurigaussian models are considered for the unknown underlying random sets. Overall, good agreement is found between the SEM images and three-phases models based on plurigaussian random sets, for all morphological measurements considered in the present work: covariances, granulometry and linear erosion. The spatial distribution and shapes of the phases produced by the plurigaussian model are visually very close to the real material. Furthermore, the proposed models require no numerical optimization and are straightforward to generate using the covariance functions measured on the SEM images.Lay description
High resolution microscopy images are commonly used for studying anode layers and other components used in fuel cells. The performance of fuel cell materials is largely governed by their transport properties. Fluid, ions and electronic conductivity in particular depend on the microstructure. Understanding and modeling their morphology at the microscopic scale is therefore critical to develop new devices with improved properties. In this study, several methods are employed to model these media, based on three types of anode layers of different origins and aspect. A general methodology is used to represent materials made of three different phases, based on two random 3D sets which are independently chosen. The independent models are computed according to statistical measurement carried out on the experimental images. The best model, which reproduces the correlation function of the experimental images and other statistical features, is shown to model accurately the three types of anode layers investigated. The correlation function of the anode layers is also modeled, which can serves as the basis for generic models of these materials.