Correlation between microstructures and macroscopic properties of nickel/yttria-stabilized zirconia (Ni-YSZ) anodes: Meso-scale modeling and deep learning with convolutional neural networks

Abstract

A deep learning based homogenization framework is proposed to link the microstructures of porous nickel/yttria-stabilized zirconia anodes in solid oxide fuel cells (SOFCs) to their effective macroscopic properties. A variety of microstructures are generated by the discrete element method and the meso‑scale kinetic Monte Carlo method. Then, the finite element method and the homogenization theory are used to calculate the effective elastic modulus (E), Poisson's ratio (υ), shear modulus (G) and coefficient of thermal expansion (CTE) of representative volume elements. In addition, the triple-phase boundary length density (LTPB) is also calculated. The convolutional neural network (CNN) based deep learning model is trained to find the potential relationship between the microstructures and the five effective macroscopic properties. The comparison between the ground truth and the predicted values of the new samples proves that the CNN model has an excellent predictive performance. This indicates that the CNN model could be used as an effective alternative to numerical simulations and homogenization because of its accurate and rapid prediction performance. Hence the deep learning-based homogenization framework could potentially accelerate the continuum modeling of SOFCs for microstructure optimization.