A phase-field model for describing three-phase electrode microstructure (i.e., electrode-phase, electrolyte-phase, and pore-phase) in solid oxide fuel cells is proposed using the diffuse-interface theory. Conserved composition and non-conserved grain orientation order parameters are simultaneously used to describe the coupled phase coarsening and grain growth in the three-phase electrode. The microstructural evolution simulated by the phase-field approach demonstrates the significant dependence of morphological microstructure and output statistic material features on the prescribed kinetic parameters and three-phase volume fractions. The triple-phase boundary fraction is found to have a major degradation in the early evolution.

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.