A time-continuous approach to analyzing anode aging in solid-oxide fuel cells via stochastic 3D microstructure modeling and physics-based simulations

Abstract

Solid-oxide fuel cells (SOFCs) are a promising energy conversion technology, offering a low environmental impact, low costs and high flexibility regarding the choice of the fuel. However, electrochemical performance of SOFCs decreases with time as a result of complex structural aging mechanisms of their anodes that are not yet fully understood. An option to quantitatively investigate this aging behavior could be tomographic imaging of the 3D microstructure of SOFC anodes for different aging durations, which is expensive and time-consuming. To overcome this issue, physics-based aging simulations resolving the 3D microstructural evolution can be exploited, which use tomographic image data of pristine SOFC anodes consisting of nickel, gadolinium-doped ceria (GDC) and pore space, as initial state. This microstructure simulation method is based on a grand-chemical potential multi-phase-field approach including surface diffusion. Computations conducted with the simulation framework are capable to predict the coarsening of the multiphase polycrystalline electrode. A promising approach to further accelerate the quantitative investigation of SOFC degradation is to combine physics-based aging simulation with data-driven stochastic 3D microstructure modeling, which is typically less computationally intensive compared to phase-field simulations. More precisely, an excursion set model based on Gaussian random fields is used to characterize the 3D microstructure of SOFC anodes by means of a small number of interpretable model parameters. Moreover, the evolution of the parameter vector of the calibrated stochastic 3D model over time is modeled by analytical functions that make fast predictive simulations possible. The prediction robustness is investigated by first assuming that the evolution of the 3D microstructure is known up to a certain point in time. Then, in a second step, the 3D microstructure of SOFC anodes is predicted for further future points in time and, through geometrical descriptors, compared with the results of physics-based aging simulation. • Combining multi-phase field simulations with stochastic modeling to quantitatively analyze the aging behavior of SOFC anodes. • Time-continuous stochastic 3D model based on excursion sets of random fields for SOFC anodes. • Predictive microstructure simulations by interpolation of model parameters. • Analytical regression formulas to investigate the relationship between model parameters and geometrical descriptors.

Topics & Concepts