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Solid oxide fuel cells have become a promising candidate in the development of high-density clean energy sources for the rapidly increasing demands in energy and global sustainability. In order to understand more about solid oxide fuel cells, the important step is to understand how to model heterogeneous materials. Heterogeneous materials are abundant in nature and also created in various processes. The diverse properties exhibited by these materials result from their complex microstructures, which also make it hard to model the material. Microstructure modeling and reconstruction on a meso-scale level is needed in order to produce heterogeneous models without having to shave and image every slice of the physical material, which is a destructive and irreversible process. Yeong and Torquato [1] introduced a stochastic optimization technique that enables the generation of a model of the material with the use of correlation functions. Spatial correlation functions of each of the various phases within the heterogeneous structure are collected from a two-dimensional micrograph representing a slice of a solid oxide fuel cell through computational means. The assumption is that two-dimensional images contain key structural information representative of the associated full three-dimensional microstructure. The collected spatial correlation functions, a combination of one-point and two-point correlation functions are then outputted and are representative of the material. In the reconstruction process, the characteristic two-point correlation functions is then inputted through a series of computational modeling codes and software to generate a three-dimensional visual model that is statistically similar to that of the original two-dimensional micrograph. Furthermore, parameters of temperature cooling stages and number of pixel exchanges per temperature stage are utilized and altered accordingly to observe which parameters has a higher impact on the reconstruction results. Stochastic optimization techniques to produce three-dimensional visual models from two-dimensional micrographs are therefore a statistically reliable method to understanding heterogeneous materials.
ContributorsPhan, Richard Dylan (Author) / Jiao, Yang (Thesis director) / Ren, Yi (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In this study, the viability of doped ceria for SOFC electrolyte application is investigated through calculation of the oxygen anion diffusion through undoped, Zr-doped, Pr-doped, and Gd-doped ceria. DFT calculations are performed to determine the oxygen vacancy formation and activation energy to vacancy migration barriers for each material. All dopants were found to increase the activation energy to vacancy migration and decrease the oxygen vacancy formation energy. These energy barriers are then integrated into a kinetic Monte Carlo simulation that models the oxygen vacancy diffusion over time. From the simulation results, the diffusivity of oxygen anion through each material is calculated as a function of dopant concentration and temperature. It was discovered that diffusivity increased with temperature and decreased with dopant concentration in all dopant cases. Gd-doped ceria exhibited the highest overall oxygen diffusion rates, making it the most effective choice for SOFC electrolyte application, while Zr-doped ceria would be the least effective choice with the lowest diffusion rates.
ContributorsMandal, Bennett (Author) / Muhich, Christopher (Thesis director) / Milcarek, Ryan (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12