Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling
Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling
复制标题
具有动态差异减少建模的固体氧化物燃料电池电极微观结构演化预测的降阶模型
DOI：
10.1016/j.jpowsour.2019.01.046
复制
发表时间：
2019
影响因子：
9.2
通讯作者：
Y. Wen
中科院分区：
文献类型：
--
作者：
Yinkai Lei;Tian;D. Mebane;Y. Wen
固体氧化物燃料电池电极的微观结构演变是降低氧化还原反应活性位点和电导率的重要降解机制。用于微观结构演化模拟的相场模型对于大规模模拟来说通常是昂贵的。本文采用动态差异约简模型，通过在奥斯特瓦尔德成熟动力学方程中插入高斯过程随机函数来降低模型阶数，建立了一种降阶粗化模型。在相场模型生成的数据集上对降阶模型进行了标定，并得到了实验验证。生成了一个验证数据集，该数据集与模型预测结果吻合良好。该模型进一步应用于预测不同SOFC电极的长期微观结构演变。这项工作是使用数据科学技术构建SOFC退化模型的第一次尝试。
Microstructure evolution in the electrodes of solid oxide fuel cell is an important degradation mechanism which reduces active sites for redox reaction and the electric conductivity. Phase field models for microstructure evolution simulation are usually expensive for large scale simulations. In this work, a reduced-order coarsening model is developed using dynamic discrepancy reduced modeling, which reduces the model order by inserting Gaussian process stochastic functions into the dynamic equations of Ostwald ripening. The reduced order model has been calibrated on a dataset generated by a phase field model that has been well validated to experiments. A validating dataset has also been generated with which the model prediction show good agreement. This model is further applied to predict long term microstructure evolution in different SOFC electrodes. This work is the first attempt of building a degradation model of SOFC using data science techniques.
影响因子：
17.6
作者：
Bertei, A.;Ruiz-Trejo, E.;Brandon, N. P.
通讯作者：
Brandon, N. P.