Correlation between microstructures and macroscopic properties of nickel/yttria-stabilized zirconia (Ni-YSZ) anodes: Meso-scale modeling and deep learning with convolutional neural networks
Correlation between microstructures and macroscopic properties of nickel/yttria-stabilized zirconia (Ni-YSZ) anodes: Meso-scale modeling and deep learning with convolutional neural networks
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DOI：
10.1016/j.egyai.2021.100122
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Xuhao Liu;Shihao Zhou;Zilin Yan;Zheng Zhong;N. Shikazono;S. Hara
中科院分区：
文献类型：
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作者：
Xuhao Liu;Shihao Zhou;Zilin Yan;Zheng Zhong;N. Shikazono;S. Hara
提出了一种基于深度学习的均匀化框架，将固体氧化物燃料电池（SOFCs）中多孔镍/钇稳定氧化锆阳极的微观结构与其有效的宏观性能联系起来。采用离散元法和中尺度动力学蒙特卡罗法生成了多种微观结构。然后，采用有限元法和均匀化理论计算了具有代表性的体积单元的有效弹性模量(E)、泊松比（υ）、剪切模量(G)和热膨胀系数（CTE）。此外，还计算了三相边界长度密度（LTPB）。训练基于卷积神经网络（CNN）的深度学习模型，寻找微观结构与五种有效宏观性质之间的潜在关系。将新样本的真实值与预测值进行比较，证明了CNN模型具有良好的预测性能。这表明，CNN模型具有准确、快速的预测性能，可以作为数值模拟和均匀化的有效替代方法。因此，基于深度学习的均匀化框架可能会加速sofc的连续建模，以实现微观结构优化。
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.