Conditional generative adversarial network for generation of three-dimensional porous structure of solid oxide fuel cell anodes with controlled volume fractions
Conditional generative adversarial network for generation of three-dimensional porous structure of solid oxide fuel cell anodes with controlled volume fractions
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DOI：
10.1016/j.jpowsour.2023.233411
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发表时间：
2023-10
影响因子：
9.2
通讯作者：
M. Kishimoto;Yodai Matsui;H. Iwai
中科院分区：
文献类型：
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作者：
M. Kishimoto;Yodai Matsui;H. Iwai
提出了一种基于生成对抗网络（GAN）的结构生成模型，用于合成固体氧化物燃料电池（SOFC）阳极的人工多孔微结构。与传统的gan框架不同的是，对生成器进行额外的训练，以控制生成结构的统计参数，即体积分数。通过将合成结构与三维显微分析得到的真实电极微观结构进行比较，验证了所建立的模型的有效性。显微结构参数，如体积分数，比表面积，三相边界密度，用于比较除了目测。研究了发生器输入矢量大小和损失定义对生成真实结构和控制结构体积分数的能力的影响。开发的模型成功地生成了具有精确控制体积分数的真实阳极微结构，即使对于未包含在训练数据集中的成分也是如此。研究还发现，损失之间的平衡影响了体积分数控制的准确性和生成结构的多样性。开发的GAN模型有望有助于构建电极制造和评估过程的数字孪生。
A structure generation model based on a generative adversarial network (GAN) is developed to synthesize artificial porous microstructures of solid oxide fuel cell (SOFC) anodes. Different from the conventional framework of GANs, additional training is performed for the generator to control statistical parameters, namely, volume fractions, of the generated structures. The developed model is validated by comparing the synthesized structures with the real electrode microstructures obtained by three-dimensional microscopy analysis. Microstructural parameters, such as volume fraction, specific surface area, and triple-phase boundary density, are used for the comparison in addition to the visual observation. The effect of the input vector size for the generator and the definition of the loss on the ability to generate realistic structures and control the volume fractions of the structures is investigated. The developed model successfully generates realistic anode microstructures with accurately controlled volume fractions, even for compositions not included in the training datasets. It is also found that the balance between the losses influences the accuracy of the volume fraction control and diversity of the generated structures. The GAN model developed is expected to be helpful in constructing a digital twin of electrode fabrication and evaluation processes.