Deep convolutional neural network for quantification of tortuosity factor of solid oxide fuel cell anode

Deep convolutional neural network for quantification of tortuosity factor of solid oxide fuel cell anode

A deep convolutional neural network model (DCNN) is developed to quantify the tortuosity factor of porous electrodes of solid oxide fuel cells (SOFCs). The DCNN model is first trained using synthetic three-dimensional (3D) structure datasets generated by either a generative adversarial network (GAN)...

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Bibliographic Details
Main Authors: Masashi KISHIMOTO, Yodai MATSUI, Hiroshi IWAI
Format: Article
Language:English
Published: The Japan Society of Mechanical Engineers 2025-05-01
Series:Journal of Thermal Science and Technology
Subjects:
solid oxide fuel cell
machine learning
convolutional neural network
porous material
tortuosity factor
surrogate model
Online Access:https://www.jstage.jst.go.jp/article/jtst/20/1/20_24-00500/_pdf/-char/en
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