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)...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Japan Society of Mechanical Engineers
2025-05-01
|
| Series: | Journal of Thermal Science and Technology |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/jtst/20/1/20_24-00500/_pdf/-char/en |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|