Investigating electrode pattern effects on piezoelectric energy harvesting using cGAN and eFEA integration
Journal
Active and Passive Smart Structures and Integrated Systems XIX
Part Of
Active and Passive Smart Structures and Integrated Systems XIX
Start Page
38
Date Issued
2025-03-17
Author(s)
Editor(s)
Tol, Serife
Nouh, Mostafa A.
Yang, Jinkyu
Huang, Guoliang
Li, Xiaopeng
Chen, Yangyang
Sugino, Christopher
Abstract
This paper proposes an innovative approach to investigate the impact of electrode patterns on the energy harvesting performance of piezoelectric plates across various vibration modes. By integrating a conditional generative adversarial network (cGAN) with electromechanical finite element analysis (eFEA), realistic synthetic datasets are generated to evaluate the energy harvesting efficiency. The results demonstrate that cGAN-generated datasets significantly enhance the accuracy of power output predictions, with the proposed model achieving the average of Structural Similarity Index Measure (SSIM) values exceeding 0.9. The findings also highlight the critical influence of electrode configurations on modal frequency responses. Additionally, preliminary results suggest that this approach could be extended to segmented piezoelectric layer patterns on the plate.
Event(s)
Active and Passive Smart Structures and Integrated Systems XIX 2025, Vancouver, 17 March 2025 through 21 March 2025. Code 209150
Subjects
conditional Generative Adversarial Network (cGAN)
deep learning
electrode patterns
electromechanical finite element analysis (eFEA)
piezoelectric energy harvesting
plate vibration modes
Publisher
SPIE
Type
conference paper