Deep adversarial learning models for distribution patterns of piezoelectric plate energy harvesting

Abstract

This paper presents a novel approach utilizing piezoelectric plate structures with random electrode distribution patterns for energy harvesting applications across various vibration modes. For the first time, leveraging electromechanical Finite Element Analysis (eFEA) and data extraction techniques, we investigate the integration of conditional Generative Adversarial Networks (cGAN)-based dynamic models. The cGAN offers an effective technique for generating realistic synthetic data conditioned on input parameters, thereby enabling the creation of diverse and representative datasets for training energy harvesting systems. The integration of eFEA with cGAN opens up new possibilities for optimizing the design and performance of piezoelectric energy harvesters across various applications. Specifically, we explore four distinct cGAN models-based mechanics of energy harvesters by deploying distribution patterns. These models include training data generated by stacking simultaneously mode images, utilizing separate cGAN models for each mode, labeling images by mode, and concatenating all mode images into one. Our study focuses on assessing the effectiveness of these models in minimizing loss in cGAN-based power generation and predicting Structural Similarity Index Measure (SSIM) values, and more importantly, identifying the predicted data point outputs from the generated pixel image extractions. By analyzing the generated data from numerical model and its application in deep learning, we aim to enhance the understanding of the effects of distribution patterns and image processing techniques for optimal power generation and the effectiveness of piezoelectric energy harvesting systems across different vibration modes. The studies explore how different distribution patterns affect the power harvesting efficiency and frequency bandwidth, utilizing the generated datasets.