Theoretical Modeling of Triboelectric Receiver Transducer for Mechanic-Electrical Transformations
NANO ENERGY（2024）
South China Normal Univ
Abstract
In recent years, receiver transducers have gained extensive attention in both research and industrial fields. In particular, the triboelectric receiver transducer (TE-RT) stands out due to their self-powered capability, low cost, and excellent output performance. While theoretical methods for analyzing the output performance are still lacked, which may limit the further development of this field. In this study, we propose an equivalent electromechanical conversion circuit that couples the acoustic field and electric field to predict the performance characteristics of the TE-RT. Specifically, the relationship between the deformation of the diaphragm and output performance of the TE-RT is analyzed through theoretical modeling and clarification of governing equations. Subsequently, a specific transformer revealing the mechanical-electrical conversion mechanism for the TE-RT was proposed using the equivalent circuit method (ECM), and an ECM model was established to analyze the performance of TE-RT quickly and accurately. To further improve the output performance of the TE-RT, piezoelectric materials are integrated into the model to enhance its capacity for converting acoustic energy. Verification for the two types of receiver transducer was conducted using the finite element method (FEM), and the equivalent circuit model fit the simulation results well. The acoustic energy conversion efficiency of the TE-RT can also be further evaluated by the number of turns (n) in the specialized transformer. This study can provide a solid theoretical foundation for the design and development of receiver transducer.
MoreTranslated text
Key words
Receiver transducer,Triboelectric,Equivalent circuit method,Finite element method
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined