材料科学
压电
声学
聚偏氟乙烯
能量收集
灵敏度(控制系统)
声能
能量(信号处理)
复合材料
工程类
电子工程
聚合物
声音(地理)
物理
统计
数学
作者
Ran Zhang,Hao Shao,Tong Lin,Xu Wang
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2022-06-01
卷期号:151 (6): 3838-3846
被引量:3
摘要
Sound pollution has been capturing more and more attention around the world. Piezoelectric materials convert acoustic energy into electrical energy and actively attenuate the sound simultaneously. In this paper, an electro-spun nonwoven polyvinylidene difluoride nanofiber membrane as a high-performance piezoelectric material is found to have an ultra-high acoustoelectric conversion capability at the low sound frequency range. The novelty of the material in this paper is the proposed electro-spun piezoelectric nano-fiber web, which presents a strong acoustic-to-electric conversion performance. The piezoelectric acoustic energy harvester consists of the polyvinylidene difluoride nanofiber membrane that vibrates under the sound wave excitation. The piezoelectric acoustic energy harvester device can precisely detect the sound of 72.5 Hz with a sensitivity as high as 711.3 mV Pa−1 which is higher than the sensitivity of a commercial piezoelectric poly (vinylidene fluoride) membrane device. The energy harvesting performance of the piezoelectric acoustic energy harvester device is simulated by the comsol software and then validated with the experimental results to illustrate its excellent energy harvesting ability. Based on the validated simulation model, a regression parameter model is developed from the comsol software simulation results using the response surface method. The empirical regression parameter model is applied to predict the energy harvesting performance of the acoustic energy harvester from input design parameters or material property parameters where the sensitivity of the design parameters or material property parameters and their interactions can be analyzed. The design or material property parameters can be optimized for the best energy harvesting performance based on the regression parameter model. The optimization results show a significant improvement in the energy harvesting performance. The sensitivity of the parameters on the energy harvesting performance also indicates the potential of the large-scale application of this acoustic energy harvester.
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