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  5. Self‐powered In‐Phase Sensing and Regulating Mechanical System Enabled by Nanogenerator and Electrorheological Fluid

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Article
en
2022

Self‐powered In‐Phase Sensing and Regulating Mechanical System Enabled by Nanogenerator and Electrorheological Fluid

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en
2022
Vol 33 (9)
Vol. 33
DOI: 10.1002/adfm.202212248

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Jianfeng Sun
Lingjun Zhang
Xindan Hui
+6 more

Abstract

Abstract Mechanical system with adjustable stiffness and damping (ASAD) shows great potential in vibration suppression of aircraft, vehicle, precise instrument, etc. However, current ASAD systems consist of power, sensing, and controlling components, which bring huge challenges of system reliability and integrability, and critically limit its application diversity. Herein, we proposed the first strategy for regulating ASAD system that is system simplification, self‐powered and no‐delay by deeply coupling triboelectric nanogenerator (TENG) and electrorheological fluid (ERF). Under the time‐varying mechanical triggering, the local and time correlated output of TENG instantaneously applied on the ERF regulates the entire mechanical system dynamically and “in‐phase”. As an illustration, the resonance of a cantilever system is effectively suppressed naturally without complicated controlling strategy, and the vibration isolating efficiency reaches up to 85.2%. Finally, a mechanical transmission system is demonstrated utilizing a rotary TENG and ERF, excellent soft‐start performance is enabled by this self‐regulating ASAD strategy. This work may provide the opening arsenal of methodologies used in self‐powered and self‐regulating mechanical systems.

How to cite this publication

Jianfeng Sun, Lingjun Zhang, Xindan Hui, Yingzhou Huang, Jie Chen, Chenguo Hu, Hengyu Guo, Song Qi, Zhong Lin Wang (2022). Self‐powered In‐Phase Sensing and Regulating Mechanical System Enabled by Nanogenerator and Electrorheological Fluid. , 33(9), DOI: https://doi.org/10.1002/adfm.202212248.

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Publication Details

Type

Article

Year

2022

Authors

9

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/adfm.202212248

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