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  5. Bladeless Wind Turbine Triboelectric Nanogenerator for Effectively Harvesting Random Gust Energy

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Article
en
2024

Bladeless Wind Turbine Triboelectric Nanogenerator for Effectively Harvesting Random Gust Energy

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en
2024
DOI: 10.1002/aenm.202401543

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Mingkang Zhu
Jianyang Zhu
Jinzhi Zhu
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Abstract

Abstract In the environment, there is an abundance of gust energy which is challenging to harvest with conventional rotating wind turbines, such as the gusts generated by passing vehicles along roadsides. Addressing the irregular and low‐frequency characteristics of gusts, a bladeless wind turbine triboelectric nanogenerator (BWT‐TENG) with enhanced aerodynamic performance is proposed, enabling effective harvesting of random gust energy. First, a bladeless wind turbine with a cylindrical bluff body shape is designed, and its aerodynamic principles under gust‐driven conditions are elucidated through the computational fluid dynamics method. Subsequently, parameter optimization is conducted for the multilayered TENG. Systematic experiments demonstrated that the BWT‐TENG achieved a peak power density of 4.08 W m −3 driven by a gust of 10 m s −1 , and can even operate at frequencies as low as 0.1 Hz. Finally, experiments showcased the BWT‐TENG powering a warning light in a simulated rainfall environment and harvesting gust energy from vehicles passing by real roadside to power wireless gyroscopic sensors, thereby achieving self‐powered structural health monitoring of roads or bridges. This work provides a novel strategy for utilizing TENGs in the harvest of environmental gust energy and demonstrates the vast potential of TENGs in the field of self‐powered structural health monitoring.

How to cite this publication

Mingkang Zhu, Jianyang Zhu, Jinzhi Zhu, Zilong Zhao, Hengyu Li, Xiaojun Cheng, Zhong Lin Wang, Tinghai Cheng (2024). Bladeless Wind Turbine Triboelectric Nanogenerator for Effectively Harvesting Random Gust Energy. , DOI: https://doi.org/10.1002/aenm.202401543.

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Publication Details

Type

Article

Year

2024

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aenm.202401543

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