Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 166, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2021.108410
Keywords
Galloping energy harvester; Fluctuating wind; Mean output power; Stochastic averaging
Categories
Funding
- National Natural Science Foundation of China [11872061, 12072267, 12111530105]
- 111 Project [BP0719007]
- Zhejiang Provincial Natural Science Foundation of China [LY20A020006]
- Fundamental Research Funds for the Provincial Universities of Zhejiang [2020YW07]
- Royal Society International Exchanges [IEC\NSFC\201127]
Ask authors/readers for more resources
The study investigates the dynamic characteristics of the galloping energy harvester by separating wind speed into mean and fluctuating components, considering the latter as Gaussian white noise. Decoupling the output voltage using a generalized harmonic transformation and deriving mean output power through stochastic averaging are key approaches employed. Critical wind speed and optimal parameters of the harvester were determined, with the analytical procedure validated through Monte Carlo simulation.
The galloping energy harvesting technique has been extensively applied to harvest fluid energy and utilize the aeroelastic instability of mechanical systems in a flow field for low-powered electronic devices. However, the flow field is random in the real world. In previous studies, fluid motion was regarded as a deterministic process (i.e., with a constant wind speed). However, to determine the actual dynamic characteristics of the galloping energy harvester (GEH), fluctuations in wind speeds should be fully considered. In this study, the wind speed is separated into a mean component and a fluctuating component with the latter being considered Gaussian white noise. The mechanical and electrical dynamical statuses of the GEH are described by electromechanical coupled equations. The output voltage is decoupled by applying a generalized harmonic transformation and is approximated by the mechanical subsystem status. The mean output power is derived by applying stochastic averaging. The critical wind speed and optimal parameters of the harvester were determined in detail. The presented analytical procedure was verified by Monte Carlo simulation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available