Dynamic response mechanism of the galloping energy harvester under fluctuating wind conditions. (1st March 2022)

• Record Type: Journal Article
• Authors: Xu, Ming; Wang, Bin; Li, Xiaoya; Zhou, Shengxi; Yurchenko, Daniil
• Is Part Of: Mechanical systems and signal processing. Volume 166(2022)
• Publication Date: 2022-03-01
• Subjects: Galloping energy harvester -- Fluctuating wind -- Mean output power -- Stochastic averaging; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108410
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Dynamic performance of the galloping energy harvester under fluctuating wind. conditions is theoretically analyzed. Stochastic averaging is provided to determine the random responses. The critical wind speed for different parameters is explored. The ratios of time constant and mechanical-electrical coupling coefficient are optimized for energy harvesting enhancement. Abstract: 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.