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Systematic study of dual resonant rectilinear-to-rotary motion converter for low frequency vibrational energy harvesting
Author(s): Jingfan Chen; Ya Wang; Benjamin L. Grisso
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Paper Abstract

Energy harvesting of environmental vibrations has been an intensive research field for several years, but there are lingering challenges regarding low frequency applications. Building off our recent invention of a multi-stable, broadband electromagnetic harvester relying on a dual resonant, rectilinear-to-rotary motion converter, research detailed in this paper outlines the creation of theoretical models for the dual resonator and systematically examines these models through experiments to further understand the interrelation of key design parameters and to optimize the harvester’s performance. More specifically, the dual resonant system converts broadband rectilinear vibrations to rotational motions via magnetic coupling, while frequency up-conversion via magnetic plucking converts low frequency vibrations into high frequency rotations. By combining the advantages of multi-stable nonlinearity, the invented dual resonant energy harvester possesses high power density at low operating frequencies. In this paper, a nonlinear electromechanical model of the dual resonant harvester is established, and the parametric study is conducted for various repulsive magnets configurations. Subsequent experiments validate the theoretical analysis. Systematic analysis of both theoretical and experimental results shows that the initial rotation angle and the configurations among the coupling magnets are critical to determining the operating patterns of the rotor and fully exploiting the potential of magnetic plucking to increase voltage output. The optimized dual resonator is advantageous over the linear harvesters by providing wider low frequency bandwidth via the inherent nonlinear dynamics.

Paper Details

Date Published: 15 March 2018
PDF: 13 pages
Proc. SPIE 10595, Active and Passive Smart Structures and Integrated Systems XII, 105952T (15 March 2018); doi: 10.1117/12.2296817
Show Author Affiliations
Jingfan Chen, Stony Brook Univ. (United States)
Ya Wang, Stony Brook Univ. (United States)
Benjamin L. Grisso, Naval Surface Warfare Ctr. Carderock (United States)


Published in SPIE Proceedings Vol. 10595:
Active and Passive Smart Structures and Integrated Systems XII
Alper Erturk, Editor(s)

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