Rotational energy harvesting from a novel arc-cylinder type vibro-impact dielectric elastomer generator
Source abstract
Abstract
A novel arc-cylinder type dielectric elastomer generator (AVI-DEG) is proposed in this paper to scavenge energy from rotational environment. The proposed AVI-DEG consists of a hollow arc cylinder, an inner rigid ball, two pairs of identical cylindrical frames and two pre-stretched dielectric elastomer membranes (DEMs). When the system is subjected to rotations, the rotational energy can be harvested through the impacts between the ball and the membranes. To simplify the complex rotations resulting from torsional vibrations and pendulum, etc., a harmonic rotational excitation is considered to act on the proposed AVI DEG. The dynamical behaviors of the proposed AVI-DEG are first analyzed theoretically, based on which the system’s energy harvesting (EH) process is further derived. The experiments measuring the output voltages of a DEM under the impacts of a ball using a single-sided impact (SSI) model, which has been conducted previously, are introduced to verify the EH process of the AVI-DEG at each impact. Furthermore, the numerical simulations are conducted to present the dynamical and electrical responses of the system under different rotational excitations, and the parametric influences of the rotational excitation (frequency and amplitude) and system’s dimensions on the system’s EH performance are discussed in detail. Research results show that appropriately setting these parameters can significantly improve the system’s EH performance. This work is also beneficial to investigating the EH performance of the system with given dimensions, or optimizing the system’s dimensions under a given rotational excitation.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Innovative Energy Harvesting Technologies · Dielectric materials and actuators
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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