Stochastic response of an energy harvesting system with viscoelastic element under Gaussian white noise excitation. (October 2021)

• Record Type: Journal Article
• Authors: Guo, Shu-Ling; Yang, Yong-Ge; Sun, Ya-Hui
• Is Part Of: Chaos, solitons and fractals. Volume 151(2021)
• Publication Date: 2021-10
• Subjects: Energy harvesting -- Viscoelastic element -- Stochastic averaging approach -- Gaussian white noise; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2021.111231
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: In this paper, a new energy harvesting system with viscoelastic property influenced by Gaussian white noise is presented. The numerical solutions of Monte Carlo method verifies the correctness of the analytical method presented in this paper. According to probability density function of the amplitude, we discuss the influences of system parameters on the mean square value and mean output power. Abstract: Energy harvesting system is a facility that converts waste kinetic energy into serviceable electric energy. Vibration energy harvesting (VEH)) technology can be used in many different domains, such as controllers, medical implants, wireless sensors. Therefore, for the past few years, energy harvesting has become a prominent and growing field of research. There were few works focused on the vibration energy harvesting with viscoelastic property under random excitation. The crucial difference of the present study and previous studies is that we consider the viscoelastic property and the random excitation simultaneously: the stochastic response of energy harvesting and viscoelastic system subjected to Gaussian white noise excitation is investigated in this paper. First, the term associated with the viscoelasticity can be approximately replaced by stiffness and damping forces via variables transformation. Thus, an equivalent stochastic system without viscoelastic element can be obtained. Then, the approximately analytical solutions of the equivalent stochastic system can be given by the stochastic averaging approach. Furthermore, the theoretical analysis results are compared with the numerical results obtained by Monte Carlo method. The numerical results indicate that the method presented in this paper has a satisfactory level of accuracy. The last, the effects of system parameters on the mean square value and mean output power are discussed. The results show that the noise intensity, electro-magnetic coupling term and the ratio of the resistance and inductance has a positive effect on the mean output power.