Stochastic analysis of galloping piezoelectric energy harvesters under turbulent flow conditions based on the probability density evolution method

Abstract

Harvesting wind energy to power wireless sensors is a very promising technology. Galloping piezoelectric energy harvesters (GPEHs) are considered to be an excellent solution that can be miniaturized but high efficiency. In order to better understand the working mechanism and determine the design method, it is very necessary to develop accurate theoretical model of GPEHs. In conventional method, the wind was usually simplified as uniform flow with constant wind speed. In practice, however, the wind is turbulent flow that consists of uniform and fluctuating components. The influence of fluctuating component on GPEHs is usually neglected in existing literatures, which will lead to significant errors. To address this issue, this work proposes a stochastic method for GPEHs based on the probability density evolution method (PDEM). Aerodynamic coefficients are regarded as random variables to describe the random excitation induced by turbulent flow. The confidence bands of the output power and the cut-in wind speed of GPEHs under turbulent flow condition are predicted, which satisfy specific probability density distribution. The probabilistic concentrated phenomenon of the output power at low wind speed and far from the optimal resistance is observed. Its mechanism is explored in detail. In addition, a parametric study is conducted to highlight the influence of the variability of the aerodynamic coefficients and the probability distributions of the aerodynamic coefficients on the output power. The proposed method is verified by Monte Carlo Simulation (MCS) and experiment.