Global sensitivity analysis of asymmetric energy harvesters

TL;DR

This paper uses global sensitivity analysis to identify key parameters affecting the power output of asymmetric bistable energy harvesters with nonlinear piezoelectric coupling, aiding robust design and optimization.

Contribution

It applies Sobol indices for sensitivity analysis to determine critical parameters influencing energy harvester performance under variability.

Findings

Frequency and amplitude of excitation are critical parameters.

Electrical properties of piezoelectric coupling significantly impact power.

Parameter importance varies with the stability of the system's dynamic response.

Abstract

Parametric variability is inevitable in actual energy harvesters. It can significantly affect crucial aspects of the system performance, especially in harvesting systems that present geometric parameters, material properties, or excitation conditions that are susceptible to small perturbations. This work aims to develop an investigation to identify the most critical parameters in the dynamic behavior of asymmetric bistable energy harvesters with nonlinear piezoelectric coupling, considering the variability of their physical and excitation properties. For this purpose, a global sensitivity analysis based on orthogonal variance decomposition, employing Sobol indices, is performed to quantify the effect of the harvester parameters on the variance of the recovered power. This technique quantifies the variance concerning each parameter individually and collectively regarding the total variation of the model. The results indicate that the frequency and amplitude of excitation, asymmetric terms and electrical proprieties of the piezoelectric coupling are the most critical parameters that affect the mean power harvested. It is also shown that the order of importance of the parameters can change according to the stability of the harvester's dynamic response. In this way, a better understanding of the system under analysis is obtained since the study allows the identification of vital parameters that rule the change of dynamic behavior and therefore constitutes a powerful tool in the robust design, optimization, and response prediction of nonlinear harvesters.