Exploration of the nonlinear stochastic dynamics of a bi-stable energy harvester
Résumé
Energy harvesting is a very promising technology to provide low levels of power for small autonomous systems, which the applicability encompass a very wide range of areas, that spans from micro/nano sensors in engineering to state of art implants in medicine. Motivated by this context, the present work proposes to explore in deep the nonlinear stochastic dynamics of a bi-stable energy harvester. In particular, it is of interesting to investigate the effects of model parameters uncertainties on the energy harvesting process efficiency, as well as the influence of forcing noise intensity. It presents the construction of a consistent stochastic model of uncertainties to describe the nonlinear dynamic behavior of this bi-stable system. The physical system of interest consists of a energy harvesting device based on a piezo-magneto-elastic beam, subject to effects of large displacements, modeled by a system of 3 nonlinear differential equations. The underlying uncertainties are take into account through a parametric probabilistic approach, where model parameters are described as random variables and realizations of the random external excitation are constructed via Karhunen-Loève decomposition. Monte Carlo method is employed to compute the propagation of uncertainties through the stochastic model. Numerical experiments show that the system randomness may be beneficial or not the harvester efficiency.