Reliability-Based Design Optimization for Nonlinear Energy Harvesters

Author(s):  
Sumin Seong ◽  
Christopher Mullen ◽  
Soobum Lee

This paper presents reliability-based design optimization (RBDO) and experimental validation of the purely mechanical nonlinear vibration energy harvester we recently proposed. A bi-stable characteristic was embodied with a pre-stressed curved cantilever substrate on which piezoelectric patches were laminated. The curved cantilever can be simply manufactured by clamping multiple beams with different lengths or by connecting two ends of the cantilever using a coil spring. When vibrating, the inertia of the tip mass activates the curved cantilever to cause snap-through buckling and makes the nature of vibration switch between two equilibrium positions. The reliability-based design optimization study for maximization of power density and broadband energy harvesting performance is performed. The benefit of the proposed design in terms of excellent reliability, design compactness, and ease of implementation is discussed. The prototype is fabricated based on the optimal design result and energy harvesting performance between the linear and nonlinear energy harvesters is compared. The excellent broadband characteristic of the purely mechanical harvester will be validated.

Author(s):  
Ioannis Petromichelakis ◽  
Apostolos F. Psaros ◽  
Ioannis A. Kougioumtzoglou

Abstract A methodology based on the Wiener path integral technique (WPI) is developed for stochastic response determination and reliability-based design optimization of a class of nonlinear electromechanical energy harvesters endowed with fractional derivative elements. In this regard, first, the WPI technique is appropriately adapted and enhanced to account both for the singular diffusion matrix and for the fractional derivative modeling of the capacitance in the coupled electromechanical governing equations. Next, a reliability-based design optimization problem is formulated and solved, in conjunction with the WPI technique, for determining the optimal parameters of the harvester. It is noted that the herein proposed definition of the failure probability constraint is particularly suitable for harvester configurations subject to space limitations. Several numerical examples are included, while comparisons with pertinent Monte Carlo simulation data demonstrate the satisfactory performance of the methodology.


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