Method of reliability design optimization using evidence theory and interval analysis

2008 ◽  
Vol 44 (12) ◽  
pp. 35 ◽  
Author(s):  
Huixin GUO
2017 ◽  
Vol 56 (3) ◽  
pp. 647-661 ◽  
Author(s):  
Z. L. Huang ◽  
C. Jiang ◽  
Z. Zhang ◽  
T. Fang ◽  
X. Han

2019 ◽  
Vol 60 (2) ◽  
pp. 565-580 ◽  
Author(s):  
Z. L. Huang ◽  
C. Jiang ◽  
Z. Zhang ◽  
W. Zhang ◽  
T. G. Yang

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.


2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Lixiong Cao ◽  
Jie Liu ◽  
Chao Jiang ◽  
Zhantao Wu ◽  
Zheng Zhang

Abstract Evidence theory has the powerful feature to quantify epistemic uncertainty. However, the huge computational cost has become the main obstacle of evidence theory on engineering applications. In this paper, an efficient uncertainty quantification (UQ) method based on dimension reduction decomposition is proposed to improve the applicability of evidence theory. In evidence-based UQ, the extremum analysis is required for each joint focal element, which generally can be achieved by collocating a large number of nodes. Through dimension reduction decomposition, the response of any point can be predicted by the responses of corresponding marginal collocation nodes. Thus, a marginal collocation node method is proposed to avoid the call of original performance function at all joint collocation nodes in extremum analysis. Based on this, a marginal interval analysis method is further developed to decompose the multidimensional extremum searches for all joint focal elements into the combination of a few one-dimensional extremum searches. Because it overcomes the combinatorial explosion of computation caused by dimension, this proposed method can significantly improve the computational efficiency for evidence-based UQ, especially for the high-dimensional uncertainty problems. In each one-dimensional extremum search, as the response at each marginal collocation node is actually calculated by using the original performance function, the proposed method can provide a relatively precise result by collocating marginal nodes even for some nonlinear functions. The accuracy and efficiency of the proposed method are demonstrated by three numerical examples and two engineering applications.


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