Response analysis and reliability-based design optimization of structural-acoustic system under evidence theory

2018 ◽  
Vol 59 (3) ◽  
pp. 959-975 ◽  
Author(s):  
Rugao Gao ◽  
Shengwen Yin ◽  
Feng Xiong
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.


2010 ◽  
Vol 29-32 ◽  
pp. 1258-1262
Author(s):  
Hin Xin Guo ◽  
Juan Dai ◽  
Guan Yu Hu ◽  
Li Zhi Cheng

The design optimization of valve-spring is achieved under the condition of expected reliability. The restrain conditions about the reliability of static strength and fatigue strength are considered, and then a dual-objective optimal problem of valve spring is modeled to obtain the lightest mass of valve-spring and the minimum error of spring stiffness. The feasibility of reliability condition is discussed based on evidence theory in order to improve the computational efficiency. By means of the combination of evidence, the upper and lower bounds of reliability are obtained, and a substituting model of restrain condition about reliability is proposed based on the obtained bounds of reliability. After the weighted combination of two objective functions is made, the optimization model is solved by using genetic algorithm. An example is given and it shows that the proposed method is effective.


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