Special Issue "Novel Methods for Reliability Evaluation and Optimization of Complex Mechanical Structures"

Submission Deadline: 31 July 2020 (closed)
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Guest Editors
Prof. Yangjun Luo, Dalian University of Technology, China
Dr. Feng Zhang, Northwestern Polytechnical University, China

Summary

Reliability of mechanical structures is a very important issue in the engineering field, especially in the field of aerospace and other high-tech sophisticated fields. Up to now, reliability-based evaluation and optimization methods have been widely applied in the safety analysis and design of mechanical structures. Many advanced algorithms for greatly improving the calculation efficiency and accuracy of solutions have been developed and gained successful applications in real engineering problems. Recently, with the advantages of strong versatility and well global-searching ability, intelligent algorithms for reliability optimization design of mechanical structures have also attracted ever-increasing interest. For the failure of complex mechanical structures under multi-physical coupling field, the reliability optimization method based on intelligent algorithms is applicable to solving design failure problems.

In this special issue, "Novel methods for reliability evaluation and optimization of complex mechanical structures", we thus invite researchers and practitioners to present their original ideas and novel algorithms in the reliability-based evaluation and reliability-based optimization. Suggested topics include, but are not limited to:
• Reliability Evaluation Index of Complex Mechanical Structures

• Innovation and Improvement of Intelligent Algorithms

• Fault Analysis of Complex Mechanical Structures

• Reliability Evaluation Based on Intelligent Algorithms

• Reliability Optimization Design in Intelligent Algorithms



Keywords
Structure Reliability; Intelligent Algorithms; Reliability Evaluation; Reliability Optimization

Published Papers
  • Subinterval Decomposition-Based Interval Importance Analysis Method
  • Abstract The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty. When an input variable is described by a specific interval rather than a certain probability distribution, the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods. Generally, the non-probabilistic importance analysis methods involve the Monte Carlo simulation (MCS) and the optimization-based methods, which both have high computational cost. In order to overcome this problem, this study proposes an interval important analytical method avoids the time-consuming optimization process. First, the original performance function is… More
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  • A Local Sparse Screening Identification Algorithm with Applications
  • Abstract Extracting nonlinear governing equations from noisy data is a central challenge in the analysis of complicated nonlinear behaviors. Despite researchers follow the sparse identification nonlinear dynamics algorithm (SINDy) rule to restore nonlinear equations, there also exist obstacles. One is the excessive dependence on empirical parameters, which increases the difficulty of data pre-processing. Another one is the coexistence of multiple coefficient vectors, which causes the optimal solution to be drowned in multiple solutions. The third one is the composition of basic function, which is exclusively applicable to specific equations. In this article, a local sparse screening identification algorithm (LSSI) is proposed… More
  •   Views:401       Downloads:259        Download PDF