scholarly journals Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Xin-Jia Meng ◽  
Shi-Kai Jing ◽  
Ye-Dong Wang ◽  
Jing-Tao Zhou ◽  
Li-Xiang Zhang ◽  
...  
Keyword(s):  
Reliability Analysis ◽  
Computational Efficiency ◽  
System Optimization ◽  
Collaborative Optimization ◽  
Analysis Method ◽  
Linear Approximations ◽  
Inverse Reliability Analysis ◽  
Failure Point ◽  
Nonnormal Distribution ◽  
Probable Failure

Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO). However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO) is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most probable failure point (MPP) search of inverse reliability, and then the process of searching for MPP of multidisciplinary inverse reliability is performed based on the framework of CLA-CO. This method improves the MPP searching process through two elements. One is treating the discipline analyses as the equality constraints in the subsystem optimization, and the other is using linear approximations corresponding to subsystem responses as the replacement of the consistency equality constraint in system optimization. With these two elements, the proposed method realizes the parallel analysis of each discipline, and it also has a higher computational efficiency. Additionally, there are no difficulties in applying the proposed method to problems with nonnormal distribution variables. One mathematical test problem and an electronic packaging problem are used to demonstrate the effectiveness of the proposed method.

scholarly journals An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 229
Author(s):  
Fangyi Li ◽  
Yufei Yan ◽  
Jianhua Rong ◽  
Houyao Zhu
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Random Variables ◽  
Equivalent Model ◽  
Independent Random Variables ◽  
Problem Analysis ◽  
Analysis Method ◽  
Calculation Algorithm ◽  
Reliability Problem ◽  
Interval Variables

In practical engineering, due to the lack of information, it is impossible to accurately determine the distribution of all variables. Therefore, time-variant reliability problems with both random and interval variables may be encountered. However, this kind of problem usually involves a complex multilevel nested optimization problem, which leads to a substantial computational burden, and it is difficult to meet the requirements of complex engineering problem analysis. This study proposes a decoupling strategy to efficiently analyze the time-variant reliability based on the mixed uncertainty model. The interval variables are treated with independent random variables that are uniformly distributed in their respective intervals. Then the time-variant reliability-equivalent model, containing only random variables, is established, to avoid multi-layer nesting optimization. The stochastic process is first discretized to obtain several static limit state functions at different times. The time-variant reliability problem is changed into the conventional time-invariant system reliability problem. First order reliability analysis method (FORM) is used to analyze the reliability of each time. Thus, an efficient and robust convergence hybrid time-variant reliability calculation algorithm is proposed based on the equivalent model. Finally, numerical examples shows the effectiveness of the proposed method.

scholarly journals Comparison of reliability evaluation methods between China and Canada standards for wood structures featuring duration of load

2020 ◽  
Vol 66 (1) ◽  
Author(s):  
Qiongyao Wu ◽  
Shuang Niu ◽  
Enchun Zhu
Keyword(s):  
Reliability Analysis ◽  
Analysis Method ◽  
Safety Level ◽  
Wood Structures ◽  
Load Effect ◽  
Key Factor ◽  
Live Loads ◽  
High Level ◽  
Partial Factor ◽  
Duration Of Load

Abstract Duration of load (DOL) is a key factor in design of wood structures, which makes the reliability analysis of wood structures more complicated. The importance of DOL is widely recognized, yet the methods and models through which it is incorporated into design codes vary substantially by country/region. Few investigations of the effect of different model assumptions of DOL and other random variables on the results of reliability analysis of wood structures can be found. In this paper, comparisons are made on the reliability analysis methods that underlie the China and the Canada standards for design of wood structures. Main characteristics of these two methods, especially the way how DOL is treated are investigated. Reliability analysis was carried out with the two methods employing the same set of material properties and load parameters. The resulted relationships between reliability index β and resistance partial factor γR* (the β–γR* curves) for four load combinations are compared to study the safety level indicated by the two methods. The comparison shows that the damage accumulation model (Foschi–Yao model) in the Canada analysis method is highly dependent on the type and duration of load, resulting in more conservative design than the China analysis method in loading cases dominated by dead load, but less conservative design in cases of high level of live loads. The characteristics of the load effect term of the performance function are also found to make considerable difference in reliability levels between the two methods. This study aims to provide references for researchers and standard developers in the field of wood structures.

Reliability Analysis Method on Repairable System with Standby Structure Based on Goal Oriented Methodology

2015 ◽  
Vol 32 (7) ◽  
pp. 2505-2517 ◽  
Author(s):  
Xiao-jian Yi ◽  
B.S. Dhillon ◽  
Jian Shi ◽  
Hui-na Mu ◽  
Hai-ping Dong
Keyword(s):  
Reliability Analysis ◽  
Repairable System ◽  
Analysis Method

scholarly journals Reliability-Based Topology Optimization Using Stochastic Response Surface Method with Sparse Grid Design

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Qinghai Zhao ◽  
Xiaokai Chen ◽  
Zheng-Dong Ma ◽  
Yi Lin
Keyword(s):  
Topology Optimization ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Computational Efficiency ◽  
Sparse Grid ◽  
Stochastic Response ◽  
Stochastic Response Surface Method ◽  
Surface Method ◽  
Grid Design

A mathematical framework is developed which integrates the reliability concept into topology optimization to solve reliability-based topology optimization (RBTO) problems under uncertainty. Two typical methodologies have been presented and implemented, including the performance measure approach (PMA) and the sequential optimization and reliability assessment (SORA). To enhance the computational efficiency of reliability analysis, stochastic response surface method (SRSM) is applied to approximate the true limit state function with respect to the normalized random variables, combined with the reasonable design of experiments generated by sparse grid design, which was proven to be an effective and special discretization technique. The uncertainties such as material property and external loads are considered on three numerical examples: a cantilever beam, a loaded knee structure, and a heat conduction problem. Monte-Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach. Based on the results, it is demonstrated that application of SRSM with SGD can produce an efficient reliability analysis in RBTO which enables a more reliable design than that obtained by DTO. It is also found that, under identical accuracy, SORA is superior to PMA in view of computational efficiency.

A Probabilistic and Interval Hybrid Reliability Analysis Method for Structures with Correlated Uncertain Parameters

2015 ◽  
Vol 12 (04) ◽  
pp. 1540006 ◽  
Author(s):  
C. Jiang ◽  
J. Zheng ◽  
B. Y. Ni ◽  
X. Han
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Correlation Coefficients ◽  
Analysis Method ◽  
Analysis Model ◽  
Probability Interval ◽  
Uncertain Variables ◽  
Uncertainty Model ◽  
Sample Correlation ◽  
Hybrid Reliability

This paper proposes a probability-interval mixed uncertainty model considering parametric correlations and a corresponding structural reliability analysis method. First of all, we introduce the sample correlation coefficients to express the correlations between different kinds of uncertain variables including probability and interval variables. Then dependent parameters are transformed into independent ones through a matrix transformation. A reliability analysis model is put forward, and an efficient method is built to obtain the reliability index or failure probability interval of the structure. Finally, four numerical examples are provided to verify the validity of the method.

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