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An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method 认领 引用 被引量：2
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作者 Xiaoyi Wang Xinyue Chang +2 位作者 Wenxuan Wang Zijie Qiao Feng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1775-1796,共22页
The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the effi... The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method. 展开更多
关键词 Reliability-based design optimization high-dimensional model decomposition point estimation method Lagrange interpolation aviation hydraulic piping system
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Nataf transformation based point estimate method 认领 引用 被引量：45
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作者 LI HongShuang LV ZhenZhou YUAN XiuKai 《Chinese Science Bulletin》 2008年第17期2586-2592,共7页
Structural probabilistic analysis quantifies the effect of input random variables,such as material proper-ties,geometrical parameters and loading conditions,on the structural responses.The point estimate method(PEM)is... Structural probabilistic analysis quantifies the effect of input random variables,such as material proper-ties,geometrical parameters and loading conditions,on the structural responses.The point estimate method(PEM)is a direct and easy-used way to perform the structural probabilistic analysis in practice.In this paper,a novel and efficient point estimate method is proposed for computing the first four statistical moments of structural response which is a function of input random variables.The method adopts Nataf transformation to replace Rosenblatt transformation in conventional point estimate method.Because of the nature of engineering problems and limited statistical data,the joint probability density function(PDF)of all input random variables is hard to acquire,but it must be known in Rosenblatt transformation.A more common case is that the marginal PDF of each random variable and the correlation matrix are available,which just satisfy the service condition of Nataf transformation.Hence the Nataf transformation based point estimate method is particularly suitable for engineering applications.The comparison between the proposed method and the conventional point estimate method shows that(1)they are equivalent when all random variables are mutually independent;(2)if the marginal PDFs and the correlation matrix are known,the conventional PEM cannot be applicable,but the proposed method can give a rational approximation.Finally,the procedure is demonstrated in detail through a simple illustration. 展开更多
关键词 structural probabilistic analysis point estimate method Nataf transformation statistical moment correlation matrix
Seismic safety assessment with non-Gaussian random processes for train-bridge coupled systems 认领 引用 被引量：2
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作者 Zhao Han Gao Lei +4 位作者 Wei Biao Tan Jincheng Guo Peidong Jiang Lizhong Xiang Ping 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2024年第1期241-260,共20页
Extensive high-speed railway(HSR)network resembled the intricate vascular system of the human body,crisscrossing mainlands.Seismic events,known for their unpredictability,pose a significant threat to both trains and b... Extensive high-speed railway(HSR)network resembled the intricate vascular system of the human body,crisscrossing mainlands.Seismic events,known for their unpredictability,pose a significant threat to both trains and bridges,given the HSR’s extended operational duration.Therefore,ensuring the running safety of train-bridge coupled(TBC)system,primarily composed of simply supported beam bridges,is paramount.Traditional methods like the Monte Carlo method fall short in analyzing this intricate system efficiently.Instead,efficient algorithm like the new point estimate method combined with moment expansion approximation(NPEM-MEA)is applied to study random responses of numerical simulation TBC systems.Validation of the NPEM-MEA’s feasibility is conducted using the Monte Carlo method.Comparative analysis confirms the accuracy and efficiency of the method,with a recommended truncation order of four to six for the NPEM-MEA.Additionally,the influences of seismic magnitude and epicentral distance are discussed based on the random dynamic responses in the TBC system.This methodology not only facilitates seismic safety assessments for TBC systems but also contributes to standard-setting for these systems under earthquake conditions. 展开更多
关键词 train-bridge coupled(TBC)system random vibration new point estimate method(NPEM) seismic safety assessment moment expansion approximation(MEA) non-Gaussian distributions
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An improved probabilistic load flow in distribution networks based on clustering and Point estimate methods 认领 引用 被引量：2
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作者 Morsal Salehi Mohammad Mahdi Rezaei 《Energy and AI》 EI 2023年第4期253-261,共9页
Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simu... Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion. 展开更多
关键词 Probabilistic load flow(PLF) Distribution network(DN) Monte Carlo simulation(MCS) k-means clustering(KMC) Point estimate method(PEM)
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