The escalating need for reliability analysis(RA)and reliability-based design optimization(RBDO)within engineering challenges has prompted the advancement of saddlepoint approximationmethods(SAM)tailored for such probl...The escalating need for reliability analysis(RA)and reliability-based design optimization(RBDO)within engineering challenges has prompted the advancement of saddlepoint approximationmethods(SAM)tailored for such problems.This article offers a detailed overview of the general SAM and summarizes the method characteristics first.Subsequently,recent enhancements in the SAM theoretical framework are assessed.Notably,the mean value first-order saddlepoint approximation(MVFOSA)bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation(MVSOSA);the latter serves as an auxiliary approach to the former.Their distinction is rooted in the varying expansion orders of the performance function as implemented through the Taylor method.Both the saddlepoint approximation and third-moment(SATM)and saddlepoint approximation and fourth-moment(SAFM)strategies model the cumulant generating function(CGF)by leveraging the initial random moments of the function.Although their optimal application domains diverge,each method consistently ensures superior relative precision,enhanced efficiency,and sustained stability.Every method elucidated is exemplified through pertinent RA or RBDO scenarios.By juxtaposing them against alternative strategies,the efficacy of these methods becomes evident.The outcomes proffered are subsequently employed as a foundation for contemplating prospective theoretical and practical research endeavors concerning SAMs.The main purpose and value of this article is to review the SAM and reliability-related issues,which can provide some reference and inspiration for future research scholars in this field.展开更多
One caveat to the dinosaur’s extinction is the conclusion that avian dinosaurs survived and became ancestors of birds. Their mobility enabled them to migrate great distances and find the nutrients needed to survive. ...One caveat to the dinosaur’s extinction is the conclusion that avian dinosaurs survived and became ancestors of birds. Their mobility enabled them to migrate great distances and find the nutrients needed to survive. Given this scenario, could the current observable migration of birds (the “dinosaurian offspring”) now be related? Migration is the regular seasonal movement undertaken by many species of birds, with the most common pattern, flying north in the Northern spring to breed in the temperate or Arctic summer and returning in the Northern autumn to wintering grounds in warmer regions of the south. The primary motivation for migration appears to be food. None of the major North-South migratory pathways fly over the Caribbean but three main fly ways, past to the west of the theorized K-T impact centre. Due to their ability to fly, the “avian Dinosaurs” adapted and survived very quickly in response to the disaster that marked the K-T boundary. It is an interesting speculation that the avian migration that we witness today is rooted in an event that occurred 66 million years ago! But it does explain why the migratory birds mostly fly from Polar summer to polar summer when they could just be as easily fly from Polar zone to the warmer equatorial region and back. In the recent article in Nature by Melanie During about identifying the late spring timing of the “Astro disaster”, it can be cited as consistent with my speculation. A late April early May Impact as suggested by During would have seen these migrations completely. The western migratory routes would have been found to be “luxurious” in vegetation in that first northern autumn after the “Astro-impact” while all eastern routes would have still been barren.展开更多
Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex en...Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.展开更多
The application of the saddlepoint approximation to reliability analysis of dynamic systems is investigated. The failure event in reliability problems is formulated as the exceedance of a single performance variable o...The application of the saddlepoint approximation to reliability analysis of dynamic systems is investigated. The failure event in reliability problems is formulated as the exceedance of a single performance variable over a prescribed threshold level. The saddlepoint approximation technique provides a choice to estimate the cumulative distribution function （CDF） of the performance variable. The failure probability is obtained as the value of the complement CDF at a specified threshold. The method requires computing the saddlepoint from a simple algebraic equation that depends on the cumulant generating function （CGF） of the performance variable. A method for calculating the saddlepoint using random samples of the performance variable is presented. The applicable region of the saddlepoint approximation is discussed in detail. A 10-story shear building model with white noise excitation illustrates the accuracy and efficiency of the proposed methodology.展开更多
基金funded by the National Natural Science Foundation of China under Grant No.52175130the Sichuan Science and Technology Program under Grants Nos.2022YFQ0087 and 2022JDJQ0024+1 种基金the Guangdong Basic and Applied Basic Research Foundation under Grant No.2022A1515240010the Students Go Abroad for Scientific Research and Internship Funding Program of University of Electronic Science and Technology of China.
文摘The escalating need for reliability analysis(RA)and reliability-based design optimization(RBDO)within engineering challenges has prompted the advancement of saddlepoint approximationmethods(SAM)tailored for such problems.This article offers a detailed overview of the general SAM and summarizes the method characteristics first.Subsequently,recent enhancements in the SAM theoretical framework are assessed.Notably,the mean value first-order saddlepoint approximation(MVFOSA)bears resemblance to the conceptual framework of the mean value second-order saddlepoint approximation(MVSOSA);the latter serves as an auxiliary approach to the former.Their distinction is rooted in the varying expansion orders of the performance function as implemented through the Taylor method.Both the saddlepoint approximation and third-moment(SATM)and saddlepoint approximation and fourth-moment(SAFM)strategies model the cumulant generating function(CGF)by leveraging the initial random moments of the function.Although their optimal application domains diverge,each method consistently ensures superior relative precision,enhanced efficiency,and sustained stability.Every method elucidated is exemplified through pertinent RA or RBDO scenarios.By juxtaposing them against alternative strategies,the efficacy of these methods becomes evident.The outcomes proffered are subsequently employed as a foundation for contemplating prospective theoretical and practical research endeavors concerning SAMs.The main purpose and value of this article is to review the SAM and reliability-related issues,which can provide some reference and inspiration for future research scholars in this field.
文摘One caveat to the dinosaur’s extinction is the conclusion that avian dinosaurs survived and became ancestors of birds. Their mobility enabled them to migrate great distances and find the nutrients needed to survive. Given this scenario, could the current observable migration of birds (the “dinosaurian offspring”) now be related? Migration is the regular seasonal movement undertaken by many species of birds, with the most common pattern, flying north in the Northern spring to breed in the temperate or Arctic summer and returning in the Northern autumn to wintering grounds in warmer regions of the south. The primary motivation for migration appears to be food. None of the major North-South migratory pathways fly over the Caribbean but three main fly ways, past to the west of the theorized K-T impact centre. Due to their ability to fly, the “avian Dinosaurs” adapted and survived very quickly in response to the disaster that marked the K-T boundary. It is an interesting speculation that the avian migration that we witness today is rooted in an event that occurred 66 million years ago! But it does explain why the migratory birds mostly fly from Polar summer to polar summer when they could just be as easily fly from Polar zone to the warmer equatorial region and back. In the recent article in Nature by Melanie During about identifying the late spring timing of the “Astro disaster”, it can be cited as consistent with my speculation. A late April early May Impact as suggested by During would have seen these migrations completely. The western migratory routes would have been found to be “luxurious” in vegetation in that first northern autumn after the “Astro-impact” while all eastern routes would have still been barren.
基金support from the National Natural Science Foundation of China(Grant No.52175130)the Sichuan Science and Technology Program(Grant No.2021YFS0336)+4 种基金the China Postdoctoral Science Foundation(Grant No.2021M700693)the 2021 Open Project of Failure Mechanics and Engineering Disaster Prevention,Key Lab of Sichuan Province(Grant No.FMEDP202104)the Fundamental Research Funds for the Central Universities(Grant No.ZYGX2019J035)the Sichuan Science and Technology Innovation Seedling Project Funding Project(Grant No.2021112)the Sichuan Special Equipment Inspection and Research Institute(YNJD-02-2020)are gratefully acknowledged.
文摘Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.
基金Research Committee of University of Macao Under Grant No. G074/05-06S/YKV/FST UMAC.
文摘The application of the saddlepoint approximation to reliability analysis of dynamic systems is investigated. The failure event in reliability problems is formulated as the exceedance of a single performance variable over a prescribed threshold level. The saddlepoint approximation technique provides a choice to estimate the cumulative distribution function （CDF） of the performance variable. The failure probability is obtained as the value of the complement CDF at a specified threshold. The method requires computing the saddlepoint from a simple algebraic equation that depends on the cumulant generating function （CGF） of the performance variable. A method for calculating the saddlepoint using random samples of the performance variable is presented. The applicable region of the saddlepoint approximation is discussed in detail. A 10-story shear building model with white noise excitation illustrates the accuracy and efficiency of the proposed methodology.
