Journal of Verification, Validation and Uncertainty Quantification最新文献

A Curved Surface Integral Method for Reliability Analysis of Multiple Failure Modes System with Non-Overlapping Failure Domains 用于非重叠失效域多失效模式系统可靠性分析的曲面积分法

IF 0.5

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-07-02 DOI: 10.1115/1.4065857

Zhenzhong Chen, H. Mu, Xiaoke Li

{"title":"A Curved Surface Integral Method for Reliability Analysis of Multiple Failure Modes System with Non-Overlapping Failure Domains","authors":"Zhenzhong Chen, H. Mu, Xiaoke Li","doi":"10.1115/1.4065857","DOIUrl":"https://doi.org/10.1115/1.4065857","url":null,"abstract":"\u0000 In the study of reliability of systems with multiple failure modes, approximations can be obtained by calculating the probability of failure for each state function. The first-order reliability method and the second-order reliability method are effective, but they may introduce significant errors when dealing with certain nonlinear situations. Simulation methods such as line sampling method and response surface method can solve implicit function problems, but the large amount of calculation results in low efficiency. The curved surface integral method (CSI) has good accuracy in dealing with nonlinear problems. Therefore, a system reliability analysis method (CSIMMS) is proposed on the basis of CSI for solving multiple failure modes system reliability problems with non-overlapping failure domains. The order of magnitude of the failure probability is evaluated based on the reliability index and the degree of nonlinearity, ignoring the influence of low order of magnitude failure modes, and reducing the calculation of the system failure probability. Finally, CSIMMS and other methods are compared by three numerical examples, and the results show the stability and accuracy of the proposed method.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Framework for Developing Systematic Testbeds for Multi-Fidelity Optimization Techniques 为多保真度优化技术开发系统测试平台的框架

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-06-12 DOI: 10.1115/1.4065719

Siyu Tao, Chaitra Sharma, Srikanth Devanathan

{"title":"A Framework for Developing Systematic Testbeds for Multi-Fidelity Optimization Techniques","authors":"Siyu Tao, Chaitra Sharma, Srikanth Devanathan","doi":"10.1115/1.4065719","DOIUrl":"https://doi.org/10.1115/1.4065719","url":null,"abstract":"\u0000 Multi-fidelity (MF) models abound in simulation-based engineering fields. Many MF strategies have been proposed to improve the efficiency in engineering processes, especially in design optimization. When it comes to assessing the performance of MF optimization techniques, existing practice usually relies on test cases involving contrived MF models of seemingly random math functions, due to limited access to real-world MF models. While it is acceptable to use contrived MF models, these models are often manually written up rather than created in a systematic manner. This gives rise to the potential pitfall that the test MF models may be not representative of general scenarios. We propose a framework to generate test MF models systematically and characterize tested MF optimization methods' performances comprehensively. In our framework, the MF models are generated based on given high-fidelity (HF) model and come with two parameters to control their fidelity levels and allow model randomization. In our testing process, MF case problems are systematically formulated using our model creation method. Running the given MF optimization technique on these problems produces what we call “savings curve” that characterizes the method's performance similarly to how ROC curves characterize machine learning classifiers. Our test results also allow plotting “optimality curves” that serve similar functionality to savings curves in certain types of problems. The flexibility of our MF model creation facilitates the development of testing processes for general MF problem scenarios, and our framework can be easily extended to other MF applications than optimization.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability Analysis for RV Reducer by Combining PCE and Saddlepoint Approximation Considering Multi-Failure Modes 考虑多失效模式，结合 PCE 和鞍点逼近法对 RV 减速器进行可靠性分析

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-06-05 DOI: 10.1115/1.4065690

Shunqi Yang, Huipeng Xiao, Pan Lu, Guohua Xu, Hao Li, Xiaoling Zhang

{"title":"Reliability Analysis for RV Reducer by Combining PCE and Saddlepoint Approximation Considering Multi-Failure Modes","authors":"Shunqi Yang, Huipeng Xiao, Pan Lu, Guohua Xu, Hao Li, Xiaoling Zhang","doi":"10.1115/1.4065690","DOIUrl":"https://doi.org/10.1115/1.4065690","url":null,"abstract":"\u0000 RV (Rotate Vector) reducer is an essential mechanical transmission device extensively used in industrial machinery, robotics, aerospace and other fields. The dynamic transmission characteristics and strength of the cycloidal pin gear, turning arm bearing of RV reducer significantly affect the motion accuracy and reliability of the whole equipment. Uncertainties from manufacturing and assembly error, working loads add complexity to these effects. Developing effective methods for uncertainty propagation and reliability analysis for the RV reducer is crucial. In this work, the mail failure modes of RV reducer are studied, and an effective reliability analysis method for RV reducer considering the correlation between multi-failure modes by combining polynomial chaos expansions (PCE) and saddlepoint approximation method (SPA) is proposed. This paper develops an uncertainty propagation strategy for RV reducer based on dynamic simulation and PCE method with high accuracy. On this basis, a surrogated cumulant generating function (CGF) and SPA are combined to analyze the stochastic characteristic for the failure behavior. Based on the probability density function (PDF) and cumulative distribution function (CDF) calculated by SPA, copula function is employed to quantify the correlations between the multi-failure modes. Then, the system reliability with multi-failure modes is estimated by SPA and optimal copula function. The proposed method provides an effective reliability assessment technology with high-accuracy for complex system under unknown physical model and distribution characteristics. The validity of the proposed approach is illustrated RV-320E reducer reliability estimation, offering a basis to improve the performance of complex dynamic system. .","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning-Based Resilience Modeling and Assessment of High Consequence Systems Under Uncertainty 基于机器学习的复原力建模和不确定性条件下的高后果系统评估

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-05-06 DOI: 10.1115/1.4065466

Liu Cong, Fengjun Wang, Chaoyang Xie

{"title":"Machine Learning-Based Resilience Modeling and Assessment of High Consequence Systems Under Uncertainty","authors":"Liu Cong, Fengjun Wang, Chaoyang Xie","doi":"10.1115/1.4065466","DOIUrl":"https://doi.org/10.1115/1.4065466","url":null,"abstract":"\u0000 This study proposes a theoretical model and assessment method for the resilience of High Consequence System (HCS), addressing the risk assessment and decision-making needs in critical system engineering activities. By analyzing various resilience theories in different domains and considering the characteristics of risk decision-making for HCS, a comprehensive theoretical model for the resilience of HCS is developed. This model considers the operational capability under normal environment (consisting of reliability and maintainability) and the safety capability under abnormal environment (consisting of resistance and emergence response ability). A case study is conducted on a spent fuel transportation packaging system, where the sealing performance after sealing ring aging is regarded as the reliability of the system and calculated using reliability methods, and impact resistance after impact is regard as resistance the impact safety of the packaging system is assessed using finite element analysis and surrogate modelling methods. The surrogate model fits the deformation output results of finite elements. Maintainability and emergency response ability are also essential elements of the resilience model for HCS facing exceptional events. The resilience variation of the spent fuel transportation packaging system is computed under the uncertainty of yielding stress of buffer material. The resilience of the packaging system is evaluated for different buffer thicknesses. The system's resilience decreases with higher uncertainty in the yielding stress of the buffer material, while it increases with thicker buffer materials. The improvement of emergency rescue ability will also lead to the improvement of system resilience.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141006679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Posterior Covariance Matrix Approximations 后协方差矩阵近似值

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-04-23 DOI: 10.1115/1.4065378

Abigail Schmid, Stephen Andrews

引用次数: 0

Influence of Sampling Rate on Reproducibility and Accuracy of Miniature Kolsky Bar Experiments 取样率对微型科尔斯基棒实验重现性和准确性的影响

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-03-28 DOI: 10.1115/1.4065207

Thomas H. Hannah, V. Martin, Stephen Ellis, Reuben H. Kraft

{"title":"Influence of Sampling Rate on Reproducibility and Accuracy of Miniature Kolsky Bar Experiments","authors":"Thomas H. Hannah, V. Martin, Stephen Ellis, Reuben H. Kraft","doi":"10.1115/1.4065207","DOIUrl":"https://doi.org/10.1115/1.4065207","url":null,"abstract":"\u0000 The purpose of this work is to develop and verify a method for quantitatively analyzing data collected from Kolsky bar experiments and to confirm its validity by comparing it to a finite element (FE) model. This study also aims to demonstrate the need for higher sample rate capture in miniature Kolsky bars, 3.16mm diameter used in this work, by comparing results from two different data acquisition setups on identically sized experimental setups. We identified that the sample capture rate needed to accurately depict experimental results on small scale systems is at least 400 kHz, which is far greater than what is typically assumed for lager bar systems. Finally, a statistical method for evaluating results is presented an expanded upon which removes the dependence on the knowledge and experience of the experimentalist to interpret the data. Using this analysis technique on the two different systems examined in this study, we find upwards of 3.5 times better loading condition reproducibility and up to a 20 MPa reduction in the standard deviation of the sample stress profile, confirming the need for higher quality frequency capture rates.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of Imperfect Kolsky Bar Experiments Across Different Scales Assessed Using Finite Elements 使用有限元评估不同尺度的不完善科尔斯基棒实验的影响

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-03-28 DOI: 10.1115/1.4065206

Thomas H. Hannah, V. Martin, Stephen Ellis, Reuben H. Kraft

{"title":"Impact of Imperfect Kolsky Bar Experiments Across Different Scales Assessed Using Finite Elements","authors":"Thomas H. Hannah, V. Martin, Stephen Ellis, Reuben H. Kraft","doi":"10.1115/1.4065206","DOIUrl":"https://doi.org/10.1115/1.4065206","url":null,"abstract":"\u0000 Typical Kolsky bars are 10-20mm in diameter with lengths of each main bar being on the scale of meters. To push 104+ strain rates, smaller systems are needed. As the diameter and mass decreases the precision in the alignment must increase to maintain the same relative tolerance, and the potential impacts of gravity and friction change. Finite Element models are typically generated assuming a perfect experiment with exact alignment and no gravity. Additionally, these simulations tend to take advantage of the radial symmetry of an ideal experiment which removes any potential for modeling non-symmetric effects, but has the benefit of reducing computational load. In this work we discuss results from these fast-running symmetry models to establish a baseline and demonstrate their first-order use case. We then take advantage of high-performance computing techniques to generate half symmetry simulations using Abaqu to model gravity and misalignment. The imperfection is initially modeled using a static general step followed by a dynamic explicit step to simulate the impact events. This multi-step simulation structure can properly investigate the impact of these real-world, non-axis symmetric effects. These simulations explore the impacts of these experimental realities and are described in detail to allow other researchers to implement a similar FE modeling structure to aid in experimentation and diagnostic efforts. It is shown that of the two sizes evaluated, the smaller 3.16mm system is more sensitive than the larger 12.7mm system to such imperfections","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximate Integral Method for Nonlinear Reliability Analysis 非线性可靠性分析的近似积分法

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-03-26 DOI: 10.1115/1.4065183

Zhenzhong Chen, Guiming Qiu, Xiaoke Li, Rui Jin

{"title":"Approximate Integral Method for Nonlinear Reliability Analysis","authors":"Zhenzhong Chen, Guiming Qiu, Xiaoke Li, Rui Jin","doi":"10.1115/1.4065183","DOIUrl":"https://doi.org/10.1115/1.4065183","url":null,"abstract":"In the realm of reliability analysis methods, the First-Order Reliability Method (FORM) exhibits excellent computational accuracy and efficiency in linear problems. However, it fails to deliver satisfactory performance in nonlinear ones. Therefore, this paper proposes an Approximate Integral Method (AIM) to calculate the failure probability of nonlinear problems. Firstly, based on the Most Probable Point (MPP) of failure and the reliability index β obtained from the FORM, the Limit State Function (LSF) can be equivalent to an Approximate Parabola (AP) which divides the hypersphere space into feasible and failure domains. Secondly, through the ratio of the approximate region occupied by a parabolic curve to the entire hypersphere region, the failure probability can be calculated by integration. To avoid the computational complexity in the parabolic approximate area due to high dimensionality, this paper employs a hyper-rectangle, constructed from chord lengths corresponding to different curvatures, as a substitute for the parabolic approximate area. Additionally, a function is utilized to adjust this substitution, ensuring accuracy in the calculation. Finally, compared with the calculated result of the Monte Carlo simulation (MCS) and the FORM, the feasibility of this method can be demonstrated through five numerical examples.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140379416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty Quantification for Multi-Dimensional Correlated Flow Field Responses 多维相关流场响应的不确定性量化

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-03-13 DOI: 10.1115/1.4065070

Wei Zhao, Luogeng Lv, Jiao Zhao, Wei Xiao, Jiangtao Chen, Xiaojun Wu

{"title":"Uncertainty Quantification for Multi-Dimensional Correlated Flow Field Responses","authors":"Wei Zhao, Luogeng Lv, Jiao Zhao, Wei Xiao, Jiangtao Chen, Xiaojun Wu","doi":"10.1115/1.4065070","DOIUrl":"https://doi.org/10.1115/1.4065070","url":null,"abstract":"\u0000 The inherent randomness of fluid dynamics problems or human cognitive limitations results in non-negligible uncertainties in CFD modeling and simulation, leading to doubts about the credibility of CFD results. Therefore, scientific and rigorous quantification of these uncertainties is crucial for assessing the reliability of CFD predictions and informed engineering decisions. Although mature uncertainty propagation methods have been developed for individual output quantities, the challenges lie in the multi-dimensional correlated flow field variables. This article proposes an advanced uncertainty propagation modeling approach based on proper orthogonal decomposition and artificial neural networks. By projecting the multi-dimensional correlated responses onto an orthogonal basis function space, the dimensionality of output is significantly reduced, simplifying the subsequent model training process. An artificial neural network that maps the uncertain parameters of the CFD model to the coefficients of the basis functions is established. Due to the bidirectional representation of flow field variables and basis function coefficients through proper orthogonal decomposition, combined with artificial neural network modeling, rapid prediction of flow field variables under any model parameters is achieved. To effectively identify the most influential model parameters, we employ a multi-output global sensitivity analysis method based on covariance decomposition. Through two exemplary cases of NACA0012 airfoil and M6 wing, we demonstrate the accuracy and efficacy of our proposed approach in predicting multi-dimensional flow field variables under varying model coefficients. Large-scale random sampling is conducted to quantify the uncertainties and identify the key factors that significantly impact the overall flow field.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reviewer's Recognition 评审员表彰

IF 0.6

Journal of Verification, Validation and Uncertainty Quantification Pub Date : 2024-03-01 DOI: 10.1115/1.4064715

引用次数: 0