Library Subscription: Guest
International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A MIXED UNCERTAINTY QUANTIFICATION APPROACH USING EVIDENCE THEORY AND STOCHASTIC EXPANSIONS

Volume 5, Issue 1, 2015, pp. 21-48
DOI: 10.1615/Int.J.UncertaintyQuantification.2015010941
Get accessDownload

ABSTRACT

Uncertainty quantification (UQ) is the process of quantitative characterization and propagation of input uncertainties to the response measure of interest in experimental and computational models. The input uncertainties in computational models can be either aleatory, i.e., irreducible inherent variations, or epistemic, i.e., reducible variability which arises from lack of knowledge. Previously, it has been shown that Dempster Shafer theory of evidence (DSTE) can be applied to model epistemic uncertainty in case of uncertainty information coming from multiple sources. The objective of this paper is to model and propagate mixed uncertainty (aleatory and epistemic) using DSTE. In specific, the aleatory variables are modeled as Dempster Shafer structures by discretizing them into sets of intervals according to their respective probability distributions. In order to avoid excessive computational cost associated with large scale applications, a stochastic response surface based on point-collocation non-intrusive polynomial chaos has been implemented as the surrogate model for the response. A convergence study for accurate representation of aleatory uncertainty in terms of minimum number of subintervals required is presented. The mixed UQ approach is demonstrated on a numerical example and high fidelity computational fluid dynamics study of transonic flow over RAE 2822 airfoil.

CITED BY
  1. Zhanpeng Shen, Xueqian Chen, Xinen Liu, Chaoping Zang, Uncertainties Quantification and Propagation of Multiple Correlated Variables with Limited Samples, Journal of Physics: Conference Series, 744, 2016. Crossref

  2. Riley Matthew E., Hoffman William M., Risk Analysis of Reactor Pressure Vessels Considering Modeling-Induced Uncertainties, Journal of Verification, Validation and Uncertainty Quantification, 1, 4, 2016. Crossref

  3. Cao Lixiong, Liu Jie, Han Xu, Jiang Chao, Liu Qiming, An efficient evidence-based reliability analysis method via piecewise hyperplane approximation of limit state function, Structural and Multidisciplinary Optimization, 58, 1, 2018. Crossref

  4. Roelofs Martijn, Vos Roelof, Uncertainty-Based Design Optimization and Technology Evaluation: A Review, 2018 AIAA Aerospace Sciences Meeting, 2018. Crossref

  5. Li Y., Zhang F. P., Yan Y., Zhou J. H., Li Y. F., Multi-source uncertainty considered assembly process quality control based on surrogate model and information entropy, Structural and Multidisciplinary Optimization, 59, 5, 2019. Crossref

  6. Cao Lixiong, Liu Jie, Wang Qingyun, Jiang Chao, Zhang Lianyi, An efficient structural uncertainty propagation method based on evidence domain analysis, Engineering Structures, 194, 2019. Crossref

  7. Cao Lixiong, Liu Jie, Jiang Chao, Wu Zhantao, Zhang Zheng, Evidence-Based Structural Uncertainty Quantification by Dimension Reduction Decomposition and Marginal Interval Analysis, Journal of Mechanical Design, 142, 5, 2020. Crossref

  8. West Thomas K., Phillips Ben D., Multifidelity Uncertainty Quantification of a Commercial Supersonic Transport, Journal of Aircraft, 57, 3, 2020. Crossref

  9. Yang Xufeng, Zeqing Liu, Cheng Xin, An enhanced active learning Kriging model for evidence theory-based reliability analysis, Structural and Multidisciplinary Optimization, 64, 4, 2021. Crossref

  10. Zhang Yinsheng, Tensor Computations of Stochastic Dynamic Fields, Journal of Physics: Conference Series, 1828, 1, 2021. Crossref

  11. Meng Debiao, Yang Shiyuan, He Chao, Wang Hongtao, Lv Zhiyuan, Guo Yipeng, Nie Peng, Multidisciplinary design optimization of engineering systems under uncertainty: a review, International Journal of Structural Integrity, 13, 4, 2022. Crossref

Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections Prices and Subscription Policies Begell House Contact Us Language English 中文 Русский Português German French Spain