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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 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

ORTHOGONAL POLYNOMIAL EXPANSIONS FOR SOLVING RANDOM EIGENVALUE PROBLEMS

Volume 1, Edição 2, 2011, pp. 163-187
DOI: 10.1615/Int.J.UncertaintyQuantification.v1.i2.40
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RESUMO

This paper examines two stochastic methods stemming from polynomial dimensional decomposition (PDD) and polynomial chaos expansion (PCE) for solving random eigenvalue problems commonly encountered in dynamics of mechanical systems. Although the infinite series from PCE and PDD are equivalent, their truncations endow contrasting dimensional structures, creating significant differences between the resulting PDD and PCE approximations in terms of accuracy, efficiency, and convergence properties. When the cooperative effects of input variables on an eigenvalue attenuate rapidly or vanish altogether, the PDD approximation commits a smaller error than does the PCE approximation for identical expansion orders. Numerical analyses of mathematical functions or simple dynamic systems reveal markedly higher convergence rates of the PDD approximation than the PCE approximation. From the comparison of computational efforts, required to estimate with the same precision the frequency distributions of dynamic systems, including a piezoelectric transducer, the PDD approximation is significantly more efficient than the PCE approximation.

CITADO POR
  1. Yadav Vaibhav, Rahman Sharif, A hybrid polynomial dimensional decomposition for uncertainty quantification of high-dimensional complex systems, Probabilistic Engineering Mechanics, 38, 2014. Crossref

  2. Yadav Vaibhav, Rahman Sharif, Adaptive-sparse polynomial dimensional decomposition methods for high-dimensional stochastic computing, Computer Methods in Applied Mechanics and Engineering, 274, 2014. Crossref

  3. Kundu A., Adhikari S., Friswell M. I., Stochastic finite elements of discretely parameterized random systems on domains with boundary uncertainty, International Journal for Numerical Methods in Engineering, 100, 3, 2014. Crossref

  4. Hossain Md. Nurtaj, Sarkar Soumyadipta, Ghosh Debraj, Identification of dominant modes in random dynamical and aeroelastic systems, Journal of Sound and Vibration, 357, 2015. Crossref

  5. Ren Xuchun, Yadav Vaibhav, Rahman Sharif, Reliability-based design optimization by adaptive-sparse polynomial dimensional decomposition, Structural and Multidisciplinary Optimization, 53, 3, 2016. Crossref

  6. Bibliography, in Uncertainty Quantification and Stochastic Modeling with Matlab, 2015. Crossref

  7. Rahman Sharif, Mathematical Properties of Polynomial Dimensional Decomposition, SIAM/ASA Journal on Uncertainty Quantification, 6, 2, 2018. Crossref

  8. Borgonovo Emanuele, Morris Max D., Plischke Elmar, Functional ANOVA with Multiple Distributions: Implications for the Sensitivity Analysis of Computer Experiments, SIAM/ASA Journal on Uncertainty Quantification, 6, 1, 2018. Crossref

  9. Lu Kuan, Statistical moment analysis of multi-degree of freedom dynamic system based on polynomial dimensional decomposition method, Nonlinear Dynamics, 93, 4, 2018. Crossref

  10. Tang Kunkun, Massa Luca, Wang Jonathan, Freund Jonathan B., An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation, Journal of Computational Physics, 361, 2018. Crossref

  11. Zhang Rui-jun, Wang Chen, Zhang Qing, Response analysis of the composite random vibration of a high-speed elevator considering the nonlinearity of guide shoe, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40, 4, 2018. Crossref

  12. Lu Kuan, Jin Yulin, Chen Yushu, Yang Yongfeng, Hou Lei, Zhang Zhiyong, Li Zhonggang, Fu Chao, Review for order reduction based on proper orthogonal decomposition and outlooks of applications in mechanical systems, Mechanical Systems and Signal Processing, 123, 2019. Crossref

  13. Rahman Sharif, Global sensitivity analysis by polynomial dimensional decomposition, Reliability Engineering & System Safety, 96, 7, 2011. Crossref

  14. Fan Wenliang, Liu Runyu, Ang Alfredo H-S, Li Zhengliang, A new point estimation method for statistical moments based on dimension-reduction method and direct numerical integration, Applied Mathematical Modelling, 62, 2018. Crossref

  15. Rahman Sharif, A surrogate method for density-based global sensitivity analysis, Reliability Engineering & System Safety, 155, 2016. Crossref

  16. Lu Kuan, Hou Lei, Chen Yushu, Application of the polynomial dimensional decomposition method in a class of random dynamical systems, Journal of Vibroengineering, 19, 7, 2017. Crossref

  17. Tang Kunkun, Congedo Pietro M., Abgrall Rémi, Adaptive surrogate modeling by ANOVA and sparse polynomial dimensional decomposition for global sensitivity analysis in fluid simulation, Journal of Computational Physics, 314, 2016. Crossref

  18. Ghosh Debraj, Ghanem Roger, An invariant subspace-based approach to the random eigenvalue problem of systems with clustered spectrum, International Journal for Numerical Methods in Engineering, 91, 4, 2012. Crossref

  19. Li Keyan, Wu Di, Gao Wei, Song Chongmin, Spectral stochastic isogeometric analysis of free vibration, Computer Methods in Applied Mechanics and Engineering, 350, 2019. Crossref

  20. Rahman Sharif, Ren Xuchun, Yadav Vaibhav, High-Dimensional Stochastic Design Optimization by Adaptive-Sparse Polynomial Dimensional Decomposition, in Sparse Grids and Applications - Stuttgart 2014, 109, 2016. Crossref

  21. Rahman Sharif, Dimensionwise multivariate orthogonal polynomials in general probability spaces, Applied Mathematics and Computation, 362, 2019. Crossref

  22. Fenzi Luca, Michiels Wim, Polynomial (chaos) approximation of maximum eigenvalue functions, Numerical Algorithms, 82, 4, 2019. Crossref

  23. Lu Kuan, Yang Yongfeng, Xia Yebao, Fu Chao, Statistical moment analysis of nonlinear rotor system with multi uncertain variables, Mechanical Systems and Signal Processing, 116, 2019. Crossref

  24. Allaix Diego Lorenzo, Carbone Vincenzo Ilario, Karhunen-Loève decomposition of random fields based on a hierarchical matrix approach, International Journal for Numerical Methods in Engineering, 94, 11, 2013. Crossref

  25. Giovanis Dimitris G., Papadopoulos Vissarion, Stavroulakis George, An adaptive spectral Galerkin stochastic finite element method using variability response functions, International Journal for Numerical Methods in Engineering, 104, 3, 2015. Crossref

  26. Rahman Sharif, Uncertainty quantification under dependent random variables by a generalized polynomial dimensional decomposition, Computer Methods in Applied Mechanics and Engineering, 344, 2019. Crossref

  27. Yadav Vaibhav, Rahman Sharif, Uncertainty quantification of high-dimensional complex systems by multiplicative polynomial dimensional decompositions, International Journal for Numerical Methods in Engineering, 94, 3, 2013. Crossref

  28. Gur Sourav, Frantziskonis George N, Design of porous and graded NiTi smart energy absorbers considering synthetic uncertainty in parameters, Journal of Intelligent Material Systems and Structures, 32, 16, 2021. Crossref

  29. Rahman Sharif, Jahanbin Ramin, Orthogonal spline expansions for uncertainty quantification in linear dynamical systems, Journal of Sound and Vibration, 512, 2021. Crossref

  30. Nath Kamaljyoti, Dutta Anjan, Hazra Budhaditya, Iterative Polynomial Dimensional Decomposition approach towards solution of structural mechanics problems with material randomness, Probabilistic Engineering Mechanics, 66, 2021. Crossref

  31. Yadav Vaibhav, Rahman Sharif, Multiplicative polynomial dimensional decompositions for uncertainty quantification of high-dimensional complex systems, 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2012. Crossref

  32. Rahman Sharif, Wiener–Hermite polynomial expansion for multivariate Gaussian probability measures, Journal of Mathematical Analysis and Applications, 454, 1, 2017. Crossref

  33. Fragkoulis Vasileios C., Kougioumtzoglou Ioannis A., Pantelous Athanasios A., Beer Michael, Joint Statistics of Natural Frequencies Corresponding to Structural Systems with Singular Random Parameter Matrices, Journal of Engineering Mechanics, 148, 3, 2022. Crossref

  34. Sheng Xiangqian, Fan Wenliang, Zhang Qingbin, Li Zhengling, An adaptive polynomial dimensional decomposition method and its application in reliability analysis, Engineering Computations, 39, 7, 2022. Crossref

  35. Zheng Zhibao, Beer Michael, Nackenhorst Udo, An efficient reduced‐order method for stochastic eigenvalue analysis, International Journal for Numerical Methods in Engineering, 2022. Crossref

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