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International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 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

PROPAGATION OF HYBRID UNCERTAINTIES IN TRANSIENT HEAT CONDUCTION PROBLEMS

Volumen 9, Edición 6, 2019, pp. 543-568
DOI: 10.1615/Int.J.UncertaintyQuantification.2019030736
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SINOPSIS

In this paper, the propagation of hybrid uncertainties is studied in transient heat conduction problems. Based on the layer-by-layer analysis strategy, a novel mixed method using the stochastic theory and the convex model is presented. Two types of models for the uncertainties are considered: random parameters and uncertain-but-bounded parameters. Firstly, the matrix perturbation theory is utilized to deal with random parameters, obtaining the temperature response expectation and variance. Then using the Taylor series expansion and the Lagrange multiplier method to analyze the convex model, we derive the intervals of the temperature response probabilistic characters. Four numerical examples are presented to address transient heat conduction problems with random and uncertain-but-bounded parameters or pure uncertainties. The results are compared with those of the Monte Carlo method to verify the feasibility and practicality of the proposed method. In addition, the proposed method is also applicable to the steady-state problems.

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