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International Journal for Multiscale Computational Engineering

Erscheint 6 Ausgaben pro Jahr

ISSN Druckformat: 1543-1649

ISSN Online: 1940-4352

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: 1.4 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: 1.3 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: 2.2 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: 0.00034 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: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

A SIMULATION-BASED UPSCALING TECHNIQUE FOR MULTISCALE MODELING OF ENGINEERING SYSTEMS UNDER UNCERTAINTY

Volumen 12, Ausgabe 6, 2014, pp. 549-566
DOI: 10.1615/IntJMultCompEng.2014011519
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ABSTRAKT

A simulation-based upscaling procedure is proposed to construct coarse-scale representation of a fine-scale model for use in engineering systems. The complexity of the fine-scale heterogeneity under uncertainty is replaced with the homogenized coarse-scale parameters by seeking agreement between the responses at both scales. Generalized polynomial chaos expansion is implemented to reduce the dimensionality of propagating uncertainty through scales and the computational costs of the upscaling method. It is integrated into a hybrid optimization procedure with the genetic algorithm and sequential quadratic programming. Two structural engineering problems that involve uncertainties in elastic material properties and geometric properties at fine scales are presented to demonstrate the applicability and merit of the proposed technique.

REFERENZIERT VON
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