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International Journal for Multiscale Computational Engineering

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ISSN Печать: 1543-1649

ISSN Онлайн: 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

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RECONCILED TOP-DOWN AND BOTTOM-UP HIERARCHICAL MULTISCALE CALIBRATION OF BCC FE CRYSTAL PLASTICITY

Том 15, Выпуск 6, 2017, pp. 505-523
DOI: 10.1615/IntJMultCompEng.2017021859
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Краткое описание

In this paper, a test for connections between models via parameter sets is developed. A set of parameters from the flow rule of a crystal plasticity model for bcc Fe is identified for connecting top-down and bottom-up information. The top-down calibration is performed using experimental measurements of single-crystal yield strength at multiple temperatures and crystallographic orientations, where a likelihood function in parameter space is informed using second-order regression surrogate modeling. A bottom-up calibration of the same model uses the parameter estimates from atomistic simulations to inform penalty functions. A constrained likelihood function incorporates the top-down and bottom-up information in one calibration of parameters. Decision making within hierarchical multiscale modeling is approached. The benefit to calibration precision brought by incorporating additional data from bottom up is considered against the uncertainty in the requisite multiscale connection. This trade-off is formulated into an empirical test of connections. Hypothetical decision making is demonstrated between multiple alternative bottom-up estimates.

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