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International Journal for Multiscale Computational Engineering

Published 6 issues per year

ISSN Print: 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

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ATOMISTICALLY INFORMED TEMPERATURE AND RATE-DEPENDENT MECHANICAL RESPONSE OF FUSED SILICA

Volume 18, Issue 4, 2020, pp. 409-420
DOI: 10.1615/IntJMultCompEng.2020035707
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ABSTRACT

This manuscript studies the mechanical response of amorphous silica based on data mining from molecular dynamics simulations. The temperature- and densification-dependent yield criteria have been established. The proposed modified Drucker-Prager-cap yield criterion adequately captures the temperature effect on the initial yield surface. The main focus of this study has been on understanding the critical state, which defines the onset of an inhomogeneous plastic deformation. The machine learning-based nonparametric regression method has been employed to describe complex shear critical state dependence on temperature, pressure, and strain rate.

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