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International Journal for Multiscale Computational Engineering

年間 6 号発行

ISSN 印刷: 1543-1649

ISSN オンライン: 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

IDENTIFICATION OF OPTIMAL REDUCED ORDER HOMOGENIZATION MODELS FOR FAILURE OF HETEROGENEOUS MATERIALS

巻 11, 発行 3, 2013, pp. 185-200
DOI: 10.1615/IntJMultCompEng.2013005373
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要約

This manuscript presents a new methodology for the identification of optimal reduced order models for the inelastic and failure response of heterogeneous materials. The proposed methodology employs the eigendeformation-based reduced order homogenization approach. The identification of the optimal reduced order model is posed as an integer optimization problem and the genetic algorithm method is used to evaluate the optimization problem. A second optimization problem is posed to ensure that the errors associated with the optimal reduced order model are minimized through scaling of the failure parameters. The performance and capabilities of the optimal reduced order models identified based on the proposed approach are demonstrated by comparing model predictions with the computational homogenization method with full resolution of the material microstructure. Numerical simulations conducted using unidirectional reinforced matrix microstructures reveal that the reduced order models accurately describe the response characteristics of the composite material for a wide range of loading regimes.

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によって引用された
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