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合成材料:力学,计算和应用

每年出版 4 

ISSN 打印: 2152-2057

ISSN 在线: 2152-2073

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: 0.2 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: 0.3 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: 0.00004 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: 0.08 SJR: 0.153 SNIP: 0.178 CiteScore™:: 1 H-Index: 12

Indexed in

DYNAMIC NEURAL NETWORK AS A MODEL OF VISCOELASTIC MEDIA

卷 9, 册 4, 2018, pp. 345-362
DOI: 10.1615/CompMechComputApplIntJ.2018026199
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摘要

The paper utilizes a nonlinear viscoelastic model of a medium with associative and hereditary memory in the form of a system of integro-differential equations. The hereditary memory is contained (for a long time) in the Volterra integral operator, and the associative (short-term) memory is determined by the differential operator. Identification of the model is solved using neural networks in the version of the finite-dimensional approximation with discrete time for a composite material based on a matrix of natural rubber (polyisoprene), filled by 20% with N-330 carbon black. The study is carried out in both the small strain mode and the finite strain mode. The issues studied in the paper are the accuracy of reproduction by the model of the real nonlinearity function and its ability to generalize the experimental material on the basis of the training sample. The identification of this model showed a good reproduction of the actual function of nonlinearity of a real viscoelastic material in the finite strain mode.

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