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Special Topics & Reviews in Porous Media: An International Journal

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ISSN Печать: 2151-4798

ISSN Онлайн: 2151-562X

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: 1.1 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: 1.5 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: 0.5 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: 0.00018 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: 0.42 SJR: 0.217 SNIP: 0.362 CiteScore™:: 2.3 H-Index: 19

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PREDICTION OF EFFECTIVE THERMAL CONDUCTIVITY OF POLYMER COMPOSITES USING AN ARTIFICIAL NEURAL NETWORK APPROACH

Том 3, Выпуск 2, 2012, pp. 115-123
DOI: 10.1615/SpecialTopicsRevPorousMedia.v3.i2.30
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Краткое описание

The effective thermal conductivity (ETC) of polymer composites is studied using artificial neural networks. Artificial neural networks are a form of artificial intelligence, which attempt to mimic the function of the human brain and nervous system. Artificial neural networks provide a great deal of promise but they suffer from a number of shortcomings, such as knowledge extraction, extrapolation, and uncertainty. This paper presents the use of the artificial neural network for prediction of ETC of metal-filled polymer composites due to their increasing importance in many fields of engineering applications and technological developments. Artificial neural networks models are based on a radial basis with the training function: the more efficient design radial basis network (NEWRB) and the feedforward backpropagation network with training functions conjugate gradient with Powell-Beale restarts, Levenberg-Marquardt, one-step secant, random order incremental, and resilient backpropagation. The volume fraction and thermal conductivity of continuous (matrix) and dispersed (filler) phases are input parameters to predict the ETC. The resultant predictions of ETC using the different models of artificial neural networks agree well with the available experimental data.

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