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International Journal for Multiscale Computational Engineering

Publicou 6 edições por ano

ISSN Imprimir: 1543-1649

ISSN On-line: 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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Three-Dimensional Reconstruction of Statistically Optimal Unit Cells of Multimodal Particulate Composites

Volume 8, Edição 5, 2010, pp. 489-507
DOI: 10.1615/IntJMultCompEng.v8.i5.50
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RESUMO

In the current digital age, it is befitting that complex heterogeneous materials, such as solid propellants, are characterized by digital computational and/or experimental techniques. Of those, microcomputer tomography (micro-CT) and advanced packing algorithms are the most popular for identifying the statistics of multimodal, random, particulate composites. In this work, we develop a procedure for the characterization and reconstruction of periodic unit cells of highly filled, multimodal, particulate composites from a packing algorithm. Rocpack, a particle packing software, is used to generate the solid propellant microstructures, and one-, two-, and three-point probability functions are used to describe their statistical morphology. However, both the experimentally scanned or computationally designed packs are usually nonoptimal in size and likely too big to be fully numerically resolved when complex nonlinear processes, such as combustion, decohesion, matrix tearing, etc., are modeled. Thus, domain reduction techniques, which can reconstruct the optimal periodic unit cell, are important to narrow the problem size while preserving the statistics. The three-dimensional reconstruction is carried out using a parallel augmented simulated annealing algorithm. Then, the resulting cell geometries are discretized, taking into consideration the periodic layout using our master/slave approach implemented into a sophisticated meshing generator T3D. Final discretized geometries show only a small loss of volume fraction. Particulate systems composed of 40 and 70% volume fractions are investigated, and the unit cells are reconstructed such that the statistical correspondence to the original packs is maintained.

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