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International Journal for Multiscale Computational Engineering

Publicado 6 números por año

ISSN Imprimir: 1543-1649

ISSN En Línea: 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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INVESTIGATION OF MICRO-MACROSCALE INTERACTION OF HETEROGENEOUS MATERIALS BY A PARALLEL-BONDED PARTICLE MODEL AND INTRODUCTION OF NEW MICROPARAMETER DETERMINATION FORMULATIONS

Volumen 12, Edición 1, 2014, pp. 1-21
DOI: 10.1615/IntJMultCompEng.2014006142
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SINOPSIS

The distinct element method (DEM) is becoming an effective method of investigating engineering problems in granular and heterogeneous materials, especially in granular flows, powder mechanics, advanced ceramics, and rock mechanics. Creation of a DEM model requires some microscale material parameters, unable to be physically measured in laboratories; a calibration process is typically used in order to select the proper microparameters using DEM simulations. The calibration process is basically trial and error, which depends on the experience of the modeler. Therefore such a process may be complicated and time consuming for the user. In this study, a parametric study is performed in order to determine the relations of microparameters used in three dimensional DEM model and the macroscale material parameters (i.e., Young's modulus, Poisson's ratio, compressive strength). According to dependencies and independencies between microparameters and macroparameters, empirical fitting functions are obtained by using a stepwise regression method. The macroparameters calculated by empirical fitting functions reveal a good agreement with DEM results. The predictive ability of fitting functions is confirmed with the creation of further data sets in DEM simulations. Comparison of the fitting values with the literature shows that the fitting functions may also be used two dimensional DEM simulations. The micro-macro scale interactions and empirical fitting functions provided in this study would be very helpful for the user in order to observe the relationship between micro- and macroparameters and determine the approximate proper microparameters.

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CITADO POR
  1. Su Hui, Li Hongliang, Hu Baowen, Yang Jiaqi, A Research on the Macroscopic and Mesoscopic Parameters of Concrete Based on an Experimental Design Method, Materials, 14, 7, 2021. Crossref

  2. Ren Junqing, Xiao Ming, Liu Guoqing, Rock Macro–Meso Parameter Calibration and Optimization Based on Improved BP Algorithm and Response Surface Method in PFC 3D, Energies, 15, 17, 2022. Crossref

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