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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

Indexed in

MULTISCALE IDENTIFICATION OF THE RANDOM ELASTICITY FIELD AT MESOSCALE OF A HETEROGENEOUS MICROSTRUCTURE USING MULTISCALE EXPERIMENTAL OBSERVATIONS

Volume 13, Edição 4, 2015, pp. 281-295
DOI: 10.1615/IntJMultCompEng.2015011435
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RESUMO

This paper deals with a multiscale statistical inverse method for performing the experimental identification of the elastic properties of materials at macroscale and at mesoscale within the framework of a heterogeneous microstructure which is modeled by random elastic media. New methods are required for carrying out such multiscale identification using experimental measurements of the displacement fields carried out at macroscale and at mesoscale with only a single specimen submitted to a given external load at macroscale. In this paper, for a heterogeneous microstructure, a new identification method is presented and formulated within the framework of the three-dimensional linear elasticity. It permits the identification of the effective elasticity tensor at macroscale, and the identification of the tensor-valued random field, which models the apparent elasticity field at mesoscale. A validation is presented first with simulated experiments using a numerical model based on the hypothesis of 2D-plane stresses. Then, we present the results given by the proposed identification procedure for experimental measurements obtained by digital image correlation (DIC) on cortical bone.

CITADO POR
  1. Soize C., Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm, Computational Mechanics, 62, 3, 2018. Crossref

  2. Soize Christian, Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media, in Uncertainty Quantification, 47, 2017. Crossref

  3. Staber B., Guilleminot J., A random field model for anisotropic strain energy functions and its application for uncertainty quantification in vascular mechanics, Computer Methods in Applied Mechanics and Engineering, 333, 2018. Crossref

  4. Vigliotti A., Csányi G., Deshpande V.S., Bayesian inference of the spatial distributions of material properties, Journal of the Mechanics and Physics of Solids, 118, 2018. Crossref

  5. Nguyen Manh-Tu, Allain Jean-Marc, Gharbi Hakim, Desceliers Christophe, Soize Christian, Experimental multiscale measurements for the mechanical identification of a cortical bone by digital image correlation, Journal of the Mechanical Behavior of Biomedical Materials, 63, 2016. Crossref

  6. Perrin Guillaume, Soize Christian, Adaptive method for indirect identification of the statistical properties of random fields in a Bayesian framework, Computational Statistics, 35, 1, 2020. Crossref

  7. Guilleminot Johann, Modeling non-Gaussian random fields of material properties in multiscale mechanics of materials, in Uncertainty Quantification in Multiscale Materials Modeling, 2020. Crossref

  8. Zhang Tianyu, Pled Florent, Desceliers Christophe, Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements, Materials, 13, 12, 2020. Crossref

  9. Pled Florent, Desceliers Christophe, Zhang Tianyu, A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network, Computer Methods in Applied Mechanics and Engineering, 373, 2021. Crossref

  10. Soize Christian, Computational stochastic homogenization of heterogeneous media from an elasticity random field having an uncertain spectral measure, Computational Mechanics, 68, 5, 2021. Crossref

  11. Le Tien-Thinh, Probabilistic modeling of surface effects in nano-reinforced materials, Computational Materials Science, 186, 2021. Crossref

  12. Pitz Emil, Rooney Sean, Pochiraju Kishore, Estimation of spatial uncertainty in material property distributions within heterogeneous structures using optimized convolutional neural networks, Engineering Applications of Artificial Intelligence, 117, 2023. Crossref

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