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International Journal for Multiscale Computational Engineering

年間 6 号発行

ISSN 印刷: 1543-1649

ISSN オンライン: 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

INVERSE STOCHASTIC HOMOGENIZATION ANALYSIS FOR A PARTICLE-REINFORCED COMPOSITE MATERIAL WITH THE MONTE CARLO SIMULATION

巻 9, 発行 4, 2011, pp. 409-423
DOI: 10.1615/IntJMultCompEng.v9.i4.50
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要約

This paper proposes a numerical method for identifying microscopic randomness in an elastic property of a component material of a particle-reinforced composite material. Some reports on the stochastic homogenization analysis considering a microscopic random variation can be found in the literature. A microscopic stress field is influenced by the microscopic variation, and stochastic microscopic stress analysis is also important. In the previous reports it is assumed that the microscopic random variation is known. However, it is sometimes difficult to identify a microscopic random variation in a composite material, especially after the manufacturing process. Therefore, an identification process for microscopic randomness by solving an inverse problem is needed for the stochastic microscopic stress analysis. This kind of problem is called "inverse stochastic homogenization." In this paper solving an inverse stochastic homogenization problem is attempted with inverse homogenization analysis and Monte Carlo simulation is used for the stochastic homogenization analysis. The inverse homogenization analysis is performed with the homogenization method and an optimization technique. Some techniques for the inverse stochastic homogenization analysis with the Monte Carlo simulation are developed. With numerical results, validity and accuracy of the methods are discussed.

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によって引用された
  1. SAKATA Sei-ichiro, ASHIDA Fumihiro, IWAHASHI Daiki, Stochastic Homogenization Analysis of a Particle Reinforced Composite Material using an Approximate Monte-Carlo Simulation with the Weighted Least Square Method, Journal of Computational Science and Technology, 7, 1, 2013. Crossref

  2. SAKATA Sei-ichiro, ASHIDA Fumihiro, OHSUMIMOTO Ken-ichi, A Multiscale Stochastic Stress Analysis of a Heterogeneous Material considering Nonuniform Microscopic Random Variation, Journal of Computational Science and Technology, 7, 2, 2013. Crossref

  3. SAKATA Sei-ichiro, ASHIDA Fumihiro, OHSUMIMOTO Ken-ichi, Multiscale Stochastic Stress Analysis of a Porous Material with the Perturbation-Based Stochastic Homogenization Method for a Microscopic Geometrical Random Variation, Journal of Computational Science and Technology, 7, 1, 2013. Crossref

  4. Hu Nan, Fish Jacob, McAuliffe Colin, An adaptive stochastic inverse solver for multiscale characterization of composite materials, International Journal for Numerical Methods in Engineering, 109, 12, 2017. Crossref

  5. SAKATA Sei-ichiro, KOBAYASHI Susumu, Polynomial-based Approximate Inverse Stochastic Homogenization Analysis of a Particle Reinforced Composite Material Considering Correlated Multiple Microscopic Random Variations, Journal of Smart Processing, 5, 1, 2016. Crossref

  6. Wu Feng, Gao Qiang, Xu Xiao-Ming, Zhong Wan-Xie, A Modified Computational Scheme for the Stochastic Perturbation Finite Element Method, Latin American Journal of Solids and Structures, 12, 13, 2015. Crossref

  7. Fish Jacob, Yuan Zifeng, Kumar Rajesh, Computational certification under limited experiments, International Journal for Numerical Methods in Engineering, 114, 2, 2018. Crossref

  8. Sakata S., Ashida F., Ohsumimoto K., Stochastic homogenization analysis of a porous material with the perturbation method considering a microscopic geometrical random variation, International Journal of Mechanical Sciences, 77, 2013. Crossref

  9. Sakata S., Ashida F., Enya K., A Microscopic failure probability analysis of a unidirectional fiber reinforced composite material via a multiscale stochastic stress analysis for a microscopic random variation of an elastic property, Computational Materials Science, 62, 2012. Crossref

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