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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
International Journal for Multiscale Computational Engineering
Импакт фактор: 1.016 5-летний Импакт фактор: 1.194 SJR: 0.452 SNIP: 0.68 CiteScore™: 1.18

ISSN Печать: 1543-1649
ISSN Онлайн: 1940-4352

Выпуски:
Том 17, 2019 Том 16, 2018 Том 15, 2017 Том 14, 2016 Том 13, 2015 Том 12, 2014 Том 11, 2013 Том 10, 2012 Том 9, 2011 Том 8, 2010 Том 7, 2009 Том 6, 2008 Том 5, 2007 Том 4, 2006 Том 3, 2005 Том 2, 2004 Том 1, 2003

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v9.i4.50
pages 409-423

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

Sei-ichiro Sakata
Department of Electronic and Control Systems Engineering, Interdisciplinary Faculty of Science and Engineering, Shimane University, Japan
F. Ashida
Department of Electronic and Control Systems Engineering, Interdisciplinary Faculty of Science and Engineering, Shimane University, Japan
Y. Shimizu
Graduate School of Shimane University, Japan

Краткое описание

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.

ЛИТЕРАТУРА

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