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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: 0.554 SNIP: 0.68 CiteScore™: 1.18

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

Выпуски:
Том 18, 2020 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.2016015857
pages 273-290

PERTURBATION-BASED SURROGATE MODELS FOR DYNAMIC FAILURE OF BRITTLE MATERIALS IN A MULTISCALE AND PROBABILISTIC CONTEXT

Junwei Liu
Department of Civil Engineering, Johns Hopkins University, Baltimore, MD
Lori Graham-Brady
Department of Civil Engineering, Johns Hopkins University, Baltimore, MD

Краткое описание

Localization of failure in many materials is associated with the heterogeneity in the material microstructure. Multiscale models often address this heterogeneity by passing field variables back and forth between a macroscale model and subscale analyses at each integration point. Although this technique is often effective, it can be extremely costly to perform distinct microscale analyses for every integration point in the domain. The proposed work uses a perturbation-based approach, conceptually similar to in situ adaptive tabulation, which provides a straightforward surrogate model that can be orders of magnitude more efficient than the microscale model. The approach is demonstrated specifically for models of dynamic brittle failure, in which crack populations are tracked from one load step to the next. Furthermore, following an approach similar to that used in perturbation-based stochastic finite elements, this technique streamlines the process of probabilistic characterization of the instantaneous stress and the uniaxial compressive strength. Numerical examples show that the approach is accurate and highly efficient when considering random perturbations in both the underlying flaw population and the strain history in these brittle materials.