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Title: A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Abstract

Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

Authors:
 [1]; ;  [2];  [1]
  1. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556 (United States)
  2. Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven (Netherlands)
Publication Date:
OSTI Identifier:
22622246
Resource Type:
Journal Article
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Volume: 330; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0021-9991
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; COMPUTERIZED SIMULATION; GLOBAL ASPECTS; MATERIALS TESTING; NONLINEAR PROBLEMS; PERFORMANCE; REDUCTION; REVIEWS; SUPERCOMPUTERS; VALIDATION; VERIFICATION

Citation Formats

Matouš, Karel, Geers, Marc G.D., Kouznetsova, Varvara G., and Gillman, Andrew. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials. United States: N. p., 2017. Web. doi:10.1016/J.JCP.2016.10.070.
Matouš, Karel, Geers, Marc G.D., Kouznetsova, Varvara G., & Gillman, Andrew. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials. United States. https://doi.org/10.1016/J.JCP.2016.10.070
Matouš, Karel, Geers, Marc G.D., Kouznetsova, Varvara G., and Gillman, Andrew. 2017. "A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials". United States. https://doi.org/10.1016/J.JCP.2016.10.070.
@article{osti_22622246,
title = {A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials},
author = {Matouš, Karel and Geers, Marc G.D. and Kouznetsova, Varvara G. and Gillman, Andrew},
abstractNote = {Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.},
doi = {10.1016/J.JCP.2016.10.070},
url = {https://www.osti.gov/biblio/22622246}, journal = {Journal of Computational Physics},
issn = {0021-9991},
number = ,
volume = 330,
place = {United States},
year = {2017},
month = {2}
}