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. Here 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:
-
- University of Notre Dame, IN (United States)
- Eindhoven University of Technology (Netherlands)
- Publication Date:
- Research Org.:
- University of Notre Dame, IN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); European Research Council (ERC)
- OSTI Identifier:
- 1533953
- Alternate Identifier(s):
- OSTI ID: 1397825
- Grant/Contract Number:
- NA0002377; 339392
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Computational Physics
- Additional Journal Information:
- Journal Volume: 330; Journal Issue: C; Journal ID: ISSN 0021-9991
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; predictive science; image-based multiscale modeling; computational homogenization; high performance computing; co-designed simulations and experiments; verification and validation; model reduction; big data
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. Wed .
"A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials". United States. https://doi.org/10.1016/j.jcp.2016.10.070. https://www.osti.gov/servlets/purl/1533953.
@article{osti_1533953,
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. Here 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},
journal = {Journal of Computational Physics},
number = C,
volume = 330,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}
}
Web of Science
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