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

Matouš, Karel ; Geers, Marc G.D. ; Kouznetsova, Varvara G. ; Gillman, Andrew

Journal of computational physics, 2017-02, Vol.330 (C), p.192-220 [Periódico revisado por pares]

Cambridge: Elsevier Inc

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Título:
A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
Autor: Matouš, Karel ; Geers, Marc G.D. ; Kouznetsova, Varvara G. ; Gillman, Andrew
Assuntos: Big Data ; Co-designed simulations and experiments ; Computation ; Computational homogenization ; Computational physics ; Computer simulation ; Digital data ; Formulations ; High performance computing ; Image-based multiscale modeling ; Innovations ; Materials ; Materials science ; Materials testing ; Mathematical models ; Medical electronics ; Model reduction ; Multifunctional materials ; Nonlinear equations ; Predictive science ; Product design ; Software ; Supercomputers ; Technological change ; Verification and validation
É parte de: Journal of computational physics, 2017-02, Vol.330 (C), p.192-220
Notas: USDOE
NA0002377
Descrição: 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.
Editor: Cambridge: Elsevier Inc
Idioma: Inglês

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

Matouš, Karel ; Geers, Marc G.D. ; Kouznetsova, Varvara G. ; Gillman, Andrew

Cambridge: Elsevier Inc

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