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

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• 作者：Karel Matou&scaron ; ^a ; ^{kmatous@nd.edu} ; Marc G.D. Geers^b ; Varvara G. Kouznetsova^b ; Andrew Gillman^a
• 关键词：Predictive science ; Image-based multiscale modeling ; Computational homogenization ; High performance computing ; Co-designed simulations and experiments ; Verification and validation ; Model reduction ; Big Data
• 刊名：Journal of Computational Physics
• 出版年：2017
• 出版时间：1 February 2017
• 年：2017
• 卷：330
• 期：Complete
• 页码：192-220
• 全文大小：3134 K
• 卷排序：330

文摘

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.