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

JOURNAL OF COMPUTATIONAL PHYSICS

卷:330

Review

Matous, Karel¹  Geers, Marc G. D.²  Kouznetsova, Varvara G.²  Gillman, Andrew¹ 

[1] Univ Notre Dame, Dept Aerosp & Mech Engn, Notre Dame, IN 46556 USA

[2] Eindhoven Univ Technol, Dept Mech Engn, Eindhoven, Netherlands

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;   

DOI  :  10.1016/j.jcp.2016.10.070

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Inc. All rights reserved.

【 授权许可】

Free