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Multi-scale micromorphic theory for hierarchical materials

Authors
Journal
Journal of the Mechanics and Physics of Solids
0022-5096
Publisher
Elsevier
Publication Date
Volume
55
Issue
12
Identifiers
DOI: 10.1016/j.jmps.2007.04.008
Keywords
  • Multi-Scale Micromorphic Theory
  • Microstructures
  • Plastic Collapse
  • Inhomogeneous Material
  • Finite Elements
Disciplines
  • Computer Science
  • Design

Abstract

Abstract For the design of materials, it is important to faithfully model macroscopic materials response together with mechanisms and interactions occurring at the microstructural scales. While brute-force modeling of all the details of the microstructure is too costly, many of the current homogenized continuum models suffer from their inability to capture the correct underlying deformation mechanisms—especially when localization and failure are concerned. To overcome this limitation, a multi-scale continuum theory is proposed so that kinematic variables representing the deformation at various scales are incorporated. The method of virtual power is then used to derive a system of coupled governing equations, each representing a particular scale and its interactions with the macro-scale. A constitutive relation is then introduced to preserve the underlying physics associated with each scale. The inelastic behavior is represented by multiple yield functions, each representing a particular scale of microstructure, but collectively coupled through the same set of internal variables. The theory is illustrated by two applications. First, a one-dimensional example of a three-scale material is presented. After the onset of softening, the model shows that the localization zone is distributed according to two distinct length scale determined by the model. Second, a two-scale continuum model is introduced for the failure of porous metals. By comparing the theory to a direct numerical simulation (DNS) of the microstructure for a specimen in tension, we show that the model capture the main physics, and at the same time, remains computationally affordable.

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