Abstract. This paper is concerned with the effective modeling of deformation microstructures within a concurrent multiscale computing framework. We present a rigorous formulation of concurrent multiscale computing based on relaxation; we establish the connection between concurrent multiscale computing and enhanced-strain elements; and we illustrate the approach in an important area of application, namely, single-crystal plasticity, for which the explicit relaxation of the problem is derived analytically. This example demonstrates the vast effect of microstructure formation on the macroscopic behavior of the sample, e.g., on the force/travel curve of a rigid indentor. Thus, whereas the unrelaxed model results in an overly stiff response, the relaxed model exhibits a proper limit load, as expected. Our numerical examples additionally illustrate that ad hoc element enhancements, e.g., based on polynomial, trigonometric, or similar representations, are unlikely to result in any significant relaxation in general.Key words. multiscale computing, relaxation, microstructure, finite elements, enhanced strain, single-crystal plasticity
AMS subject classifications. 74G65, 74C05DOI. 10.1137/0606623321. Introduction. The problem addressed in this paper concerns the effective modeling of deformation microstructures within a concurrent multiscale computing framework. In many applications of interest, materials develop fine microstructure on multiple length and time scales in response to loading [5,53,58,49]. Examples of such microstructures include martensite; subgrain dislocation structures; dislocation walls and networks; ferroelectric domains; shear bands; spall planes; and others. In addition, materials such as polycrystalline metals may exhibit processing microstructure from the outset, prior to the onset of deformation. The macroscopic behavior of such materials is too complex to be amenable to modeling based on simple representational schemes, such as afforded by continuum thermodynamics, symmetry groups, linearization, polynomial approximations, empirical fitting and calibration, and other similar schemes. Indeed, empirical models are a major source of error and uncertainty in engineering applications, and the empirical paradigm does not offer a systematic means of reducing such error and uncertainty.Multiscale modeling aims to eliminate empiricism and uncertainty from material models by systematically identifying the rate-controlling mechanisms at all scales and the fundamental laws that govern those mechanisms, and by bridging the relevant