Adaptive Model Selection Procedure for Concurrent Multiscale Problems

An adaptive method for the selection of models in a concurrent multiscale approach is presented. Different models from a hierarchy are chosen in different subdomains of the problem domain adaptively in an automated problem simulation. A concurrent atomistic to continuum (AtC) coupling method [27], based on a blend of the continuum stress and the atomistic force, is adopted for the problem formulation. Two error indicators are used for the hierarchy of models consisting of a linear elastic model, a nonlinear elastic model, and an embedded atom method (EAM) based atomistic model. A nonlinear indicator ηNL−L , which is based on the relative error in the energy between the nonlinear model and the linear model, is used to select or deselect the nonlinear model subdomain. An atomistic indicator is a stress-gradient-based criterion to predict dislocation nucleation, which was developed by Miller and Acharya [6]. A material-specific critical value associated with the dislocation nucleation criterion is used in selecting and deselecting the atomistic subdomain during an automated simulation. An adaptive strategy uses limit values of the two indicators to adaptively modify the subdomains of the three different models. Example results are illustrated to demonstrate the adaptive method.

[1]  James B. Adams,et al.  Interatomic Potentials from First-Principles Calculations: The Force-Matching Method , 1993, cond-mat/9306054.

[2]  Mark S. Shephard,et al.  Concurrent AtC coupling based on a blend of the continuum stress and the atomistic force , 2007 .

[3]  M. Musgrave,et al.  Crystal Acoustics: Introduction to the Study of Elastic Waves and Vibrations in Crystals , 1970 .

[4]  J. Tinsley Oden,et al.  Modeling error and adaptivity in nonlinear continuum mechanics , 2001 .

[5]  Amit Acharya,et al.  Constitutive analysis of finite deformation field dislocation mechanics , 2004 .

[6]  J. Z. Zhu,et al.  The superconvergent patch recovery and a posteriori error estimates. Part 1: The recovery technique , 1992 .

[7]  Amit Acharya,et al.  A model of crystal plasticity based on the theory of continuously distributed dislocations , 2001 .

[8]  J. Z. Zhu,et al.  The superconvergent patch recovery and a posteriori error estimates. Part 2: Error estimates and adaptivity , 1992 .

[9]  Gregory J. Wagner,et al.  Coupling of atomistic and continuum simulations using a bridging scale decomposition , 2003 .

[10]  Fredrik Larsson,et al.  Modeling and discretization errors in hyperelasto-(visco-)plasticity with a view to hierarchical modeling , 2004 .

[11]  J. Ericksen The Cauchy and Born Hypotheses for Crystals. , 1983 .

[12]  M. Baskes,et al.  Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals , 1984 .

[13]  Alexandre Ern,et al.  A Posteriori Control of Modeling Errors and Discretization Errors , 2003, Multiscale Model. Simul..

[14]  Fredrik Larsson,et al.  Adaptive computational meso-macro-scale modeling of elastic composites , 2006 .

[15]  J. Tinsley Oden,et al.  MultiScale Modeling of Physical Phenomena: Adaptive Control of Models , 2006, SIAM J. Sci. Comput..

[16]  D. Hull,et al.  Introduction to Dislocations , 1968 .

[17]  J. B. Adams,et al.  Anisotropic surface segregation in AlMg alloys , 1997 .

[18]  Amit Acharya,et al.  A stress-gradient based criterion for dislocation nucleation in crystals , 2004 .

[19]  Michael Ortiz,et al.  Nanovoid cavitation by dislocation emission in aluminum. , 2004, Physical review letters.

[20]  Rolf Rannacher,et al.  An optimal control approach to a posteriori error estimation in finite element methods , 2001, Acta Numerica.

[21]  Mark S. Shephard,et al.  Composite Grid Atomistic Continuum Method: An Adaptive Approach to Bridge Continuum with Atomistic Analysis , 2004 .

[22]  William A. Curtin,et al.  A coupled atomistics and discrete dislocation plasticity simulation of nanoindentation into single crystal thin films , 2004 .

[23]  J. Tinsley Oden,et al.  Estimation of local modeling error and goal-oriented adaptive modeling of heterogeneous materials: I. Error estimates and adaptive algorithms , 2000 .

[24]  S. Ohnimus,et al.  Coupled model- and solution-adaptivity in the finite-element method , 1997 .

[25]  A. Cemal Eringen,et al.  Mechanics of continua , 1967 .

[26]  S. Ohnimus,et al.  Anisotropic discretization- and model-error estimation in solid mechanics by local Neumann problems , 1999 .

[27]  J. Oden,et al.  A Posteriori Error Estimation in Finite Element Analysis , 2000 .

[28]  Paul T. Bauman,et al.  On the extension of goal-oriented error estimation and hierarchical modeling to discrete lattice models , 2005 .

[29]  J. Tinsley Oden,et al.  Estimation of modeling error in computational mechanics , 2002 .

[30]  Ronald E. Miller,et al.  The Quasicontinuum Method: Overview, applications and current directions , 2002 .

[31]  M. Ortiz,et al.  Quasicontinuum analysis of defects in solids , 1996 .