International Journal for Multiscale Computational Engineering

ISSN for PRINT: 1543-1649

For Online Access

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2005, Volume3

Issue 1

135 pages

DOI: 10.1615/IntJMultCompEng.v3.i1

Issue price - $178.00

Abhijit Chatterjee
Department of Chemical Engineering Center for Catalytic Science and Technology (CCST), University of Delaware, Newark, DE 19716

Dionisios G. Vlachos
Department of Chemical Engineering Center for Catalytic Science and Technology (CCST), University of Delaware, Newark, DE 19716-3110

Markos A. Katsoulakis
Department of Mathematics and Statistics University of Massachusetts, Amherst, MA 01003

A coarse-grained kinetic Monte Carlo (CG-KMC) method was recently introduced as a hierarchical multiscale modeling tool for extending the length scales reached by stochastic simulations. Coarse-graining causes errors due to loss of degrees of freedom. To quantify these errors, theoretical error estimates derived using information loss theory are first presented. Simulations are subsequently carried out in the canonical ensemble for various combinations of key parameters suggested by theoretical estimates. Numerically evaluated errors are compared to theoretical error estimates to assess whether the latter can qualitatively capture the loss of information during coarse-graining. Finally, a standing wave example is presented to illustrate how these error estimates can be used to control accuracy in CG-KMC by employing adaptive meshes.

DOI: 10.1615/IntJMultCompEng.v3.i1.50 Download article, 59-70 pages

Article price - $35.00

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