Publication 13-CNA-003
Materials Microstructures: Entropy and Curvature-Driven Coarsening
Katayun Barmak
Department of Applied Physics and Applied Mathematics
Columbia University
New York, NY 10027
kb2612@columbia.edu
Eva Eggeling
Fraunhofer Austria Research GmbH
Visual Computing
A-8010 Graz, Austria
eva.eggeling@fraunhofer.at
Maria Emelianenko
Department of Mathematical Sciences
George Mason University
Fairfax, VA 22030
memelian@gmu.edu
Yekaterina Epshteyn
Department of Mathematics
The University of Utah
Salt Lake City, UT, 84112
epshteyn@math.utah.edu
David Kinderlehrer
Department of Mathematical Sciences
Carnegie Mellon University
Pittsburgh, PA 15213
davidk@andrew.cmu.edu
Richard Sharp
Microsoft Corporation
One Microsoft Way
Redmond, WA 98052
rsharp@gmail.com
Shlomo Ta'asan
Department of Mathematical Sciences
Carnegie Mellon University
Pittsburgh, PA 15213
shlomo@andrew.cmu.edu
Abstract: Cellular networks are ubiquitous in nature. Most engineered materials are polycrystalline microstructures composed of a myriad of small grains separated by grain boundaries, thus comprising cellular networks. The grain boundary character distribution (GBCD) is an empirical distribution of the relative length (in 2D) or area (in 3D) of interface with a given lattice misorientation and normal. Material microstructures evolve by curvature driven growth, seeking to decrease their interfacial energy. During the growth, or coarsening, process, an initially random grain boundary arrangement reaches a steady state that is strongly correlated to the interfacial energy density. In simulation, if the given energy density depends only on lattice misorientation, then the steady state GBCD and the energy are related by a Boltzmann distribution. This is among the simplest non-random distributions, corresponding to independent trials with respect to the energy.
Here we describe an entropy based theory which suggests that the evolution of the GBCD satisfies a Fokker-Planck Equation, an equation whose stationary state is a Boltzmann distribution. The properties of the evolving network that characterize the GBCD must be identified and appropriately upscaled or `coarse-grained'. This entails identifying the evolution of the statistic in terms of the recently discovered Monge-Kantorovich- Wasserstein implicit scheme. The undetermined diffusion coefficient or temperature parameter is found by means of a convex optimization problem reminiscent of large deviation theory.
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