View source for Reduction Strategies for Kinetic Monte Carlo Models of Thin Film Growth

{{HTDB paper
| authors = Martha A. Gallivan, Richard M. Murray, David G. Goodwin
| title = Reduction Strategies for Kinetic Monte Carlo Models of Thin Film Growth
| source = Electrochemical Society, May 2001
| year = 2001
| type =  Conference 
  Paper
| funding = AFOSR
| url = http://www.cds.caltech.edu/~murray/preprints/gmg01-ecs.pdf
| abstract = 
Thin film deposition encompasses a variety of physical processes,
which occur over a wide range of length and time scales. A major challenge in modeling and simulating thin film deposition is this disparity
in scales. In this study we focus on an atomic-scale lattice model of
surface processes. Kinetic Monte Carlo simulations provide stochastic
realizations of the surface evolution, which may then inform a reacting flow or heat transfer model. Unfortunately, these simulations are
extremely computationally intensive, particularly for design and optimization studies in which many cases are considered.
<p>
We develop reduced-order models of thin film growth using two techniques: balanced truncation and eigensystem realization. After identifying the underlying structure of the lattice model as a linear differential
equation, we apply the reduction techniques to obtain reduced-order
models of root-mean-square roughness and step edge density. Three
modes are needed for a very small model system, while only five modes
capture the evolution of a 200x200-site system over a range of growth
modes, from stochastic roughening to island nucleation and coalescence.
| flags = 
| tag = gmg01-ecs
| id = 2001f
}}