A large-scale statistical study of the coarsening rate in models of Ostwald-Ripening
Lennon \'O N\'araigh, Andrew Gloster

TL;DR
This study investigates the coarsening rate in models of Ostwald Ripening, using large-scale simulations to test a conjecture that the long-time average rate does not exceed 1/3, with results supporting this in some models.
Contribution
The paper provides the first large-scale computational analysis of the coarsening rate in Ostwald Ripening models, testing the conjecture across different systems.
Findings
Droplet population model aligns with the conjecture that average coarsening rate ≤ 1/3.
Cahn--Hilliard model for asymmetric mixtures supports the conjecture.
Symmetric mixtures sometimes exceed the 1/3 rate, but long-term bounds remain uncertain.
Abstract
In this article we look at the coarsening rate in two standard models of Ostwald Ripening. Specifically, we look at a discrete droplet population model, which in the limit of an infinite droplet population reduces to the classical Lifshitz--Slyozov--Wagner model. We also look at the Cahn--Hilliard equation with constant mobility. We define the coarsening rate as , where is the total free energy of the system and is time. There is a conjecture that the long-time average value of should not exceed -- this result is summarized here as . We explore this conjecture for the two considered models. Using large-scale computational resources (specifically, GPU computing employing thousands of threads), we are able to construct ensembles of simulations and thereby build up a statistical picture of . Our results show…
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Taxonomy
TopicsTheoretical and Computational Physics · nanoparticles nucleation surface interactions · Stochastic processes and statistical mechanics
