Abstract

We introduce a general family of Weighted Flow Algorithms for simulating particle coagulation, generate a method to optimally tune these methods, and prove their consistency and convergence under general assump- tions. These methods are especially effective when the size distribution of the particle population spans many orders of magnitude, or in cases where the concentration of those particles that significantly drive the population evolu- tion is small relative to the background density. We also present a family of simulations demonstrating the efficacy of the method.

Original languageEnglish (US)
Pages (from-to)69-94
Number of pages26
JournalJournal of Computational Dynamics
Volume6
Issue number1
DOIs
StatePublished - Jan 1 2019

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Coagulation
Efficacy
Simulation
Family

Keywords

  • Martingales
  • Particle coagulation
  • Smolu- chowski equation
  • Stochastic simulation

ASJC Scopus subject areas

  • Computational Mechanics
  • Computational Mathematics

Cite this

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title = "Convergence of a generalized weighted flow algorithm for stochastic particle coagulation",
abstract = "We introduce a general family of Weighted Flow Algorithms for simulating particle coagulation, generate a method to optimally tune these methods, and prove their consistency and convergence under general assump- tions. These methods are especially effective when the size distribution of the particle population spans many orders of magnitude, or in cases where the concentration of those particles that significantly drive the population evolu- tion is small relative to the background density. We also present a family of simulations demonstrating the efficacy of the method.",
keywords = "Martingales, Particle coagulation, Smolu- chowski equation, Stochastic simulation",
author = "DeVille, {Lee Xavier} and Nicole Riemer and Matthew West",
year = "2019",
month = "1",
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journal = "Journal of Computational Dynamics",
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AB - We introduce a general family of Weighted Flow Algorithms for simulating particle coagulation, generate a method to optimally tune these methods, and prove their consistency and convergence under general assump- tions. These methods are especially effective when the size distribution of the particle population spans many orders of magnitude, or in cases where the concentration of those particles that significantly drive the population evolu- tion is small relative to the background density. We also present a family of simulations demonstrating the efficacy of the method.

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KW - Smolu- chowski equation

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