Accelerated simulation of stochastic particle removal processes in particle-resolved aerosol models

J. H. Curtis, M. D. Michelotti, N. Riemer, M. T. Heath, M. West

Research output: Contribution to journalArticlepeer-review

Abstract

Stochastic particle-resolved methods have proven useful for simulating multi-dimensional systems such as composition-resolved aerosol size distributions. While particle-resolved methods have substantial benefits for highly detailed simulations, these techniques suffer from high computational cost, motivating efforts to improve their algorithmic efficiency. Here we formulate an algorithm for accelerating particle removal processes by aggregating particles of similar size into bins. We present the Binned Algorithm for particle removal processes and analyze its performance with application to the atmospherically relevant process of aerosol dry deposition. We show that the Binned Algorithm can dramatically improve the efficiency of particle removals, particularly for low removal rates, and that computational cost is reduced without introducing additional error. In simulations of aerosol particle removal by dry deposition in atmospherically relevant conditions, we demonstrate about 50-times increase in algorithm efficiency.

Original languageEnglish (US)
Pages (from-to)21-32
Number of pages12
JournalJournal of Computational Physics
Volume322
DOIs
StatePublished - Oct 1 2016

Keywords

  • Aerosol modeling
  • Aerosol removal
  • Particle methods
  • Stochastic simulations

ASJC Scopus subject areas

  • Numerical Analysis
  • Modeling and Simulation
  • Physics and Astronomy (miscellaneous)
  • Physics and Astronomy(all)
  • Computer Science Applications
  • Computational Mathematics
  • Applied Mathematics

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