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

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, m...

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Bibliographic Details
Published inJournal of computational physics Vol. 322
Main Authors Curtis, J.H., Michelotti, M.D., Riemer, N., Heath, M.T., West, M.
Format Journal Article
LanguageEnglish
Published United States 01.10.2016
Subjects
AEROSOLS
ALGORITHMS
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
DEPOSITION
EFFICIENCY
ERRORS
PARTICLES
SIMULATION
STOCHASTIC PROCESSES
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Summary: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.
ISSN:0021-9991
1090-2716
DOI:10.1016/J.JCP.2016.06.029