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3458838 
Journal Article 
Accelerated simulation of stochastic particle removal processes in particle-resolved aerosol models 
Curtis, JH; Michelotti, MD; Riemer, N; Heath, MT; West, M 
2016 
Yes 
Journal of Computational Physics
ISSN: 0021-9991
EISSN: 1090-2716 
322 
21-32 
WOS:000381585100002 
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. (C) 2016 Elsevier Inc. All rights reserved. 
Aerosol modeling; Stochastic simulations; Particle methods; Aerosol removal