Abstract
A highly efficient Monte Carlo (MC) algorithm is developed for the numerical simulation of aerosol dynamics, that is, nucleation, surface growth, and coagulation. Nucleation and surface growth are handled with deterministic means, while coagulation is simulated with a stochastic method (Marcus-Lushnikov stochastic process). Operator splitting techniques are used to synthesize the deterministic and stochastic parts in the algorithm. The algorithm is parallelized using the Message Passing Interface (MPI). The parallel computing efficiency is investigated through numerical examples. Near 60% parallel efficiency is achieved for the maximum testing case with 3.7 million MC particles running on 93 parallel computing nodes. The algorithm is verified through simulating various testing cases and comparing the simulation results with available analytical and/or other numerical solutions. Generally, it is found that only small number (hundreds or thousands) of MC particles is necessary to accurately predict the aerosol particle number density, volume fraction, and so forth, that is, low order moments of the Particle Size Distribution (PSD) function. Accurately predicting the high order moments of the PSD needs to dramatically increase the number of MC particles.
Highlights
Population balance equations (PBEs), describing particulate entities conservation, have applications in many branches, such as aerosol dynamics [1, 2], polymerization [3], and so forth
The discussion mainly focuses on aerosol dynamics
In this paper we propose a parallel hybrid algorithm of stochastic simulation and deterministic integration for solving the PBE, which is called Operator Splitting Monte Carlo (OSMC)
Summary
Population balance equations (PBEs), describing particulate entities conservation, have applications in many branches, such as aerosol dynamics [1, 2], polymerization [3], and so forth. Included) goes to diverse directions, time or event driven, constant volume or number MC simulations [23,24,25] Most of these algorithms share the same idea [26] to randomly choose a aerosol dynamic process (either nucleation, or coagulation, or surface growth) at a time to determine the aerosol particles evolution. Other than selecting all processes randomly, Patterson et al [27] proposed the linear process deferment algorithm, which separates the surface reaction from other dynamic processes, and models the surface reaction independently for some steps, and includes the other processes Their idea to accelerate the simulation is similar to the τ leaping method in some sense. The paper first introduces the OSMC algorithm, discusses the simulation results of the testing cases
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