摘要：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.
