Reducing Statistical Noise and Extending the Size Spectrum by Applying Weighted Simulation Particles in Monte Carlo Simulation of Coagulation

The direct simulation Monte Carlo (DSMC) method is widely utilized to simulate microscopic dynamic processes in dispersed systems that give rise to the population balance equation. In conventional DSMC approaches, simulation particles are equally weighted, even for broad size distributions where number concentrations in different size intervals are significantly different. The resulting statistical noise and limited size spectrum severely restrict the application of these DSMC methods. This study proposes a new Monte Carlo (MC) method, the differentially weighted time-driven method, which captures the coagulation dynamics in dispersed systems with low noise and is simultaneously able to track the size distribution over the full size range. Key elements of this method include constructing a new jump Markov process based on a new coagulation rule for two differentially weighted simulation particles, and restricting the number of simulation particles in each size interval within prescribed bounds. The method is validated by using an ideal coagulation kernel with a known analytical solution and a real coagulation kernel for which an accurate solution can be found numerically (self-preserving particle size distribution in the continuum regime).