Monte Carlo Aggregation Code (MCAC) Part 2: Application to soot agglomeration, highlighting the importance of primary particles

Abstract

During the agglomération of nanoparticles, notably soot, a change in both the flow régime (from free molecular to near continuum) as well as the change of agglomeration regime (from ballistic to diffusive) is expected. However, these effects are rarely taken into account in numerical simulations of particles agglomeration and yet, they are suspected to have an important impact on the agglomeration kinetics, particle morphologies and size distributions. This work intends to study these properties by using the MCAC presented in the preceding work (part 1) by focusing on the physical impact of varying the particle volume fraction and monomers size and polydispersity. The results show an important sensitivity of the kinetics of agglomeration, coagulation homogeneity and agglomerates morphology to the size of monomers. First, for smaller monomers diameters the agglomeration kinetic is enhanced and agglomerates are found to be more compact, characterized by larger fractal dimensions. Secondly, for large monomer diameters, fractal dimensions down to 1.67 can be found being therefore, smaller than the classical 1.78 for DLCA mechanism. One important conclusion is that variation in time of both the agglomeration and flow regimes has to be considered for a more accurate simulation of the agglomerates size distribution and morphology.

Models for Simulation of Fractal-like Particle Clusters with Prescribed Fractal Dimension

“…Based on a binomial approach and the discretization of Langevin Dynamics trajectories of individual particles, the definitions of specific particle persistent distance (λ p = √ 18Dτ, where D is the particle's diffusion coefficient, and τ is the momentum relaxation time defined as the ratio between particle's mass and friction coefficient), its corresponding time step (∆t = 3τ) and subsequent probabilities for particle displacements were found. The model combines the advantages of MC, i.e., the ability to efficiently simulate the complex agglomerates with fractal dimensions in the range 1.62 < D f < 1.88 [104], and the advantages of Langevin Dynamics to account for physical dynamics.…”