A coupled CFD-Monte Carlo method for simulating complex aerosol dynamics in turbulent flows

Abstract

A coupled computational fluid dynamics (CFD)-Monte Carlo method is presented to simulate complex aerosol dynamics in turbulent flows. A Lagrangian particle method-based probability density function (PDF) transport equation is formulated to solve the population balance equation (PBE) of aerosol particles. The formulated CFD-Monte Carlo method allows investigating the interaction between turbulence and aerosol dynamics and incorporating individual aerosol dynamic kernels as well as obtaining full particle size distribution (PSD). Several typical cases of aerosol dynamic processes including turbulent coagulation, nucleation and growth are studied and compared to the sectional method with excellent agreement. Coagulation in both laminar and turbulent flows is simulated and compared to demonstrate the effect of turbulence on aerosol dynamics. The effect of jet Reynolds (Re j) number on aerosol dynamics in turbulent flows is fully investigated for each of the studied cases. The results demonstrate that Re j number has significant impact on a single aerosol dynamic process (e.g., coagulation) and the simultaneous competitive aerosol dynamic processes in turbulent flows. This newly modified CFD-Monte Carlo/PDF method renders an efficient method for simulating complex aerosol dynamics in turbulent flows and provides a better insight into the interactions between turbulence and the full PSD of aerosol particles.

A new weighted fraction Monte Carlo method for particle coagulation

“…The PBE is a partial integral-differential equation; analytical solutions of the PBE are available for only a few ideal cases (Von Smoluchowski, 1916). In general, approximate solutions can be obtained using various numerical methods such as the sectional method (SM) (Gelbard et al , 1980; Prakash et al , 2003), method of moments (Frenklach, 2002; Yu et al , 2008; Yu et al , 2016; Chan et al , 2018) and Monte Carlo (MC) method (Zhao et al , 2009; Zhou and He, 2014; Zhou et al , 2014; Liu and Chan, 2017a, 2017b, 2018a). However, there are two disadvantages to MC methods, i.e.…”

A quasi-Monte Carlo based flocculation model for fine-grained cohesive sediments in aquatic environments

“…The QMC models produce a new stochastic way to solve PBM and help determining various micro-scale flocculation behaviors, which provides new prospects to improve low cost bio-flocculation models to couple with large-scale model. Possibly coupled with computational fluid dynamics (CFD) in turbulent flow (Liu and Chan, 2017;Xu et al, 2017), the QMC based PBMs also have the potential to simulate the interactions between turbulence and flocculated particles in the future.…”

Modelling and experimental study of hydrate formation kinetics of natural gas‐water‐surfactant system in a multi‐tube bubble column reactor

“…For its generalized form, the PBE includes an accumulation term, convection and diffusion terms for each dimension of the phase space, and the source terms ascribed to the birth and death of particles with a distribution of properties . To solve the PBE, numerous methods have been developed; for example, the Monte Carlo method, the discrete method, the standard method of moments (SMM), the quadrature method of moments (QMOM), and the direct quadrature method of moments (DQMOM) …”