Monte Carlo Simulation of Particle Aggregation and Simultaneous Restructuring

Tandon, Pushkar; Rosner, Daniel E.

doi:10.1006/jcis.1998.6036

Cited by 129 publications

(110 citation statements)

References 44 publications

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“…Monte-Carlo methods proceed via a statistical sampling of particles undergoing the growth and interaction processes to simulate the evolution of the particle ensemble. Early work in that area was carried out in the aerosol community and a review of it can be found in [53]; recent work has applied these methods to nanoparticle production [239], [3], [82]. These methods are more suitable for the simulation of multidimensional PBEs, as they do not scale exponentially with dimensionality.…”

Section: • Monte Carlo Methodsmentioning

confidence: 99%

Population balance modelling of polydispersed particles in reactive flows

Rigopoulos

2010

Progress in Energy and Combustion Science

135

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Polydispersed particles in reactive flows is a wide subject area encompassing a range of dispersed flows with particles, droplets or bubbles that are created, transported and possibly interact within a reactive flow environment -typical examples include soot formation, aerosols, precipitation and spray combustion. One way to treat such problems is to employ as a starting point the Newtonian equations of motion written in a Lagrangian framework for each individual particle and either solve them directly or derive probabilistic equations for the particle positions (in the case of turbulent flow). Another way is inherently statistical and begins by postulating a distribution of particles over the distributed properties, as well as space and time, the transport equation for this distribution being the core of this approach. This transport equation, usually referred to as population balance equation (PBE) or general dynamic equation (GDE), was initially developed and investigated mainly in the context of spatially homogeneous systems. In the recent years, a growth of research activity has seen this approach being applied to a variety of flow problems such as sooting flames and turbulent precipitation, but significant issues regarding its appropriate coupling with CFD pertain, especially in the case of turbulent flow. The objective of this review is to examine this body of research from a unified perspective, the potential and limits of the PBE approach to flow problems, its links with Lagrangian and multifluid approaches and the numerical methods employed for its solution. Particular emphasis is given to turbulent flows, where the extension of the PBE approach is met with challenging issues. Finally, applications including reactive precipitation, soot formation, nanoparticle synthesis, sprays, bubbles and coal burning are being reviewed from the PBE perspective. It is shown that popualtion balance methods have been applied to these fields in varying degrees of detail, and future prospects are discussed.

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Section: • Monte Carlo Methodsmentioning

confidence: 99%

Population balance modelling of polydispersed particles in reactive flows

Rigopoulos

2010

Progress in Energy and Combustion Science

135

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“…Monte Carlo methods to simulate particulate growth processes are not new, and the theoretical foundations have been discussed extensively in the literature [14,24,25]. Basically the Monte Carlo approach utilizes probabilistic tools to study a finite dimensional subsystem in order to infer the properties of the whole system.…”

Section: Monte-carlo Methodsmentioning

confidence: 99%

Modeling and simulation of multi-component aerosol dynamics

Efendiev¹

2004

Comput. Appl. Math.

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Abstract. In this paper we consider the modeling of heterogeneous aerosol coagulation where the heterogeneous aerosol particles (called droplets) contain smaller particles (enclosures).Droplets and enclosures coagulate with different collision kernels. We discuss macroscopic modeling and simulation of these processes using both deterministic and Monte-Carlo methods.Mathematical subject classification: 65C05, 65C35.

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“…As an alternative to deterministic population balance models, stochastic or so-called Monte Carlo methods have gained increasing popularity for the simulation of par ticulate processes [2][3][4][5][6] . There, the evolution of a set of representative, discrete particles is tracked.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Structure Evolution for Size-Dependent Aggregation by Means of Monte Carlo Simulations

Briesen

2007

KONA

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Aggregation during crystallization and precipitation processes often leads to complex-shaped particle aggregates. As an alternative to low-dimensional deterministic population balance models, where assumptions on the particle shape must be made, stochastic or so-called Monte Carlo methods can be employed. In previous work a hierarchical characterization of aggregates has been proposed (Briesen, AIChE J., 52, 2436-2446, 2006

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Monte Carlo Simulation of Particle Aggregation and Simultaneous Restructuring

Cited by 129 publications

References 44 publications

Population balance modelling of polydispersed particles in reactive flows

Population balance modelling of polydispersed particles in reactive flows

Modeling and simulation of multi-component aerosol dynamics

Aggregate Structure Evolution for Size-Dependent Aggregation by Means of Monte Carlo Simulations

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