Simulation of nanoparticle coagulation under Brownian motion and turbulence in a differential–algebraic framework: Developments and applications

Investigating the Nanoparticles Penetration Efficiency through Horizontal Tubes Using an Experimental Approach

“…Nanoparticles deposition inside tubes occurs in many important technological problems in biology, immunology, crystallization, and colloid and polymer science, such as enhanced heat transfer in heat exchangers [1], toxic particle transport in human lung contamination [2], pollutant particle emission from coal combustion in power plants, and the rail tube of a moving car [3]. For badly stable suspension of microsized particles, nanoparticles suspended inside a tube may coagulate or deposit on the wall due to various mechanisms that may act in combination with Brown motion and fluid turbulence [4]. The process of coagulation causes the particle clusters to grow up, and then the dynamic characteristics of large clusters are different from that of small clusters.…”

Simulation of airborne nanoparticles transport, deposition and aggregation: Experimental validation of a CFD-QMOM approach

“…To overcome this difficulty, we use the DAE-QMOM technique described in Guichard et al (2014b), which was initially proposed in Gimbun et al (2009) in a chemical engineering context. It is possible to keep a low computational cost since Marchisio et al (2003a) showed that the first six moments were sufficient to describe a particle size distribution, i.e., only six transport equations are solved for tracking the dispersed phase flow.…”

“…is the aggregation kernel between particles of size L and particles of size ' L . The Brownian and turbulent aggregation kernel described in Guichard et al (2014b) is used. It corresponds to the kernel of Zaichik and Solov'ev (2002) which has been adapted to account for the morphology of aggregates, as: g L which are defined in equation 18.…”

“…In this context, we showed (Guichard et al, 2014b) that the DAE-QMOM approach of Gimbun et al (2009) was particularly well suited to solve the modelling of aerosol undergoing aggregation in turbulent indoor air conditions with a high quadrature order. A complete CFD model, which used this DAE-QMOM approach for the aggregation of the particulate phase was then presented (Guichard et al, 2014a).…”

“…This model was assessed in very simple configurations, like ducts, bends and well-mixed chambers, each configuration allowing the validation of an isolated physical phenomenon (deposition, Brownian or turbulent aggregation, transport, etc.). More recent developments were also proposed in (Guichard et al, 2014b) in order to take into account the effect of morphology on aggregation kinetics and to demonstrate the influence of turbulence. Comparisons with reference experimental data showed the ability of the modelling approach to predict the PSD shift due to aggregation.…”