CFD-PBM modelling for predicting the size and distribution of silica nanoparticles synthesized in an ultrasound-assisted swirling vortex flow reactor.

Abstract

A coupled computational fluid dynamics-population balance model (CFD-PBM) framework is developed and experimentally validated to predict the size and distribution of silica nanoparticles in an ultrasound-assisted swirling vortex flow reactor (SVFR). Two key aggregation parameters (i.e., bridge strength parameter A_p, and the initial particle number coefficient C₁) are identified and corrected against experimental data, enabling accurate prediction of primary and secondary peaks in the particle size distribution. The modelling results show that increasing vortex Reynolds number promotes particle collisions, leading to a high frequency of particle collisions and thus relatively large particle sizes, while ultrasound exerts a dual effect by enhancing collisions and simultaneously suppressing excessive aggregation via turbulence-induced shear. These findings highlight the critical role of ultrasound-assisted turbulence in controlling nanoparticle formation, providing insights for reactor design and scale-up.