Precipitation of nanoparticles in a T-mixer: Coupling the particle population dynamics with hydrodynamics through direct numerical simulation

Mixing is a key parameter to tailor the particle size distribution (PSD) in nanoparticle precipitation. In this study we present two model approaches to simulate the impact of mixing on the PSD, using barium sulfate as exemplary material. In the first model, we combine a Lagrangian micromixing model with the population balance equation. This approach was found successful in predicting the influence of mixing on mean particle sizes but fails to predict the shape and width of the PSD. This is attributed to the neglect of spatial and temporal fluctuations in that model. Therefore, an improved CFD-based approach using direct numerical simulation (DNS) in combination with Lagrangian particle tracking strategy is applied. We found that the full DNS-approach, coupled to the population balance and a derived micromixing model, is capable of predicting the full PSD in nanoparticle precipitation.