Predictive simulation of nanoparticles-precipitation in a T-mixer by coupling direct numerical simulation with population balance equations

Mixing and consequently fluid dynamics are key parameters to tailor the particle size distribution (PSD) in nanoparticle precipitation. The influence of the operating conditions on the flow and concentration field of the applied mixer is investigated experimentally by PIV and LIF. 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, a Lagrangian micromixing model is combined 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 DNS-approach including macro and micro mixing, coupled to the population balance is capable of predicting the full PSD in nanoparticle precipitation. Additionally to the resulting PSD this approach delivers 3D-information on all running subprocesses in the mixer, i.e. supersaturation built-up or nucleation, which are visualized for different process variables.