Mixing and consequently fluid dynamic is a key parameter to tailor the particle size distribution (PSD) in nanoparticle precipitation. Due to fast and intensive mixing a static T-mixer configuration is capable for synthesizing continuously nanoparticles. The flow and concentration field of the applied mixer is investigated experimentally at different flow rates by Particle Image Velocimetry (PIV) and Laser Induced Fluorescence (LIF). Due to the PIV measurements the flow field in the mixer was characterized qualitatively and the mixing process itself is quantified by the subsequent LIF-measurements. A special feature of the LIF set up is to detect structures in the flow field, which are smaller than the Batchelor length. Thereby a detailed insight into the mixing process in a static T-Mixer is given. In this study a CFD-based approach using Direct Numerical Simulation (DNS) in combination with the solid formation kinetics solving population balance equations (PBE) is applied, using barium sulfate as modeling material. A Lagrangian Particle Tracking strategy is used to couple the flow field information with a micro mixing model and with the classical theory of nucleation. We found that the DNS-PBE approach including macro and micro mixing, combined with the population balance is capable of predicting the full PSD in nanoparticle precipitation for different operating parameters. Additionally to the resulting PSD, this approach delivers a 3D-information about all running subprocesses in the mixer, i.e. supersaturation built-up or nucleation, which is visualized for different process variables.

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