Precipitation crystallization is one possibility of producing nanoscaled solid particles from the liquid phase. High nucleation and growth rates are generated by mixing two well soluble reactants and by their subsequent reaction to a sparingly soluble product. Primary processes, such as nucleation and growth, can be especially very fast (< 1 s). Therefore, experimental access to such internal flow-dependent rates is insufficient due to predominantly very short process times. Computational fluid dynamics (CFD)-based approaches combined with a suitable population balance algorithm are a promising simulative alternative to gain insight into such swift processes. They enable the precipitation process to be resolved, starting from the turbulent mixing of reactants, followed by the supersaturation build-up as the driving force for nucleation and growth and, finally, the solid formation of nanoparticles. The aim of this paper is to predict the influence of mixing on the particle formation process using the valuable synergies of CFD and the population balance solver. In this paper, a confined impinging jet mixer is used as a benchmark apparatus. This type of apparatus enables the adjustment of highly reproducible mixing conditions. Precipitation is carried out using the sparingly soluble model system BaSO4-water. Formation of solids at low Reynolds numbers is mixing-controlled, which anticipates that local gradients, inhomogeneous mixing and the characteristic residence time distribution influences the particle formation process significantly. Firstly, this contribution compares the standard scalar transport model for the ion concentration involved to results from a micromixing model (joint probability density function). Secondly, transient coupled CFD-population balance equations (PBE) simulations using the sophisticated detached eddy turbulence model and a direct quadrature method of moments for the solution of the PBE are compared to experimental results. (C) 2016 Elsevier Ltd. All rights reserved.