(382e) Modeling and Simulation of Flash Nanoprecipitation in a Multi-Inlet Vortex Reactor
Flash nanoprecipitation (FNP) is an emerging micellization technique applicable to making functional nanoparticles for drug delivery, drug targeting, bio-imaging, sunscreen, and food technology. FNP utilizes rapid mixing of an anti-solvent stream and a solvent stream containing stabilizing block copolymer and nanoparticles. The mixing of these two streams induces supersaturation, leading to the formation of functional nanoparticles. One constraint of FNP is to have a fast mixing time compared to nanoparticle formation time, which lead to the design and scale-up study of the multi-inlet vortex reactor (MIVR). Experiments have shown promising results for tunable, consistent nanoparticle sizes, but in order to optimize the MIVRâ??s design, the mixing behavior of the reactor needs to be examined. Computational fluid dynamics (CFD) simulations have been used as an aid to benchmarking the mixing and determining the flow patterns of the MIVR. In order to model FNP in the MIVR correctly, the turbulence field inside the MIVR has to be predicted correctly first. Considering the complex turbulent swirling flow inside the MIVR, our recent work has applied a hybrid RANS/LES method for turbulence modeling successfully. Population Balance Equations (PBE) can be coupled with CFD simulations to describe particle size evolution over time, but this direct coupling is computationally intractable. As an alternative, Quadrature Based Moment Methods (QBMM) are used to compress computational costs by tracking the moments of the particle size distributions. The Conditional Quadrature Method of Moments (CQMOM) is used to describe the aggregation of block copolymers and nanoparticles. As a means to streamlining the simulation process, OpenQBMM, an extension of OpenFOAM, is used to solve the moment transport equations along with appropriate mixing and aggregation kernels. By utilizing recently developed and improved CFD models and QBMM, this work will increase the understanding of FNP in the MIVR.