PDF-LES modelling of nanodroplet nucleation and growth

Aerosol formation and the generation of particulate matter from a bulk gas phase are widely spread in both nature and industrial processes, with prominent examples being the formation of rain drops, soot generation or nanoparticle production by flame synthesis. Industrial nanoparticle production requires a high level of process control to obtain the desired aerosol particle size distribution (PSD). This project aims to develop a fundamental understanding of the physical processes that govern nanodroplet formation in turbulent flows by means of numerical simulation. Turbulence is described by the accurate large eddy simulation (LES) technique. To describe homogeneous droplet nucleation and surface growth a probability density function (PDF) model based on the transport of stochastic particles that represent local, instantaneous samples of the gas mixture and droplet ensemble is employed. The PSD is modelled by a sectional approach, where a set of discrete droplet sizes is stored on thestochastic particles. Two PDF model formulations are developed. Initially, a conventional PDF method -the accuracy of which relies on large numbers of stochastic particles per LES cell- is used and extended to account for the evolution of the PSD. Then, we take advantage of the multiple mapping conditional (MMC) concept, which allows us to use a sparse particle set with significantly less stochastic particles than LES cells, to model the droplet formation and growth processes. Both modelling approaches are validated by comparison to experimental data on dibutyl-phthalate (DBP) nanodroplets that condense in a heated nitrogen jet cooled by ambient air.