MMC-LES modelling of droplet nucleation and growth in turbulent jets. (10th August 2017)

• Record Type: Journal Article
• Authors: Neuber, G.; Kronenburg, A.; Stein, O.T.; Cleary, M.J.
• Is Part Of: Chemical engineering science. Volume 167(2017)
• Page Start: 204
• Page End: 218
• Publication Date: 2017-08-10
• Subjects: Aerosol -- Nucleation -- Condensation -- PBE -- MMC -- LES -- PDF method; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2017.04.008
• Languages: English
• ISSNs: 0009-2509

Abstract

Highlights: Sparse-Lagrangian particle method for particle nucleation and growth. Sparse particle and standard (dense) particle methods yield near to identical results. New particle method captures the correct trends when varying the precursor concentration. The interactions between turbulence and nucleation can modify the averaged nucleation rates by up to 250%. Abstract: An Eulerian large-eddy simulation (LES) is coupled with a sparse-Lagrangian particle method to solve the population balance equation for aersol nucleation and growth in turbulent flows. We use the LES for the solution of the filtered velocity and mixing fields while the particle method provides one-point statistics of the gaseous species and the condensed phase such that the non-linear aerosol nucleation and growth terms appear naturally in closed form. A sparse particle implementation requires additional localisation in a reference space, and this localisation is realised here by employing the generalised multiple mapping conditioning (MMC) mixing model. The complete model, called MMC-LES, is validated by comparison with experimental data from nucleation studies in a turbulent, hot, nitrogen jet laden with dibutyl-phthalate (DBP) that condenses during mixing with a coflow of cold air. Acceptable agreement is found between the MMC-LES predictions and the experimental data. The average DBP droplet sizes are well predicted, and predictions of the total droplet number and the dependencies of droplet statistics on precursor concentrations are satisfactory. A comparison of the sparse particle method with results from conventional (dense) Monte Carlo-LES simulations demonstrates the capabilities of MMC-LES to predict aerosol nucleation and growth at relatively low computational cost. An additional quantitative analysis of the interactions between the turbulence and the non-linear nucleation source and growth terms shows that sub-grid effects must not be neglected and interactions between turbulence and nucleation can modify averaged nucleation rates by more than 250%.