EFFICIENT STATISTICAL TRANSPORT MODEL FOR TURBULENT PARTICLE DISPERSION IN SPRAYS

A statistical transport model for turbulent particle dispersion is formulated having significantly improved computational efficiency in comparison to the conventional stochastic discrete-particle methodology. In the proposed model, a computational parcel representing a group of physical particles is characterized by a normal (Gaussian) probability density function (pdf) in space. The mean of each pdf is determined by Lagrangian tracking of each computational parcel, either deterministically or stochastically. The variance of each pdf is represented by a statistically derived turbulence-induced mean square dispersion from the linearized direct modeling formulation for particle-eddy interactions. Convolution of the computational parcel pdfs produces the probable distribution of particles in the spray. The validity of the new model is established for time-averaged dispersion by comparison with the conventional stochastic direct modeling method, wherein each parcel is represented by a discrete delta function in space, for nonevaporating particles injected into simple turbulent air flows.