Abstract
A novel model for turbulent droplet dispersion is formulated having significantly improved computational efficiency in comparison to the conventional point source stochastic sampling methodology. In the proposed model, a computational parcel representing a group of physical particles is considered to have a normal (Gaussian) probability density function (PDF) in three-dimensional space. The mean of each PDF is determined by Lagrangian tracking of each computational parcel, either deterministically or stochastically. The variance is represented by a turbulence-induced mean squared dispersion which is based on statistical inferences from the linearized direct modeling formulation for particle/eddy interactions. Convolution of the computational parcel PDF's produces a single PDF for the physical particle distribution profile. The validity of the new model is established by comparison with the conventional stochastic sampling method, where in each parcel is represented by a delta function distribution, for non-evaporating particles injected into simple turbulent air flows.
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