PPI-Theory for Particle Dispersion
For many problems with turbulent dispersion the Lagrangian approach as introduced by Taylor (1921) is the most appropriate. Extension of this theory to atmospheric dispersion problems is, however, complicated by the inhomogeneity and instationarity of atmospheric turbulence. Here we first describe a method based on the Preferred Path Integration (PPI) theory relating the probability distribution of particle displacement to the probability for a particle to move along the most probable or preferred path. The theory is based on the assumption that the particle velocity fluctuations are governed by a first order autoregressive process equivalent to the Langevin model of dispersion (Lin and Ried (1962), Novikov (1963), Smith (1968), Hanna (1978) and Gifford (1982)). The PPI-model yields conditional probability distributions in the case of stationary conditions. The Langevin model has been proposed also for instationary conditions in Monte-Carlo simulations of particle dispersion (Durbin (1980), Wilson et al. (1981)), and here are presented some analytical results based on this type of model for the case of dispersion in decaying turbulence.
KeywordsVelocity Variance Particle Dispersion Atmospheric Turbulence Turbulent Dispersion Boundary Layer Model
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