Efficient computation of particle dispersion in turbulent flows with a stochastic-probabilistic model

This paper describes a three-dimensional, stochastic-probabilistic, efficiency-enhanced dispersion (SPEED) model for the prediction of particle dispersion in turbulent hows. A stochastic procedure is used to compute particle-trajectory mean and variance, and a probabilistic procedure is used to compute a physical-particle spatial distribution among Eulerian control volumes. An additional Lagrangian equation is derived to govern the evolution of particle-trajectory variance. The SPEED model is aimed al tracking a relatively small number of particle trajectories, while efficiently reducing computational shot noise in the conventional stochastic dispersion model. Two tests cases with available measurements are used to validate the efficiency of the SPEED model. Numerical results of the SPEED model using only 5 x 10(2) particle trajectories are compared with those of the conventional stochastic model using as high as 1.5 x 10(4) particle trajectories. It is found that the SPEED model offers better agreement with the experimental measurements, and that the computational efficiency can be substantially enhanced by a Factor of 20. (C) 1997 Elsevier Science Ltd.