STOCHASTIC-PROBABILISTIC EFFICIENCY ENHANCED DISPERSION MODELING OF TURBULENT POLYDISPERSED SPRAYS

A stochastic-probabilistic efficiency enhanced dispersion (SPEED) mode l is developed for the prediction of turbulent two-phase hows, The SPE ED model computes both the mean and variance of droplet positions at e ach Lagrangian integral time step, The mean position is determined wit h an improved conventional stochastic model, whereas the variance is d etermined by a newly derived Lagrangian equation with a Lagrangian aut ocorrelation function, A memoryless Markovian chain is used to determi ne the autocorrelation function. The distribution of a physical drople t in space is determined with a prescribed probability density functio n, The efficiency of the SPEED model is that a minimal number of dropl et trajectories are required for Lagrangian trajectory computations du ring which a large amount of smooth noise-free solution can be attaine d, The developed SPEED model is first validated against a benchmark te st where the measured mean-squared dispersion width is available. Then the results include the prediction of a polydispersed turbulent spray with detailed experimental measurements. Numerical results of the SPE ED model, using only a total number of 6 x 10(2) droplet trajectories, are compared with those of a conventional stochastic discrete delta-f unction model using a total number of 2.1 x 10(4) trajectories, and wi th a previous stochastic dispersion-width transport model. It is found that the SPEED model is numerically more efficient than the dispersio n-width transport model and needs much fewer number of droplet traject ories than the standard model.