Stochastic tracking accounts for local variations in the flow properties. It adds random turbulent dispersion to each particle track. Tracks that start from the same point can be all different at the final location.

But it requires a large number of stochastic tries to achieve statistically significant sampling. It depends on the density of the mesh.

Insufficient number of tries can result in convergence issues.

It is recommended for use in complex geometries.

Cloud tracking uses statistical methods to trace the turbulent dispersion of particles about a mean trajectory. The mean trajectory is calculated from the ensemble average of the equation of motion for the particles represented in cloud. The distribution of particles inside a cloud is represented by a

Gaussian probability density function.

In cloud tracking, the local variations in the flow get averaged inside the particle cloud. It has a smooth distribution of particle coupling source terms. Each particle diameter requires its own cloud trajectory calculation.