
As mentioned in Section 22.12.4, you can choose for each injection stochastic tracking or cloud tracking as the method for modeling turbulent dispersion of particles.
Stochastic Tracking
For turbulent flows, if you choose to use the stochastic tracking technique, you must enable it and specify the "number of tries''. Stochastic tracking includes the effect of turbulent velocity fluctuations on the particle trajectories using the DRW model described in Section 22.2.2.
When a sufficient number of tries is requested, the trajectories computed will include a statistical representation of the spread of the particle stream due to turbulence. Note that for unsteady particle tracking, the Number of Tries is set to 1 if Stochastic Tracking is enabled.
If you want the characteristic lifetime of the eddy to be random (Equation 22.231), enable the Random Eddy Lifetime option. You will generally not need to change the Time Scale Constant ( in Equation 22.222) from its default value of 0.15, unless you are using the Reynolds Stress turbulence model (RSM), in which case a value of 0.3 is recommended.
Figure 22.12.10 illustrates a discrete phase trajectory calculation computed via the "mean'' tracking (number of tries = 0) and Figure 22.12.11 illustrates the "stochastic'' tracking (number of tries 1) option.
When multiple stochastic trajectory calculations are performed, the momentum and mass defined for the injection are divided evenly among the multiple particle/droplet tracks, and are thus spread out in terms of the interphase momentum, heat, and mass transfer calculations. Including turbulent dispersion in your model can thus have a significant impact on the effect of the particles on the continuous phase when coupled calculations are performed.
Cloud Tracking
For turbulent flows, you can also include the effects of turbulent dispersion on the injection. When cloud tracking is used, the trajectory will be tracked as a cloud of particles about a mean trajectory, as described in Section 22.2.2.
You may want to restrict the Max. Cloud Diameter to a relevant length scale for the problem to improve computational efficiency in complex domains where the mean trajectory may become stuck in recirculation regions.