Dear Elise,
we use a semi-implicit scheme to compute the particle motion. The time step is adaptive. The time step size is computed in each step such that the distance traveled by the particle in one step is bound.
We have not published the details of the implementation.
It is possible to increase or decrease the step size by a user-defined factor. For this, you can use the TimeStepScaling expert setting:

But there are some caveats: if you decrease the time steps too much, some particles might erroneously be detected as "not moving" or "filtered". If you increase the time step size with a factor larger than 1.0, particle-wall collisions might go undetected which can lead to various errors.
Not all the computed steps are stored for particle trajectory visualization - the typical distance traveled in a time step is much smaller than 0.5 voxel. You can change this distance on the Output options tab - be aware that smaller numbers will lead to much larger *.gpt files.
Regards,
Jürgen
we use a semi-implicit scheme to compute the particle motion. The time step is adaptive. The time step size is computed in each step such that the distance traveled by the particle in one step is bound.
We have not published the details of the implementation.
It is possible to increase or decrease the step size by a user-defined factor. For this, you can use the TimeStepScaling expert setting:
But there are some caveats: if you decrease the time steps too much, some particles might erroneously be detected as "not moving" or "filtered". If you increase the time step size with a factor larger than 1.0, particle-wall collisions might go undetected which can lead to various errors.
Not all the computed steps are stored for particle trajectory visualization - the typical distance traveled in a time step is much smaller than 0.5 voxel. You can change this distance on the Output options tab - be aware that smaller numbers will lead to much larger *.gpt files.
Regards,
Jürgen
