This python script runs a two-population dust evolution model according to Birnstiel, Klahr, Ercolano, A&A (2012). Parameters can be set by using the arguments, otherwise default parameters are used (see output). The parameters, their meaning and units can be seen by executing ./two-pop-py.py -h.
This code is published on github.com/birnstiel.
For bug reports, questions, ... contact me via my website.
If you use this code in a publication, please cite at least Birnstiel, Klahr, Ercolano, A&A (2012), and possibly Birnstiel et al. (ApJL) 2015 if you use the size distribution reconstruction. I addition to that, it would be best practice to include the hash of the version you used to make sure results are reproducible, as the code can change.
- v0.2: restructuring of the code and packaging as a python package. The code can now be installed with
python setup.py install. This includes an executable script.
Output is written in the folder data/ by default (can be specified with option -dir).
The following files are created:
| File | Description | Units |
|---|---|---|
x.dat |
Radial grid | cm |
T.dat |
Temperature | K |
a.dat |
Grain size grid | cm |
a_df.dat |
drift-fragmentation limit on radial grid | cm |
a_dr.dat |
drift size limit on radial grid | cm |
a_fr.dat |
fragmentation limit on radial grid | cm |
a_t.dat |
maximum particle size as function of radius and time | cm |
constants.dat |
lists several constants | see file contents |
sigma_d.dat |
dust surface density as function of radius and time | g cm^-2 |
sigma_d_a.dat |
final dust surface density distribution (fct. of particle size and radius) | g cm^-2 |
sigma_g.dat |
gas surface density as function of radius and time | g cm^-2 |
time.dat |
times at which the snapshots were taken | s |
v_0.dat |
small grain velocity as function of radius and time | cm s^-1 |
v_1.dat |
large grain velocity as function of radius and time | cm s^-1 |
v_gas.dat |
gas velocity as function of radius and time | cm s^-1 |
astropy, numpy, scipy, configobj
- proper integration of
$da/dt$ instead of using exponential approximation.
