A vector parameter study of the SWASH wave-flow model driven by the Dakota iterative systems analysis toolkit.
This study is broken into two stages.
In the first stage,
Dakota,
through the dakota_run_driver.py script,
creates a series of independent PBS submissions,
one for each iteration of the parameter study (currently 7),
each using the submission script
run_swash.sh.
The submission script uses mpiexec
to call SWASH in parallel
using 8 processors
on one compute node.
Output from each run is collected and stored
in PBS_O_WORKDIR
in a directory run.N,
where N = 1, 2, ..., 7.
In the second stage, Dakota analyses the results of each iteration with the dakota_analysis_driver.py script and creates the tabular output file dakota.dat, which summarizes the results of the parameter study.
On beach, add Dakota paths with:
export DAKOTA_DIR=/usr/local/dakota
PATH=$DAKOTA_DIR/bin:$DAKOTA_DIR/test:$PATH
export LD_LIBRARY_PATH=$DAKOTA_DIR/bin:$DAKOTA_DIR/lib:$LD_LIBRARY_PATH
Also, I recommend using the Anaconda Python distribution instead of the default Python:
PATH=/usr/local/anaconda/bin:$PATH
Run the first stage of the study with:
$ dakota -i dakota_run.in -o dakota_run.out &> run.log
After the first stage completes, run the second stage with:
$ dakota -i dakota_analysis.in -o dakota_analysis.out &> analysis.log
View the results of the study in dakota.dat:
$ cat dakota.dat
%eval_id interface BOT-sand Ufric_x_002800_000-mean Ufric_x_002800_000-stdev
1 0 2 0.00145875 0.0223389
2 0 2.5 0.00474198 0.0264774
3 0 3 -0.000528284 0.0221227
4 0 3.5 -0.0115249 0.0196799
5 0 4 -0.0152711 0.0199635
6 0 4.5 -0.0163003 0.0195528
7 0 5 -0.017241 0.0196005