Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/mdpiper/dakota-swash-parameter-study
A Dakota vector parameter study with the SWASH wave-flow model
https://github.com/mdpiper/dakota-swash-parameter-study
Last synced: 27 days ago
JSON representation
A Dakota vector parameter study with the SWASH wave-flow model
- Host: GitHub
- URL: https://github.com/mdpiper/dakota-swash-parameter-study
- Owner: mdpiper
- License: mit
- Created: 2015-10-30T16:13:09.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2015-11-19T21:39:45.000Z (about 9 years ago)
- Last Synced: 2024-06-12T17:33:23.640Z (7 months ago)
- Language: Python
- Size: 15.6 KB
- Stars: 2
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SWASH parameter study with Dakota
A vector parameter study
of the [SWASH](http://swash.sourceforge.net/)
wave-flow model
driven by the
[Dakota](https://dakota.sandia.gov/)
iterative systems analysis toolkit.
## Description
This study is broken into two stages.
In the first stage,
Dakota,
through the [dakota_run_driver.py](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](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](dakota_analysis_driver.py) script
and creates the tabular output file **dakota.dat**,
which summarizes the results of the parameter study.
## Setup
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
```
## Execution
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
```
## Results
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
```