GRAL
A Lagrangian dispersion model with reasonable demands on computational times and sensible accuracy.
https://github.com/GralDispersionModel/GRAL
Category: Atmosphere
Sub Category: Atmospheric Dispersion and Transport
Last synced: about 20 hours ago
JSON representation
Repository metadata
Gral calculation core
- Host: GitHub
- URL: https://github.com/GralDispersionModel/GRAL
- Owner: GralDispersionModel
- License: gpl-3.0
- Created: 2019-12-27T15:05:08.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-11-27T19:37:28.000Z (5 months ago)
- Last Synced: 2025-04-17T21:24:50.261Z (10 days ago)
- Language: C#
- Size: 538 KB
- Stars: 19
- Watchers: 9
- Forks: 13
- Open Issues: 1
- Releases: 7
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Metadata Files:
- Readme: README.md
- Contributing: Contributing.md
- License: License.md
- Code of conduct: Code_of_Conduct.md
README.md
GRAL Dispersion Model
Atmospheric dispersion modeling in complex terrain and in situations with low wind speeds is still challenging. Nevertheless, air pollution has to be assessed in such environments.
It is therefore necessary to develop models and methods, which allow for such assessments with reasonable demands on computational times and with sensible accuracy.
This has been the motivation for the development of the Lagrangian dispersion model GRAL at the Graz University of Technology, Institute for Internal Combustion Engines and Thermodynamics, ever since 1999.
Dr. Dietmar Oettl from the Office of the Provincial Government of Styria (2006 - 2020) and Markus Kuntner (since 2016), Austria, are further developing the model. In 2019, the decision was made to publish GRAL as Open Source.
The basic principle of Lagrangian models is the tracing/tracking of a multitude of fictitious particles moving on trajectories within a 3-d windfield. GRAL provides a CFD model for the flow calculation around buildings or micrsocale terrain structures. To take the presence of topography into account, GRAL can be linked with the prognostic wind field model GRAMM.
To speed up the calculation, GRAL is parallelized, the CFD model supports SSE/AVX vectorization and the latest performance optimizations of the .NET framework are used.
Validation
GRAL has been used to calculate a large number of validation data sets, which are continuously updated and documented in the GRAL manual. The manual is available on the GRAL homepage in the download section. Individual validation projects are also published on Github.
Usage
It is possible to create or evaluate all input files and output files, which are text files and/or binary files, by yourself. The file formats are documented in the GRAL manual.
We are also developing a comprehensive graphical user interface (GUI), in order to simplify the numerous input values, display the results and allow verification of your model input and output. Our goal is to provide the user with a simple and comprehensive model checking tool to support the quality requirements of dispersion calculations. Also the GUI is free and completely published as Open Source.
Built With
Official Release and Documentation
The current validated and signed GRAL version, the documentation and a recommendation guide is available at the GRAL homepage
Contributing
Everyone is invited to contribute to the project Contributing
Versioning
The version number includes the release year and the release month, e.g. 20.01.
License
This project is licensed under the GPL 3.0 License - see the License file for details
Owner metadata
- Name: GralDispersionModel
- Login: GralDispersionModel
- Email: [email protected]
- Kind: organization
- Description:
- Website:
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/56819015?v=4
- Repositories: 4
- Last ynced at: 2023-03-10T04:50:38.341Z
- Profile URL: https://github.com/GralDispersionModel
GitHub Events
Total
- Create event: 1
- Release event: 1
- Issues event: 4
- Watch event: 3
- Delete event: 1
- Issue comment event: 2
- Push event: 1
- Pull request event: 2
- Fork event: 1
Last Year
- Create event: 1
- Release event: 1
- Issues event: 4
- Watch event: 3
- Delete event: 1
- Issue comment event: 2
- Push event: 1
- Pull request event: 2
- Fork event: 1
Committers metadata
Last synced: 6 days ago
Total Commits: 171
Total Committers: 3
Avg Commits per committer: 57.0
Development Distribution Score (DDS): 0.023
Commits in past year: 42
Committers in past year: 2
Avg Commits per committer in past year: 21.0
Development Distribution Score (DDS) in past year: 0.071
| Name | Commits | |
|---|---|---|
| Markus | m****r@t****t | 167 |
| JoshLovesFun | i****l@g****m | 3 |
| Markus Kuntner | m****r@g****t | 1 |
Committer domains:
- gmx.at: 1
- tirol.gv.at: 1
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 9
Total pull requests: 38
Average time to close issues: 16 days
Average time to close pull requests: 3 days
Total issue authors: 6
Total pull request authors: 3
Average comments per issue: 1.11
Average comments per pull request: 0.21
Merged pull request: 32
Bot issues: 0
Bot pull requests: 0
Past year issues: 4
Past year pull requests: 7
Past year average time to close issues: 29 days
Past year average time to close pull requests: 8 days
Past year issue authors: 4
Past year pull request authors: 2
Past year average comments per issue: 0.75
Past year average comments per pull request: 1.14
Past year merged pull request: 5
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- MarkusKuntner (4)
- Asche98 (1)
- maccardo (1)
- Gweiqi (1)
- Code-Signing-25 (1)
- ireneormazabal (1)
Top Pull Request Authors
- MarkusKuntner (35)
- JoshLovesFun (2)
- Dietmar-Oettl (1)
Top Issue Labels
Top Pull Request Labels
Dependencies
Score: 4.0943445622221