https://github.com/vasulab/sco2es
Python package for sCO2 energy storage modeling.
https://github.com/vasulab/sco2es
Last synced: 11 months ago
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Python package for sCO2 energy storage modeling.
- Host: GitHub
- URL: https://github.com/vasulab/sco2es
- Owner: VasuLab
- License: other
- Created: 2023-05-25T18:43:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-20T19:55:58.000Z (over 1 year ago)
- Last Synced: 2025-01-25T20:43:46.340Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage: https://vasulab.github.io/sCO2ES/
- Size: 2.49 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Supercritical Carbon Dioxide Energy Storage (sCO₂ES)
[](https://python-poetry.org/)
`sco2es` is intended for modeling of energy storage for supercritical carbon dioxide (sCO₂)
packed bed thermal energy storage systems. This work is based on the model detailed by Battisti et al.[^1].
> [!IMPORTANT]
> This software is licensed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license
> The software can be used for academic purposes, but if you are interested in using this
> software for commercial purposes, please contact the University of Central Florida Research Foundation's
> [Technology Transfer Office](https://tt.research.ucf.edu/) to negotiate a commercial license.
>
> Copyright © 2024 UCFRF, Inc. All Rights Reserved
## Performance
`sco2es` uses Numba for optimization of functions and utilizes NumPy's linear algebra routines
and SciPy's sparse linear algebra routines. For optimal performance, a version of SciPy built against a well
optimized LAPACK/BLAS library is required. Therefore, it is recommended to run this code using the
Anaconda distribution of SciPy, which is built against Intel’s MKL[^2].
[^1]: F. G. Battisti, L. A. de Araujo Passos, and A. K. da Silva, "Performance mapping of packed-bed thermal energy
storage systems for concentrating solar-powered plants using supercritical carbon dioxide," Applied Thermal Engineering,
vol. 183, 2021, doi: 10.1016/j.applthermaleng.2020.116032.
[^2]: https://numba.readthedocs.io/en/stable/user/performance-tips.html