pyforwind
=========
An open-source package to generate synthetic IEC-conform wind fields with extended turbulence characteristics.
Installation
------------
The ``pyforwind`` package is available on pypi and can be installed using pip
.. code-block:: shell
pip install pyforwind
How to use this package
-----------------------
Generate superstatistical Kaimal wind field
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To use ``pyforwind`` to generate a Kaimal wind field ``u_kaimal`` and its superstatistical (i.e., non-Gaussian)
extension ``u_super_kaimal``, import the ``SFW`` with the model parameters: integral length scale ``L``, intermittency coefficient ``mu``,
horizontal wind speed at rotor hub ``V_hub``, hub height ``h_hub``, time length and diameter ``(T, diam)``, resolution ``(N_T, N_rotor)``,
and the wind field type ``kind``.
.. code-block:: python
from pyforwind import SFW
swf_kaimal = SWF(L, mu, V_hub, h_hub, (T, diam), (N_T, N_rotor), kind='gauss')
swf_super_kaimal = SWF(L, mu, V_hub, h_hub, (T, diam), (N_T, N_rotor), kind='spatiotemporal')
u_kaimal = swf_kaimal.field(seed)
u_super_kaimal = swf_super_kaimal.field(seed)
References
----------
Friedrich, J., Moreno, D., Sinhuber, M., Wächter, M., & Peinke, J. (2022). Superstatistical wind fields from pointwise atmospheric turbulence measurements. PRX Energy, 1(2), 023006.
Acknowledgments
---------------
This project is funded by the European Union Horizon Europe Framework program (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205, and by the German Federal Ministry for Economic Affairs and Energy in the scope of the projects EMUwind (03EE2031A/C) and PASTA (03EE2024A/B).
Raw data
{
"_id": null,
"home_page": "https://github.com/fiddir/pyforwind",
"name": "pyforwind",
"maintainer": "Jan Friedrich",
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "synthetic wind fields, inflow turbulence, wind energy",
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/73/6d/93c648e2947372247863239997f6dd49ee59aa4289a5c3b5f871909e9239/pyforwind-0.1.3.tar.gz",
"platform": "any",
"description": "pyforwind\n=========\n\nAn open-source package to generate synthetic IEC-conform wind fields with extended turbulence characteristics. \n\nInstallation\n------------\n\nThe ``pyforwind`` package is available on pypi and can be installed using pip\n\n.. code-block:: shell\n\n pip install pyforwind\n\nHow to use this package\n-----------------------\n\nGenerate superstatistical Kaimal wind field\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nTo use ``pyforwind`` to generate a Kaimal wind field ``u_kaimal`` and its superstatistical (i.e., non-Gaussian)\nextension ``u_super_kaimal``, import the ``SFW`` with the model parameters: integral length scale ``L``, intermittency coefficient ``mu``,\nhorizontal wind speed at rotor hub ``V_hub``, hub height ``h_hub``, time length and diameter ``(T, diam)``, resolution ``(N_T, N_rotor)``,\nand the wind field type ``kind``.\n\n.. code-block:: python\n\n from pyforwind import SFW\n\n swf_kaimal = SWF(L, mu, V_hub, h_hub, (T, diam), (N_T, N_rotor), kind='gauss')\n swf_super_kaimal = SWF(L, mu, V_hub, h_hub, (T, diam), (N_T, N_rotor), kind='spatiotemporal')\n u_kaimal = swf_kaimal.field(seed)\n u_super_kaimal = swf_super_kaimal.field(seed)\n\nReferences\n----------\nFriedrich, J., Moreno, D., Sinhuber, M., W\u00e4chter, M., & Peinke, J. (2022). Superstatistical wind fields from pointwise atmospheric turbulence measurements. PRX Energy, 1(2), 023006.\n\nAcknowledgments\n---------------\nThis project is funded by the European Union Horizon Europe Framework program (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205, and by the German Federal Ministry for Economic Affairs and Energy in the scope of the projects EMUwind (03EE2031A/C) and PASTA (03EE2024A/B).\n",
"bugtrack_url": null,
"license": "LGPL-3.0",
"summary": "Synthetic IEC-conform wind fields with extended turbulence characteristics",
"version": "0.1.3",
"project_urls": {
"Homepage": "https://github.com/fiddir/pyforwind"
},
"split_keywords": [
"synthetic wind fields",
" inflow turbulence",
" wind energy"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "736d93c648e2947372247863239997f6dd49ee59aa4289a5c3b5f871909e9239",
"md5": "409dd1cd962e4a7603607b4bf8b8fa35",
"sha256": "0dc5b3053edf7c183193842fa3bbb92905c97ce355c9c5dddb5ede2b8f8a2c65"
},
"downloads": -1,
"filename": "pyforwind-0.1.3.tar.gz",
"has_sig": false,
"md5_digest": "409dd1cd962e4a7603607b4bf8b8fa35",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 17959,
"upload_time": "2024-10-25T12:40:02",
"upload_time_iso_8601": "2024-10-25T12:40:02.872902Z",
"url": "https://files.pythonhosted.org/packages/73/6d/93c648e2947372247863239997f6dd49ee59aa4289a5c3b5f871909e9239/pyforwind-0.1.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-25 12:40:02",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "fiddir",
"github_project": "pyforwind",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "pyforwind"
}
JSON
