The software foxes is a modular wind farm simulation and wake modelling toolbox which is based on engineering wake models. It has many applications, for example

• Wind farm optimization, e.g. layout optimization or wake steering,
• Wind farm post-construction analysis,
• Wake model studies, comparison and validation,
• Wind farm simulations invoking complex model chains.

The fast performance of foxes is owed to vectorization and parallelization, and it is intended to be used for large wind farms and large timeseries inflow data. The parallelization on local or remote clusters is supported, based on mpi4py or dask.distributed. The wind farm optimization capabilities invoke the foxes-opt package which as well supports vectorization and parallelization.

foxes is build upon many years of experience with wake model code development at IWES, starting with the C++ based in-house code flapFOAM (2011-2019) and the Python based direct predecessor flappy (2019-2022).

Documentation: https://fraunhoferiwes.github.io/foxes

Source code: https://github.com/FraunhoferIWES/foxes

PyPi reference: https://pypi.org/project/foxes/

Anaconda reference: https://anaconda.org/conda-forge/foxes

Please cite the JOSS paper "FOXES: Farm Optimization and eXtended yield Evaluation Software"

Bibtex:

@article{ Schmidt2023, author = {Jonas Schmidt and Lukas Vollmer and Martin Dörenkämper and Bernhard Stoevesandt}, title = {FOXES: Farm Optimization and eXtended yield Evaluation Software}, doi = {10.21105/joss.05464}, url = {https://doi.org/10.21105/joss.05464}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {86}, pages = {5464}, journal = {Journal of Open Source Software} } 

The supported Python versions are Python 3.9...3.13.

Either install via pip:

pip install foxes

Alternatively, install via conda:

conda install foxes -c conda-forge

For detailed examples of how to run foxes, check the examples and notebooks folders in this repository. A minimal running example is the following, based on provided static csv data files:

import foxes if __name__ == "__main__": states = foxes.input.states.Timeseries("timeseries_3000.csv.gz", ["WS", "WD","TI","RHO"]) farm = foxes.WindFarm() foxes.input.farm_layout.add_from_file(farm, "test_farm_67.csv", turbine_models=["NREL5MW"]) algo = foxes.algorithms.Downwind(farm, states, ["Jensen_linear_k007"]) farm_results = algo.calc_farm() print(farm_results)

For testing, please clone the repository and install the required dependencies:

git clone https://github.com/FraunhoferIWES/foxes.git cd foxes pip install -e .[test]

The tests are then run by

pytest tests

1. Fork foxes on github.
2. Create a branch (git checkout -b new_branch)
3. Commit your changes (git commit -am "your awesome message")
4. Push to the branch (git push origin new_branch)
5. Create a pull request here

The development of foxes and its predecessors flapFOAM and flappy (internal - non public) has been supported through multiple publicly funded research projects. We acknowledge in particular the funding by the Federal Ministry of Economic Affairs and Climate Action (BMWK) through the projects Smart Wind Farms (grant no. 0325851B), GW-Wakes (0325397B) and X-Wakes (03EE3008A), as well as the funding by the Federal Ministry of Education and Research (BMBF) in the framework of the project H2Digital (03SF0635). We furthermore acknowledge funding by the Horizon Europe project FLOW (Atmospheric Flow, Loads and pOwer for Wind energy - grant id 101084205).