foxes 0.6.2

Farm Optimization and eXtended yield Evaluation Software

Welcome to foxes

Overview

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 calculation is fully vectorized and its fast performance is owed to dask. Also the parallelization on local or remote clusters is enabled via dask. The wind farm optimization capabilities invoke the iwopy package which as well supports vectorization.

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.docs/index.html

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

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

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

Citation

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} 
}

Requirements

The supported Python versions are:

• Python 3.8
• Python 3.9
• Python 3.10
• Python 3.11

Installation via pip

Virtual Python environment

First create a new venv environment, for example called foxes and located at ~/venv/foxes (choose any other convenient name and location in your file system if you prefer), by

python3 -m venv ~/venv/foxes

Then activate the environment every time you work with foxes, by

source ~/venv/foxes/bin/activate

You can leave the environment by

deactivate

The pip installation commands below should be executed within the active foxes environment.

Standard users

As a standard user, you can install the latest release via pip by

pip install foxes

This commands installs the version that correspond to the main branch at github. Alternatively, you can decide to install the latest pre-release developments (non-stable) by

pip install git+https://github.com/FraunhoferIWES/foxes@dev#egg=foxes

Developers

For developers using pip, simply invoke the -e flag in the installation command in your local clone:

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

The last line makes sure that all your code changes are included whenever importing foxes. Concerning the git clone line, we actually recommend that you fork foxes on GitHub and then replace that command by cloning your fork instead.

Installation via conda

Preparation

It is strongly recommend to use the libmamba dependency solver instead of the default solver. Install it once by

conda install conda-libmamba-solver -n base -c conda-forge

We recommend that you set this to be your default solver, by

conda config --set solver libmamba

Virtual Python environment

First create a new conda environment, for example called foxes, by

conda create -n foxes -c conda-forge

Then activate the environment every time you work with foxes, by

conda activate foxes

You can leave the environment by

conda deactivate

The conda installation commands below should be executed within the active foxes environment.

Standard users

The foxes package is available on the channel conda-forge. You can install the latest version by

conda install foxes -c conda-forge --solver=libmamba

The --solver=libmamba is optional. Note that it is not necessary if you have set the libmamba solver as your default, see above.

Developers

For developers using conda, we recommend first installing foxes as described above, then removing only the foxes package while keeping the dependencies, and then adding foxes again from a git using conda develop:

conda install foxes conda-build -c conda-forge --solver=libmamba
conda remove foxes --force
git clone https://github.com/FraunhoferIWES/foxes.git
cd foxes
conda develop .

The last line makes sure that all your code changes are included whenever importing foxes. The --solver=libmamba is optional. Note that it is not necessary if you have set the libmamba solver as your default, see above.

Concerning the git clone line, we actually recommend that you fork foxes on GitHub and then replace that command by cloning your fork instead.

Usage

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

states = foxes.input.states.Timeseries("timeseries_3000.csv.gz", ["WS", "WD","TI","RHO"])

mbook = foxes.ModelBook()

farm = foxes.WindFarm()
foxes.input.farm_layout.add_from_file(farm, "test_farm_67.csv", turbine_models=["NREL5MW"])

algo = foxes.algorithms.Downwind(mbook, farm, states, ["Jensen_linear_k007"])
farm_results = algo.calc_farm()

print(farm_results)

Testing

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

Contributing

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

Acknowledgements

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).