foxes 0.3.4

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

Requirements

The supported Python versions are:

• Python 3.7
• Python 3.8
• Python 3.9
• Python 3.10
• Python 3.11

Installation via conda

Virtual Python environment

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

conda create -c conda-forge --name foxes

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 -c conda-forge foxes

Developers

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

conda install -c conda-forge foxes conda-build
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. 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 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.

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("NREL-5MW-D126-H90.csv")

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

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

print(farm_results)

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).