Welcome to FOXES

Farm Optimization and eXtended yield Evaluation Software

FOXES is a modular wind farm and wake modelling code written in Python by Fraunhofer IWES. 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.

Quick Start:

pip install foxes

Source code repository (and issue tracker):

https://github.com/FraunhoferIWES/foxes

Contact (please report code issues under the github link above):

License:

MIT

Contents:

• Installation
  • Requirements
  • Installation
  • Usage

• Examples
  • The model book
  • Single row of turbines
  • Timeseries data
  • Multi-height wind data
  • Wind rose data
  • Wind sector management
  • Heterogeneous flow
  • Power mask
  • Layout optimization

• API

• Static data
  • Wind farm layout data
  • Ambient states data
  • Power and thrust curves
  • File paths

• History
  • v0.1.0-alpha
  • v0.1.1-alpha
  • v0.1.2-alpha
  • v0.1.3-alpha
  • v0.1.4-alpha
  • v0.2.0-alpha
  • v0.2.1-alpha
  • v0.2.2-alpha
  • v0.2.3-alpha
  • v0.2.4-alpha
  • v0.2.5-alpha

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

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 p rojects 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).