Changed in version 1.7.0: The engine parameter in the Algorithm constructor has been removed. If no engine context is specified the default engine is used in the background. For other choices computations have to be run within an explicit engine context, i.e. within a with engine block (see examples).
Changed in version 1.7.0: Image creating outputs like FlowPlots2D now separate plot data computation from figure generation. This improves thread-safety of the plotting functions.
Changed in version 1.7.0: Support for Python 3.14 dropped, will be re-added at a later stage.
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 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.
- Source code repository (and issue tracker):
Please report code issues under the github link above.
License¶
Contents¶
- Examples
- The model book
- Single row of turbines
- Timeseries data
- Multi-height wind data
- Wind rose data
- Wind sector management
- Heterogeneous flow
- Power mask
- Yawed rotor wakes
- Dynamic Wakes 1
- Dynamic Wakes 2
- Dynamic Wakes 3
- Blockage modelling 1
- Blockage modelling 2
- Turbine operation flags
- Partial wakes verification
Contributing¶
Fork foxes on github.
Create a branch (git checkout -b new_branch)
Commit your changes (git commit -am “your awesome message”)
Push to the branch (git push origin new_branch)
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 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). We furthermore acknowledge funding by the Horizon Europe project FLOW (Atmospheric Flow, Loads and pOwer for Wind energy - grant id 101084205).