Added in version 1.7.6: All algorithms now accept the new optional parameter max_wake_length_km, reducing the number of target points in wake calculations. Note that this replaces the correcponding parameter in some of the wake frames, like DynamicWakes or Streamlines2D.

Changed in version 1.7.4: Removed the pre_rotor flag from turbine models. The position before or after the rotor model is now automatically determined based on the overlap of model output variables and the newly introduced input variables of the selected rotor model.

Added in version 1.7.4: New command line application wrf2foxes, converting data files from WRF-NEWA NetCDF format into a regular UTM based grid that is readable by the FieldData states class. The subsequent foxes run might then be faster than using the NEWAStates class.

Added in version 1.7.4: New ambient states SingleStateField, for heterogeneous data in NetCDF formatw ithout state dimension, e.g. the mean field output of the application foxes_create_mean_dataset.

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):: https://github.com/FraunhoferIWES/foxes

Please report code issues under the github link above.

License¶

MIT

Contents¶

Citation

Journal paper

Version specific DOI

Installation

Requirements

TLDR; Quick installation guide

Installation via pip

Installation via conda

Overview - how to setup foxes

Inputs

Wind farm layouts

Ambient inflow states

Models

Model types

Rotor models

Turbine types

Wake models

Wake frames

Wake deflections

Partial wakes

Turbine models

Farm controllers

Ground models

Point models

Vertical profiles

Parallelization

Available engines

Default engine

Engine selection through a with-block

Remarks & recommendations

Input parameter files

foxes_yaml

foxes_windio

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

ICON-DREAM data

Turbine-based ambient flow data

Optimization

API

Static data

Wind farm layout data

Ambient states data

Power and thrust curves

File paths

Testing

Changelog

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).