WEIS
WEIS is a framework that combines multiple tools to enable design optimization of floating offshore wind turbines.
https://github.com/nlrwindsystems/weis
Category: Renewable Energy
Sub Category: Wind Energy
Last synced: about 7 hours ago
JSON representation
Repository metadata
Workflow for Energy with Integrated Servo-controls Toolset
- Host: GitHub
- URL: https://github.com/nlrwindsystems/weis
- Owner: NLRWindSystems
- License: apache-2.0
- Created: 2020-08-21T16:54:28.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2026-02-06T19:49:51.000Z (2 days ago)
- Last Synced: 2026-02-07T01:39:24.184Z (2 days ago)
- Language: Python
- Homepage: https://weis.readthedocs.io/en/latest/
- Size: 239 MB
- Stars: 67
- Watchers: 12
- Forks: 53
- Open Issues: 10
- Releases: 18
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Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
WEIS
WEIS, Wind Energy with Integrated Servo-control, performs multifidelity co-design of wind turbines. WEIS is a framework that combines multiple NREL-developed tools to enable design optimization of floating offshore wind turbines.
Author: NREL WISDEM & OpenFAST & Control Teams
Part of the WETO Stack
WEIS is primarily developed with the support of the U.S. Department of Energy and is part of the WETO Software Stack. For more information and other integrated modeling software, see:
Documentation
See local documentation in the docs-directory or access the online version at https://weis.readthedocs.io/en/latest/
Packages
WEIS integrates in a unique workflow four models:
- WISDEM is a set of models for assessing overall wind plant cost of energy (COE).
- OpenFAST is the community model for wind turbine simulation to be developed and used by research laboratories, academia, and industry.
- TurbSim is a stochastic, full-field, turbulent-wind simulator.
- ROSCO provides an open, modular and fully adaptable baseline wind turbine controller to the scientific community.
In addition, three external libraries are added:
- pCrunch is a collection of tools to ease the process of parsing large amounts of OpenFAST output data and conduct loads analysis.
- pyOptSparse is a framework for formulating and efficiently solving nonlinear constrained optimization problems.
The core WEIS modules are:
- aeroelasticse is a wrapper to call OpenFAST
- control contains the routines calling ROSCO and the routines supporting distributed aerodynamic control devices, such trailing edge flaps
- gluecode contains the scripts glueing together all models and libraries
- multifidelity contains the codes to run multifidelity design optimizations
- optimization_drivers contains various optimization drivers
- schema contains the YAML files and corresponding schemas representing the input files to WEIS
Installation
On laptop and personal computers, installation with Anaconda is the recommended approach because of the ability to create self-contained environments suitable for testing and analysis. WEIS requires Anaconda 64-bit. However, the conda command has begun to show its age and we now recommend the one-for-one replacement with the Miniforge3 distribution, which is much more lightweight and more easily solves for the package dependencies. Sometimes, using mamba in place of conda with this distribution speeds up the installation process. WEIS is supported on Linux, MAC, Windows Sub-system for Linux (WSL), and native Windows.
The installation instructions below use the environment name, "weis-env," but any name is acceptable. For those working behind company firewalls, you may have to change the conda authentication with conda config --set ssl_verify no. Proxy servers can also be set with conda config --set proxy_servers.http http://id:pw@address:port and conda config --set proxy_servers.https https://id:pw@address:port.
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If you are NOT installing WEIS on DOE's HPC system Kestrel, skip step 0 and run step 1 and 2 (skip step 3). If you are on Kestrel, follow steps 0, 1, and 3, and skip step 2. On Kestrel, start by purging existing modules and load conda
module purge module load conda -
In a terminal, setup and activate the Anaconda environment
conda config --add channels conda-forge conda install git git clone https://github.com/WISDEM/WEIS.git cd WEIS git checkout branch_name # (Only if you want to switch branches, say "develop") conda env create --name weis-env -f environment.yml conda activate weis-env # (if this does not work, try source activate weis-env) conda install -y petsc4py mpi4py pyoptsparse # (Mac / Linux only, sometimes Windows users may need to install mpi4py) -
If you are NOT on Kestrel, install the software
pip install -e . -
If you are on Kestrel, first load some modules and then install:
module load intel-oneapi-compilers intel-oneapi-mpi intel-oneapi-mkl conda pip install --no-deps -e . -v
NOTE: To use WEIS again after installation is complete, you will always need to activate the conda environment first with conda activate weis-env (or source activate weis-env). On Kestrel, make sure to reload the necessary modules
For Windows users, we recommend installing git and the m264 packages in separate environments as some of the libraries appear to conflict such that WISDEM cannot be successfully built from source. The git package is best installed in the base environment.
Developer guide
If you plan to contribute code to WEIS, please first consult the developer guide.
Feedback
For software issues please use https://github.com/WISDEM/WEIS/issues.
Owner metadata
- Name: NLRWindSystems
- Login: NLRWindSystems
- Email: systems.engineering@nrel.gov
- Kind: organization
- Description: The home of wind energy systems models developed, released, and maintained by the National Laboratory of the Rockies, formerly named NREL
- Website: https://www.nrel.gov/wind/systems-engineering.html
- Location: United States of America
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/5444272?v=4
- Repositories: 40
- Last ynced at: 2026-01-15T00:49:55.882Z
- Profile URL: https://github.com/NLRWindSystems
GitHub Events
Total
- Delete event: 1
- Issues event: 6
- Issue comment event: 5
- Push event: 9
Last Year
- Delete event: 1
- Issues event: 6
- Issue comment event: 5
- Push event: 9
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 2
Total pull requests: 3
Average time to close issues: N/A
Average time to close pull requests: 11 days
Total issue authors: 2
Total pull request authors: 2
Average comments per issue: 2.0
Average comments per pull request: 1.0
Merged pull request: 3
Bot issues: 0
Bot pull requests: 0
Past year issues: 2
Past year pull requests: 3
Past year average time to close issues: N/A
Past year average time to close pull requests: 11 days
Past year issue authors: 2
Past year pull request authors: 2
Past year average comments per issue: 2.0
Past year average comments per pull request: 1.0
Past year merged pull request: 3
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- nRiccobo (1)
- fangjianju (1)
Top Pull Request Authors
- dzalkind (2)
- gbarter (1)
Top Issue Labels
Top Pull Request Labels
Dependencies
- marmot-agents *
- matplotlib *
- numpy *
- numpydoc *
- openmdao >=3.2
- scipy *
- simpy *
- sphinx >2.0
- sphinx-rtd-theme *
- sphinxcontrib-bibtex <2.0.0
- control *
- dill *
- fatpack *
- jsonmerge *
- nlopt *
- numpy *
- openmdao >=3.4
- scipy *
- smt *
- actions/checkout v2 composite
- s-weigand/setup-conda v1 composite
- actions/checkout v2 composite
- s-weigand/setup-conda v1 composite
- marmot-agents >=0.2.5
- mat4py *
- moorpy *
- nlopt *
- numpy *
- openmdao <3.28
- openpyxl *
- openraft *
- osqp *
- pcrunch *
- pyhams *
- rosco *
- scipy *
- smt *
- wisdem *
- anyio
- argon2-cffi
- arrow
- async-lru
- babel
- beautifulsoup4
- bleach
- cffi
- comm
- debugpy
- decorator
- dill
- executing
- fqdn
- git
- ipython
- isoduration
- jedi
- jinja2
- json5
- jsonmerge
- jupyter
- jupyterlab
- markupsafe
- mat4py
- matplotlib
- meson
- mistune
- moorpy
- ninja
- nlopt
- notebook
- openfast
- openpyxl
- openraft
- osqp
- overrides
- pandas
- parso
- patsy
- pcrunch
- pexpect
- pip
- platformdirs
- psutil
- ptyprocess
- pydoe3
- pygments
- pyhams
- pyoptsparse
- python-json-logger
- pyzmq
- qdldl-python
- qtpy
- rosco
- scikit-learn
- setuptools
- smt
- tornado
- traitlets
- treon
- webencodings
- wisdem
Score: -Infinity