WISDEM®

The Wind-Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website.

Author: NREL WISDEM Team

Part of the WETO Stack

WISDEM 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://wisdem.readthedocs.io/en/master/

Packages

WISDEM® is a family of modules. The core modules are:

CommonSE includes several libraries shared among modules
FloatingSE works with the floating platforms
DrivetrainSE sizes the drivetrain and generator systems (formerly DriveSE and GeneratorSE)
TowerSE is a tool for tower (and monopile) design
RotorSE is a tool for rotor design
NREL CSM is the regression-based turbine mass, cost, and performance model
ORBIT is the process-based balance of systems cost model for offshore plants
LandBOSSE is the process-based balance of systems cost model for land-based plants
Plant_FinanceSE runs the financial analysis of a wind plant

The core modules draw upon some utility packages, which are typically compiled code with python wrappers:

Airfoil Preppy is a tool to handle airfoil polar data
CCBlade is the BEM module of WISDEM
pyFrame3DD brings libraries to handle various coordinate transformations
MoorPy is a quasi-static mooring line model
pyOptSparse provides some additional optimization algorithms to OpenMDAO

Installation

Installation with Anaconda is the recommended approach because of the ability to create self-contained environments suitable for testing and analysis. WISDEM® 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 WISDEM package dependencies.

Installation as a "library"

Create a conda environment with your preferred name (wisdem-env in the following example) and favorite, approved Python version:
```
conda create -n wisdem-env python=3.13 -y
```
Activate the environment:
```
conda activate wisdem-env
```
Install WISDEM via a conda or pip. We highly recommend via conda.
```
conda install wisdem
```
or
```
pip install wisdem
```

To use WISDEM's modules as a library for incorporation into other scripts or tools, WISDEM is available via conda install wisdem or pip install wisdem, assuming that you have already setup your python environment. Note that on Windows platforms, we suggest using conda exclusively.

Installation for direct use or development

These instructions are for interaction with WISDEM directly, the use of its examples, and the direct inspection of its source code.

The installation instructions below use the environment name, "wisdem-env," but any name is acceptable. Below are a series of considerations:

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.
To setup an environment based on a different Github branch of WISDEM, simply substitute the branch name for master in the setup line.

Note For Windows users, we recommend installing git and the m2w64 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.

Direct use

We still highly recommend users use conda install wisdem into an environment, but if there is a reason that is not desired, please use the following instructions.

Setup and activate the Anaconda environment from a prompt (Anaconda3 Power Shell on Windows or Terminal.app on Mac)

[!IMPORTANT] In the environment.yaml please uncomment out the OS-dependent dependencies at the top

Install git if you don't already have it:
```
conda install git
```

Clone the repository and enter it:

git clone https://github.com/NLRWindSystems/WISDEM.git
cd WISDEM

Checkout the desired branch, if necessary:
```
git checkout <branch>
```
Create and activate your wisdem-env environment, substituting "wisdem-env" with a different desired name.
```
conda env create --name wisdem-env -f environment.yml
conda activate wisdem-env
```
Install WISDEM.
```
pip install --no-deps . -v
```

Development

In order to directly use the examples in the repository and peek at the code when necessary, we recommend all users install WISDEM in developer / editable mode using the instructions here. If you really just want to use WISDEM as a library and lean on the documentation, you can always do conda install wisdem and be done. Note the differences between Windows and Mac/Linux build systems.

[!IMPORTANT] In the environment_dev.yaml please uncomment out the OS-dependent dependencies at the top

For Linux, we recommend using the native compilers (for example, gcc and gfortran in the default GNU suite).

Please follow steps 1-3 in the Direct Use section above, replacing steps 4 & 5 with the following to ensure the development dependencies for building, testing, and documentation are also installed:

Create and activate your wisdem-env environment, substituting "wisdem-env" with a different desired name.
```
conda env create --name wisdem-env -f environment_dev.yml
conda activate wisdem-env
```
Install WISDEM. Please note the -e (editable) flag used to ensure your code changes are registered dynamically every time you save modifications.
```
pip install --no-deps -e . -v
```

Run Unit Tests

Each package has its own set of unit tests, and the project runs the examples as integration tests. These can be run in batch with the following command:

pytest

Users can add either the --unit or --integration flags if they would like to skip running the examples or just run the examples. Otherwise, all tests will be run.

[!Note] Legacy users can continue to run python test/test_all.py to run the scipts, though it is recommend to adopt the simpler pytest call. In a future version, test_all.py will be removed.

Feedback

For software issues please use https://github.com/NLRWindSystems/WISDEM/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.

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wisdem 4.2.5

WISDEM®

Part of the WETO Stack

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BLAKE2b-256	`32322a00f40de2a93f2b8047cb3a3b53b396a33b40e5952b4b63c115e18bda26`

Algorithm	Hash digest
SHA256	`bfaf143696be9d66078cb6e95caca34bcfd7c78df0b3cc8c9aaceaef51eb1790`
MD5	`7ba739adb6888644164704d419b7b91d`
BLAKE2b-256	`f2c69ffb39f8169d2f98217cc60f0658d6a85764e5aad8dbe712c7f2896599fa`

Algorithm	Hash digest
SHA256	`0a642497d4ca97f662819a54c712b0e832cc78faa1b488e1805cc7c73553f4ff`
MD5	`8d42bb34f68e8c3b1f9e4b47d9968798`
BLAKE2b-256	`6807c45b080f2ca28b8d39853d9b57149bf3f1fb698145039a6bf8e635569a8a`

Algorithm	Hash digest
SHA256	`351f4015a8d162fcf63fe88f4ff50ee7529ec832cbb00d6b03086430b3cd2c7f`
MD5	`14d4aded217dbe2de2e3c23f64768a3e`
BLAKE2b-256	`bcef1d26ef5a8d230db03b227463d9698eae7c288cc720fdc5416e961dec6dd3`

Algorithm	Hash digest
SHA256	`923b0c8b89d30aac1980374146f6fcb8744c9578b14ac6a9693847ddbd34f90c`
MD5	`ac2deb85776b6501fb59a63b634b6479`
BLAKE2b-256	`ed563d7ee4f846b25461490edcc3284feccf05d8b74ba5f634c7cf42a03ce8d0`

Algorithm	Hash digest
SHA256	`232ec0780bbdec8061c41a9dbf49283782434f7356f9a738ad1340435d4b0d01`
MD5	`3be32fab4ad5abc8838029a3b723df30`
BLAKE2b-256	`dea9ed84bb8221360b7a2c19e5cb032c056390afc122d29aa99f1d2b181f8981`

Algorithm	Hash digest
SHA256	`89868b0ddd79d3dd378fcdf6012f69f828aa808e3c0f74a4a782237da96def11`
MD5	`c2e3c03da56eb4fd7989f6ed30219956`
BLAKE2b-256	`8fb1c9055185fc0ef261c144770f2c97e2b96944a80bf6d70557698d54df15e6`

Algorithm	Hash digest
SHA256	`69163f3ae6ce03e39bee1460b446c101fa0146513d668a7e4ffc49f4b6b56955`
MD5	`981aaa8d20cb98c49251f94579aed3ef`
BLAKE2b-256	`c3ef11f158da67b5ff2b4388028036b6de50a625fffccea1ad2e798833d2c4cd`