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CCBlade

A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.

Author: NREL WISDEM Team

Part of the WETO Stack

CCBlade 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 http://wisdem.github.io/CCBlade/

Prerequisites

CCBlade execution requires: numpy, scipy, openmdao CCBlade installation requires: meson, ninja, gfortran

Installation

CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:

$ pip install CCBlade

Run Unit Tests

To check if installation was successful, run the unit tests

$ python test/test_ccblade.py
$ python test/test_gradients.py

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

About

A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).

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Readme
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65 stars

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20 watching

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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
Resources
Readme
License
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Stars
65 stars
Watchers
20 watching
Forks
45 forks
Report repository
Releases
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Used by
Contributors
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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
Resources
Readme
License
Activity
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Stars
65 stars
Watchers
20 watching
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45 forks
Report repository
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Used by
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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
Resources
Readme
License
Activity
Custom properties
Stars
65 stars
Watchers
20 watching
Forks
45 forks
Report repository
Releases
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Used by
Contributors
Languages

, 'i');
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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
Resources
Readme
License
Activity
Custom properties
Stars
65 stars
Watchers
20 watching
Forks
45 forks
Report repository
Releases
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Used by
Contributors
Languages

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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
Resources
Readme
License
Activity
Custom properties
Stars
65 stars
Watchers
20 watching
Forks
45 forks
Report repository
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Used by
Contributors
Languages

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GitHub - NLRWindSystems/CCBlade: A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). · GitHub
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CCBlade
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable). Analytic gradients are also (optionally) provided for the distributed loads, thrust, torque, and power with respect to design variables of interest.
Author: NREL WISDEM Team
Part of the WETO Stack
CCBlade 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 http://wisdem.github.io/CCBlade/
Prerequisites
CCBlade execution requires: numpy, scipy, openmdao
CCBlade installation requires: meson, ninja, gfortran
Installation
CCBlade is available as a WISDEM module and WISDEM is both pip-installable and conda-installable. For building CCBlade from source as a standalone library, first make sure that you have the necessary prerequisites installed. After cloning the repository, do:
$ pip install CCBlade

Run Unit Tests
To check if installation was successful, run the unit tests
$ python test/test_ccblade.py
$ python test/test_gradients.py

For software issues please use https://github.com/WISDEM/CCBlade/issues. For functionality and theory related questions and comments please use the NWTC forum for Systems Engineering Software Questions.
About
A blade element momentum method for analyzing wind turbine aerodynamic performance that is robust (guaranteed convergence), fast (superlinear convergence rate), and smooth (continuously differentiable).
Resources
Readme
License
Activity
Custom properties
Stars
65 stars
Watchers
20 watching
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45 forks
Report repository
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