COMPAS CEM

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.. _COMPAS: https://compas.dev/ .. _COMPAS CEM Docs: https://arpastrana.github.io/compas_cem/latest/index.html .. _CEM Framework: https://arxiv.org/abs/2111.02607 .. _Rafael Pastrana: https://pastrana.xyz/ .. _Princeton: https://soa.princeton.edu/ .. _Ole Ohlbrock: https://schwartz.arch.ethz.ch/Team/patrickoleohlbrock.php?lan=en .. _Pierluigi D'Acunto: https://www.professoren.tum.de/en/dacunto-pierluigi .. _Stefana Parascho: https://soa.princeton.edu/content/stefana-parascho .. _Anaconda: https://www.anaconda.com/ .. _Rhino: https://www.rhino3d.com/ .. _Blender: https://www.blender.org/ .. _Grasshopper: https://grasshopper3d.com/ .. _metaverse: https://apnews.com/article/meta-facebook-explaining-the-metaverse-f57e01cd5739840945e89fd668b0fa27

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The Combinatorial Equilibrium Modeling (CEM) _ framework for COMPAS_.

The CEM framework_ is a numerical form-finding approach to generate forms in static equilibrium for spatial bar structures subjected to combinations of tension-compression forces and design constraints. COMPAS CEM encapsulates the CEM framework_ into an open-source structural design tool that enables the formulation and the solution of constrained form-finding problems in plain and simple Python _ code.

Main features

• Mix tension and compression forces: Explore a wider spectrum of structural typologies by combining internal tension and compression forces in the same structure. Design space frames, bridges, tensegrities, and staircases and go beyond the conventional catalog of compression-only shells and cable-nets!

• Solve constrained form-finding problems efficiently via automatic differentiation: Generate forms in static equilibrium that simultaneously meet a priori design constraints such as best-fitting a global target shape, restraining bar lengths, and controlling the reaction forces at the supports of a structure.

• Usable across different 3D modeling software and operating systems: COMPAS CEM runs on Windows, MacOS and Linux (perhaps one day in the metaverse) and it does not depend on any CAD software to work. However, it provides the necessary interfaces to be seamlessly used inside popular design environments like Rhino, Blender, and Grasshopper. As a COMPAS_ extension, COMPAS CEM offers native integration and data exchange with other extensions and plugins in the COMPAS ecosystem.

• Move those sliders with the Grasshopper plugin: Are you a Grasshopper_ person? Worry not. COMPAS CEM is also shipped as a precooked Grasshopper plugin to readily integrate our constrained form-finding engine into your next spaghetti pipeline 🍝.

Installation

These are succint instructions to install COMPAS CEM and its Grasshopper_ plugin. For detailed guidance, please refer to the COMPAS CEM Docs_.

Install COMPAS CEM in a dedicated Anaconda_ environment via pip:

::

pip install compas-cem

To double-check that everything is up and running, type the following in the command line and hit enter:

::

python -c "import compas_cem"

If no errors show up, celebrate 🎉! You have a working installation of COMPAS CEM.

Grasshopper plugin

Once COMPAS CEM was installed from the comment line, we can additionally link it to Rhino_ and use it as Grasshopper_ plugin:

::

python -m compas_rhino.install -v 7.0

The flag -v 7.0 indicates that we will be installing COMPAS CEM and company in Rhino 7. If you are working with Rhino 6, replace that last bit with -v 6.0.

First steps

• COMPAS CEM Docs_
• COMPAS CEM Examples _
• COMPAS CEM API Reference _
• COMPAS Tutorials _
• COMPAS API Reference _

Are you a bug hunter?

If you find a bug or want to suggest a potential enhancement, please help us tackle it by filing a report _.

Questions and feedback

We encourage the use of the COMPAS framework forum _ for questions and discussions.

Contributing

Pull requests are warmly welcome! Check the Contributor's Guide _ for more details.

Citing

If you use COMPAS CEM for a project or research, please cite us using these references _.

Acknowledgements

This work has been supported in part by the U.S. National Science Foundation under grant OAC-2118201 and the NSF Institute for Data Driven Dynamical Design _.

License

MIT