sigmaepsilon.solid.material 0.0.1a0

Material models for solid structures.

SigmaEpsilon.Solid.Material - Classes and algorithms for solids in Python

Note Here and there, implementation of the performance critical parts of the library rely on the JIT-compilation capabilities of Numba. This means that the library performs well even for large scale problems, on the expense of a longer first call.

What is sigmaepsilon.solid.material?

It is part of the sigmaepsilon.solid namespace, a set of namespace pacakes dedicated to different areas of solid mechanics. In this namespace, sigmaepsilon.solid.material is responsible for calculations related to mechanical investingations of solid materials. Similar to sectionproperties, but we are extending the possibilities by supporting similar functionality for membranes, plates, shells and 3d solids as well, for general anisotropy and utilization with a customizable failure mechanism. For 1d members we rely on sectionproperties.

Highlights

• Classes to handle linear elastic materials of all kinds.
• Elastic stiffness calculations for all kinds of models like Uflyand-Mindlin shells, Kirchhoff-Love shells, Timoshenko-Ehrenfest and Euler-Bernoulli beams, 3d bodies, etc.
• Utilization calculations.
• Fitting of failure models to observed data.
• NumPy-compilant data classes to handle stiffness, strains and stresses.
• Fast and efficient code with GPU support.

Documentation

The documentation is built with Sphinx using the PyData Sphinx Theme and hosted on ReadTheDocs. Check it out for the user guide, an ever growing set of examples, and API Reference.

Installation

sigmaepsilon.solid.material can be installed from PyPI using pip on Python >= 3.8:

>>> pip install sigmaepsilon.solid.material

or chechkout with the following command using GitHub CLI

gh repo clone sigma-epsilon/sigmaepsilon.solid.material

and install from source by typing

>>> pip install .

If you want to run the tests, you can install the package along with the necessary optional dependencies like this

>>> pip install ".[test]"

If want to execute on the GPU, you need to manually install the necessary requirements. Numba is a direct dependency, so even in this case you have to care about having the prover version of the cuda toolkit installed. For this, you need to know the version of the cuda compute engine, which depends on the version of GPU card you are having.

Development mode

If you are a developer and want to install the library in development mode, the suggested way is by using this command:

>>> pip install "-e .[test, dev]"

Checking your installation

You should be able to import sigmaepsilon.mesh from the Python prompt:

$ python
Python 3.10.2 (tags/v3.10.2:3d8993a, May  3 2023, 11:48:03) [MSC v.1928 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import sigmaepsilon.solid.material
>>> sigmaepsilon.solid.material.__version__
'0.0.1a'

Testing and coverage

The following command runs all tests and creates a html report in a folder named htmlcov (the settings are governed by the .coveragerc file):

python -m pytest --cov-report html --cov-config=.coveragerc --cov sigmaepsilon.mesh

Open htmlcov/index.html to see the results.

Changes and versioning

See the changelog, for the most notable changes between releases.

The project adheres to semantic versioning.

How to contribute?

Contributions are currently expected in any the following ways:

• finding bugs If you run into trouble when using the library and you think it is a bug, feel free to raise an issue.
• feedback All kinds of ideas are welcome. For instance if you feel like something is still shady (after reading the user guide), we want to know. Be gentle though, the development of the library is financially not supported yet.
• feature requests Tell us what you think is missing (with realistic expectations).
• examples If you've done something with the library and you think that it would make for a good example, get in touch with the developers and we will happily inlude it in the documention.
• sharing is caring If you like the library, share it with your friends or colleagues so they can like it too.

In all cases, read the contributing guidelines before you do anything.

Acknowledgements

A lot of the packages mentioned on this document here and the introduction have a citable research paper. If you use them in your work through sigmaepsilon.mesh, take a moment to check out their documentations and cite their papers.

Also, funding of these libraries is partly based on the size of the community they are able to support. If what you are doing strongly relies on these libraries, don't forget to press the :star: button to show your support.

License

This package is licensed under the MIT license.