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Python library that loads modal composites from the file containing the parametric model of buckling and free vibration in prismatic shell structures, as computed by the fsm_eigenvalue project.

Project description

About

Python library that loads modal composites from the file containing the parametric model of buckling and free vibration in prismatic shell structures, as computed by the fsm_eigenvalue project.

This work is a part of the investigation within the research project [ON174027], supported by the Ministry for Science and Technology, Republic of Serbia. This support is gratefully acknowledged.

References

[ON174027]“Computational Mechanics in Structural Engineering”

Installation

To install fsm_load_modal_composites run:

$ pip install fsm_load_modal_composites

Usage examples

Quick start:

>>> import logging
>>> logging.basicConfig(level=logging.DEBUG)

>>> from pprint import pprint
>>> from fsm_load_modal_composites import load_modal_composites

>>> results_file = 'examples/barbero-viscoelastic.hdf5'
>>> modal_composites, column_units, column_descriptions = load_modal_composites(
...     results_file, a_max=600, t_b_min=6.0
... )

>>> modal_composites.shape
(143,)

>>> pprint(modal_composites.dtype)
[('a', '<f8'),
 ('t_b', '<f8'),
 ('m_dominant', '<i4'),
 ('omega', '<f8'),
 ('omega_approx', '<f8'),
 ('omega_rel_err', '<f8'),
 ('sigma_cr', '<f8'),
 ('sigma_cr_approx', '<f8'),
 ('sigma_cr_rel_err', '<f8')]

 >>> pprint(column_descriptions)
 {'a': 'strip length',
  'm_dominant': 'dominant mode, modal composite via sigma_cr',
  'omega': 'natural frequency',
  'omega_approx': 'natural frequency approximated from critical buckling stress',
  'omega_rel_err': 'natural frequency relative approximation error',
  'sigma_cr': 'critical buckling stress',
  'sigma_cr_approx': 'critical buckling stress approximated from natural frequency',
  'sigma_cr_rel_err': 'critical buckling stress relative approximation error',
  't_b': 'base strip thickness'}

Please see the fsm_modal_analysis source code for more examples.

Contribute

If you find any bugs, or wish to propose new features please let us know.

If you’d like to contribute, simply fork the repository, commit your changes and send a pull request. Make sure you add yourself to AUTHORS.

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