Abstract
pyvisco is a Python library that supports the identification of Prony series parameters for linear viscoelastic materials described by a Generalized Maxwell model. The necessary material model parameters are identified by fitting a Prony series to the experimental measurement data. pyvisco allows for the identification of Prony series parameters from experimental data measured in either the frequency-domain (via Dynamic Mechanical Thermal Analysis) or time-domain (via relaxation measurements). The experimental data can be provided as raw measurement sets at different temperatures or as pre-processed master curves. An optional minimization routine is included to reduce the number of Prony elements. This routine is helpful in Finite Element simulations where reducing the computational complexity of the linear viscoelastic material models can shorten the simulation time.
See also, https://pypi.org/project/pyvisco/
- Developers:
-
Springer, Martin [1] ; Owen-Bellini, Michael [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2022-03-21
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
Jupyter Notebook
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies OfficePrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 72248
- Site Accession Number:
- NREL SWR-22-30
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Springer, Martin, and Owen-Bellini, Michael.
pyvisco [SWR-22-30].
Computer Software.
https://github.com/NREL/pyvisco.
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office.
21 Mar. 2022.
Web.
doi:10.5281/zenodo.6384954.
Springer, Martin, & Owen-Bellini, Michael.
(2022, March 21).
pyvisco [SWR-22-30].
[Computer software].
https://github.com/NREL/pyvisco.
https://doi.org/10.5281/zenodo.6384954.
Springer, Martin, and Owen-Bellini, Michael.
"pyvisco [SWR-22-30]." Computer software.
March 21, 2022.
https://github.com/NREL/pyvisco.
https://doi.org/10.5281/zenodo.6384954.
@misc{
doecode_72248,
title = {pyvisco [SWR-22-30]},
author = {Springer, Martin and Owen-Bellini, Michael},
abstractNote = {pyvisco is a Python library that supports the identification of Prony series parameters for linear viscoelastic materials described by a Generalized Maxwell model. The necessary material model parameters are identified by fitting a Prony series to the experimental measurement data. pyvisco allows for the identification of Prony series parameters from experimental data measured in either the frequency-domain (via Dynamic Mechanical Thermal Analysis) or time-domain (via relaxation measurements). The experimental data can be provided as raw measurement sets at different temperatures or as pre-processed master curves. An optional minimization routine is included to reduce the number of Prony elements. This routine is helpful in Finite Element simulations where reducing the computational complexity of the linear viscoelastic material models can shorten the simulation time.
See also, https://pypi.org/project/pyvisco/},
doi = {10.5281/zenodo.6384954},
url = {https://doi.org/10.5281/zenodo.6384954},
howpublished = {[Computer Software] \url{https://doi.org/10.5281/zenodo.6384954}},
year = {2022},
month = {mar}
}