Abstract
A constrained particle swarm optimization algorithm (C-PSO) is introduced and modified for hyperelastic and visco-hyperelastic characterization of bovine brain tissue at three different strain rates. Using the elasticity compatibility and Drucker’s stability criterion, the constraints of the hyperelastic and visco-hyperelastic models are identified and implemented in the C-PSO algorithm and its performance is compared with the classic curve fitting algorithms including Levenberg-Marquardt and trust region reflective. The accuracy of the C-PSO was found to be superior for visco-hyperelastic characterization, as for some strain rates, the trust region reflective algorithm failed to provide a reasonable approximation.
| Original language | English |
|---|---|
| Pages (from-to) | 169-184 |
| Number of pages | 16 |
| Journal | International Journal for Computational Methods in Engineering Science and Mechanics |
| Volume | 21 |
| Issue number | 4 |
| DOIs | |
| State | Published - May 28 2020 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2020 Taylor & Francis Group, LLC.
ASJC Scopus Subject Areas
- Computational Mechanics
- Computational Mathematics
Keywords
- constitutive modeling
- Constrained optimization
- curve fitting
- hyperelastic
- material characterization
- particle swarm optimization
- soft tissues
- visco-hyperelastic
Disciplines
- Computer Engineering
- Biomedical Engineering and Bioengineering
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