Title
Data Driven Constitutive Model Discovery from In Silico Myocardial Biaxial Stretch Tests
Author
Krijnen, Rogier (TU Delft Mechanical, Maritime and Materials Engineering)
Contributor
Peirlinck, M. (mentor) 
Kumar, S. (graduation committee)
Gijsen, F.J.H. (graduation committee)
Joshi, A. (graduation committee)
Degree granting institution
Delft University of Technology
Programme
Mechanical Engineering | BioMechanical Design
Date
2023-11-26
Abstract
This thesis investigates the ability of Bayesian EUCLID to retrieve a predictive approximate material model for the myocardium in the presence of heterogeneous deformation fields due to simulated biaxial stretch tests. The Holzapfel-Ogden material model is used as the ground-truth material model of the simulation, since it is capable of describing the orthotropic and hyperelastic behavior of the myocardium. In total, four material models were retrieved from Bayesian-EUCLID by considering two fiber orientations within the sample and two displacement measurement techniques. The two fiber directions included one in which the sheet-like structure of the sample lies within the test plane and one in which it lies perpendicular to the test plane. The displacement field measurements considered were a full displacement field measurement and an approximation thereof based on a stereo digital image correlation method. The results show that when the sheet-like structure of the myocardium lies within the test plane of the biaxial stretch test and the full displacement field is available, Bayesian EUCLID is capable of finding orthotropic and hyperelastic material models that correspond well to the ground truth. This shows that a biaxial stretch test can adequately characterize the material behavior of the myocardium.
Subject
data-driven modeling
constitutive modeling
myocardium
biaxial stretch test
hyperelasticity
heterogeneous deformation
fiber orientation
Neuromusculoskeletal Biomechancis
To reference this document use:
http://resolver.tudelft.nl/uuid:adf007c3-4b5e-47d9-865e-9d52ca5b4d70
Embargo date
2025-11-26
Part of collection
Student theses
Document type
master thesis
Rights
© 2023 Rogier Krijnen