thesis

Biaxial mechanical characterization and microstructure-driven modeling of elastic pulmonary artery walls of large mammals under hypertensive conditions

Abstract

Pulmonary Hypertension (PH) is a disease of the pulmonary vasculature which causes right heart failure. It is known that PH causes significant remodeling of the pulmonary arterial vasculature, but the effects of this remodeling are not well-understood. In addition, there is a dearth of research in large mammals for PH. Modeling of the arteries is also important in the simulation of deformation due to blood flow. Current models either do not reflect the microstructure, or are too complex for clinical use. This work presents mechanical characterization and analysis of the artery wall, in addition to a constitutive model driven by the microstructure of the artery. In this work, mechanical characterization of the artery wall is performed via multiaxial deformation using a custom-fabricated planar biaxial tester. This test device provides higher fidelity than the standard uniaxial tests. Using the data gathered from the biaxial tester, trends in aspects of the mechanical behavior due to PH can be elucidated. Specifically, in this work, the anisotropy of the elastin protein network has been quantified, with the circumferential direction being 1.4x stiffer than the longitudinal direction. In addition to this new finding, PH has been shown to slightly decrease the anisotropy of the pulmonary artery trunk. A new microstructurally-based constitutive model for the artery wall was developed to reflect this finding. This model uses decoupled anisotropy for the elastin and collagen networks, reflecting the true behavior of the artery wall. The model uses a sinusoidal elastic beam to model the collagen fibers, reflecting the microstructure. This microstructural basis is then verified through histology and correlation of material parameters to histological images. Using information from this data, prospective future analysis of mechanical behavior will be proposed

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oai:scholar.colorado.edu:mcen_gradetds-1013Last time updated on 11/12/2016View original full text link

This paper was published in CU Scholar Institutional Repository.

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