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An inverse fitting strategy to determine the constrained mixture model parameters: application in patient-specific aorta.
- Source :
-
Frontiers in bioengineering and biotechnology [Front Bioeng Biotechnol] 2023 Nov 20; Vol. 11, pp. 1301988. Date of Electronic Publication: 2023 Nov 20 (Print Publication: 2023). - Publication Year :
- 2023
-
Abstract
- The Constrained Mixture Model (CMM) is a novel approach to describe arterial wall mechanics, whose formulation is based on a referential physiological state. The CMM considers the arterial wall as a mixture of load-bearing constituents, each of them with characteristic mass fraction, material properties, and deposition stretch levels from its stress-free state to the in-vivo configuration. Although some reports of this model successfully assess its capabilities, they barely explore experimental approaches to model patient-specific scenarios. In this sense, we propose an iterative fitting procedure of numerical-experimental nature to determine material parameters and deposition stretch values. To this end, the model has been implemented in a finite element framework, and it is calibrated using reported experimental data of descending thoracic aorta. The main results obtained from the proposed procedure consist of a set of material parameters for each constituent. Moreover, a relationship between deposition stretches and residual strain measurements (opening angle and axial stretch) has been numerically proved, establishing a strong consistency between the model and experimental data.<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2023 Navarrete, Utrera, Rivera, Latorre, Celentano and García-Herrera.)
Details
- Language :
- English
- ISSN :
- 2296-4185
- Volume :
- 11
- Database :
- MEDLINE
- Journal :
- Frontiers in bioengineering and biotechnology
- Publication Type :
- Academic Journal
- Accession number :
- 38053847
- Full Text :
- https://doi.org/10.3389/fbioe.2023.1301988