Fracture of porcine aorta-Part 2: FEM modelling and inverse parameter identification

The mechanics of vascular tissue, particularly its fracture properties, are crucial in the onset and progres-sion of vascular diseases. Vascular tissue properties are complex, and the identification of fracture me-chanical properties relies on robust and efficient numerical tools. In this study, we propose a parameter identification pipeline to extract tissue properties from force-displacement and digital image correlation (DIC) data. The data has been acquired by symconCT testing porcine aorta wall specimens. Vascular tissue is modelled as a non-linear viscoelastic isotropic solid, and an isotropic cohesive zone model describes tissue fracture. The model closely replicated the experimental observations and identified the fracture energies of 1 . 57 & PLUSMN; 0 . 82 kJ m-2 and 0 . 96 & PLUSMN; 0 . 34 kJ m-2 for rupturing the porcine aortic media along the circumferential and axial directions, respectively. The identified strength was always below 350 kPa, a value significantly lower than identified through classical protocols, such as simple tension, and sheds new light on the resilience of the aorta. Further refinements to the model, such as considering rate ef-fects in the fracture process zone and tissue anisotropy, could have improved the simulation results. Statement of significance This paper identified porcine aorta's biomechanical properties using data acquired through a previously developed experimental protocol, the symmetry-constraint compact tension test. An implicit finite ele-ment method model mimicked the test, and a two-step approach identified the material's elastic and fracture properties directly from force-displacement curves and digital image correlation-based strain measurements. Our findings show a lower strength of the abdominal aorta as compared to the litera-ture, which may have significant implications for the clinical evaluation of the risk of aortic rupture. & COPY; 2023 The Author(s). Published by Elsevier Ltd on behalf of Acta Materialia Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

In this study, a parameter identification pipeline was proposed to extract tissue properties from force-displacement and digital image correlation (DIC) data, which play a crucial role in understanding the fracture properties of vascular tissue. The model closely replicated the experimental observations and identified the fracture energies of the porcine aorta. The findings reveal the lower strength of the abdominal aorta and have significant implications for the clinical evaluation of the risk of aortic rupture.