Το work with title Study of nonlinear models of aneurysmal aortas using the finite element method by Xydias Charalampos-Faidon is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
Charalampos-Faidon Xydias, "Study of nonlinear models of aneurysmal aortas using the finite element method", Diploma Work, School of Production Engineering and Management, Technical University of Crete, Chania, Greece, 2025
https://doi.org/10.26233/heallink.tuc.102253
Computational technology offers significant capabilities for studying and simulating complex medical conditions. In this thesis, finite element method (FEM) simulations are employed to investigate the mechanical behavior of abdominal aortic aneurysms (AAA), a serious vascular condition that can lead to rupture and pose a life-threatening risk. The study focuses on comparing different simulation approaches by considering both linear and nonlinear material models, as well as single-layer and multi-layer models of the aortic wall structure. The data used in this research were obtained from real patients, processed to create three-dimensional geometries, and analyzed under physiological loading conditions. The analysis revealed that nonlinear models more accurately capture the mechanical behavior of the aortic wall, identifying higher stress concentrations in high-risk regions, such as the aneurysm dome. Multi-layer models provided a more realistic representation of stress and strain distribution, highlighting the importance of differentiating between the wall layers. On the other hand, linear models offered a simplified approach, sufficient for initial assessments but inadequate for predicting extreme conditions. The objective of this study was to examine the impact of material models on simulation outcomes and to identify regions of elevated stress and deformation, which are directly correlated with rupture risk. The findings contribute to improving diagnostic methodologies and developing personalized treatment strategies. Moreover, they provide valuable insights for the creation of mechanical digital twins of patients, which can be used for predicting disease progression and supporting clinical decision-making.