• PCA
• CFD
• flow eccentricity
• Womersley
• abdominal aortic aneurysm

Abdominal Aortic Aneurysm (AAA) typically forms in the aorta's infra-renal region when the vessel diameter is greater than 4 cm or exceeds the normal aortic diameter by 50%. If a AAA is not treated, the diameter will gradually grow, increasing the risk of rupture. This disease has a near-100% mortality rate.
Because the majority of people with intact AAA are asymptomatic, screening of at-risk groups is the most effective method of preventing AAA-related death.
In this work, the hemodynamics of AAA was investigated by focusing on the effect of inlet boundary conditions (IBCs). Indeed, there is a gap in CFD research, which has a rich literature on aneurysm geometry, but less on IBCs, and even less works dealing systematically with large data sets of cases.
A specific mathematical model has been developed and implemented to obtain a big variety of 4D map that were then imported in the software used to perform the CFD simulations. Several CFD simulations were therefore performed while varying 4D velocity profiles at the inlet depending on several factors, such as Womerlsey number, flow eccentricity, stroke volume, and systole duration.
Finally, it has been studied quantitatively, by using Principal Components Analysis (PCA), how different results of different quantities of interest, such as pressure and velocity, can be obtained at different instants in the cardiac cycle.
For instance, if there are a small number of velocity structures in systole, so that the maximum three principal components explain 100% of the variability, this is not possible in diastole.
In the case of diastole, there are so many velocity structures that more complex mathematical models, possibly requiring the inclusion of turbulence models, or even more sophisticated simulations, would be required to study the phenomenon.
This was a preliminary study; it may be an interesting future development to compare the model's introduced variables with uncertainty quantification to quantify the effect of each these variables on the output quantities of interest.