## 3 Results
### Numerical Modelling Augmentation
#### 3.7.1 Relevance of Modelling Certain Physiological Features  
The results that we gathered mainly focused on determining wall motion, patient-specific material properties, turbulence and non-Newtonian blood behaviour's impact on the numerical results. The aortic wall possesses hyperelastic properties, mainly due to the presence of elastin fibres [9], which allows the aorta to expand and contract during the cardiac cycle. This effect is known as the Windkessel effect and contributes to reducing the range between systolic and diastolic pressures, as well as assisting with blood flow. Given this, two questions arise from this rationale. Firstly, diseased aortas usually present stiffer aortas and lower diameter variations over the cardiac cycle. The studied group in [PERSON] et al. [13] presented an average variation of \(4.2\pm 2.4\%\). It may be not significant to consider as it introduces complexity to the model without a relevant impact on the numerical results. On this topic, [PERSON] et al. [73] compared the results of CFD and FSI simulations and found that instantaneous WSS are extremely influenced by wall motion. However, averaged values such as TAWSS and OSI seem not to be significantly influenced by FSI simulations. [PERSON] et al. [107] performed the same comparison on idealised models of the aortic root and concluded that aortic compliance did not impact the transvalvular haemodynamics. [PERSON] et al. [108] noticed a 15-fold increase in reporting time from CFD to FSI models. They also found that FSI-driven WSS distributions presented closer agreement with in vivo patient-specific data. [PERSON] et al. [14] found that rigid wall simulations tend to overestimate blood pressure and WSS magnitude. [PERSON] et al. [13] also assessed the differences between CSM, CFD and FSI simulations. Their results suggested that both WSS and WS presented significant differences between the simplest approaches (CSM and CFD) and FSI models. As reported by [PERSON] et al. [40], these differences were greater for more compliant aortas. [PERSON] et al. [25] studied the importance of modelling realistic wall and tear motion to AD haemodynamics and also found that FSI simulations contributed to improving the agreement of numerical results with MRI data. \begin{table}
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\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**Front Author** & **Year** & **Simulation** & **Solver** & **Geometry** & **BC** & **Flow** & **Wall** & **Main Remanks** & **Aim of the work** & **GRADE** \\ \hline \hline \multirow{3}{*}{[PERSON]. [PERSON]} & \multirow{3}{*}{2015} & FSI (FEM) & \multirow{3}{*}{Ansys} & \multirow{3}{*}{AD (PS-CT)} & \multirow{3}{*}{IFR; OWM} & \(\frac{E}{E}-\omega\) & \multirow{3}{*}{C-Y} & \multi  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**First Author** & **Year** & **Simulation** & **Solver** & **Geometry** & \begin{tabular}{c} **BC** \\ **conditions** \\ \end{tabular}  & **Flow** & **Wall** & **Main Remaks** & **Alim of the work** & **GRADE** \\ \hline [PERSON] & 2009 & CFD (PEM) & - & Acorta (I) & LVP; OPoT & L & N & - & Under transient conditions, & Asense the sensitivity of \\  & & & & & & & & & & & & & \\ [PERSON] & 2009 & CFD (ID) & MatLab & Acorta & VP; FoT & L & N & - & - & \begin{tabular}{c} Under transient conditions, \\ inlet and outlet BC significantly \\ \end{tabular}  & \begin{tabular}{c} Asense the sensitivity of \\ numerical results to the \\ chosen BC \\ \end{tabular}  & Low \\ [PERSON] & 2009 & CFD (ID) & MatLab & Acorta & VP; FoT & L & N & - & \begin{tabular}{c} The developed model \\ presented good agreement with \\ two data with \\ \end{tabular}  & \begin{tabular}{c} Novel method to estimate \\ WS distribution \\ Explore the \\ handcomings in WS were \\ fundamental problems in the \\ rheological models \\ \end{tabular}  & High \\ [PERSON] & 2008 & CFD (PEM) & Fluent & \ \begin{tabular}{c} Abdominal \\ aorta (I) \\ \end{tabular}