• Numerical
• Mock
• Loop
• Hemodynamics
• Flow
• Conditions
• CFD
• Boundary
• Stochastic

In the present work experiments have been carried out on a circulatory mock loop for thoracic aorta hemodynamics analysis.
First, a stochastic sensitivity analysis on the effects of the inlet conditions in experiments is presented.
Then, once defined the experimental set-up, the in-vitro measurements are compared with the numerical predictions obtained by using the open-source code SimVascular.
Experiments on the circulatory mock loop are performed at the BioCardio Lab of Massa a sub unit of the Adult Cardiology Division of the G. Monasterio Foundation.
In the first part of the work, the impact of stroke, period of the cardiac cycle and distribution in space of the inlet velocity in \textit{in-vitro} measurements is investigated for three different geometries of the aorta: healthy aorta, aneurysm and coarctation.
This study is carried out with a stochastic sensitivity analysis on experimental results. The stochastic approach is based on the generalized Polynomial Chaos (gPC), in which continuous response surfaces of the quantities of interest in the parameter space can be obtained from a limited number of simulations. We use beta PDFs reproducing clinical data for the uncertain parameters fitted on the available in-vivo medical data. Among the three selected input parameters, the stroke volume and, in minor part, the cardiac cycle period are the most important for the variability of the flow-rate and pressure waveforms.
In the second part of the work, the Echo-Cardiodoppler velocity fields obtained from the mock loop are compared with the numerical predictions. Again three geometries of the aorta are considered.
In-vitro experiments and CFD results are in a very good agreement in terms of instantaneous flow streamlines.


Techniques based on computational fluid dynamics (CFD) have been extensively used in the last few years to investigate hemodynamics inside arteries in both healthy and diseased subjects for its capacity of providing informations of pressure and flow fields at a time and space unachievable, in term of resolution, by any in-vivo measurement.
In fact, CFD permits to compute a variety of quantities and indicators that are difficult to be obtained from Magnetic Resonance Imaging (MRI), as e.g. wall shear stresses and a full developed velocity field on the whole control volume, which suffers of spatial and temporal resolution limitations.
Another important result, with numerical simulations, is the possibility to investigates hemodynamics in patient specific geometries, since medical images are capable to provide accurate morphological features and CFD techniques are mature to tackle complex 3D geometries. Furthermore, since it has been shown from literature that geometric vessel details may deeply affect the flow features, as e.g. the regions where the wall shear stresses takes very-high or very-low values, the model shape accuracy is became an important and essential requirement for real applications.
On the other hand, different sources of uncertainties are present in CFD models which can propagate and affect the output quantities of interest, especially in the reconstruction of the domain geometry, the choice of the mechanical properties of the arterial wall and the imposition of suitable inflow/outflow boundary conditions, which should reproduce the effect of of organs and vessels outside the control volume.
Specifically, we simulated the effect of inflow conditions with an imposition of an ideal flow rate waveform inspired by patient-specific measured volumetric flow rate waveform.
Therefore, the present work is focused on the effects of different inlet conditions of the thoracic aorta have on the output values of interest and the way in which they effects hemodynamics inside the vessel.
These assumptions are possible thanks to the supports of two instruments, as anticipated: the 3D Phase Contrast Magnetic Resonance Imaging (3D PC-MRI) technique, is a clinical imaging tool able to provide in-vivo hemodynamic informations in a non-invasive manner, and Computational Fluid Dynamics (CFD), in particular with the open source software SimVascular.
So, in practical terms, the integration of patient medical imaging data could be useful to reduce modeling assumptions, in particular for morphological determination and for inlet/outlet boundary conditions, that strongly affect the accuracy of predictions.
It is also possible to compare physical and visual data from different sources to calibrate and to improve all the singular approaches and to give a more wide knowledge of the whole phenomenon under analysis.