ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

vitro (40 vs 34%), at the expense of QP (33 vs 38%).
The oscillating shape of the experimental pressure tracings (bright colors) at pressure
ports a and b and, conversely, the lack of pulsatility in the descending aorta (c) were
well captured by the computational model (dark colors) at the corresponding locations.
Mean values of in-vitro and in-silico pressures in the descending aorta showed good
agreement (9.2% and 0.8% difference at location b and c, respectively), while pressure
in the aortic arch upstream the AC (a) was 20% higher in-vitro than in-silico.
Fig. 3 Comparison of experimental and computational results. Left: in-vitro (solid) and in-silico
(dashed) flows through the upper body (QUB), lower body (QLB) and shunt (QP) outlets. Right:
in-vitro (brighter) and in-silico (darker) pressures at location a, b and c of the 3D model.
Figure 4 shows the velocity map (right) on a frontal plane of the 3D model (left) used
in-silico. Taken at the time instant (0.22 s) of maximum pressure drop across the AC, it
depicts the sudden increase of velocity in the AC and the non-uniform velocity field
along the proximal descending aorta. This is an example of the local information, hardly
or even not accessible in-vitro, which can be easily extracted from the numerical model.
Fig. 4 Velocity map
(right) on a frontal plane,
including the coarctation
(AC), of the 3D meshed
model (left), taken at the
time instant (0.22 s) of
maximum pressure drop
across the coarctation.
Comparison of the experimentally measured hemodynamic variables with those
calculated in-silico suggested that, due to high flow pulsatility, in-vitro resistive
components should be computationally modeled as non-linear terms, although they do
not reproduce the linear behavior of peripheral vascular resistances in-vivo. However,
the effects of using constant resistances could be easily explored thanks to the in-silico