The complex hemodynamics observed in the human aorta make this district a site of election for an in depth investigation of the relationship between fluid structures, transport and pathophysiology. In recent years, the coupling of imaging techniques and computational fluid dynamics (CFD) has been applied to study aortic hemodynamics, because of the possibility to obtain highly resolved blood flow patterns in more and more realistic and fully personalized flow simulations [1]. However, the combination of imaging techniques and computational methods requires some assumptions that might influence the predicted hemodynamic scenario. Thus, computational modeling requires experimental cross-validation. Recently, 4D phase contrast MRI (PCMRI) has been applied in vivo and in vitro to access the velocity field in aorta [2] and to validate numerical results [3]. However, PCMRI usually requires long acquisition times and suffers from low spatial and temporal resolution and a low signal-to-noise ratio. Anemometric techniques have been also applied for in vitro characterization of the fluid dynamics in aortic phantoms. Among them, 3D Particle Tracking Velocimetry (PTV), an optical technique based on imaging of flow tracers successfully used to obtain Lagrangian velocity fields in a wide range of complex and turbulent flows [4], has been very recently applied to characterize fluid structures in the ascending aorta [5].
Productivity analysis in tubular photobioreactors using a dynamic photosynthesis model coupled to computational fluid dynamics particle tracking
