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Abstract
Cardiovascular disease remains the leading cause of morbidity and mortality globally, necessitating extensive research into the hemodynamics of blood flow under pathological conditions, such as atherosclerosis in carotid arteries. In vitro studies, particularly Computational Fluid Dynamics (CFD), are crucial for advancing our understanding of arterial blood flow and predicting pathological states. However, the accuracy of CFD simulations relies heavily on their validation against empirical data, such as those obtained from Particle Image Velocimetry (PIV). This study focuses on the comparative analysis of CFD predictions and PIV measurements of blood velocity vectors in a stented carotid artery bifurcation model under steady flow conditions derived from patient-specific data. The methodology involves simulating blood flow within a CFD framework and conducting PIV experiments using a blood-mimicking fluid seeded with particles in a carotid artery bifurcation phantom. The results indicate a reasonable agreement between the axial velocity vector profiles obtained via PIV and those predicted by CFD, with CFD predicting 10% higher than that recorded by PIV, especially in terms of recirculation areas and velocity values, despite some discrepancies in the velocity contours distribution, highlighting potential differences in how each method captures flow separation or recirculation areas. Despite some discrepancies in velocity contour distribution, which highlight potential differences in capturing flow separation or recirculation areas, the findings confirm that CFD simulations can effectively replicate the hemodynamics observed in carotid arteries and potentially other arterial segments. This study emphasizes the importance of integrating CFD simulations with experimental PIV data to validate and refine our understanding of arterial flow dynamics, significantly contributing to cardiovascular research and the development of interventions for arterial diseases.
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