Wall Shear Stress

Wall shear stress (WSS) quantifies the frictional force of blood flow against vessel walls, a crucial factor in cardiovascular health and other fluid dynamics applications. Current research focuses on improving WSS quantification through advanced computational methods, including deep learning architectures trained on both numerical simulations and experimental data (like Particle Image Velocimetry), and employing novel approaches such as graph neural networks and mesh neural networks to efficiently handle complex geometries. Accurate WSS prediction is vital for personalized medicine (e.g., aneurysm risk assessment), optimizing designs in various engineering fields, and advancing our understanding of fundamental fluid mechanics.