International Journal of Science, Engineering and TechnologyAuthors: M.Sirisha, Pothapragada Himabindu, Assistant Professor
Abstract: Cardiovascular diseases remain the leading cause of death globally, driving an urgent need for predictive, patient-specific modeling of blood flow and vascular mechanics. This paper synthesizes recent advances in numerical methods for cardiovascular fluid mechanics, emphasizing their transition from research tools to clinical applicability. We analyze four core methodological domains: (1) immersed fluid–structure interaction (FSI) methods for deformable vessels and heart valves; (2) machine learning–enhanced reduced-order modeling (ROM) for real-time hemodynamic assessment; (3) multiphysics integration of hemodynamics, tissue mechanics, and biological processes for disease progression modeling; and (4) turbulence modeling and uncertainty quantification in large arteries. Drawing on peer-reviewed studies from 2025–2026, we demonstrate that modern numerical frameworks achieve patient-specific digital twins capable of predicting wall shear stress (WSS), fractional flow reserve (FFR), and rupture risk in aortic dissection, coronary artery disease, and cerebral aneurysms. We conclude with an integrated computational pipeline for clinical decision support and identify key challenges for future translation.
DOI: http://doi.org/10.5281/zenodo.20583045
