CFD Modeling of Venous Valve Flow Characteristics at Various Reynolds Numbers

Venous valves play a crucial role in maintaining unidirectional blood flow and preventing reflux in the lower extremities. Dysfunction of these valves can contribute to deep vein thrombosis (DVT), a serious and life-threatening condition. In this study, we used computational fluid dynamics (CFD) to model blood flow through a two-dimensional venous valve over a range of relevant Reynolds numbers. Simulations in ANSYS Fluent analyze flow separation, vortex shear, shear stress, and the formation of recirculation and stagnation regions, all of which are closely linked to thrombus formation. To deepen our understanding, we conducted simulations under both normal and dysfunctional valve configurations to assess the influence of valve competence on hemodynamics. We also compared results across various fluid models, including Newtonian and non-Newtonian representations of blood rheology, and observed significant differences in flow behavior, particularly at transitional Reynolds numbers. Both linear and nonlinear blood flow models were examined to capture the shear-thinning, non-Newtonian nature of blood under different physiological conditions. Simulations included scenarios with variable leaflet positions, such as fully open, partially closed, and asymmetric closure, to study how leaflet motion and positioning influence local vortex shedding and overall circulation patterns. While recent work has focused on broad digital twin frameworks and experimental platforms, our study offers high-resolution, physics-based insights into the specific flow structures that contribute to clot formation. By isolating and quantifying the fluid dynamic mechanisms behind valve dysfunction and DVT risk, our work provides a level of mechanistic clarity and predictive detail that is often generalized in hybrid or data-driven approaches. These findings inform the design of more effective valves, risk stratification, and the development of targeted therapeutic strategies.