The hemodynamic parameters in arteries are difficult to measure non-invasively, and the analysis and prediction of hemodynamic parameters based on computational fluid dynamics (CFD) has become one of the important research hotspots in biomechanics. This article establishes 15 idealized left coronary artery bifurcation models with concomitant stenosis and aneurysm lesions, and uses CFD method to numerically simulate them, exploring the effects of left anterior descending branch (LAD) stenosis rate and curvature radius on the hemodynamics inside the aneurysm. This study compared models with different stenosis rates and curvature radii and found that as the stenosis rate increased, the oscillatory shear index (OSI) and relative residence time (RRT) showed a trend of increase; In addition, the decrease in curvature radius led to an increase in the degree of vascular curvature and an increased risk of vascular aneurysm rupture. Among them, when the stenosis rate was less than 60%, the impact of stenosis rate on aneurysm rupture was greater, and when the stenosis rate was greater than 60%, the impact of curvature radius was more significant. Based on the research results of this article, it can be concluded that by comprehensively considering the effects of stenosis rate and curvature radius on hemodynamic parameters, the risk of aneurysm rupture can be analyzed and predicted. This article uses CFD methods to deeply explore the effects of stenosis rate and curvature radius on the hemodynamics of aneurysms, providing new theoretical basis and prediction methods for the assessment of aneurysm rupture risk, which has important academic value and practical guidance significance.
Atherosclerosis, aneurysms, and other vascular pathologies are closely associated with hemodynamic parameters. Non-invasive measurement of these hemodynamic parameters is of great significance for the prevention and treatment of cardiovascular diseases. In this study, eight idealized models of diseased vessels with varying stenosis rates and dilation extents were constructed. Transient simulations were performed under realistic physiological boundary conditions, and spatiotemporal coordinates as well as hemodynamic parameters were extracted from the models over one cardiac cycle to construct the dataset. Subsequently, by integrating hard boundary constraint methods with a physics-informed neural network (PINN), a prediction model for hemodynamic parameters in diseased vessels was established. The superiority of this model was demonstrated through validation on steady-state problems. With only limited supervised data, the proposed model achieves high-accuracy predictions of key hemodynamic parameters in lesioned vessels, including velocity, pressure, wall shear stress (WSS), time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). The results indicate that the developed hemodynamic prediction model can accurately capture flow field characteristics such as velocity and pressure distributions, and exhibits excellent performance in predicting WSS and TAWSS. This study provides a novel approach and scientific basis for mechanistic investigations, clinical diagnosis, therapeutic strategies, and risk assessment of atherosclerosis, aneurysms, and related cardiovascular diseases.
