It is well established that the blood flow within a healthy aorta exhibits laminar flow, while the presence of turbulence is well-documented in patients with obstructive diseases in major vessels. However, catheter-based measurements in both human and canine studies have demonstrated that turbulence can develop in the aorta not only in the presence of disease but also with normal aortic valves 8,20,21. Despite these observations, comprehensive description of turbulence in a healthy aorta has been lacking.
In this study, LES was employed to investigate turbulent flow characteristics in an idealized thoracic aortic model, with the focus on analyzing turbulence development and its extent in the aorta. This research enhances the broader understanding of cardiovascular health and disease progression, particularly in relation to conditions such as aortic dissection, aortic aneurysm and atherosclerotic stenosis. By examining the mechanisms and extent of hemodynamic changes during the transition from laminar to turbulent flow, this research offers critical implications for clinical management and treatment strategies.
In recent years, biomechanical research has increasingly focused on aortic flow through steady-state simulations employing various CFD models and patient-specific geometries of aortas 22–25. While steady-state simulations show overall wall WSS behavior, they fail to capture the dynamic changes induced by pulsatile blood velocity, especially in regions such as the aortic arch and branch junctions 26. These areas are significantly influenced by the acceleration phase where turbulent effects are pronounced, particularly at the curved section of the aorta. Some studies have considered the k-ε and k-ω models, which provide mean values of turbulence parameters and overall flow behavior 27–29. However, small eddies in transitional flow increase WSS, potentially contributing to further aneurysm dilation, as the aorta adjusts its diameter to maintain shear stress below physiological thresholds 30. In medical contexts with relatively high Reynolds numbers, high-resolution visualization and accuracy are crucial. Reynolds-Averaged Navier-Stokes (RANS) models are based solely on mean flow equations, relying on averaged quantities to approximate turbulence effects, which are the least computationally expensive turbulence modeling methods 31. In contrast, Direct Numerical Simulation (DNS) represents the gold standard of simulation because it accounts for the effects of every scale of eddy when solving the flow equations. This approach maintains high fidelity by using computational cells smaller than or equal to the size of the smallest eddy, but it demands substantial computational resources 32. DNS has been applied effectively in various studies: Dimakopoulos 33 performed a DNS analysis on a 2D stent-type aortic valve, while Lee et al. 34 used the spectral element method to simulate weakly turbulent flow in a patient-specific stenosed carotid bifurcation, predicting the complex flow field, turbulence levels, and distribution of hemodynamic parameters. Tullio et al. 35 conducted a detailed DNS flow analysis on an aortic bileaflet mechanical heart valve, verifying the strong agreement between DNS and experimental results. Despite its accuracy, DNS is limited by its high computational cost. LES, which resolves larger turbulent eddies while modeling smaller ones, offers a more computationally efficient alternative to DNS. This method captures most of the flow dynamics while significantly reducing computational costs compared to DNS.
The LES conducted in our study provides detailed insights into the complex flow patterns in a generic thoracic aorta. During the acceleration phase, flow in the aorta is predominantly laminar from the ascending aorta to the aortic arch. However, during the mid-deceleration phase, there was a noticeable transition from well-organized laminar flow patterns observed during peak systole to small-scale irregular vortical structures. The aortic arch appears to be the beginning of the transition from laminar to turbulent flow. This transition is crucial, as it is characterized by high turbulence intensity and elevated shear stress, which can lead to potential vascular risks. High velocity fluctuations during this transition can lead to increased shear stress and vibrations in the aortic wall, potentially damaging the vessel and may contribute to vascular occlusion 36. Our observations of complex vortices and reverse flow in the aortic arch during this transition are consistent with the findings of Fukuda et al. 37 from PIV experiments, demonstrating a similar flow pattern. Helical flow is observed in the ascending aorta through streamlines plots in our study. A similar helical flow pattern was also observed in MRI flow filed study by Kilner et al. 38, and CFD study by Tse et al. 39 which validates our qualitative results.
In our simulation results, WSS is highest during near-peak systole and mid-deceleration phase. The aortic arch exhibits significantly elevated WSS, measuring 2–5 times higher than surrounding areas. This elevated WSS can be a contributing factor to the development of aortic dissection. For type A dissection, which involves the ascending aorta and aortic arch, we observed high WSS at the proximal ascending aorta and the aortic arch. These coincide with the predilection sites for aortic dissection reported by Chi et al. 22. High WSS is also observed at the proximal segments of the arterial branches. Notably LSA exhibits higher WSS compared to the BCA and LCCA, indicating a potentially higher risk of tearing at the LSA. Furthermore, the elevated WSS at the proximal segments of arterial branches is associated with typical locations reported for atherosclerotic lesions in the thoracic aorta 40.
For type B dissection, which typically affects the descending aorta, we observed high WSS at the entrance and distal location of the descending aorta. This high WSS is consistent with known high-risk sites for type B dissection. However, due to the complex nature of type B aortic dissection and the variability in patient-specific anatomy, while high WSS might contribute to wall thinning or increased biological stress, accurately quantifying and predicting the risk of type B aortic dissection remains challenging in healthy aortas 41,42. In addition to aortic dissection, the regions of high WSS in our study, particularly near the aortic arch and branch bifurcations, may also be one of the contributing factors in the formation of aortic aneurysms. The persistent elevated WSS in these areas can lead to localized degeneration of the aortic wall, which, over time, may result in vessel wall dilation and an increased risk of aneurysm formation.
The diffusion of vorticity during mid-deceleration and end-systole result in turbulent flow with repeated reverse flow, which leads to repeated oscillations of WSS. These oscillating can contribute to the development of atherosclerosis, as studies have shown that low-level and oscillating WSS can induce endothelial changes, contributing to plaque formation 43–45, which may subsequently lead to thrombus formation 46–48. In our study, WSS levels decrease significantly from systole to the beginning of diastole, with higher shear stress localized near the branches and the inner curvature of the aortic arch. These findings are consistent with the LES results of Lantz et al. 6.
Despite the comprehensive insights gained from our study, several limitations should be noted. Our study does not account for individual variations in aortic anatomy. While patient-specific models offer precise representations of individual anatomy, they may not be as widely applicable. An idealized model, such as ours, offers general insights that can be relevant to a broad range of individuals. Our model assumes rigid walls and a Newtonian fluid. While the rigid wall assumption may not fully capture the dynamic interactions between the flow and the vessel wall, Alimohammadi et al. 49 noted that this assumption in CFD can slightly overestimate WSS. However, it does not significantly alter the overall WSS distribution. Although non-Newtonian and Newtonian fluids exhibit different behaviors, many studies have shown similar vortex structures and WSS distributions for both type of fluids, with the main differences primarily observed in low Reynolds number regions. Therefore, the Newtonian fluid assumption is appropriate for simulating flow in arteries with high Reynolds numbers 50–52. Lastly, we used a predefined flow division (10% of the total flow was directed to the BCA, and 10% was equally divided between the LCCA and LSA). This approach may not fully reflect the natural variations in flow distribution among individuals. Anatomical differences can result in varying flow distribution, and incorporating subject-specific flow data could provide a more accurate representation of the blood flow dynamics within the aorta.