Application of computational fluid dynamics and physics informed neural networks in predicting rupture risk of thoracoabdominal aneurysms with fluid-structure interaction analysis
M. Abaid Ur Rehman , Özgür Ekici , M. Asif Farooq , Rashid M. Talha , Sadaf Amir
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引用次数: 0
Abstract
An aneurysm’s rupture is often linked to its maximum diameter, but biomechanical studies highlight the crucial role of hemodynamic factors, such as blood flow pattern and pressure within the blood vessel, in this process. This study investigates six cases, exploring both axisymmetric (fusiform) and asymmetric (saccular) aneurysm shapes while maintaining a consistent aneurysm diameter and varying the bulge shape factor to induce asymmetry. Hemodynamic factors, including wall shear stress (WSS), wall shear stress gradient (WSSG), and von Mises stress distributions, are computed under both laminar and turbulent flow conditions, reflecting the diastolic and systolic phases, respectively. Our results reveal that recirculation zones, particularly prominent in asymmetric cases, generate vortices within the aneurysm, increasing blood residence time and the likelihood of thrombus formation. Thrombus formation can impede blood flow, raising the risk of embolism or ischemic events. Rupture occurs when WSS exceeds tissue strength, and our findings suggest that rupture risk varies with aneurysm asymmetry. Specifically, hemodynamic factors are more severe in turbulent cases, with the highest values observed in case 2-T, an asymmetric aneurysm with a significant posterior bulge. This case also shows the highest rupture risk, indicated by elevated WSS, WSSG, and von Mises stress, particularly on the anterior side, towards the distal end of the thoracic aortic aneurysm. At this location, the high WSS and WSSG indicate intense fluctuations and flow disturbances, while von Mises stress is also significantly elevated, further increasing the likelihood of rupture. Additionally, we used a synergistic approach that integrates Computational Fluid Dynamics (CFD) with Physics-Informed Neural Networks (PINNs) to compare pressure and velocity distributions along the medial and transverse planes, demonstrating strong agreement. A novel aspect of this study lies in its comprehensive analysis of thoracoabdominal aneurysms, accounting for the influence of aneurysm asymmetry on WSS, WSSG, and von Mises stress. Additionally, both laminar and turbulent flow conditions are explored to reflect the diastolic and systolic phases, respectively.
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