Fluid Dynamics - understanding fluid dynamics to improve our lives

脑动脉瘤是一种危及生命的血管扩张。对于选定的动脉瘤，微创治疗包括插入血管内血流转向器，将血流重新引导离开动脉瘤。这会触发血栓形成途径，导致动脉瘤充满结构性纤维血栓，最终导致动脉瘤完全消除。然而，某些类型的血栓在大型动脉瘤中的作用也被认为与延迟破裂有关。该项目将集中于：(I)基于粒子的方法(LBM)，它提供了一种生物激励的方法来跟踪剪切应力下的血小板激活(Ii)通过利用物理信息的神经网络与粒子分辨的流场相结合来模拟生化反应来加速计算。最近的方法将探索使用水平集函数来定义感兴趣的计算区域，并使用浸没边界方法解决流动问题。基于粒子的模型将与传统CFD解算器的参考解决方案和Tuail Patankar教授提供的临床血流成像数据进行验证。为了预测稳定的凝块形态，将定义一组生化传输-反应方程，并用基于粒子的方法和物理信息的神经网络进行比较研究。

A cerebral aneurysm is a life-threatening vascular distension. A minimally invasive treatment for selected aneurysms involves catheter insertion of an endovascular flow diverter that redirects blood flow away from the aneurysm. This triggers thrombus formation pathways and causes the aneurysm to fill with a structured fibrous thrombus, eventually leading to the complete elimination of the aneurysm. However, the role of certain types of thrombus within large aneurysms is also thought to be related to delayed ruptures. The project will focus on:(i) particle-based methods (LBM) that provide a biologically motivated way to track platelet activation under shear stresses (ii) acceleration of the computations by modelling the biochemical reactions with physics-informed neural networks coupled with the particle-resolved flow field. Recent approaches will be explored using level-set functions to define the computational region of interest and solve the flow problem using the immersed boundary method. The particle-based models will be validated against reference solutions from traditional CFD solvers and clinical flow imaging data provided by Prof Tufail Patankar. To predict the stable clot configuration, a set of biochemical transport-reaction equations will be defined and solved in a comparative study both with particle-based methods and physics-informed neural networks.