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Fluid Dynamics - understanding fluid dynamics to improve our lives

流体动力学 - 了解流体动力学以改善我们的生活

基本信息

批准号：
2883288
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2883288
关键词：
Fluid Dynamics understanding fluid dynamics

项目摘要

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.

脑动脉瘤是一种危及生命的血管扩张。对于选定的动脉瘤，一种微创治疗方法是将导管插入血管内血流分流器，使血流远离动脉瘤。这会触发血栓形成途径，使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动脉瘤中的作用也被认为与延迟破裂有关。该项目将侧重于：(i)基于粒子的方法（LBM），它提供了一种生物动机的方法来跟踪剪切应力下的血小板激活；（ii）通过将物理信息神经网络与粒子解析流场相结合，对生化反应进行建模来加速计算。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，并使用浸入边界法解决流动问题。基于颗粒的模型将根据传统CFD求解器的参考解决方案和Tufail 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结构，将定义一组生化转运反应方程，并在基于粒子的方法和物理信息神经网络的比较研究中求解。