## 1 Introduction  
A cerebral or intracranial aneurysm occurs due to the weakness in the walls of the cerebral artery, which causes abnormal focal dilation of the vessel. As the artery wall becomes thinner over time, the aneurysm is likely to rupture, causing blood to flow into the space in the brain. The rupture of an intracranial aneurysm produces a subarachnoid hemorrhage (SAH) and leads to high mortality and morbidity rates [1]. In order to diagnose the aneurysms, noninvasive angiographic imaging techniques such as computed tomography angiography (CTA), magnetic resonance angiography (MRA), and digital subtraction angiography (DSA) have been commonly used [2]. After detection of an aneurysm, various methods may be used to avoid its rupture, including (open surgery) clipping and endovascular therapy (coils or stents, flow diversion devices). These treatments are based on the exclusion of aneurysms from the blood circulation [3]. A number of devices have been developed over the last decade to treat cerebral aneurysms. Among them, the flow-diverting (FD) stent--a self-expandable, strut-braided, mesh device--shows considerable promise by promoting thrombosis within the aneurysm cavity by diverting the blood flow away from the aneurysm [4]. Despite encouraging results with FD stents, post-treatment complications such as failure of occlusion and subarachnoid hemorrhaging have been reported, so clinicians need to consider some vital issues, for example, discriminating between ruptured and unruptured aneurysms to make a decision on the treatment process, and assessing the performance of selected devices [5; 6]. Computational fluid dynamics (CFD) is a promising, yet economic, means to tackle these problems [6; 7]. High-resolution images of the brain can be acquired to create detailed 3D models of aneurysm geometry. Governing equations on blood flow have been solved using CFD methods to calculate vascular flow patterns in the aneurysm [6; 7; 8]. The computational simulations can be performed before and after treatment to analyze the hemodynamic alterations due to the implantation of the devices [3]. The post-treatment hemodynamic parameters, including time-averaged inflow rate, aneurysm-averaged velocity, wall shear stress, total absolute circulation, and turnover time (the aneurysm sac volume divided by the inflow rate at the neck plane), are provided using computational fluid dynamics analysis [9]. The flow-related parameters are used to anticipate the flow diverter performance in aneurysm occlusion, and this information helps clinicians to select the optimal devices before treatment [10]. CFD studies have been conducted on aneurysm treatment using flow diverting stents. Most studies aim to identify the effects of flow-diverter placements by comparing the distribution of hemodynamic parameters in patient-specific or idealized models of cerebral aneurysms before and after treatment. The distribution of velocity, pressure, and wall shear stress has been presented to assess the potential risk of aneurysm rupture [11], evaluate the accuracy of computational fluid dynamics methods by comparing with clinical imaging results [12], or consider the effect of mechanical properties of the stent, as well as its hemodynamic performance [4]. Previous studies have illustrated that detailed knowledge of hemodynamics can be affected by stent placement, thus being critical to the prediction of treatment outcome. Some studies have focused on the simulation of the mechanical deployment of flow-diverter stents using finite element analysis to capture the clinically realistic FD deployment processes [3; 13]. Some researchers have conducted stent deployment and CFD simulations simultaneously to investigate the influence of different stent deployment approaches and the location of stents on the hemodynamics of aneurysms by comparing untreated and stented vessels [9; 14; 15].