Near-Wall Flow in Cerebral Aneurysms

The region where the vascular lumen meets the surrounding endothelium cell layer, hence the interface region between haemodynamics and cell tissue, is of primary importance in the physiological functions of the cardiovascular system. The functions include mass transport to/from the blood and tissue, and signalling via mechanotransduction, which are primary functions of the cardiovascular system and abnormalities in these functions are known to affect disease formation and vascular remodelling. This region is denoted by the near-wall region in the present work, and we outline simple yet effective numerical recipes to analyse the near-wall flow field. Computational haemodynamics solutions are presented for six patient specific cerebral aneurysms, at three instances in the cardiac cycle: peak systole, end systole (taken as dicrotic notch) and end diastole. A sensitivity study, based on Newtonian and non-Newtonian rheological models, and different flow rate profiles, is effected for a selection of aneurysm cases. The near-wall flow field is described by the wall shear stress (WSS) and the divergence of wall shear stress (WSSdiv), as descriptors of tangential and normal velocity components, respectively, as well as the wall shear stress critical points. Relations between near-wall and free-stream flow fields are discussed

Isolating the Effect of Arch Architecture on Aortic Hemodynamics Late After Coarctation Repair: A Computational Study

OBJECTIVES: Effective management of aortic coarctation (CoA) affects long-term cardiovascular outcomes. Full appreciation of CoA hemodynamics is important. This study aimed to analyze the relationship between aortic shape and hemodynamic parameters by means of computational simulations, purposely isolating the morphological variable. METHODS: Computational simulations were run in three aortic models. MRI-derived aortic geometries were generated using a statistical shape modeling methodology. Starting from n = 108 patients, the mean aortic configuration was derived in patients without CoA (n = 37, “no-CoA”), with surgically repaired CoA (n = 58, “r-CoA”) and with unrepaired CoA (n = 13, “CoA”). As such, the aortic models represented average configurations for each scenario. Key hemodynamic parameters (i.e., pressure drop, aortic velocity, vorticity, wall shear stress WSS, and length and number of strong flow separations in the descending aorta) were measured in the three models at three time points (peak systole, end systole, end diastole). RESULTS: Comparing no-CoA and CoA revealed substantial differences in all hemodynamic parameters. However, simulations revealed significant increases in vorticity at the site of CoA repair, higher WSS in the descending aorta and a 12% increase in power loss, in r-CoA compared to no-CoA, despite no clinically significant narrowing (CoA index >0.8) in the r-CoA model. CONCLUSIONS: Small alterations in aortic morphology impact on key hemodynamic indices. This may contribute to explaining phenomena such as persistent hypertension in the absence of any clinically significant narrowing. Whilst cardiovascular events in these patients may be related to hypertension, the role of arch geometry may be a contributory factor

Near-wall flow deconstruction via mapping and polynomial fit

A mapping technique for enhancing the visualisation and analysis of the flow structure in regions near the wall is presented. After identifying a near-wall region of interest, the output of the proposed mapping technique is an analytical expression of the flow variables, satisfying the governing PDEs and boundary conditions, on a stencil of standardised morphology.The approach firstly involves selecting a local surface region of interest from the computational domain to be mapped. Subsequently a structured mesh of arbitrary height on top of the cropped surface is generated, thus forming the target volume region, which is termed the physical space. The solution data comprising of flow properties such as velocity and pressure from the computational domain is interpolated onto the physical space. The physical space and the data are consequently mapped onto an unwrapped domain with standard shape, termed the mapped space. For simplicity, the mapped space is chosen here to be a cuboid. Finally, the data is expressed as a best fit polynomial, satisfying the governing PDEs and boundary conditions.The method is validated by direct pointwise comparison and from the velocity streamlines mapped from the physical space, for a set of test problems. The mapping technique effectiveness is demonstrated firstly on a 90 degree bend pipe as a benchmark investigation and subsequently on a nasal cavity anatomy. For the latter, three scenarios covering different flow structures in the near-wall region are scrutinised, demonstrating the ability of the techniques proposed to uncover the details of the near-wall flow in complex physiological flows. The regions of interest can be identified using near-wall measures such as wall shear stress, shear lines, and wall shear stress critical points.The mapping technique has potential applications in the fields of fluid dynamics and specifically near-wall flows, as the interface region describing the dynamics of exchanges. It is furthermore capable of inferring the velocity field from reduced data available to enhance the use of deep learning or regression methods