The spatio-temporal evolution of a turbulent swirling jet undergoing vortex breakdown has been investigated. Experiments suggest the existence of a self-excited global mode having a single dominant frequency. This oscillatory mode is shown to be absolutely unstable and leads to a rotating counter-winding helical structure that is located at the periphery of the recirculation zone. The resulting time-periodic 3D velocity field is predicted theoretically as being the most unstable mode determined by parabolized stability analysis employing the mean flow data from experiments. The 3D oscillatory flow is constructed from uncorrelated 2D snapshots of particle image velocimetry data, using proper orthogonal decomposition, a phase-averaging technique and an azimuthal symmetry associated with helical structures. Stability-derived modes and empirically derived modes correspond remarkably well, yielding prototypical coherent structures that dominate the investigated flow region. The proposed method of constructing 3D time-periodic velocity fields from uncorrelated 2D data is applicable to a large class of turbulent shear flows.

In this paper methods for extraction of local features in crisp vector fields are extended to uncertain fields. While in a crisp field local features are either present or absent at some location, in an uncertain field they are present with some probability. We model sampled uncertain vector fields by discrete Gaussian random fields with empirically estimated spatial correlations. The variability of the random fields in a spatial neighborhood is characterized by marginal distributions. Probabilities for the presence of local features are formulated in terms of low-dimensional integrals over such marginal distributions. Specifically, we define probabilistic equivalents for critical points and vortex cores. The probabilities are computed by Monte Carlo integration. For identification of critical points and cores of swirling motion we employ the Poincaré index and the criterion by Sujudi and Haimes. In contrast to previous global methods we take a local perspective and directly extract features in divergence-free fields as well. The method is able to detect saddle points in a straight forward way and works on various grid types. It is demonstrated by applying it to simulated unsteady flows of biofluid and climate dynamics.

Computational fluid dynamics (CFD) methods based on three-dimensional (3D) vessel reconstructions have recently been shown to provide prognostically relevant hemodynamic data. However, the geometry reconstruction and the assessment of clinically relevant hemodynamic parameters may depend on the used imaging modality. In this study, the silicon model of the left coronary artery (LCA) was acquired with a biplane angiography. The geometry reconstruction was done using commercial CAAS 5.2 QCA 3D software and compared with an original geometry. The original model is an optically digitized post-mortem vessel cast. The biplane angiography reconstruction achieved a Hausdorff surface distance of 0.236 mm to the original geometry that is comparable with results obtained in our earlier study for computed tomography (CT) and magnetic resonance imaging (MRI) reconstructions. Steady flow simulations were performed with a commercial CFD program FLUENT. A comparison of the calculated wall shear stress (WSS) shows good correlation for histograms (r=0.97) and good agreement among the four modalities with a mean WSS of 0.65 Pa in the original model, of 0.68 Pa in the CT-based model, of 0.67 Pa in the MRI based model, and of 0.69 Pa in the biplane angiography-based model. We can conclude that the biplane angiography-based reconstructions can be used for the WSS profiling of the coronary arteries.

Computational fluid dynamics (CFD) methods based on in vivo three-dimensional vessel reconstructions have recently been shown to provide prognostically relevant hemodynamic data. However, the geometry reconstruction and the assessment of clinically relevant hemodynamic parameters may depend on the used imaging modality. This study compares geometric reconstruction and calculated wall shear stress (WSS) values based on magnetic resonance imaging (MRI) and computed tomography (CT). Both imaging methods were applied to a same 2.5-fold upscale silicon model of the left coronary artery (LCA) main bifurcation. The original model is an optically digitized post mortem vessel cast. This digitized geometry is considered as a "gold standard" or original geometry for the MRI versus CT comparative study. The use of the upscale model allowed generating a high resolution CT raw data set with voxel size of 0.156 x 0.156 x 0.36 mm(3) and a high resolution MRI data set with an equivalent voxel size of 0.196 x 0.196 x 0.196 mm(3) for corresponding in vivo conditions. MRI based reconstruction achieved a mean Hausdorff surface distance of 0.1 mm to the original geometry. This is 2.5 times better than CT based reconstruction with mean Hausdorff surface distance of 0.252 mm. A comparison of the calculated mean WSS shows good correlation (r = 0.97) and good agreement among the three modalities with a WSS of 0.65 Pa in the original model, of 0.68 Pa in the CT based model and of 0.67 Pa in the MRI based model.

BACKGROUND AND PURPOSE:Coil embolization procedures change the flow conditions in the cerebral aneurysm and, therefore, in the near-wall region. Knowledge of these flow changes may be helpful to optimize therapy. The goal of this study was to investigate the effect of the coil-packing attenuation on the near-wall flow and its variability due to differences in the coil structure.