Streamline Predicates

Salzbrunn, Tobias; Scheuermann, Gerik

doi:10.1109/tvcg.2006.104

Cited by 67 publications

(27 citation statements)

References 21 publications

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“…Salzbrunn and Scheuermann [26] propose combined Boolean predicates based on predefined scalar quantities, which determine for each streamline whether it has a desired property. Predicates on pathlines are applied to the visual analysis of measured blood flow in aortic aneurysms [27].…”

Section: User-guided Flow Partitioningmentioning

confidence: 99%

Blood Flow Clustering and Applications inVirtual Stenting of Intracranial Aneurysms

Oeltze

Lehmann

Kühn

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Understanding the hemodynamics of blood flow in vascular pathologies such as intracranial aneurysms is essential for both their diagnosis and treatment. Computational fluid dynamics (CFD) simulations of blood flow based on patient-individual data are performed to better understand aneurysm initiation and progression and more recently, for predicting treatment success. In virtual stenting, a flow-diverting mesh tube (stent) is modeled inside the reconstructed vasculature and integrated in the simulation. We focus on steady-state simulation and the resulting complex multiparameter data. The blood flow pattern captured therein is assumed to be related to the success of stenting. It is often visualized by a dense and cluttered set of streamlines.We present a fully automatic approach for reducing visual clutter and exposing characteristic flow structures by clustering streamlines and computing cluster representatives. While individual clustering techniques have been applied before to streamlines in 3D flow fields, we contribute a general quantitative and a domain-specific qualitative evaluation of three state-of-the-art techniques. We show that clustering based on streamline geometry as well as on domain-specific streamline attributes contributes to comparing and evaluating different virtual stenting strategies. With our work, we aim at supporting CFD engineers and interventional neuroradiologists.

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Section: User-guided Flow Partitioningmentioning

confidence: 99%

Blood Flow Clustering and Applications inVirtual Stenting of Intracranial Aneurysms

Oeltze

Lehmann

Kühn

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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“…These time-consuming methods, largely-limited by the number of advection steps required, have been improved by the visualization community through GPU acceleration [11,25], adaptive mesh refinement techniques [10], and interpolation over sparse particles [2]. Others have used the Lagrangian methods by incorporating flow maps into the Eulerian representation of a flow field [28,26,17].…”

Section: Related Workmentioning

confidence: 99%

Lagrangian representations of flow fields with parameter curves

Bujack

Joy

2015

2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV)

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There are two main principles in which a flow field can be described. In its Eulerian representation, the immediate direction of the flow is used. In its Lagrangian representation, the paths of fluid parcels are stored. The latter way has proven less prone to error in cases when only a sparse subset of the original flow field can be used due to restrictions in computational capacity, which are inevitable in practice. But so far, a main drawback of the Lagrangian representation is that it leads to unsightly lines with sharp edges, because the spanning trajectories are stored as polygonal chains to minimize storage space.In this paper, we tackle this issue by proposing to represent the trajectories by means of parameter curves, like composite Bézier curves and cubic Hermite splines, instead of polygonal chains. We demonstrate that a parameter representation results in smoother lines with comparable approximation error under equal conditions of limited storage capacity.

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“…In addition to visualization, particle traces can be further analyzed to understand flow fields. The user can query for particle traces of interest by sketching [30] or explicit mathematical expressions [27] [28], or statistics from pathlines can be used to detect flow features [18][24] [29]. For these applications, it is desirable to densely place seeds in the domain in order to capture salient flow structures, thus requiring an efficient algorithm to advect a large number of particles.…”

Section: A Particle Advectionmentioning

confidence: 99%

Parallel particle advection and FTLE computation for time-varying flow fields

Nouanesengsy

Lee

et al. 2012

2012 International Conference for High Performance Computing, Networking, Storage and Analysis

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Flow fields are an important product of scientific simulations. One popular flow visualization technique is particle advection, in which seeds are traced through the flow field. One use of these traces is to compute a powerful analysis tool called the Finite-Time Lyapunov Exponent (FTLE) field, but no existing particle tracing algorithms scale to the particle injection frequency required for high-resolution FTLE analysis. In this paper, a framework to trace the massive number of particles necessary for FTLE computation is presented. A new approach is explored, in which processes are divided into groups, and are responsible for mutually exclusive spans of time. This pipelining over time intervals reduces overall idle time of processes and decreases I/O overhead. Our parallel FTLE framework is capable of advecting hundreds of millions of particles at once, with performance scaling up to tens of thousands of processes.

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Streamline Predicates

Cited by 67 publications

References 21 publications

Blood Flow Clustering and Applications inVirtual Stenting of Intracranial Aneurysms

Blood Flow Clustering and Applications inVirtual Stenting of Intracranial Aneurysms

Lagrangian representations of flow fields with parameter curves

Parallel particle advection and FTLE computation for time-varying flow fields

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