Flow visualization for turbomachinery design

Röth,; Peikert,

doi:10.1109/visual.1996.568137

Cited by 34 publications

(28 citation statements)

References 3 publications

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“…These methods are commonly used to visualize spatially complex fluid flows 9,10 and create low-dimensional models of turbulence. 11,12 However, their use in analyzing biofluids is novel.…”

mentioning

confidence: 99%

Quantifying the Large-Scale Hemodynamics of Intracranial Aneurysms

Byrne

Mut

Cebral

2013

American Journal of Neuroradiology

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BACKGROUND AND PURPOSE Hemodynamics play an important role in the mechanisms that govern the initiation, growth, and possible rupture of intracranial aneurysms. The purpose of this study was to objectively characterize these dynamics, classify them, and connect them to aneurysm rupture. MATERIALS AND METHODS Image-based computational fluid dynamic simulations were used to re-create the hemodynamics of 210 patient-specific intracranial aneurysm geometries. The hemodynamics were then classified according to their spatial complexity and temporal stability by using quantities derived from vortex core lines and proper orthogonal decomposition. RESULTS The quantitative classification was compared with a previous qualitative classification performed by visual inspection. Receiver operating characteristic curves provided area-under-the-curve estimates for spatial complexity (0.905) and temporal stability (0.85) to show that the 2 classifications were in agreement. Statistically significant differences were observed in the quantities describing the hemodynamics of ruptured and unruptured intracranial aneurysms. Specifically, ruptured aneurysms had more complex and more unstable flow patterns than unruptured aneurysms. Spatial complexity was more strongly associated with rupture than temporal stability. CONCLUSIONS Complex-unstable blood flow dynamics characterized by longer core line length and higher entropy could induce biologic processes that predispose an aneurysm for rupture.

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“…These methods are commonly used to visualize spatially complex fluid flows 9,10 and create low-dimensional models of turbulence. 11,12 However, their use in analyzing biofluids is novel.…”

mentioning

confidence: 99%

Quantifying the Large-Scale Hemodynamics of Intracranial Aneurysms

Byrne

Mut

Cebral

2013

American Journal of Neuroradiology

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“…Using the curl instead of the Jacobian yielded a circular vortex with a larger radius, which is expected due to the results of Roth and Peikert [12]. The λ 2 algorithm yields what seems to be the same curve generated by the curlbased algorithm, yet has fewer gaps with any minimum level of recursion than the curl-based algorithm with the same minimum level of recursion.…”

Section: Resultsmentioning

confidence: 52%

“…λ λ λ 2 : In 1995, Jeong and Hussain [7] claim that the swirling plane for a vortex corresponds to the plane defined by the eigenvectors corresponding to a pair of negative eigenvalues of S 2 + Ω 2 , where S and Ω are the symmetric and anti-symmetric parts of the Jacobian, respectively. Our implementation of the λ 2 algorithm is based on the contents of the literature review in Roth's and Peikert's work [12].…”

Section: Calculating the 'Swirl' Planementioning

confidence: 99%

Vortex Detection in Vector Fields Using Geometric Algebra

Pollock¹,

Mann

2013

Adv. Appl. Clifford Algebras

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“…Most line-based approaches can be generalized as extracting parallel vector descriptors from a dataset [24]. For example, a vortex core line can be defined by a locus of points, where velocity is parallel to vorticity [27,31], or as the extrema lines of pressures, where pressure gradient is parallel to vorticity [2]. Alternatively, predictor-corrector methods [3] can be used to locate vortex lines in an iterative manner.…”

Section: Vortex Visualization In Fluid Flowmentioning

confidence: 99%

Extracting, Tracking, and Visualizing Magnetic Flux Vortices in 3D Complex-Valued Superconductor Simulation Data

Guo

Phillips

Peterka

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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We propose a method for the vortex extraction and tracking of superconducting magnetic flux vortices for both structured and unstructured mesh data. In the Ginzburg-Landau theory, magnetic flux vortices are well-defined features in a complex-valued order parameter field, and their dynamics determine electromagnetic properties in type-II superconductors. Our method represents each vortex line (a 1D curve embedded in 3D space) as a connected graph extracted from the discretized field in both space and time. For a time-varying discrete dataset, our vortex extraction and tracking method is as accurate as the data discretization. We then apply 3D visualization and 2D event diagrams to the extraction and tracking results to help scientists understand vortex dynamics and macroscale superconductor behavior in greater detail than previously possible.

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Flow visualization for turbomachinery design

Cited by 34 publications

References 3 publications

Quantifying the Large-Scale Hemodynamics of Intracranial Aneurysms

Quantifying the Large-Scale Hemodynamics of Intracranial Aneurysms

Vortex Detection in Vector Fields Using Geometric Algebra

Extracting, Tracking, and Visualizing Magnetic Flux Vortices in 3D Complex-Valued Superconductor Simulation Data

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