Fig 13 - uploaded by Ulrich Rist
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Tracking of coherent structures. First, a shear layer is selected by the user (a). The time line gives feedback about the visible time step. The selected structure is drawn as opaque I 2 = 0.0031 isosurface (context), surrounded by a bounding box; the tracked structure is represented as transparent surface (focus). The evolution of the shear layer is clearly visible: first, the layer only moves from left to right (b), then a vortex structure is hit by the shear layer at time step index t = 5 (c), which is tracked in addition to the shear layer (d).
Source publication
In this paper, we present a visualization and tracking system for coherent structures. For this purpose, we propose to consider shear stress—the stretching and shear of particles inside a flow—in vortex dynamics. Based on a discussion and comparison of recent methods for computing shear stress, we introduce visualization techniques in order to prov...
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Citations
... Vortices are one of the most important features of flow fields and play a crucial role in many fluid-mechanical problems, including the understanding of turbulence (Hunt, 1987), the transportation of energy (Stephan et al., 1983), the boundary layer separation control for wings (Koehler et al., 2011;Schafhitzel et al., 2011), the risk evaluation of cerebral aneurysms (Köhler et al., 2013;Meuschke et al., 2016Meuschke et al., , 2019, and the generation and emission of aerodynamic noise (Martin & Bies, 1992). Figure 1 depicts vortices in the wake of an air plane and in the water. ...
Vortex detection plays a fundamental role in turbulence research and engineering problems. However, due to the lack of a mathematically rigorous vortex definition, as well as the absence of any vortex-oriented database, both traditional and machine learning detection methods achieve only limited performance. In this paper, we develop a deep learning model for vortex detection using a weak supervision approach. In order to avoid the need for a vast amount of manual labeling work, we employ an automatic clustering approach to encode vortex-like behavior as the basis for programmatically generating large-scale, highly reliable training labels. Moreover, to speed up the clustering method, a multi-view U-Net (MVU-Net) model is proposed to approximate the clustering results using the knowledge distillation technique. A multi-view learning strategy is further applied to integrate the information across multiple variables. In addition, we propose a physics-informed loss function, which enables our model to explicitly consider the characteristics of flow fields. The results on eight real-world scientific simulation applications show that the proposed MVU-Net model significantly outperforms other state-of-the-art methods on both efficiency and accuracy.
... Schneider et al. [37] used λ 2 -criterion with the largest contours to extract iso-surfaces. Schafhitzel et al. [38] visualized hairpin vortices with iso-surfaces. Kasten et al. [39] proposed to identify two-dimensional time-dependent vortex regions based on an acceleration magnitude, and Weißmann et al. [40] formulated a global method to identify vortex-core lines over a vector field based on the quantum mechanical analogy. ...
Vectorizing vortex-core lines is crucial for high-quality visualization and analysis of turbulence. While several techniques exist in the literature, they can only be applied to classical fluids. As quantum fluids with turbulence are gaining attention in physics, extracting and visualizing vortex-core lines for quantum fluids is increasingly desirable. In this paper, we develop an efficient vortex-core line vectorization method for quantum fluids enabling real-time visualization of high-resolution quantum turbulence structure. From a dataset obtained through simulation, our technique first identifies vortex nodes based on the circulation field. To vectorize the vortex-core lines interpolating these vortex nodes, we propose a novel graph-based data structure, with iterative graph reduction and density-guided local optimization, to locate sub-grid-scale vortex-core line samples more precisely, which are then vectorized by continuous curves. This vortex-core representation naturally captures complex topology, such as branching during reconnection. Our vectorization approach reduces memory consumption by orders of magnitude, enabling real-time visualization performance. Different types of interactive visualizations are demonstrated to show the effectiveness of our technique, which could help further research on quantum turbulence.
... Schneider et al. [38] used λ 2 -criterion with largest contours to extract iso-surfaces. Schafhitzel et al. [39] visualized hairpin vortices with iso-surface. Kasten et al. [40] proposed to identify two-dimensional time-dependent vortex regions based on an acceleration magnitude, and Weißmann et al. [41] formulated a global method to identify vortex-core lines over a vector field based on quantum mechanical analogy. ...
Vectorizing vortex-core lines is crucial for high-quality visualization and analysis of turbulence. While several techniques exist in the literature, they can only be applied to classical fluids. Recently, quantum fluids with turbulence get more and more attention in physics. It is thus desirable that vortex-core lines can also be well extracted and visualized for quantum fluids. In this paper, we aim for this goal and developed an efficient vortex-core line vectorization method for quantum fluids, which enables real-time visualization of high-resolution quantum turbulence structure. Given the datasets by simulation, our technique is developed from the vortices identified by the circulation-based method. To vectorize the vortex-core lines enclosed by those vortices, we propose a novel graph-based data structure, with iterative graph reduction and density-guided local optimization, to locate more precisely sub-grid-scale vortex-core line samples, which are then vectorized by continuous curves. This not only represents vortex-core line structures continuously, but also naturally preserves complex topology, such as branching during reconnection. By vectorization, the memory consumption can be largely reduced by orders of magnitude, enabling real-time rendering performance. Different types of interactive visualizations are demonstrated to show the effectiveness of our technique, which could assist further research on quantum turbulence.
... tices (Schafhitzel et al., 2011;Sadlo et al., 2006). Other approaches employ physical principles for visualizing advection properties, such as schlieren flow visualization (Brownlee et al., 2010) and virtual rheoscopic fluids (Barth and Burns, 2007). ...
... This method is favored by researchers because of its simplicity and practicality. In 2011, Schafhitzel et al. used λ 2 method to study the evolution and interaction of vortices in a three-dimensional time-varying flow field [13]. Koehler et al. studied the visualization of ultra-low Reynolds number vortices around wings of insects during flight [14]. ...
... (a) Evolution of vortices [13] (b) Visualization of the vortices near the wings [14] Fig.3. Applications of λ2 method The Q-Criteria and λ 2 method are effective in some simple flow field data, but they are not suitable for all flow fields, such as turbulent flow fields that contain strong curved vortex [15]. ...
... Vortices are important features in fluids with extensive amount of work devoted to study them [12], [13] and to perform visual analysis [14], [15], [16], [17], [18], [19] on them in classical fluids. Since superfluid vortices differ from their classical counterparts, mainly due to the phase singularity of the model equation, we cannot directly apply existing flow visualization methods to superfluid vortices. ...
... Schafhitzel et al. [14] visualized hairpin vortices with iso-surface rendering. Treib et al. [16] developed an interactive visualization system to show extremely detailed tera-scale turbulence simulations on a desktop PC. ...
Superfluidity is a special state of matter exhibiting macroscopic quantum phenomena and acting like a fluid with zero viscosity. In such a state, superfluid vortices exist as phase singularities of the model equation with unique distributions. This paper presents novel techniques to aid the visual understanding of superfluid vortices based on the state-of-the-art non-linear Klein-Gordon equation, which evolves a complex scalar field, giving rise to special vortex lattice/ring structures with dynamic vortex formation, reconnection, and Kelvin waves, etc. By formulating a numerical model with theoretical physicists in superfluid research, we obtain high-quality superfluid flow data sets without noise-like waves, suitable for vortex visualization. By further exploring superfluid vortex properties, we develop a new vortex identification and visualization method: a novel mechanism with velocity circulation to overcome phase singularity and an orthogonal-plane strategy to avoid ambiguity. Hence, our visualizations can help reveal various superfluid vortex structures and enable domain experts for related visual analysis, such as the steady vortex lattice/ring structures, dynamic vortex string interactions with reconnections and energy radiations, where the famous Kelvin waves and decaying vortex tangle were clearly observed. These visualizations have assisted physicists to verify the superfluid model, and further explore its dynamic behavior more intuitively.
... They used combinations of feature detection techniques, such as vortex core lines detection, with methods acting on the Jacobian of the flow field. Schafhitzel et al. [18] presented an overview on analysis of shear stress layers in flow fields and described a visualization method for simultaneous tracking of vortices and shear layers as well as their interaction. More recent studies focus on vortex core lines and vorticity magnitude detection and their variation in time [19] [20]. ...
... Vortex dominated areas are detected as regions where ‖Ω( , , )‖ > ‖ ( , , )‖, which is known as the Q-criterion [23]. Because the result of the Q-criterion is binary rather than a continuous measure, Schafhitzel et al [18] suggested to use the second invariant of S as a continuous measure for high shear stress region identification: ...
Objective:
Characteristics of vortices within intracranial aneurysmal flow patterns have been associated with increased risk of rupture. The classifications of these vortex characteristics are commonly based upon qualitative scores, and are, therefore, subjective to user interpretation. We present a quantitative method for automatic time-resolved characterization of 3-D flow patterns and vortex detection within aneurysms.
Methods:
Our approach is based upon the combination of kernel deconvolution and Jacobian analysis of the velocity field. The deconvolution approach is accurate in detecting vortex centers but cannot discriminate between vortices and high-shear regions. Therefore, this approach is combined with analysis of the Jacobian of the velocity field. Scale-space theory is used to evaluate aneurysmal flow velocity fields at various scales.
Results:
The proposed algorithm is applied to computational fluid dynamics and time-resolved 3-D phase-contrast magnetic resonance imaging of aneurysmal flow.
Conclusion:
Results show that the proposed algorithm efficiently detects, visualizes, and quantifies vortices in intracranial aneurysmal velocity patterns at multiple scales and follows the temporal evolution of these patterns.
Significance:
Quantitative analysis performed with this method has the potential to reduce interobserver variability in aneurysm classification.
... The extraction of shear layers is a crucial part in the analysis of coherent structures as stated by [7]. Shear layers are formed long before a vortex is born. ...
... The initial guess for the position of the shear layer in region I is X/h = (0,y,1) T . Comparison of Cr-criterion [7] (upper) and du/dz maxcriterion (lower). The plot shows the symmetry plane at y/h=0 and is colour coded by intensity of the single criteria. ...
The paper discusses the dynamical behaviour of the Backward Facing Step Flow (BFSF) in the transitional regime at Re h =4440. The measurements are performed via time resolved 3D Scanning Particle Tracking Velocimetry (3D SPTV) allowing the investigation in a temporal and in a spatial manner. The paper describes main outcomes concerning vortex interactions, i.e. roller and hairpin vortex interaction.
Turbulent flows are multi‐scale with vortices spanning a wide range of scales continuously. Due to such complexities, turbulence scales are particularly difficult to analyse and visualize. In this work, we present a novel and efficient optimization‐based method for continuous‐scale turbulence structure visualization with scale decomposition directly in the Kolmogorov energy spectrum. To achieve this, we first derive a new analytical objective function based on integration approximation. Using this new formulation, we can significantly improve the efficiency of the underlying optimization process and obtain the desired filter in the Kolmogorov energy spectrum for scale decomposition. More importantly, such a decomposition allows a ‘continuous‐scale visualization’ that enables us to efficiently explore the decomposed turbulence scales and further analyse the turbulence structures in a continuous manner. With our approach, we can present scale visualizations of direct numerical simulation data sets continuously over the scale domain for both isotropic and boundary layer turbulent flows. Compared with previous works on multi‐scale turbulence analysis and visualization, our method is highly flexible and efficient in generating scale decomposition and visualization results. The application of the proposed technique to both isotropic and boundary layer turbulence data sets verifies the capability of our technique to produce desirable scale visualization results.
Vortices are commonly understood as rotating motions in fluid flows. The analysis of vortices plays an important role in numerous scientific applications, such as in engineering, meteorology, oceanology, medicine and many more. The successful analysis consists of three steps: vortex definition, extraction and visualization. All three have a long history, and the early themes and topics from the 70s survived to this day, namely the identification of vortex cores, their extent and the choice of suitable reference frames. This paper provides an overview over the advances that have been made in the last forty years. We provide sufficient background on differential vector field calculus, extraction techniques like critical point search and the parallel vectors operator, and we introduce the notion of reference frame invariance. We explain the most important region-based and line-based methods, integration-based and geometry-based approaches, recent objective techniques, the selection of reference frames by means of flow decompositions, as well as a recent local optimization-based technique. We point out relationships between the various approaches, classify the literature and identify open problems and challenges for future work.
