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Author

  • Kuhn, Alexander (19)
  • Hege, Hans-Christian (13)
  • Theisel, Holger (9)
  • Günther, Tobias (6)
  • Engelke, Wito (5)
  • Flatken, Markus (3)
  • Hotz, Ingrid (3)
  • Breitkopf, Tom-Lukas (2)
  • Gross, Markus (2)
  • Hadwiger, Markus (2)
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Year of publication

  • 2017 (6)
  • 2016 (4)
  • 2015 (2)
  • 2014 (3)
  • 2013 (4)

Document Type

  • Article (7)
  • In Proceedings (4)
  • ZIB-Report (4)
  • Poster (2)
  • Doctoral Thesis (1)
  • Other (1)

Has Fulltext

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  • yes (4)

Keywords

  • Flow Visualization, Time-dependent Vector Fields (1)
  • Lagrangian Coherent Structures (LCS) (1)
  • Picture/Image Generation, Display algorithms, Three-Dimensional Graphics and Realism, Raytracing (1)
  • finite-time Lyapunov exponents (FTLE) (1)
  • flow field visualization (1)
  • multi-modal, intergrated data analysis, topology (1)
  • time lines (1)
  • time-dependent vector fields (1)

Institute

  • Visual Data Analysis (19)
  • Visual Data Analysis in Science and Engineering (9)

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Trajectory Density Projection for Vector Field Visualization (2013)
Kuhn, Alexander ; Lindow, Norbert ; Günther, Tobias ; Wiebel, Alexander ; Theisel, Holger ; Hege, Hans-Christian
Mass-Dependent Integral Curves in Unsteady Vector Fields (2013)
Günther, Tobias ; Kuhn, Alexander ; Kutz, Benjamin ; Theisel, Holger
Blood Flow Clustering and Applications in Virtual Stenting of Intracranial Aneurysms (2014)
Oeltze, Steffen ; Lehmann, Dirk J. ; Kuhn, Alexander ; Janiga, Gábor ; Theisel, Holger ; Preim, Bernhard
Atmospheric Impact of Volcano Eruptions (2014)
Engelke, Wito ; Kuhn, Alexander ; Flatken, Markus ; Chen, Fang ; Hege, Hans-Christian ; Gerndt, Andreas ; Hotz, Ingrid
The analysis of data that captures volcanic eruptions and their atmospheric aftermath plays an important role for domain experts to gain a deeper understanding of the volcanic eruption and their consequences for atmosphere, climate and air traffic. Thereby, one major challenge is to extract and combine the essential information, which is spread over various, mostly sparse data sources. This requires a careful integration of each data set with its strength and limitations. The sparse, but more reliable measurement data is mainly used to calibrate the more dense simulation data. This work combines a collection of visualization approaches into an exploitative framework. The goal is to support the domain experts to build a complete picture of the situation. But it is also important to understand the individual data sources, the wealth of their information and the quality of the simulation results. All presented methods are designed for direct interaction with the data from different perspectives rather than the sole generation of some final images.
Topology-based Analysis for Multimodal Atmospheric Data of Volcano Eruptions (2016)
Kuhn, Alexander ; Engelke, Wito ; Flatken, Markus ; Hege, Hans-Christian ; Hotz, Ingrid
Many scientific applications deal with data from a multitude of different sources, e.g., measurements, imaging and simulations. Each source provides an additional perspective on the phenomenon of interest, but also comes with specific limitations, e.g. regarding accuracy, spatial and temporal availability. Effectively combining and analyzing such multimodal and partially incomplete data of limited accuracy in an integrated way is challenging. In this work, we outline an approach for an integrated analysis and visualization of the atmospheric impact of volcano eruptions. The data sets comprise observation and imaging data from satellites as well as results from numerical particle simulations. To analyze the clouds from the volcano eruption in the spatiotemporal domain we apply topological methods. Extremal structures reveal structures in the data that support clustering and comparison. We further discuss the robustness of those methods with respect to different properties of the data and different parameter setups. Finally we outline open challenges for the effective integrated visualization using topological methods.
Intercomparison Study of Cloud Feature Extraction and Tracking Algorithms (2015)
Kuhn, Alexander ; Trömel, Silke
Clouds and precipitation systems are fundamental features in the global climate cycle and are one focus aspect of recent high resolution, cloud resolving simulations and measurement modalities. Highly resolved data sources allow for more precise methodologies to extract and track cloud features on different scales and enable novel evaluation tasks such as life-cycle tracking, feature-based statistics, and feature-based comparison of simulation and measurements. However, their complex dynamics and highly variable shape morphology makes extraction and tracking of clouds a challenging task with respect to stable and reliable algorithms. In this work we will present our efforts on establishing an community-wide inter-comparison study to provide an overview of state-of-the-art algorithms for cloud extraction and tracking. We propose a set of 2D and 3D benchmark data sets (from simulations and measurements) that are used as a common basis for comparison. In addition we describe a joint feature-based evaluation framework and provide an in depth analysis and comparison of those algorithms. The goal is to systematically compare and assess numerical extraction and tracking techniques for cloud features in meteorological data and provide a comprehensive overview of suitable application scenarios, describe current strengths and limitations, and derive statements about their variability for feature-based analysis tasks.
MCFTLE: Monte Carlo Rendering of Finite-Time Lyapunov Exponent Fields (2016)
Günther, Tobias ; Kuhn, Alexander ; Hege, Hans-Christian ; Theisel, Holger
Lagrangian Methods for Visualization and Analysis of Time-dependent Vector Fields (2013)
Kuhn, Alexander
Time-dependent vector fields are of high relevance to describe a wide range of physical phenomena based on particle motion, including stall effects in technical engineering, blood flow anomalies, and atmospheric mass trans- port. The efficient analysis and fundamental understanding of intrinsic field properties can lead to significant improvements when interacting with such phenomena based on the available field data. So called Lagrangian methods are a particularly established technique for this purpose. They are based on the evaluation of time-dependent particle trajectories. This work will present an overview of the state of the art in time-dependent vector field analysis and visualization, with special focus on topology-oriented and Lagrangian methods. The first core aspect of this work is the introduction of a novel concept for a more objective and qualitative benchmark of existing Lagrangian approaches. Based on this benchmark, this work contributes and evaluates a set of novel concepts, that offer new perspectives towards established approaches with respect to computational handling, quality and efficiency. The second core aspect is the empirical application and validation of Lagrangian methods with respect to practically relevant analysis scenarios. Each application case includes an introduction into the underlying problem statement and an efficient solution using Lagrangian methods. The validation focuses on the comparison with existing flow analysis concepts and aspects of the quantitative evaluation in each case. Together, both topics of this work contribute towards a more consistent formalization, but also to the improved applicability of Lagrangian methods.
MCFTLE: Monte Carlo Rendering of Finite-Time Lyapunov Exponent Fields (2016)
Günther, Tobias ; Kuhn, Alexander ; Hege, Hans-Christian ; Theisel, Holger
Traditionally, Lagrangian fields such as finite-time Lyapunov exponents (FTLE) are precomputed on a discrete grid and are ray casted afterwards. This, however, introduces both grid discretization errors and sampling errors during ray marching. In this work, we apply a progressive, view-dependent Monte Carlo-based approach for the visualization of such Lagrangian fields in time-dependent flows. Our ap- proach avoids grid discretization and ray marching errors completely, is consistent, and has a low memory consumption. The system provides noisy previews that con- verge over time to an accurate high-quality visualization. Compared to traditional approaches, the proposed system avoids explicitly predefined fieldline seeding structures, and uses a Monte Carlo sampling strategy named Woodcock tracking to distribute samples along the view ray. An acceleration of this sampling strategy requires local upper bounds for the FTLE values, which we progressively acquire during the rendering. Our approach is tailored for high-quality visualizations of complex FTLE fields and is guaranteed to faithfully represent detailed ridge surface structures as indicators for Lagrangian coherent structures (LCS). We demonstrate the effectiveness of our approach by using a set of analytic test cases and real-world numerical simulations.
A Graphical Representation of the Spectral Proper Orthogonal Decomposition (2015)
Sieber, Moritz ; Kuhn, Alexander ; Hege, Hans-Christian ; Paschereit, C. Oliver ; Oberleithner, Kilian
We consider the spectral proper orthogonal decomposition (SPOD) for experimental data of a turbulent swirling jet. This newly introduced method combines the advantages of spectral methods, such as Fourier decomposition or dynamic mode decomposition, with the energy-ranked proper orthogonal decomposition (POD). This poster visualizes how the modal energy spectrum transitions from the spectral purity of Fourier space to the sparsity of POD space. The transition is achieved by changing a single parameter – the width of the SPOD filter. Each dot in the 3D space corresponds to an SPOD mode pair, where the size and color indicates its spectral coherence. What we notice is that neither the Fourier nor the POD spectrum achieves a clear separation of the dynamic phenomena. Scanning through the graph from the front plane (Fourier) to the back plane (POD), we observe how three highly coherent SPOD modes emerge from the dispersed Fourier spectrum and later branch out into numerous POD modes. The spatial properties of these three individual SPOD modes are displayed in the back of the graph using line integral convolution colored by vorticity. The first two modes correspond to single-helical global instabilities that are well known for these flows. Their coexistence, however, has not been observed until now. The third mode is of double- helical shape and has not been observed so far. For this considered data set and many others, the SPOD is superior in identification of coherent structures in turbulent flows. Hopefully, it gives access to new fluid dynamic phenomena and enriches the available methods.
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