Autonomous Particles for Interactive Flow Visualization

Simple item page
dc.contributor.authorEngelke, Witoen_US
dc.contributor.authorLawonn, Kaien_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.authorHotz, Ingriden_US
dc.contributor.editorChen, Min and Benes, Bedrichen_US
dc.date.accessioned2019-03-17T09:56:54Z
dc.date.available2019-03-17T09:56:54Z
dc.date.issued2019
dc.description.abstractWe present an interactive approach to analyse flow fields using a new type of particle system, which is composed of autonomous particles exploring the flow. While particles provide a very intuitive way to visualize flows, it is a challenge to capture the important features with such systems. Particles tend to cluster in regions of low velocity and regions of interest are often sparsely populated. To overcome these disadvantages, we propose an automatic adaption of the particle density with respect to local importance measures. These measures are user defined and the systems sensitivity to them can be adjusted interactively. Together with the particle history, these measures define a probability for particles to multiply or die, respectively. There is no communication between the particles and no neighbourhood information has to be maintained. Thus, the particles can be handled in parallel and support a real‐time investigation of flow fields. To enhance the visualization, the particles' properties and selected field measures are also used to specify the systems rendering parameters, such as colour and size. We demonstrate the effectiveness of our approach on different simulated vector fields from technical and medical applications.We present an interactive approach to analyse flow fields using a new type of particle system, which is composed of autonomous particles exploring the flow. While particles provide a very intuitive way to visualize flows, it is a challenge to capture the important features with such systems. Particles tend to cluster in regions of low velocity and regions of interest are often sparsely populated. To overcome these disadvantages, we propose an automatic adaption of the particle density with respect to local importance measures. These measures are user defined and the systems sensitivity to them can be adjusted interactively. Together with the particle history, these measures define a probability for particles to multiply or die, respectively. There is no communication between the particles and no neighbourhood information has to be maintained. Thus, the particles can be handled in parallel and support a real‐time investigation of flow fields. To enhance the visualization, the particles' properties and selected field measures are also used to specify the systems rendering parameters, such as colour and size. We demonstrate the effectiveness of our approach on different simulated vector fields from technical and medical applications.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume38
dc.identifier.doi10.1111/cgf.13528
dc.identifier.issn1467-8659
dc.identifier.pages248-259
dc.identifier.urihttps://doi.org/10.1111/cgf.13528
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13528
dc.publisher© 2019 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectflow visualization
dc.subjectscientific visualization
dc.subjectvisualization
dc.subjectreal‐time rendering
dc.subjectVisualization [Human‐centered computing]: Visualization application domains–Scientific Visualization
dc.titleAutonomous Particles for Interactive Flow Visualizationen_US
Files
Collections
38-Issue 1