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Interactive visualisation techniques for large time-dependent data sets

Authors
  • Vrolijk, B. (author)
Publication Date
Jun 12, 2007
Source
TU Delft Repository
Keywords
Language
English
License
Unknown
External links

Abstract

The research described in this thesis was part of a larger research project about multi-phase flows. These flows are characterised by a sharp transition between the fluids, the so-called phase front. One of the goals of the project was to study the evolution of the phase fronts using CFD, i.e. to study the development of the surfaces over time and to understand how they change and interact with each other. In order to study the evolving fronts, methods were needed for detecting and extracting them in the first place, and subsequently for tracking the phase fronts over time, and finding a way to visualise them interactively. The focus of this research was directed towards efficient techniques for interactive isosurfacing from very large time-dependent data sets. Fast-access data structures that were designed to perform one particular visualisation task efficiently were examined first. These data structures make use of the properties of a particular visualisation algorithm and are made to fit the algorithm closely. The advantage of data structures like these is that they are designed to perform a particular visualisation task very fast. However, the drawback is that they are also limited to perform only that visualisation task. The second approach that was explored is the use of multi-resolution data structures. These structures enable the data to be accessed at several levels of resolution. A multi-resolution data structure provides the flexibility to switch between different visualisations and is designed to handle large data sets by trading off data resolution for speed. This multi-resolution approach was extended to time-dependent data sets. Techniques for region-of-interest selection and time-window management were added to provide interactive visualisation and space-time navigation of these large 4D data sets. / Electrical Engineering, Mathematics and Computer Science

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