Information

Abstract

Scientists and engineers from many domains work with fluid simulation and visualization. These tools help to understand physical phenomena like vortices that occur as weather phenomena, in ocean currents and in aerodynamics. The visualization of flow fields profits from advanced concepts like observer relativity. Existing interactive frameworks for observer-relative visualization need to pre-process the flow data to obtain meaningful reference frames. This pre-processing task is computationally expensive and potentially time consuming for large data sets. In this thesis a method without pre-processing is proposed. Starting at a user specified location we iterate the optimization of the observer field in a small subset of the data, and the integration of the observer worldline to find the next start location. The visualization of the observed pathlines is interleaved with the observer-calculation. The combination of progressive buildup, as well as low-level optimization techniques like caching, prediction, and parallelization, provides an algorithm that visualizes observer-relative flow interactively without the need of pre-processing. The avoidance of pre-processing makes this technique suitable for in-situ scenarios, and for the exploration of the parameter space of optimization techniques that are needed to compute reference-frame candidates.

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BibTeX

@bachelorsthesis{Woschizka_2021,
  title =      "Interactive Reference-Frame Computation for
               Observer-Relative Flow Visualization",
  author =     "Bernhard Woschizka",
  year =       "2021",
  abstract =   "Scientists and engineers from many domains work with fluid
               simulation and visualization. These tools help to understand
               physical phenomena like vortices that occur as weather
               phenomena, in ocean currents and in aerodynamics. The
               visualization of flow fields profits from advanced concepts
               like observer relativity. Existing interactive frameworks
               for observer-relative visualization need to pre-process the
               flow data to obtain meaningful reference frames. This
               pre-processing task is computationally expensive and
               potentially time consuming for large data sets. In this
               thesis a method without pre-processing is proposed. Starting
               at a user specified location we iterate the optimization of
               the observer field in a small subset of the data, and the
               integration of the observer worldline to find the next start
               location. The visualization of the observed pathlines is
               interleaved with the observer-calculation. The combination
               of progressive buildup, as well as low-level optimization
               techniques like caching, prediction, and parallelization,
               provides an algorithm that visualizes observer-relative flow
               interactively without the need of pre-processing. The
               avoidance of pre-processing makes this technique suitable
               for in-situ scenarios, and for the exploration of the
               parameter space of optimization techniques that are needed
               to compute reference-frame candidates. ",
  month =      dec,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Woschizka_2021/",
}