Detection and Visualization of Vortices

In general, a feature can be defined as a pattern occurring in a dataset that is the manifestation of correlations among various components of the data. For many features that occur in scientific data, these correlations can be defined precisely. For other features, they are not well understood or do not lend themselves to precise definitions. Surprisingly, the swirling feature in flow fields, commonly refered to as a vortex, is an example of a feature for which a precise definition does not exist. By most accounts [1–3], a vortex is characterized by the swirling motion of fluid around a central region. This characterization stems from our visual perception of swirling phenomena that are pervasive throughout the natural world. However, translating this intuitive description of a vortex into a formal definition has been quite a challenge. Lugt [1] proposed the following definition for a vortex: A vortex is the rotating motion of a multitude of material particles around a common center. The problem with this definition is that it is too vague. Although it is consistent with visual observations, it does not lend itself readily to implementation in a detection algorithm. In light of this, Robinson [3] attempted to provide a more concrete definition of a vortex by specifying the conditions for detecting swirling flows in three dimensions: