Group dynamics in scientific visualization

The ability to visually extract and track features is appealing to scientists in many simulations including flow fields. However, as the resolution of the simulation becomes higher, the number of features to track increases and so does the cost in large-scale simulations. Since many of these features act in groups, it seems more cost-effective to follow groups of features rather than individual ones. Very little work has been done for tracking groups of features. In this paper, we present the first full group tracking framework in which we track groups (clusters) of features in time-varying 3D fluid flow simulations. Our framework uses a clustering algorithm to group interacting features. We demonstrate the use of our framework on data output from a 3D simulation of wall bounded turbulent flow.

1 Introduction

Following the interactions of physical phenomena over time is an important problem. There have been many papers that deal with time varying phenomena focusing on individual phenomenon or features, examples can be found in the papers [1], [2], [3], [4], [5], [6] and [7]. Today's advanced simulations generate high resolution data at peta-byte or exa-byte scale. As datasets get larger, the number of features in the simulation grows. Similarly, as the resolution becomes finer, some features that appeared large in coarser resolution runs become finer. The change in resolution affects the identification of feature dynamics since some features merge at a lower resolution [9]. This is similar to identifying finer features as a single structure, i.e. group, at a lower resolution. Various interactive navigation and exploration tools proposed to help scientist explore the large scale data at different resolutions as in [9] and [10].