Autonomous Particles for In-Situ-Friendly Flow Map Sampling

Abstract

Computing and storing flow maps is a common approach to processing and analyzing large flow simulations in a Lagrangian way. Accurate Lagrangian-based visualizations require a good sampling of the flow map. We present an In-Situ-friendly flow map sampling strategy for flows using Autonomous Particles that do not need information of neighboring particles: they can be advected individually without knowing about each other. The main idea is to observe a linear neighborhood of a particle during advection. As soon as the neighborhood cannot be considered linear anymore, an adaptive splitting is performed. For observing the linear neighborhood, each particle is equipped with an ellipsoid that also gets advected by the flow. By splitting these ellipsoids into smaller ones in regions of non-linear behavior, critical and more interesting regions of the flow map are more densely sampled. Our sampling approach uses only forward integration and no adaptive integration from the past. This makes it applicable in and well-suited for in In-Situ environments. We compare our approach to existing sampling techniques and apply it to several artificial and real data sets.