µm300 µm diameter dp y t min max density x y t x y r x y |v| Figure 1: Our coordinated visualizations of inertial particle motion give insights into size-dependent separation, clustering and attraction. The left image shows the individual particle trajectories of a continuous range of differently-sized particles in space-time, which can become cluttered. Thus, the second visualization displays the trajectory density, which reveals clustering regions and easier conveys an impression of the general motion. The trajectories of differently-sized particles are clearly separated in the third view. The motion of inertial particles is governed by a size-dependent attracting manifold. The last view focuses on a selection of trajectories and displays for each the distance to the attracting manifold by connecting the trajectories to the closest curve on the manifold. As shown here, heavy particles generally converge slower due to their momentum and inertia. Here, in the BORROMEAN flow with dp = 100 µm (•), dp = 200 µm (•) and dp = 300 µm (•).
AbstractIn many scientific disciplines, the motion of finite-sized objects in fluid flows plays an important role, such as in brownout engineering, sediment transport, oceanology or meteorology. These finite-sized objects are called inertial particles and, in contrast to traditional tracer particles, their motion depends on their current position, their own particle velocity, the time and their size. Thus, the visualization of their motion becomes a high-dimensional problem that entails computational and perceptual challenges. So far, no visualization explored and visualized the particle trajectories under variation of all seeding parameters. In this paper, we propose three coordinated views that visualize the different aspects of the high-dimensional space in which the particles live. We visualize the evolution of particles over time, showing that particles travel different distances in the same time, depending on their size. The second view provides a clear illustration of the trajectories of different particle sizes and allows the user to easily identify differences due to particle size. Finally, we embed the trajectories in the space-velocity domain and visualize their distance to an attracting manifold using ribbons. In all views, we support interactive linking and brushing, and provide abstraction through density volumes that are shown by direct volume rendering and isosurface slabs. Using our method, users gain deeper insights into the dynamics of inertial particles in 2D fluids, including size-dependent separation, preferential clustering and attraction. We demonstrate the effectiveness of our method in multiple steady and unsteady 2D flows.
