filename : ibr18a.pdf entry : article conference : Eurographics Conference on Visualization 2018 pages : year : 2018 month : title : Visualizing the Phase Space of Heterogeneous Inertial Particles in 2D Flows subtitle : author : Irene Baeza Rojo and Markus Gross and Tobias Günther booktitle : Computer Graphics Forum (Proceedings of EuroVis 2018) ISSN/ISBN : editor : publisher : The Eurographics Association and John Wiley & Sons Ltd. publ.place : volume : 37 issue : 3 language : English keywords : scientific visualization abstract : In 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.