Real-Time Particle Systems

Both visualization and simulation tasks make high demands on accuracy and interactivity. The former is an evident requisite of any tool focusing on quality, the latter stands for a responsive system in which the user has real-time control. A permanent trade-off between both demands can be observed, since high accuracy is usually time-consuming. The graphics processing unit (GPU), which evolved to a powerful general purpose coprocessor during the last decade, is undoubtedly becoming an ideal instrument for performing visualization and simulation tasks accurately and in real-time. One of the main challenges in the context of geometric flow visualization and fluid dynamics is the representation of motion. In both fields, particle systems and grid-based structures constitute basic models describing the objects to which the motion is applied. This work is dedicated to the development of GPU-based particle techniques and their combination with grids in order to improve the efficiency of fluid simulation and geometric flow visualization techniques. All resulting algorithms are characterized by their parallel nature and by being entirely executable on graphics hardware. The first aim is to provide an efficient solution to particle coupling in the context of fluids based on smoothed particle hydrodynamics. A parallel processing using graphics hardware is achieved by providing a grid-based mechanism for the efficient processing of particle neighborhoods. The second aim is to provide a set of algorithms for various geometric flow visualization techniques including flow particles, flow lines and flow surfaces/volumes. The accuracy is explicitly addressed in all cases. Accordingly, a method for generating time-adaptive stream and path lines and a special refinement scheme for streak lines is presented. The flow volumes, including time surfaces, path and streak volumes, are based on a parallel reinterpretation of the particle level set (PLS) method. This reinterpretation is based on a method for grid-particle interchange which is similar to the one proposed for particle coupling. Combining a grid and a particle set takes the advantages from both models: the grid representation is robust w.r.t. deformations and topological changes, and the particles are used to reduce numerical diffusion. For the reinitialization of the level set function, which is the most time-consuming step of the PLS algorithm, a hierarchical method for computing distance transforms is proposed. It turns out that the use of a distance transform is advantageous for realizing a GPU-based PLS framework. All presented flow visualization techniques are applied to a real-world example in the context of climate research. The evaluation shows that the interactive system facilitates the efficient exploration of complex flow data sets.