Flow Simulation and Visualization

This report summarizes my research on real-time flow simulation and visualization. In particular, my research is concentrated on efficient GPU algorithms that can exploit the fine-granular thread parallelism of graphics hardware. Several established data structures and numerical solvers perform poorly on the GPU. For this reason, I study and develop new methods that are optimized for the underlying hardware architecture. In Eulerian flow simulations, the grid resolution plays an important role to capture turbulence effects on a small scale. To efficiently exploit the valuable computation and memory resources of a GPU, my work contributes an algorithm for dynamic grid refinement to interactively simulate and render smoke animations on the GPU. One of the most computationally expensive steps of a typical Navier-Stokes simulation is the numerical solution of the pressure Poisson equation. In this field, my work contributes a preconditioner for the conjugate gradient method that is optimized for the Poisson problem and for efficient GPU processing. In subsequent work, I exploited the gained performance benefit to spend more computation time for advanced visualization methods that can help control the flow by interactively manipulating boundary conditions and receive immediate feedback by visualizing Lagrangian coherent structures in real-time. For texture-based flow visualization, Semi-Lagrangian advection is often employed, which is susceptible to numerical diffusion; however, higher-order interpolation methods can be employed to reduce this effect. For this reason, I contributed an evaluation paper that studies the conservation of the frequency sprectrum for different interpolation methods.