Open Access

Multi-scale phenomena are abundant in many application fields. Representing and numerically simulating such processes is a challenging task since quite different scales have to be resolved, which often requires enormous amounts of storage and computational power. An important strategy in this context is adaptivity, i.e. local adjustment of the spatio-temporal resolution to the details to be resolved. A standard representation therefore are hierarchical, locally refined grids. A specific adaptive approach for solving partial differential equations, usually called AMR (Adaptive Mesh Refinement), was introduced in 1984. The basic idea is to combine the simplicity of structured grids and the advantages of local refinement. In this numerical scheme the computations are started on a set of coarse, potentially overlapping structured grids, that cover the computational domain. Local error criteria are applied to detect regions that require higher resolution. These are covered by subgrids with decreasing mesh spacing, which do not replace, but rather overlap the refined regions of the coarser patches. The equations are advanced on the finer subgrids and the refinement procedure recursively continues until all cells fulfill the considered error criteria, giving rise to a hierarchy of nested levels of refinement. In 1989 a variant of this scheme, called Structured Adaptive Mesh Refinement (SAMR), which reduces some of the complexity of the original approach, was proposed. While the separate subgrids in the AMR scheme could be rotated against each other, in SAMR they are aligned with the major axes of the coordinate system, which for example simplifies the computation of fluxes of (conserved) quantities through the cell faces. SAMR has become more and more popular in the last decade, and nowadays it is applied in many domains like hydrodynamics, meteorology and in particular in cosmology and relativistic astrophysics. Due to this growing popularity, an increasing number of scientists is in need of appropriate interactive visualization techniques to interpret and analyze AMR simulation data. Tools for both, 2D analysis to quantitatively convey the information within single slices and 3D representations to apprehend the overall structure are required. In this thesis we develop direct and indirect volume visualization algorithms for scalar fields that are defined on structured Adaptive Mesh Refinement (SAMR) grids. In particular algorithms for planar slicing and the display of height fields, C0-continuous isosurface extraction, software-, and hardware-based direct volume rendering and temporal interpolation for cell-, and vertex-centered data on unrestricted SAMR grids are proposed. Additionally we investigate the applicability of SAMR data structures for accelerated software-, and hardware-based volume rendering of large 3D scalar data.

In this thesis we develop new methods for visualizing vector fields which specifically address three design goals: accuracy, performance, and cognition. Our methods will be general-purpose and can be applied to arbitrary vector fields in two- and three-dimensional space. The methodology behind our approach is {\em texture-based visualization}. Texture-based visualization methods imitate techniques known from experimental flow visualization, namely, the observation of randomly dispersed particles or dye injection patterns. Instead of depicting individual lines or symbols, a contiguous high-resolution image or texture is generated. This texture clearly reveals the directional structure of the field. In this way intuitive insight can be obtained and even small details of the field become visible. However, it remains unclear what kind of textures are best suited for our purpose. How do we generate them? Can we apply these methods in three-dimensional space? In particular, we focus on a technique known as {\em line integral convolution} or LIC. This method turns out to be quite versatile and well-suited for visualizing many interesting vector fields. LIC images display the integral curves or field lines of a vector field at high spatial resolution. Although conceptually quite simple, line integral convolution implies a number of interesting mathematical and algorithmic questions.