Interactive texture based flow visualization
Guo‐Shi Li · 2008
Flow fields play an important role in a wide range of scientific, engineering, and medical disciplines. Due to the advancements in computing technologies and computational fluid dynamics (CFD), recently we have seen a large number of flow datasets with ever increasing size and complexity from numerical fluid simulations. Flow visualization methods can be highly useful to comprehend and analyze these data. In the past few years, the texture advection approach has been the de facto solution for flow visualization in the research community. This approach can be used to realize dense texture visualization and dye advection, where the former is designed to depict instantaneous local features in the entire domain, and the later focuses on highlighting the spatial-temporal relationship between the injection site of the dye material and the rest of the domain. Presented as textures, the resulting visualization from these approaches is considered easy to understand at the cost of elevated computation cost. Since both approaches can be realized as a texture generation process, tremendous performance gains can be obtained by utilizing graphics hardware originally designed for rendering purposes. Due to the difference in design paradigm and hardware constraints, however, many methods proposed by previous research have been focused on performance issues while sacrificing the faithfulness of the resulting visualization. To tackle this problem, in this dissertation we present several accuracy-oriented texture-based flow visualization methods for two-dimensional unsteady flows, unsteady flows on surfaces, and dye advection. Issues regarding the accuracy and faithfulness of the visualization are rigorously treated with algorithmically and physically correct solutions. These schemes are also designed to leverage parallelism that can be accelerated by the current generation of graphics hardware to achieve interactive performance. In this dissertation research, a number of flow datasets generated from CFD simulations are used in the experiments to assess the effectiveness and robustness of our schemes.