While real-time applications are nowadays routinely used in visualizing large nu- merical simulations and volumes, handling these large-scale datasets requires high-end graphics clusters or supercomputers to process and visualize them. However, not all users have access to powerful clusters. Therefore, it is challenging to come up with a visualization approach that provides insight to large-scale datasets on a single com- puter. Explorable images (EI) is one of the methods that allows users to handle large data on a single workstation. Although it is a view-dependent method, it combines both exploration and modification of visual aspects without re-accessing the original huge data. In this thesis, we propose a novel image-based method that applies the concept of EI in visualizing large flow-field pathlines data. The goal of our work is to provide an optimized image-based method, which scales well with the dataset size. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathlines segments. With this view-dependent method it is possible to filter, color-code and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.

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Draft tube is very important part of hydraulic turbines. Only optimum work together with turbine can bring highest performance of this machine set. Hence it is necessary to deal with character of flow in the draft tube for different operating conditions. Efficiency of the draft tube depends on many phenomena of flow. Observing these phenomena and finding their relation with energetic transformation in the draft tube is a suitable tool to judge quality of draft tube performance. Incorrect design of the draft tube can sometimes cause lower efficiency of whole machine set. The goal of this thesis is finding the main reasons causing draft tube efficiency drop for given operating conditions.