The analysis of time-dependent simulation data is a demanding task, both in terms of computing power and time. Interactive analysis using multiple linked views has been shown to be one possible solution to this problem. However, there are two significant short-comings when limited to a standard desktop-based setup: first, complex spatial relationships are hard to understand using only 2D projections of the data. Second, the size of today's simulation runs is too large to be handled even by powerful workstations. We describe a system for the interactive analysis of large, time-dependent data in virtual environments. Based on the techniques of multiple linked views and brushing, our approach allows the user to quickly formulate, visualize and assess hypotheses about the data. To enable an interactive exploration even in the face of multi-gigabyte data sets, we distribute the workload to a multi-processor parallel machine and a rendering client.

The application of virtual environments to scientific visualization provides the user with an intuitive interface to interact with the data to be analyzed. We present an approach that leverages the utilization of 3D interaction techniques to directly interact with scientific data. First, we propose an abstraction of VR-based interaction by decoupling the low-level interaction groundwork from high-level declarations of interaction behaviors. Second, we describe how recurring interaction patterns in scientific visualizations can be distilled into 3D widgets, exhibiting reusability for diverse participants in the development of interactive visualization. The benefits which follow from this approach are exchangeability of VR-toolkits and devices, faster development cycles and better code maintainability.

Abstract The analysis of unsteady phenomena is an important topic for scientific visualization. Several time‐dependent visualization techniques exist, as well as solutions for dealing with the enormous size of time‐varying data in interactive visualization. Many current visualization toolkits support displaying time‐varying data sets. However, for the interactive exploration of time‐varying data in scientific visualization, no common time model that describes the temporal properties which occur in the visualization process has been established. In this work, we propose a general time model which classifies the time frames of simulation phenomena and the connections between different time scales in the analysis process. This model is designed for intuitive interaction with time in visualization applications for the domain expert as well as for the developer of visualization tools. We demonstrate the benefits of our model by applying it to two use cases with different temporal properties.

Ventricular Assist Devices (VADs) support the heart in its vital task of maintaining circulation in the human body when the heart alone is not able to maintain a sufficient flow rate due to illness or degenerative diseases. However, the engineering of these devices is a highly demanding task. Advanced modeling methods and computer simulations allow the investigation of the fluid flow inside such a device and in particular of potential blood damage. In this paper we present a set of visualization methods which have been designed to specifically support the analysis of a tensor-based blood damage prediction model. This model is based on the tracing of particles through the VAD, for each of which the cumulative blood damage can be computed. The model's tensor output approximates a single blood cell's deformation in the flow field. The tensor and derived scalar data are subsequently visualized using techniques based on icons, particle visualization, and function plotting. All these techniques are accessible through a Virtual Reality-based user interface, which features not only stereoscopic rendering but also natural interaction with the complex three-dimensional data. To illustrate the effectiveness of these visualization methods, we present the results of an analysis session that was performed by domain experts for a specific data set for the MicroMed DeBakey VAD.

Extraction of complex data structures like vector field topologies in large-scale, unsteady flow field datasets for the interactive exploration in virtual environments cannot be carried out without parallelization strategies. We present an approach based on Nested OpenMP to find critical points, which are the essential parts of velocity field topologies. We evaluate our parallelization scheme on several multi-block datasets, and present the results for various thread counts and loop schedules on all parallelization levels. Our experience suggests that upcoming massively multi-threaded processor architectures can be very advantageously for large-scale feature extractions.

One recommended strategy for the analysis of CFD-data is the interactive exploration within virtual environments. Common visualization systems are unable to process large data sets while carrying out real-time interaction and visualization at the same time. The obvious idea is to decouple flow feature extraction from visualization. This paper covers the functionality of the parallel CFD post-processing toolkit Viracocha. Two aspects are discussed in more detail. The first approach covers strategies to reduce the loading time. Data caching and prefetching are employed to reduce access time. The second aspect concerns an approach called streaming that minimizes the time a user has to wait for first results. Viracocha already sends coarse intermediate data back to the virtual environment before the final result is available. Different streaming and data handling strategies are described. In order to emphasize the benefit of our implementation efforts, some strategies are applied to multi-block CFD data sets.