In this thesis, I present contributions towards the aim of understanding flow-related scientific data sets by communicating relation, properties, and structure. The individual papers are contributions to three different areas. First, real-world visualization challenges with domain specific tasks. The individual applications are ranging from analyzing transport behavior in a centrifugal pump, to visualization of the impact of volcano eruptions and their atmospheric aftermath, and studying circulation dynamics and eddy movements in the ocean currents of the Red Sea. Although the three individual publications target different domains, they share common demands. Furthermore, the experience shows that combining and adapting different visualization techniques to support experts is essential for these scenarios. Second, technical visualization research with a strong focus on geometry-based, interactive, and explorative techniques. In this area a new type of particle system and a novel geometry-based flow visualization technique based on evolutionary algorithms are presented. With both approaches, areas of interest can be highlighted in a semi-automatic fashion by facilitating user-defined importance measures. Lastly, a method for decoupling definition and tracking of features. Here, the development of a fast but flexible method for defining and tracking cyclonic features in pressure fields using a solid and robust mathematical basis is presented. The initial theoretical work is discussed in context of its practical applications by pointing to relevant follow-up publications.

The experience from real-world visualization tasks shows that understanding and gaining insight of scientific data with the help of visualization is an interactive, explorative, and non-linear process. Here, different methods must be combined and adapted such that they complement each other. Through this practice, relation, properties, and structure can be revealed, and a mental model can be created.

From the real-world visualization challenges and the contributions in research, demands on techniques and their embedding in a visualization toolkit can be derived. Here, the ideal software is flexible, adaptable, and allows for interactive exploration. Furthermore, the process is benefiting from a semi-automatic approach guiding the domain expert during analysis. These aspects are used as guidelines for the implementation and development work associated with the contributions of this thesis and are presented in a dedicated Chapter.

Med denna avhandling presenterar jag bidrag som syftar till att förstå flödesrelaterade vetenskapliga datamängder genom att kommunicera relationer, egenskaper och struktur. De enskilda artiklarna i avhandlingen bidrar med kunskap till tre olika områden. Det första bidraget berör verkliga visualiseringsutmaningar med domänspecifika uppgifter. De individuella tillämpningarna sträcker sig från att analysera transportbeteende i en centrifugalpump till visualisering av effekterna av vulkanutbrott och deras atmosfäriska efterverkningar, samt att studera cirkulationsdynamik och virvelrörelser i Röda havets havsströmmar. Även om de tre enskilda publikationerna riktar sig till olika domäner delar de gemensamma krav, och erfarenheten visar att kombination och anpassning av olika visualiseringstekniker för att stödja experter i deras arbete är avgörande för dessa scenarier. Det andra bidraget är undersökning av teknisk visualiseringsforskning med starkt fokus på geometribaserade, interaktiva och utforskande tekniker. Detta bidrag presenterar en ny typ av partikelsystem samt en grupp metoder baserade på evolutionära algoritmer. Med båda tillvägagångssätten kan områden av intresse lyftas fram på ett halvautomatiskt sätt genom att underlätta användardefinierade betydelseåtgärder. Det tredje bidraget är en metod för att frikoppla definition och spårning av funktioner. Detta bidrag presenterar utvecklingen av en snabb men flexibel metod för att definiera och spåra cyklonegenskaper i tryckfält med hjälp av en solid och robust matematisk grund. Det första teoretiska arbetet sätts också i praktisk tillämpning med en uppföljande publikation.

Erfarenheterna från en verklig visualiseringsuppgift visar att förståelse och insikt i vetenskapliga data med hjälp av visualisering är en interaktiv, utforskande och icke-linjär process. Här måste olika metoder kombineras och anpassas så att de kompletterar varandra. Genom att göra så kan relationer, egenskaper och struktur i data avslöjas och en mental modell kan skapas.

Utifrån detta kan sedan krav skapas, både krav på visualiseringsteknikerna och krav på hur visualiseringsverktyg ska användas. Programvaran som används bör då vara flexibel, anpassningsbar och möjliggöra interaktiv utforskning. Dessutom drar data-analysprocessen nytta av ett halvautomatiskt tillvägagångssätt som styr domänexporten under analysen. Dessa aspekter har använts som riktlinjer för genomförandet och utvecklingsarbetet i samband med bidragen i denna avhandling.

This paper documents the organization, the execution, and the results of the Topology ToolKit (TTK) hackathon that took place at the TopoInVis 2019 conference. The primary goal of the hackathon was to promote TTK in our research community as a unified software development platform for topology-based data analysis algorithms. To this end, participants were first introduced to the structure and capabilities of TTK, and then worked on their own TTK-related projects while being mentored by senior TTK developers. Notable outcomes of the hackathon were first steps towards Python and Docker packages, further integration of TTK in Inviwo, the extension of TTK with new algorithms, and the discovery of current limitations of TTK as well as future development directions.

In this paper, we propose a concept to design, track, and compare application-specific feature definitions expressed as sets of critical points. Our work has been inspired by the observation that in many applications a large variety of different feature definitions for the same concept are used. Often, these definitions compete with each other and it is unclear which definition should be used in which context. A prominent example is the definition of cyclones in climate research. Despite the differences, frequently these feature definitions can be related to topological concepts.

We present an interactive approach to analyse flow fields using a new type of particle system, which is composed of autonomous particles exploring the flow. While particles provide a very intuitive way to visualize flows, it is a challenge to capture the important features with such systems. Particles tend to cluster in regions of low velocity and regions of interest are often sparsely populated. To overcome these disadvantages, we propose an automatic adaption of the particle density with respect to local importance measures. These measures are user defined and the systems sensitivity to them can be adjusted interactively. Together with the particle history, these measures define a probability for particles to multiply or die, respectively. There is no communication between the particles and no neighbourhood information has to be maintained. Thus, the particles can be handled in parallel and support a real‐time investigation of flow fields. To enhance the visualization, the particles' properties and selected field measures are also used to specify the systems rendering parameters, such as colour and size. We demonstrate the effectiveness of our approach on different simulated vector fields from technical and medical applications.

Blood flow simulations play an important role for the understanding of vascular diseases, such as aneurysms. However, analysis of the resulting flow patterns, especially comparisons across patient groups, are challenging. Typically, the hemodynamic analysis relies on trial and error inspection of the flow data based on pathline visualizations and surface renderings. Visualizing too many pathlines at once may obstruct interesting features, e.g., embedded vortices, whereas with too little pathlines, particularities such as flow characteristics in aneurysm blebs might be missed. While filtering and clustering techniques support this task, they require the pre-computation of pathlines densely sampled in the space-time domain. Not only does this become prohibitively expensive for large patient groups, but the results often suffer from undersampling artifacts. In this work, we propose the usage of evolutionary algorithms to reduce the overhead of computing pathlines that do not contribute to the analysis, while simultaneously reducing the undersampling artifacts. Integrated in an interactive framework, it efficiently supports the evaluation of hemodynamics for clinical research and treatment planning in case of cerebral aneurysms. The specification of general optimization criteria for entire patient groups allows the blood flow data to be batch-processed. We present clinical cases to demonstrate the benefits of our approach especially in presence of aneurysm blebs. Furthermore, we conducted an evaluation with four expert neuroradiologists. As a result, we report advantages of our method for treatment planning to underpin its clinical potential.  

In this work we explore evolutionary algorithms for selected a visualization application. We demonstrate its potential using an example from flow visualization showing promising first results. Evolutionary algorithms, as guided search approach, find close-to-optimal solutions with respect to some fitness function in an iterative process using biologically motivated mechanisms like selection, mutation and recombination. As such, they provide a powerful alternative to filtering methods commonly used in visualization where the space of possible candidates is densely sampled in a pre-processing step from which the best candidates are selected and visualized. This approach however tends to be increasingly inefficient with growing data size or expensive candidate computations resulting in large pre-processing times. We present an evolutionary algorithm for the problem of streamline selection to highlight features of interest in flow data. Our approach directly optimizes the solution candidates with respect to a user selected fitness function requiring significantly less computations. At the same time the problem of possible under-sampling is solved since we are not tied to a preset resolution. We demonstrate our approach on the well-known flow around an obstacle as case with a two-dimensional search space. The blood flow in an aneurysm serves as an example with a three-dimensional search space. For both, the achieved results are comparable to line filtering approaches with much less line computations.

This paper presents an automatic selection of viewpoints, forming a camera path, to support the exploration of cerebral aneurysms. Aneurysms bear the risk of rupture with fatal consequences for the patient. For the rupture risk evaluation, a combined investigation of morphological and hemodynamic data is necessary. However, the extensive nature of the time-dependent data complicates the analysis. During exploration, domain experts have to manually determine appropriate views, which can be a tedious and time-consuming process. Our method determines optimal viewpoints automatically based on input data such as wall thickness or pressure. The viewpoint selection is modeled as an optimization problem. Our technique is applied to five data sets and we evaluate the results with two domain experts by conducting informal interviews.

The CrossDrive Project develops Distributed and Collaborative Infrastructure based on advanced Immersive Virtual Reality tools for the analysis and management of Scientific Data and Operational Activities of planetary spacecraft. The Collaborative Workspace encompasses advanced technological solutions for central storage processing, 3D visualisation and Virtual Presence in Immersive Virtual Reality environments, to support Space Data Analysis and Space Operations. Science objectives are share and correlate Atmospheric data, analysis and simulations based on the actual main Mars’ satellites (MEX and MRO); compare and correlate data for Geology and Geodesy; benchmark satellite and ground based Mars atmospheric measurements.

Atmospheric phenomena of Mars can be highly dynamic and have daily and seasonal variations. Planetary-scale wavelike disturbances, for example, are frequently observed in Mars' polar winter atmosphere. Possible sources of the wave activity were suggested to be dynamical instabilities and quasi-stationary planetary waves, i.e. waves that arise predominantly via zonally asymmetric surface properties. For a comprehensive understanding of these phenomena, single layers of altitude have to be analyzed carefully and relations between different atmospheric quantities and interaction with the surface of Mars have to be considered. The CROSS DRIVE project tries to address the presentation of those data with a global view by means of virtual reality techniques. Complex orbiter data from spectrometer and observation data from Earth are combined with global circulation models and high-resolution terrain data and images available from Mars Express or MRO instruments. Scientists can interactively extract features from those dataset and can change visualization parameters in real-time in order to emphasize findings. Stereoscopic views allow for perception of the actual 3D behavior of Mars's atmosphere. A very important feature of the visualization system is the possibility to connect distributed workspaces together. This enables discussions between distributed working groups. The workspace can scale from virtual reality systems to expert desktop applications to web-based project portals. If multiple virtual environments are connected, the 3D position of each individual user is captured and used to depict the scientist as an avatar in the virtual world. The appearance of the avatar can also scale from simple annotations to complex avatars using tele-presence technology to reconstruct the users in 3D. Any change of the feature set (annotations, cutplanes, volume rendering, etc.) within the VR is immediately exchanged between all connected users. This allows that everybody is always aware of what is visible and discussed. The discussion is supported by audio and interaction is controlled by a moderator managing turn-taking presentations. A use case execution proved a success and showed the potential of this immersive approach.

The three year European research project CROSS DRIVE (Collaborative Rover Operations and Planetary Science Analysis System based on Distributed Remote and Interactive Virtual Environments) started in January 2014. The research and development within this project is motivated by three use case studies: landing site characterization, atmospheric science and rover target selection.

Currently the implementation for the second use case is in its final phase. Here, the requirements were generated based on the domain experts input and lead to development and integration of appropriate methods for visualization and analysis of atmospheric data. The methods range from volume rendering, interactive slicing, iso-surface techniques to interactive probing. All visualization methods are integrated in DLR’s Terrain Rendering application. With this, the high resolution surface data visualization can be enriched with additional methods appropriate for atmospheric data sets. This results in an integrated virtual environment where the scientist has the possibility to interactively explore his data sets directly within the correct context. The data sets include volumetric data of the martian atmosphere, precomputed two dimensional maps and vertical profiles. In most cases the surface data as well as the atmospheric data has global coverage and is of time dependent nature. Furthermore, all interaction is synchronized between different connected application  instances, allowing for collaborative sessions between distant experts.