Abstract Uncertainties in flood predictions complicate the planning of mitigation measures. There is a consensus that many possible incident scenarios should be considered. For each scenario, a specific response plan should be prepared which is optimal with respect to criteria such as protection, costs, or realization time. None of the existing software tools is capable of creating large scenario pools, nor do they provide means for quick exploration and assessment of the associated plans. In this paper, we present an integrated solution that is based on multidimensional, time‐dependent ensemble simulations of incident scenarios and protective measures. We provide scalable interfaces which facilitate and accelerate setting up multiple time‐varying parameters for generating a pool of pre‐cooked scenarios. In case of an emergency, disaster managers can quickly extract relevant information from the pool to deal with the situation at hand. An interactive 3D‐view conveys details about how a response plan has to be executed. Linked information visualization and ranking views allow for a quick assessment of many plans. In collaboration with flood managers, we demonstrate the practical applicability of our solution. We tackle the challenges of planning mobile water barriers for protecting important infrastructure. We account for real‐world limitations of available resources and handle the involved logistics problems.

The perception of transparency and the underlying neural mechanisms have been subject to extensive research in the cognitive sciences. However, we have yet to develop visualization techniques that optimally convey the inner structure of complex transparent shapes. In this paper, we apply the findings of perception research to develop a novel illustrative rendering method that enhances surface transparency nonlocally. Rendering of transparent geometry is computationally expensive since many optimizations, such as visibility culling, are not applicable and fragments have to be sorted by depth for correct blending. In order to overcome these difficulties efficiently, we propose the illustration buffer. This novel data structure combines the ideas of the A and G-buffers to store a list of all surface layers for each pixel. A set of local and nonlocal operators is then used to process these depth-lists to generate the final image. Our technique is interactive on current graphics hardware and is only limited by the available graphics memory. Based on this framework, we present an efficient algorithm for a nonlocal transparency enhancement that creates expressive renderings of transparent surfaces. A controlled quantitative double blind user study shows that the presented approach improves the understanding of complex transparent surfaces significantly.

Artistic illustrations of important structures in fluid flow have a long-standing tradition and are appreciated as clearly perceivable, instructive, but still conveying all relevant information to the viewer. One important illustrative technique for such visualizations are cutaways. Currently cutaways are placed manually or using view-vector based approaches. We propose to optimize the visibility of important target features based on a degree-of-interest (DOI) function. The DOI is specified during interactive visual analysis, e.g., by brushing scatterplots. We show that the problem of placing cutaway boxes optimally is NP-hard in the number of boxes. To overcome this obstacle, we present an intelligent method to compute cutaways. Geometric cutaway objects are positioned using a view-dependent objective function which optimizes the visibility of all features. In order to approximate the optimal solution, we use a Monte Carlo method and exploit temporal coherence in dynamic scenes. Performance-critical parts are implemented on the GPU. The proposed method can be integrated easily into existing rendering frameworks and is general enough to be able to optimize other parameters besides cutaways as well. We evaluate the performance of the algorithm and provide a case study of vorticity visualization in a turbulent flow.

Abstract We present a dense visualization of vector fields on multi‐layered surfaces. The method is based on the illustration buffer, which provides a screen space representation of the surface, where each pixel stores a list of all surface layers. This representation is implemented on the GPU using shaders and leads to a fast output sensitive technique. In our approach, we first use procedural noise to create an initial spot pattern on the surface that has both an almost constant screen space frequency and is view independent. Then, we perform anisotropic diffusion simultaneously on all surface layers using a discretization scheme that maintains second order convergence while only accessing the four neighboring pixels. Finally, we enhance this result with illustrative techniques and composite the final image. Our method works with time‐evolving surfaces, time‐dependent vector fields, and moving cameras. We apply our method to CFD data sets from engineering and astronomy as well as synthetic velocity fields.

Room air flow and air exchange are important aspects for the design of energy-efficient buildings. As a result, simulations are increasingly used prior to construction to achieve an energy-efficient design. We present a visual analysis of air flow generated at building entrances, which uses a combination of revolving doors and air curtains. The resulting flow pattern is challenging because of two interacting flow patterns: On the one hand, the revolving door acts as a pump, on the other hand, the air curtain creates a layer of uniformly moving warm air between the interior of the building and the revolving door. Lagrangian coherent structures (LCS), which by definition are flow barriers, are the method of choice for visualizing the separation and recirculation behavior of warm and cold air flow. The extraction of LCS is based on the finite-time Lyapunov exponent (FTLE) and makes use of a ridge definition which is consistent with the concept of weak LCS. Both FTLE computation and ridge extraction are done in a robust and efficient way by making use of the fast Fourier transform for computing scale-space derivatives.