Matt Edmunds

Technical Section: Surface-based flow visualization

With increasing computing power, it is possible to process more complex fluid simulations. However, a gap between increasing data size and our ability to visualize them still remains. Despite the great amount of progress that has been made in the field of flow visualization over the last two decades, a number of challenges remain. While the visualization of 2D flow has many good solutions, the visualization of 3D flow still poses many problems. Challenges such as domain coverage, speed of computation, and perception remain key directions for further research. Flow visualization with a focus on surface-based techniques forms the basis of this literature survey, including surface construction techniques and visualization methods applied to surfaces. We detail our investigation into these algorithms with discussions of their applicability and their relative strengths and drawbacks. We review the most important challenges when considering such visualizations. The result is an up-to-date overview of the current state-of-the-art that highlights both solved and unsolved problems in this rapidly evolving branch of research.

Automatic Stream Surface Seeding: A Feature Centered Approach

The ability to capture and visualize information within the flow poses challenges for visualizing 3D flow fields. Stream surfaces are one of many useful integration based techniques for visualizing 3D flow. However seeding integral surfaces can be challenging. Previous research generally focuses on manual placement of stream surfaces. Little attention has been given to the problem of automatic stream surface seeding. This paper introduces a novel automatic stream surface seeding strategy based on vector field clustering. It is important that the user can define and target particular characteristics of the flow. Our framework provides this ability. The user is able to specify different vector clustering parameters enabling a range of abstraction for the density and placement of seeding curves and their associated stream surfaces. We demonstrate the effectiveness of this automatic stream surface approach on a range of flow simulations and incorporate illustrative visualization techniques. Domain expert evaluation of the results provides valuable insight into the users requirements and effectiveness of our approach.

Advanced, Automatic Stream Surface Seeding and Filtering

The placement or seeding of stream surfaces in 3D flow fields faces a number of challenges. These challenges include perception, occlusion, and the appropriate representation of flow characteristics. A variety of streamline seeding approaches exist, little corresponding work is presented for stream surfaces. We present a novel automatic stream surface seeding and filtering algorithm. Our approach is designed to capture the characteristics of the flow utilizing illustrative techniques to alleviate occlusion and provide options for filtering. We define and prioritize a set of seeding curves at the domain boundaries from isolines computed from a derived scalar field. We detail the generation of an initial set of surfaces from the set of seeding curves, and discuss a technique for effective surface termination. We then present an algorithm that automatically seeds new interior surfaces, to represent locations not captured by the boundary seeding, at a user specified separation from the initial surface set. The results demonstrate satisfactory domain coverage and effective visualizations on a variety of simulations.

Automatic Stream Surface Seeding

The visualisation of 3D flow poses many challenges. Difficulties can stem from attempting to capture all flow features, the speed of computation, and spatial perception. Streamlines and stream surfaces are standard tools for visualising 3D flow. Although a variety of automatic seeding approaches have been proposed for streamlines, little work has been presented for stream surfaces. We present a novel automatic approach to the seeding of stream surfaces in 3D flow fields. We first describe defining seeding curves at the domain boundaries from isolines generated from a derived scalar field. We then detail the generation of stream surfaces integrated through the flow and discuss the associated challenges of surface termination and occlusion. We also present the results of this algorithm, how we achieve satisfactory domain coverage and capture the features of the flow field. Strategies for resolving occlusion resulting from seeding multiple surfaces are also presented and analysed.

Visualization of Input Parameters for Stream and Pathline Seeding

Uncertainty arises in all stages of the visualization pipeline. However, the majority of flow visualization applications convey no uncertainty information to the user. In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to generate a simulation or on uncertainty associated with mapping visualiza-tion primitives to data. Our work is aimed at another source of uncertainty - that associated with user-controlled input param-eters. The navigation and stability analysis of user-parameters has received increasing attention recently. This work presents an investigation of this topic for flow visualization, specifically for three-dimensional streamline and pathline seeding. From a dynamical systems point of view, seeding can be formulated as a predictability problem based on an initial condition. Small perturbations in the initial value may result in large changes in the streamline in regions of high unpredictability. Analyzing this predictability quantifies the perturbation a trajectory is subjugated to by the flow. In other words, some predictions are less certain than others as a function of initial conditions. We introduce novel techniques to visualize important user input parameters such as streamline and pathline seeding position in both space and time, seeding rake position and orientation, and inter-seed spacing. The implementation is based on a metric which quantifies similarity between stream and pathlines. This is important for Computational Fluid Dynamics (CFD) engineers as, even with the variety of seeding strategies available, manual seeding using a rake is ubiquitous. We present methods to quantify and visualize the effects that changes in user-controlled input parameters have on the resulting stream and pathlines. We also present various visualizations to help CFD scientists to intuitively and effectively navigate this parameter space. The reaction from a domain expert in fluid dynamics is also reported.

Interactive stream surface placement a hybrid clustering approach supported by tree maps

The ability of a CFD engineer to study, capture, and visualise 3D flow simulation data is a challenge. Stream surfaces are a useful tool for visualising 3D flow because of their ability to convey many field attributes from their structure. It is important that the CFD engineer can interact with, and examine specific characteristics of the CFD data. We introduce an interactive, cluster based stream surface placement strategy for structured and unstructured CFD data. A two-phase hybrid clustering algorithm is used to visualise interesting subsets of the flow. An interactive tree map interface provides a visual overview and enables interactive selection of cluster details corresponding to interesting features of the data at which to place stream surfaces. We demonstrate the performance and effectiveness of our interactive framework on a range of flow simulations and provide domain expert evaluation of the results, providing valuable insight for the CFD engineers.