Volume xx (200y), Number z, pp. 1–23 Over Two Decades of Integration-Based, Geometric Flow Visualization Tony McLoughlin1, Robert S. Laramee1, Ronald Peikert2, Frits H. Post3, and Min Chen1 1Visual and Interactive Computing Group Department of Computer Science, Swansea University, United Kingdom {cstony, r.s.laramee, m.chen}@swansea.ac.uk 2Institute of Visual Computing ETH Zurich, Switzerland [email protected] 3Computer Graphics and CAD/CAM Group Faculty of Electrical Engineering, Mathematics and Computer Science TU Delft, The Netherlands [email protected] Abstract With ever increasing computing power, it is possible to process ever more complex fluid simulations. However, a gap between data set sizes and our ability to visualize them remains. This is especially true for the field of flow visualization which deals with large, time-dependent, multivariate simulation datasets. In this paper, geometry based flow visualization techniques form the focus of discussion. Geometric flow visualization methods place dis- crete objects in the velocity field whose characteristics reflect the underlying properties of the flow. A great amount of progress has been made in this field over the last two decades. However, a number of challenges remain, in- cluding placement, speed of computation, and perception. In this survey, we review and classify geometric flow visualization literature according to the most important challenges when considering such a visualization, a cen- tral theme being the seeding algorithm upon which they are based. This paper details our investigation into these techniques with discussions on their applicability and their relative merits and drawbacks. 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. It also serves as a concise introduction to the field of flow visualization research. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling 1. Introduction sualization technique for a given data set is a non-trivial task. Considerations have to be made taking into account the type Flow visualization is a classic branch of scientific visualiza- of information the user wishes to extract from the visualiza- tion. Its applications cover a broad spectrum ranging from tion along with the spatial and temporal characteristics of turbomachinery design to the modeling and simulation of the data set being analyzed. Different approaches have to be weather systems. The goal of flow visualization is to present designed for different types of data. For example, visualiza- the behavior of simulation data in a meaningful manner from tion of 2D data is much different from visualizing 3D data. which important flow features and characteristics can be eas- On top of this is the further complication of temporal dimen- ily identified and analyzed. sionality, with varying techniques more suited to steady flow Given the large variety of techniques currently utilized in compared to time-dependent flow fields and vice versa. To visualization applications, selecting the most appropriate vi- this end different tools have been developed according to the submitted to COMPUTER GRAPHICS Forum (1/2010). 2 T. McLoughlin et al. / Over Two Decades of Integration-Based, Geometric Flow Visualization challenge in the case of 3D where a balance of field cover- age, occlusion, and visual complexity must be maintained. Time-dependent data also raises a challenge because the vi- sualization then depends on when objects are seeded. Computation Time and Irregular Grids. Another chal- lenge stems from the computation time. Most of the visual- izations compute a geometry that is tangential to the veloc- ity field, e.g., the path a massless particle would take when placed into the flow. Computing such curves through 3D, un- structured grids is non-trivial. Thus much research has been devoted to this and similar topics, see Section 2.4. A recent trend to increase performance has been to move computa- tions from the CPU and perform them on the graphics pro- Figure 1: A set of streamlines exploiting the power of mod- cessing unit (GPU) [BSK∗07]. Although the resultant ren- ern programmable GPUs for faster computation [DGKP09]. dering looks the same as a CPU version (see Figure1), this may offer a significant improvement in performance as the different needs of the users and the differing dimensionality vector calculations are suited to the multiple execution units of velocity field data. on the GPU – resulting in high performance parallel process- ing. 1.1. Challenges in Flow Visualization Perception. A central challenge in flow visualization (and Flow simulations, or CFD (computational fluid dynam- visualization in general) relates to perceptual challenges in ics) simulations, are computed using methods such as the visualizing 3D and 4D velocity fields as well as multi-variate Navier-Stokes equations [CSvS86] and are used to simu- data sets. If streamlines are used to visualize flow in 3D, too late experiments such as wind-tunnel tests on cars and air- many lines causes clutter, visual complexity, and occlusion. planes. The visualization of these simulations poses many If too few are rendered, important characteristics may not challenges, the most important of which we outline here. be visualized. Thus an optimal balance between coverage and perception must be achieved. Animated flow presents its Large Data Sets. A major technical issue arises from the own unique challenges to the perception of the user. For in- sheer volume of data that may be generated from com- stance, it may not be intuitive what a user sees from a cloud plex simulations. Velocity data comprises scalar values for of moving particles or a surface deforming to the local flow each x;y;z velocity component at each sample point within characteristics. It may also be difficult to discern the move- the data domain. When coupled with several scalar data at- ment direction is 3D space. tributes and consisting of many time-steps, a large amount of data results. Advances in hardware are leading to more 1.2. Contributions computational power and the ability to process larger, more complex simulations with faster computation times. There- In light of these challenges and the more than two decades fore, flow visualization algorithms must be able to handle of research the main benefits and contributions of this paper this large amount of data and present the results (ideally) at are: interactive frame rates in order to be most useful in the in- • We review the latest developments in geometry-based vestigation and analysis of simulation data. flow visualization research. • We introduce a novel classification scheme based on chal- Interaction, Seeding and Placement. One of the main lenges including seeding. This scheme lends itself to an challenges specific to geometric flow visualization is the intuitive grouping of papers that are naturally related to seeding strategy used to place the objects within the data do- each other. This allows the reader to easily extract the rel- main. Geometric flow visualization techniques produce dis- evant literature without having to read the entire survey. crete objects whose shape, size, orientation, and position re- • Our classification highlights both unsolved problems in flect the characteristics of the underlying velocity field. The the area of geometric flow visualization and mature areas position of the objects greatly affects the final visualization. where many solutions have been provided. Different features of the velocity field may be depicted de- • This survey is the most up-to-date presentation on this pending on the final position and the spatial frequency of the popular topic. The last time this topic was addressed in objects in the data domain. It is critical that the resulting vi- the literature was over six years ago [PVH∗03]. sualization captures the features of the velocity field, e.g., • We provide a very concise introduction and overview in vortices, turbulence, sources, sinks and laminar flow, which the area of vector field visualization for those who are new the user is interested in. This aspect becomes an even greater to the topic and wishing to carry out research in this area. submitted to COMPUTER GRAPHICS Forum (1/2010). T. McLoughlin et al. / Over Two Decades of Integration-Based, Geometric Flow Visualization 3 We have made a great effort not to provide simply an enu- the same weakness of 3D domain representation as direct meration of related papers in integration-based, geometric methods and are generally more suited to 2D or surfaces. A flow visualization, but to compare different methods, related thorough investigation of texture-based flow visualization is to one another and weigh their relative merits and weak- presented by Laramee et al. [LHD∗04]. nesses. Feature-based algorithms focus the visualization on se- lected features of the data such as vortices or topological 1.3. Classification information rather than the entire data set. This may result in a large reduction of the required data and thus these tech- One of the main challenges of a survey is classifying niques are suited to large data sets that may consist of many these approaches and presenting them in a meaningful or- time-steps. Since they generally perform a search of the do- der. There are four general categories into which vector- main, these techniques require considerably more processing field visualization approaches can be divided: direct, dense before visualization. A survey of feature-based approaches is texture-based, geometric, and feature-based. This paper fo- presented by Post et al. [PVH∗03]. cuses on the geometric approaches to flow visualization, which received little coverage in previous surveys [LHD∗04, ∗ PVH 03, LHZP07]. A large volume of research work has 1.5. Integration-based, Geometric Flow Visualization been undertaken in geometry-based vector field visualiza- tion. We use four tiers of categorization.