![An example of using projection views in the pre-configured view arrangement, which user can get with one click, to explore the pathlines (about 10000 pathlines in this case). Pre-configured view arrangement consists of 3D view (top-left) and three projection views (top-right, bottom-left, and bottom-right views). Time is color mapped [12] in the 3D view going linearly from green, for the smallest, to red, for the biggest value. a : 3D view and projection views. b : All pathlines passing through middle tube are brushed in the top-right projection view.](https://www.researchgate.net/profile/Michael-Mayer-13/publication/229015903/figure/fig1/AS:300839047581710@1448737026888/An-example-of-using-projection-views-in-the-pre-configured-view-arrangement-which-user_Q320.jpg)
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Interactive Exploration and Analysis of Pathlines in Flow Data
Abstract and Figures
The rapid development of large-scale scientific computing nowadays allows to inherently respect the unsteady character of natural phenomena in computational flow simulation. With this new trend to more regularly consider time-dependent flow scenarios, an according new need for advanced exploration and analysis solutions emerges. In this paper, we now present three new concepts in pathline analysis which further improve the abilities of analysis: a multi-step analysis which helps to save time and space needed for computation, direct pathline brushing, and usage of pre-configured view arrangements. We have found that clever combining of these three concepts with already existing methods creates very powerful tool for pathline analysis. The coordinated multiple views (CMV) tool used supports iterative composite brushing which enables a quick information drill-down. We illustrate the usefulness using an example from the automotive industry. We have analyzed an exhaust manifold time-dependent simulation data set.
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... Shi et al. [28] present a similar approach together with more general path line attributes, using both local and global descriptors for the path line behavior. Lež et al. [17] enhance the utility of path line based IVA by the possibility for direct path line brushing via projections. ...
... This paper is targeting dimension reduction for interactive flow analysis along the lines of the works of Bürger et al. [1], Shi et al. [28], and Lež et al. [17]. ...
... This data set has been investigated by Lež et al. [17]. Their paper is not targeting the question of which attributes to choose for a interactive flow analysis, however, the authors suggest several attribute combinations that they found useful for the case study. ...
Recent work has shown the great potential of interactive flow analysis by the analysis of path lines. The choice of suitable attributes, describing the path lines, is, however, still an open question. This paper addresses this question performing a statistical analysis of the path line attribute space. In this way we are able to balance the usage of computing power and storage with the necessity to not loose relevant information. We demonstrate how a carefully chosen attribute set can improve the benefits of state-of-the art interactive flow analysis. The results obtained are compared to previously published work.
... The main idea is to compute pathlines and pathline attributes (some of them are scalar and others are functions of time or of the position along the pathline), and then to interactively explore the new dataset. The first tests were done using an exhaust manifold case from automotive industry [7]. Interactive visual analysis will be also used to compare results from various automatic flow segmentation methods. ...
Modern time-varying flow visualization techniques that rely on advection are able to convey fluid transport, but cannot provide an accurate insight into local flow behavior over time or locally corresponding patterns in unsteady vector fields. We overcome these limitations of purely Lagrangian approaches by generalizing the concept of function fields to time-varying flows. This representation of unsteady vector-fields as stationary function fields, where every position in space is a vector-valued function supports the application of novel analysis techniques based on function correlation, and allows to answer data analysis questions that remain unanswered with classic time-varying vector field analysis techniques. Our results demonstrate how analysis of time-varying flow fields can benefit from a conversion into function field representations and show the robustness of our presented clustering techniques.
Flow fields are an important product of scientific simulations. One popular flow visualization technique is particle advection, in which seeds are traced through the flow field. One use of these traces is to compute a powerful analysis tool called the Finite-Time Lyapunov Exponent (FTLE) field, but no existing particle tracing algorithms scale to the particle injection frequency required for high-resolution FTLE analysis. In this paper, a framework to trace the massive number of particles necessary for FTLE computation is presented. A new approach is explored, in which processes are divided into groups, and are responsible for mutually exclusive spans of time. This pipelining over time intervals reduces overall idle time of processes and decreases I/O overhead. Our parallel FTLE framework is capable of advecting hundreds of millions of particles at once, with performance scaling up to tens of thousands of processes.
Flows through tubular structures are common in many fields, including blood flow in medicine and tubular fluid flows in engineering. The analysis of such flows is often done with a strong reference to the main flow direction along the tubular boundary. In this paper we present an approach for straightening the visualization of tubular flow. By aligning the main reference direction of the flow, i.e., the center line of the bounding tubular structure, with one axis of the screen, we are able to natively juxtapose (1.) different visualizations of the same flow, either utilizing different flow visualization techniques, or by varying parameters of a chosen approach such as the choice of seeding locations for integration-based flow visualization, (2.) the different time steps of a time-dependent flow, (3.) different projections around the center line , and (4.) quantitative flow visualizations in immediate spatial relation to the more qualitative classical flow visualization. We describe how to utilize this approach for an informative interactive visual analysis. We demonstrate the potential of our approach by visualizing two datasets from two different fields: an arterial blood flow measurement and a tubular gas flow simulation from the automotive industry.
Considerable confusion surrounds the longstanding question of what constitutes a vortex, especially in a turbulent flow. This question, frequently misunderstood as academic, has recently acquired particular significance since coherent structures (CS) in turbulent flows are now commonly regarded as vortices. An objective definition of a vortex should permit the use of vortex dynamics concepts to educe CS, to explain formation and evolutionary dynamics of CS, to explore the role of CS in turbulence phenomena, and to develop viable turbulence models and control strategies for turbulence phenomena. We propose a definition of a vortex in an incompressible flow in terms of the eigenvalues of the symmetric tensor S 2 + a 2 ; here S and 0 are respectively the symmetric and antisymmetric parts of the velocity gradient tensor Vu. This definition captures the pressure minimum in a plane perpendicular to the vortex axis at high Reynolds numbers, and also accurately defines vortex cores at low Reynolds numbers, unlike a pressure-minimum criterion. We compare our definition with prior schemes/definitions using exact and numerical solutions of the Euler and Navier-Stokes equations for a variety of laminar and turbulent flows. In contrast to definitions based on the positive second invariant of V u or the complex eigenvalues of Vu, our definition accurately identifies the vortex core in flows where the vortex geometry is intuitively clear.
One of the most fundamental features of scientific visualization is the process of mapping scalar values to colors. This process
allows us to view scalar fields by coloring surfaces and volumes. Unfortunately, the majority of scientific visualization
tools still use a color map that is famous for its ineffectiveness: the rainbow color map. This color map, which naïvely sweeps
through the most saturated colors, is well known for its ability to obscure data, introduce artifacts, and confuse users.
Although many alternate color maps have been proposed, none have achieved widespread adoption by the visualization community
for scientific visualization. This paper explores the use of diverging color maps (sometimes also called ratio, bipolar, or
double-ended color maps) for use in scientific visualization, provides a diverging color map that generally performs well
in scientific visualization applications, and presents an algorithm that allows users to easily generate their own customized
color maps.
One of the reasons that topological methods have a limited popularity for the visualization of complex 3D flow fields is the fact that their topological structures contain a number of separating stream surfaces. Since these stream surfaces tend to hide each other as well as other topological features, for complex 3D topologies the visualizations become cluttered and hardly interpretable. One solution of this problem is the recently introduced concept of saddle connectors which treats separation surfaces emanating from critical points. In this paper we extend this concept to separation surfaces starting from boundary switch curves. This way we obtain a number of particular stream lines called boundary switch connectors. They connect either two boundary switch curves or a boundary switch curve with a saddle. We discuss properties and computational issues of boundary switch connectors and apply them to topologically complex flow data.
In most fluid dynamics applications, unsteady flow is a natural phenomenon and steady models are just sim- plifications of the real situation. Since computing power in - creases, the number and complexity of unsteady flow simu- lations grows, too. Besides time-dependent features, scie n- tists and engineers are essentially looking for a descripti on of the overall flow behavior, usually with respect to the re- quirements of their application domain. We call such a de- scription a flow structure, requiring a framework of defini- tions for unsteady flow structure. In this paper, we present such a framework based on pathline predicates. Using the common computer science definition, a predicate is a Boole- an function, and a pathline predicate is a Boolean function on pathlines that decides if a pathline has a property of inte r- est to the user. We will show that any suitable set of pathline predicates can be interpreted as an unsteady flow structure definition. The visualization of the resulting unsteady flow structure provides a visual description of overall flow beha v- ior with respect to the user's interest. Furthermore, this fl ow structure serves as a basis for pathline placements tailore d to the requirements of the application.
A vortex is characterized by the swirling motion of fluid around a central region. This characterization stems from the visual perception of swirling phenomena that are pervasive throughout the natural world. However, translating this intuitive description of a vortex into a formal definition has been quite a challenge. Despite the lack of a formal definition, various detection algorithms have been implemented that can adequately identify vortices in most computational datasets. This chapter presents an overview of existing detection methods; in particular, the focus is on nine methods that are representative of the state of the art. The chapter begins by presenting three taxonomies for classifying these nine detection methods. It then describes each algorithm, along with pseudo-code where appropriate. Next, the chapter describes a recently developed verification algorithm for swirling flows. The chapter also discusses the different visualization techniques for vortices.
We propose a technique for locating swirling regions of a flowfield. The technique is based on the eigenvalues of the velocity gradient tensor. We show that regions of swirling flow are characterized by complex eigenvalues for a constant velocity gradient tensor. Using results obtained in this analysis, we define an approximate parameter to indicate the tendency for the fluid to swirl about the point in question. The technique is illustrated by application to several two- and three- dimensional flowfields. In addition, the basic ideas contained here suggest the existence of a fluid property that we have termed intrinsic swirl. This new property may also be useful for control of fluid motion in many practical problems.
A new vortex detection scheme that can identify vortex centers in overlapping regions of rotational flow is introduced. This scheme is demonstrated using two computed solutions. The first case models the steady vortex rollup from a delta wing and the second simulates a wind-tunnel test of the V-22 tiltrotor blades. In addition to vortex identification, the proposed scheme can be used to guide adaptive-grid CFD methods that seek to locally improve the grid resolution for vortex wake systems.
Flow visualization has been a very attractive component of scientific visualization research for a long time. Usu-ally very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driv-ing factor for a reemergence in flow visualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio-temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.
Vector fields are a common concept for the representation of many different kinds of flow phenomena in science and engineering. Methods based on vector field topology are known for their convenience for visualizing and analysing steady flows, but a counterpart for unsteady flows is still missing. However, a lot of good and relevant work aiming at such a solution is available. We give an overview of previous research leading towards topology-based and topology-inspired visualization of unsteady flow, pointing out the different approaches and methodologies involved as well as their relation to each other, taking classical (i.e. steady) vector field topology as our starting point. Particularly, we focus on Lagrangian methods, space–time domain approaches, local methods and stochastic and multifield approaches. Furthermore, we illustrate our review with practical examples for the different approaches.