Stream bundles - cohesive advection through flow fields

Journal ArticleStreamline advection has proven an effective method for visualizing vector flow field data. Traditional streamlines do not, however, provide for investigating the coarsergrained features of complex datasets, such as the white matter tracts in the brain or the thermal conveyor belts in the ocean. In this paper, we introduce a cohesive advection primitive, called a stream bundle. Whereas traditional streamlines describe the advection patterns of single, infinitesimal micro-particles, stream bundles indicate advection paths for large macro-particles. Implementationally, stream bundles are composed of a collection of individual streamlines (here termed fibers), each of which only advects a short distance before being terminated and re-seeded in a new location. The individual fibers combine to dictate the instantaneous distribution of the bundle, and it is this collective distribution which is used in determining where fibers are reseeded. By carefully controlling the termination and re-seeding policies of the fibers, we can prevent the bundle from becoming frayed in divergent regions. By maintaining a cohesive from, the bundles can indicate the coarse structure of complex vector fields. In this paper, we use stream bundles to investigate the oceanic currents

Introduction to Vector Field Visualization

Vector field visualization techniques are essential to help us understand the complex dynamics of flow fields. These can be found in a wide range of applications such as study of flows around an aircraft, the blood flow in our heart chambers, ocean circulation models, and severe weather predictions. The vector fields from these various applications can be visually depicted using a number of techniques such as particle traces and advecting textures. In this tutorial, we present several fundamental algorithms in flow visualization including particle integration, particle tracking in time-dependent flows, and seeding strategies. For flows near surfaces, a wide variety of synthetic texture-based algorithms have been developed to depict near-body flow features. The most common approach is based on the Line Integral Convolution (LIC) algorithm. There also exist extensions of LIC to support more flexible texture generations for 3D flow data. This tutorial reviews these algorithms. Tensor fields are found in several real-world applications and also require the aid of visualization to help users understand their data sets. Examples where one can find tensor fields include mechanics to see how material respond to external forces, civil engineering and geomechanics of roads and bridges, and the study of neural pathway via diffusion tensor imaging. This tutorial will provide an overview of the different tensor field visualization techniques, discuss basic tensor decompositions, and go into detail on glyph based methods, deformation based methods, and streamline based methods. Practical examples will be used when presenting the methods; and applications from some case studies will be used as part of the motivation

Optimized profile extraction and three dimensional reconstruction techniques applied to bubble shapes

In order to predict the behavior of bubbly flows, it is necessary to know the three dimensional profiles of the bubbles present in the flow. With advancements in the field of flow visualization, accurate reconstruction of the bubble shape has become necessary. The PIV and the SIV techniques, used to acquire images of particles and bubbles, have been found to be extremely useful in this regard. The study, development, implementation, applications and limitations of a unique reconstruction technique applied to various regular and irregular bubble shapes, using the two orthogonal projections of the three-dimensional bubble profiles as captured by the SIV cameras are presented here. The technique is a blend of neural networks, combinatorial optimization and advanced computer aided design methods. The technique involves the robustness and ruggedness of the neural network approach and the flexibility and reliability of advanced computer aided design methods. The technique uses a well-known problem in neural networks and combinatorial optimization known as the Traveling Salesman Problem approach to identify the bubble boundaries on the images. An optimization solution technique known as the Simulated Annealing technique is employed to solve the Traveling Salesman Problem and obtain the bubble profiles. These results are employed to reconstruct bubble shapes using NURBS computer aided design software. Two main applications of this technique are demonstrated and the results are found to be promising. The first application included the calculation of the void fraction at a particular depth of the channel/ pipe and at a particular radius of the channel. The second application was Lagrangian tracking of bubbles, wherein the centroids of the bubbles were tracked between image frames to determine the linear and transverse velocities of the bubbles. This technique has shown scope for development including the development as integrated bubble surface reconstruction software and advanced modifications at various levels for efficient and accurate reconstruction

New techniques for the scientific visualization of three-dimensional multi-variate and vector fields

Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows

New techniques for the scientific visualization of three-dimensional multi-variate and vector fields

Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows