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

Spectral, Combinatorial, and Probabilistic Methods in Analyzing and Visualizing Vector Fields and Their Associated Flows

In this thesis, we introduce several tools, each coming from a different branch of mathematics, for analyzing real vector fields and their associated flows. Beginning with a discussion about generalized vector field decompositions, that mainly have been derived from the classical Helmholtz-Hodge-decomposition, we decompose a field into a kernel and a rest respectively to an arbitrary vector-valued linear differential operator that allows us to construct decompositions of either toroidal flows or flows obeying differential equations of second (or even fractional) order and a rest. The algorithm is based on the fast Fourier transform and guarantees a rapid processing and an implementation that can be directly derived from the spectral simplifications concerning differentiation used in mathematics. Moreover, we present two combinatorial methods to process 3D steady vector fields, which both use graph algorithms to extract features from the underlying vector field. Combinatorial approaches are known to be less sensitive to noise than extracting individual trajectories. Both of the methods are extensions of an existing 2D technique to 3D fields. We observed that the first technique can generate overly coarse results and therefore we present a second method that works using the same concepts but produces more detailed results. Finally, we discuss several possibilities for categorizing the invariant sets with respect to the flow. Existing methods for analyzing separation of streamlines are often restricted to a finite time or a local area. In the frame of this work, we introduce a new method that complements them by allowing an infinite-time-evaluation of steady planar vector fields. Our algorithm unifies combinatorial and probabilistic methods and introduces the concept of separation in time-discrete Markov chains. We compute particle distributions instead of the streamlines of single particles. We encode the flow into a map and then into a transition matrix for each time direction. Finally, we compare the results of our grid-independent algorithm to the popular Finite-Time-Lyapunov-Exponents and discuss the discrepancies. Gauss\'' theorem, which relates the flow through a surface to the vector field inside the surface, is an important tool in flow visualization. We are exploiting the fact that the theorem can be further refined on polygonal cells and construct a process that encodes the particle movement through the boundary facets of these cells using transition matrices. By pure power iteration of transition matrices, various topological features, such as separation and invariant sets, can be extracted without having to rely on the classical techniques, e.g., interpolation, differentiation and numerical streamline integration

Simulação numérica e visualização 3D interativa de objetos sob fluxos irrotacionais em tempo Quase-Real

Resumo: De uma maneira geral, qualquer fluxo irrotacional e incompressível é governado pela equação de Laplace. Esta não possui resolução analítica para problemas reais de engenharia, os quais possuem domínios e condições de contorno complexas, exceto para poucos casos particulares. A Dinâmica dos Fluidos Computacional (DFC) é um método utilizado para resolver numericamente a equação de Laplace, satisfazendo condições iniciais e de contorno. Porém, ao se refinar ou estender um domínio calculado, a quantidade de dados numéricos resultantes aumentará proporcionalmente e a análise destes valores pode se tornar complexa e onerosa. Complementariamente, para a compreensão dos resultados, é importante uma representação visual. A resolução numérica da equação de Laplace está descrita neste trabalho, com um algoritmo de solução inédito para as condições de contorno que atende qualquer forma geométrica em três dimensões. Desenvolveu-se um simulador que possibilita alterações geométricas de objetos 3D, calcula e visualiza interativamente velocidades, linhas de fluxo e força de sustentação para fluxos irrotacionais e incompressíveis em tempo quase-real. O sistema utiliza o método das diferenças finitas para a solução das equações. A interface gráfica foi desenvolvida utilizando, deste modo ineditamente para a DFC, a linguagem C++ e o VTK (Visualization Tool Kit). A quantidade, a origem das linhas de fluxo, a seleção do campo de velocidades, o cálculo da força de sustentação e a visualização estereoscópica são parâmetros que podem ser ajustados e selecionados para a visualização. O algoritmo passou por validações mostrando a capacidade de resolução em três dimensões. Assim, o simulador desenvolvido resolve, ao contrário dos softwares já existentes, o problema do cálculo e visualização interativa imediata ao se fazer modificações em objetos 3D. Este procedimento permitirá que se façam comparações entre formas geométricas imediatamente alteradas para que se possa escolher, entre elas, a que se adequar melhor às necessidades de um projeto

Topology Preserving Smoothing of Vector Fields

In this paper we propose a technique for topology preserving smoothing of sampled vector fields. In this technique, the vector field data is first converted into a scalar level-set representation. We then locally analyze the dynamic behavior of level-sets by placing geometric primitives in the scalar field and by subsequently distorting these primitives with respect to local variations in this field. Geometrical and topological considerations are then combined to successively smooth the vector data. Using the resulting algorithm, we have built a visualization system that enables us to effectively smooth dense flow fields at the same time retaining their topological structure

Topology Preserving Smoothing of Vector Fields

In this paper we propose a technique for topology preserving smoothing of sampled vector fields. The vector field data is first converted into a scalar representation in which time surfaces implicitly exist as level-sets. We then locally analyze the dynamic behavior of level-sets by placing geometric primitives in the scalar field and by subsequently distorting these primitives with respect to local variations in this field. From the distorted primitives we calculate the curvature normal and we use the normal magnitude and its direction to separate distinct flow features. Geometrical and topological considerations are then combined to successively smooth dense flow fields at the same time retaining their topological structure