Interactive visualization of computational fluid dynamics data.

This thesis describes a literature study and a practical research in the area of flow visualization, with special emphasis on the interactive visualization of Computational Fluid Dynamics (CFD) datasets. Given the four main categories of flow visualization methodology; direct, geometric, texture-based and feature-based flow visualization, the research focus of our thesis is on the direct, geometric and feature-based techniques. And the feature-based flow visualization is highlighted in this thesis. After we present an overview of the state-of-the-art of the recent developments in the flow visualization in higher spatial dimensions (2.5D, 3D and 4D), we propose a fast, simple, and interactive glyph placement algorithm for investigating and visualizing boundary flow data based on unstructured, adaptive resolution boundary meshes from CFD dataset. Afterward, we propose a novel, automatic mesh-driven vector field clustering algorithm which couples the properties of the vector field and resolution of underlying mesh into a unified distance measure for producing high-level, intuitive and suggestive visualization of large, unstructured, adaptive resolution boundary CFD meshes based vector fields. Next we present a novel application with multiple-coordinated views for interactive information-assisted visualization of multidimensional marine turbine CFD data. Information visualization techniques are combined with user interaction to exploit our cognitive ability for intuitive extraction of flow features from CFD datasets. Later, we discuss the design and implementation of each visualization technique used in our interactive flow visualization framework, such as glyphs, streamlines, parallel coordinate plots, etc. In this thesis, we focus on the interactive visualization of the real-world CFD datasets, and present a number of new methods or algorithms to address several related challenges in flow visualization. We strongly believe that the user interaction is a crucial part of an effective data analysis and visualization of large and complex datasets such as CFD datasets we use in this thesis. In order to demonstrate the use of the proposed techniques in this thesis, CFD domain experts reviews are also provided

Visualizing 2D Flows with Animated Arrow Plots

Flow fields are often represented by a set of static arrows to illustrate scientific vulgarization, documentary film, meteorology, etc. This simple schematic representation lets an observer intuitively interpret the main properties of a flow: its orientation and velocity magnitude. We propose to generate dynamic versions of such representations for 2D unsteady flow fields. Our algorithm smoothly animates arrows along the flow while controlling their density in the domain over time. Several strategies have been combined to lower the unavoidable popping artifacts arising when arrows appear and disappear and to achieve visually pleasing animations. Disturbing arrow rotations in low velocity regions are also handled by continuously morphing arrow glyphs to semi-transparent discs. To substantiate our method, we provide results for synthetic and real velocity field datasets

A Phase Field Model for Continuous Clustering on Vector Fields

A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hilliard model, which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns-the actual clustering-during which the underlying simulation data specifies preferable pattern boundaries. We introduce specific physical quantities in the simulation to control the shape, orientation and distribution of the clusters as a function of the underlying flow field. In addition, the model is expanded, involving elastic effects. In the early stages of the evolution shear layer type representation of the flow field can thereby be generated, whereas, for later stages, the distribution of clusters can be influenced. Furthermore, we incorporate upwind ideas to give the clusters an oriented drop-shaped appearance. Here, we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries. However, the method also carries provisions for other fields as well. The clusters can be displayed directly as a flow texture. Alternatively, the clusters can be visualized by iconic representations, which are positioned by using a skeletonization algorithm.

Video Data Visualization System: Semantic Classification And Personalization

We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic