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Texture-based visualization of multi-field flow data.

机译:基于纹理的多场流量数据可视化。

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摘要

Researchers have long been interested in developing a deeper understanding of the key physical mechanisms in fluid dynamics. Of particular interest are the complex relationships between the multiple scalar and vector quantities used to characterize scientific phenomena within the flow. Efforts to achieve a fundamental understanding of these key physical mechanisms remain limited mainly because of a lack of understanding of the nonlinear interactions that occur among the components of the flow.; The goal through this work is to enable researchers to obtain a succinct, meaningful visual summary of the contents of a dataset that consists of multiple, coincident variables. This is accomplished through providing techniques that allow the creation of an image in which the important features of multiple scalar or vector fields can be understood both independently and in the context of the other fields.; This research offers several new techniques for effectively using color and texture to simultaneously convey information about multiple co-located scalar and vector distributions. Specifically, we introduce: (1) color weaving, an alternative to traditional color compositing for simultaneously representing multiple distributions by allowing colors to be closely interwoven via the assignment of distinct separate hues to individual streamlines; (2) applying natural textures to streamlines to create a richly diverse set of possibilities for the visualization of multiple distributions within a flow; (3) embossing to encode the out-of-plane component of a 3D vector field defined over a 2D domain; (4) visualizing multiple 2D vector fields using strategies involving layers of disparate textures and overlapping streamlines. These methods ultimately enhance the ability to provide insight into the complicated interactions that occur within multi-field flow data.
机译:长期以来,研究人员一直对深入了解流体动力学的关键物理机制感兴趣。特别令人感兴趣的是用于表征流中科学现象的多个标量和向量之间的复杂关系。对这些关键物理机制进行基本了解的努力仍然受到限制,这主要是由于缺乏对流各成分之间发生的非线性相互作用的了解。通过这项工作的目的是使研究人员能够获得由多个重合变量组成的数据集内容的简洁,有意义的视觉摘要。这是通过提供允许创建图像的技术来实现的,在该图像中,可以独立地以及在其他字段的上下文中理解多个标量或矢量字段的重要特征。这项研究提供了几种有效利用颜色和纹理来同时传达有关多个共处标量和矢量分布的信息的新技术。具体来说,我们介绍:(1)色彩编织,这是传统色彩合成的一种替代方法,它通过将不同的单独色相分配给各个流线来使色彩紧密交织,从而同时表示多种分布; (2)将自然纹理应用于流线,以创建丰富多样的可能性,以可视化流中的多个分布; (3)压纹以编码在2D域上定义的3D矢量场的平面外分量; (4)使用涉及不同纹理层和重叠流线的策略可视化多个2D矢量场。这些方法最终增强了提供洞察在多场流数据中发生的复杂相互作用的能力。

著录项

  • 作者

    Urness, Timothy Matthew.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化技术、计算机技术;
  • 关键词

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