Flow Visualization Based on A Derived Rotation Field

Lei Zhang, Guoning Chen, R. Laramee, D. Thompson, A. Sescu
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引用次数: 10

Abstract

We identify and investigate the Φ field – a derived flow attribute field whose value at a given spatial location is determined by the integral curve initiated at the point. Specifically, we integrate the angle difference between the velocity vectors at two consecutive points along the integral curve to get the Φ field value. Important properties of the Φ field and its gradient magnitude |∇Φ| field are studied. In particular, we show that the patterns in the derived Φ field are generally aligned with the flow direction based on an inequality property. In addition, we compare the Φ field with some other attribute fields and discuss its relation with a number of flow features, such as LCS and cusp-like seeding structures. Furthermore, we introduce a unified framework for the computation of the Φ field and its gradient field, ∇Φ, and employ the Φ field and |∇Φ| field to a number of flow visualization and exploration tasks, including integral curve filtering, seeds generation and flow domain segmentation. We show that these tasks can be conducted more efficiently based on the information encoded in the Φ field.
基于导出旋转场的流场可视化
我们识别和研究Φ场-一个导出的流属性场,其值在给定的空间位置由点开始的积分曲线决定。具体来说,我们沿着积分曲线对两个连续点的速度矢量之间的角度差进行积分,得到Φ场值。研究了Φ场及其梯度幅度|∇Φ|场的重要性质。特别是,我们表明,在推导的Φ场模式通常与基于不等式性质的流动方向对齐。此外,我们将Φ字段与其他一些属性字段进行了比较,并讨论了它与一些流特征的关系,如LCS和尖状种子结构。此外,我们引入了一个统一的框架来计算Φ场及其梯度场∇Φ,并将Φ场和|∇Φ|场用于积分曲线滤波、种子生成和流域分割等一系列流动可视化和勘探任务。我们展示了基于Φ字段中编码的信息可以更有效地执行这些任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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