Visualization of Input Parameters for Stream and Pathline Seeding
Visualization of Input Parameters for Stream and Pathline Seeding
复制标题
流和路径播种输入参数的可视化
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
E. Zhang
中科院分区：
文献类型：
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作者：
Tony McLoughlin;M. Edmunds;Chao Tong;R. Laramee;I. Masters;Guoning Chen;N. Max;Harry Yeh;E. Zhang
在可视化管道的所有阶段都会出现不确定性。然而，大多数流动可视化应用程序向用户传达的不是不确定性信息。在传达不确定性的工具中，焦点通常集中在数据上，例如由用于生成模拟的数值方法引起的误差，或者与将可视化基元映射到数据相关联的不确定性。我们的工作针对另一个不确定性来源--与用户控制的输入参数相关的不确定性。近年来，用户参数的导航和稳定性分析受到越来越多的关注。这项工作提出了这一主题的研究流动可视化，特别是三维流线和路径线播种。从动力系统的观点来看，播种问题可以表示为一个基于初始条件的可预测性问题。初始值的微小扰动可能会导致高度不可预测性区域的流线发生较大变化。通过分析这种可预测性，可以量化轨迹被气流控制的扰动程度。换句话说，作为初始条件的函数，一些预测比其他预测更不确定。我们引入了新的技术来可视化重要的用户输入参数，如流线和路径线在空间和时间上的播种位置，播种犁的位置和方向，以及种子间距。该实现基于对流和路径之间的相似性进行量化的度量。这对于计算流体力学(CFD)工程师来说很重要，因为即使有各种播种策略，使用RAKE的手动播种也是无处不在的。我们提出了一些方法来量化和可视化用户控制的输入参数的变化对结果流和路径线的影响。我们还提供了各种可视化方法，以帮助CFD科学家直观、有效地导航这个参数空间。还报道了流体力学领域专家的这一反应。
Uncertainty arises in all stages of the visualization pipeline. However, the majority of flow visualization applications convey no uncertainty information to the user. In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to generate a simulation or on uncertainty associated with mapping visualiza-tion primitives to data. Our work is aimed at another source of uncertainty - that associated with user-controlled input param-eters. The navigation and stability analysis of user-parameters has received increasing attention recently. This work presents an investigation of this topic for flow visualization, specifically for three-dimensional streamline and pathline seeding. From a dynamical systems point of view, seeding can be formulated as a predictability problem based on an initial condition. Small perturbations in the initial value may result in large changes in the streamline in regions of high unpredictability. Analyzing this predictability quantifies the perturbation a trajectory is subjugated to by the flow. In other words, some predictions are less certain than others as a function of initial conditions. We introduce novel techniques to visualize important user input parameters such as streamline and pathline seeding position in both space and time, seeding rake position and orientation, and inter-seed spacing. The implementation is based on a metric which quantifies similarity between stream and pathlines. This is important for Computational Fluid Dynamics (CFD) engineers as, even with the variety of seeding strategies available, manual seeding using a rake is ubiquitous. We present methods to quantify and visualize the effects that changes in user-controlled input parameters have on the resulting stream and pathlines. We also present various visualizations to help CFD scientists to intuitively and effectively navigate this parameter space. The reaction from a domain expert in fluid dynamics is also reported.