Jankun-Kelly Monika, Jiang Ming, Thompson David, Machiraju Raghu
Computational Simulation and Design Center, Mississippi State University, USA.
IEEE Trans Vis Comput Graph. 2006 Sep-Oct;12(5):957-64. doi: 10.1109/TVCG.2006.201.
In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability.
为了理解大数据集中的复杂涡旋流，我们必须能够以自动化方式检测和可视化涡旋。在本文中，我们提出了一种基于特征的涡旋检测和可视化技术，该技术适用于在非结构化网格上计算的大型计算流体动力学数据集。特别地，我们专注于将该技术应用于锯齿形机翼上的流动可视化以及带有抖动鸭翼的旋转导弹周围的流场可视化。我们基于涡旋核心与某些标量场中的局部极值重合这一观察结果，开发了一种核心线提取技术。我们还开发了一种基于k均值聚类的处理复杂涡旋拓扑的新技术。这些技术有助于在模拟数据中可视化可能未得到最佳分辨率或采样的涡旋。文中给出的结果突出了我们方法的优点和缺点。我们通过描述如何改进我们的方法以增强鲁棒性并扩大其适用范围来得出结论。
