Department of Computer Science, Swansea University, Swansea SA2 8PP, Wales, United Kingdom.
IEEE Trans Vis Comput Graph. 2012 Feb;18(2):283-98. doi: 10.1109/TVCG.2011.25.
Vector field visualization techniques have evolved very rapidly over the last two decades, however, visualizing vector fields on complex boundary surfaces from computational flow dynamics (CFD) still remains a challenging task. In part, this is due to the large, unstructured, adaptive resolution characteristics of the meshes used in the modeling and simulation process. Out of the wide variety of existing flow field visualization techniques, vector field clustering algorithms offer the advantage of capturing a detailed picture of important areas of the domain while presenting a simplified view of areas of less importance. This paper presents a novel, robust, automatic vector field clustering algorithm that produces intuitive and insightful images of vector fields on large, unstructured, adaptive resolution boundary meshes from CFD. Our bottom-up, hierarchical approach is the first to combine the properties of the underlying vector field and mesh into a unified error-driven representation. The motivation behind the approach is the fact that CFD engineers may increase the resolution of model meshes according to importance. The algorithm has several advantages. Clusters are generated automatically, no surface parameterization is required, and large meshes are processed efficiently. The most suggestive and important information contained in the meshes and vector fields is preserved while less important areas are simplified in the visualization. Users can interactively control the level of detail by adjusting a range of clustering distance measure parameters. We describe two data structures to accelerate the clustering process. We also introduce novel visualizations of clusters inspired by statistical methods. We apply our method to a series of synthetic and complex, real-world CFD meshes to demonstrate the clustering algorithm results.
在过去的二十年中，向量场可视化技术得到了飞速发展，然而，从计算流体动力学（CFD）中对复杂边界曲面的向量场进行可视化仍然是一项具有挑战性的任务。部分原因是建模和模拟过程中使用的网格具有大、非结构化、自适应分辨率的特点。在现有的各种流场可视化技术中，向量场聚类算法具有捕获域中重要区域的详细信息的优势，同时呈现不重要区域的简化视图。本文提出了一种新颖的、鲁棒的、自动的向量场聚类算法，该算法可以从 CFD 中对大的、非结构化的、自适应分辨率的边界网格进行向量场的直观和有洞察力的可视化。我们的自底向上的层次方法是第一个将底层向量场和网格的属性组合到统一的误差驱动表示中。这种方法的动机是 CFD 工程师可以根据重要性提高模型网格的分辨率。该算法具有几个优点。无需进行曲面参数化，即可自动生成聚类，并且能够有效地处理大型网格。在可视化中，简化不太重要的区域，同时保留网格和向量场中包含的最有意义和最重要的信息。用户可以通过调整一系列聚类距离度量参数来交互控制细节级别。我们描述了两种用于加速聚类过程的数据结构。我们还引入了受统计方法启发的新聚类可视化方法。我们将我们的方法应用于一系列合成的和复杂的真实世界的 CFD 网格，以展示聚类算法的结果。
