论文标题

涡流标识的创新和自动化方法。 I.漩涡算法的描述

An innovative and automated method for vortex identification. I. Description of the SWIRL algorithm

论文作者

Cuissa, José Roberto Canivete, Steiner, Oskar

论文摘要

语境。尚未达到漩涡的普遍接受的定义。因此,我们缺乏一种明确而严格的方法来鉴定流体流中的涡旋。在高度动态和湍流系统(例如太阳气氛)中,对涡旋进行稳健的统计研究是必要的。目标。我们旨在开发一种创新且强大的自动化方法,以根据流量的本地和全球特征来识别涡流。此外,应避免使用可能阻止识别过程中弱涡旋检测的阈值。方法。我们提出了一种新方法,将数学标准的严谨性与形态学技术的全球视角结合在一起。该方法的核心在于在其附近呈现一定程度的曲率的每个点的旋转中心估计。为此,我们采用了Rortex标准,并将其与速度场的形态考虑相结合。然后,我们通过估计的旋转中心的簇来识别一致的涡旋结构。结果。我们证明,与旋转强度和从涡流流中提取物理信息的旋转强度和涡度相比,Rortex是一个更可靠的标准,因为它可以测量单独的流动的刚体旋转部分,并且并不偏向于纯或固有的剪切物。我们表明该方法在由两个羔羊 - 欧洲涡流组成的简单测试案例上表现良好。我们将所提出的方法与最先进的聚类算法结合起来,以构建自动化涡流识别算法。 (简略)

Context. A universally accepted definition of what a vortex is has not yet been reached. Therefore, we lack an unambiguous and rigorous method for the identification of vortices in fluid flows. Such a method would be necessary to conduct robust statistical studies on vortices in highly dynamical and turbulent systems, such as the solar atmosphere. Aims. We aim to develop an innovative and robust automated methodology for the identification of vortices based on local and global characteristics of the flow. Moreover, the use of a threshold that could potentially prevent the detection of weak vortices in the identification process should be avoided. Methods. We present a new method that combines the rigor of mathematical criteria with the global perspective of morphological techniques. The core of the method consists in the estimation of the center of rotation for every point of the flow that presents some degree of curvature in its neighborhood. For that, we employ the Rortex criterion and combine it with morphological considerations of the velocity field. We then identify coherent vortical structures by clusters of estimated centers of rotation. Results. We demonstrate that the Rortex is a more reliable criterion than are the swirling strength and the vorticity for the extraction of physical information from vortical flows, because it measures the rigid-body rotational part of the flow alone and is not biased by the presence of pure or intrinsic shears. We show that the method performs well on a simplistic test case composed of two Lamb-Oseen vortices. We combine the proposed method with a state of the art clustering algorithm to build an automated vortex identification algorithm. (Abridged)

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