Abstract

In this study, vortical structures are detected on sparse Shake-The-Box data sets using the Coherent-Structure Colouring (CSC) algorithm. The performance of this Lagrangian approach is assessed by comparing the CSC-coloured tracks with the baseline vorticity field. The ability to extract vortical structures from sparse data is accessed on two Lagrangian particle tracking data sets: the flow past an Ahmed body and a swirling jet flow. The effects of two normalized parameters on the identification of vortical structures were defined and studied: the mean track length and the mean inter-particle distance. The accuracy of the vortical-structure detection problem through CSC is shown to improve with decreasing inter-particle distance values, whereas little dependence on the mean track length is observed at all. Overall, the CSC algorithm showed to yield accurate detection of coherent structures for inter-particle distances smaller than 15% of the characteristic dimension of the structure. However, the results quickly deteriorate for sparser Lagrangian data.

Graphic abstract

中文翻译：

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使用相干结构着色检测稀疏拉格朗日数据中的涡旋结构

摘要

在这项研究中，使用相干结构着色（CSC）算法在稀疏的Shake-The-Box数据集上检测到涡旋结构。拉格朗日方法的性能通过将CSC色轨迹与基线涡度场进行比较来评估。可从两个拉格朗日粒子跟踪数据集访问从稀疏数据中提取旋涡结构的能力：流过Ahmed体的流和涡旋射流。定义并研究了两个归一化参数对涡旋结构识别的影响：平均轨道长度和平均粒子间距离。随着粒子间距离值的减小，通过CSC探测的旋涡结构检测问题的准确性得以提高，而对平均轨道长度的依赖性却很小。全面的，CSC算法表明，对于粒子间距离小于结构特征尺寸15％的物体，可以对相干结构进行精确检测。但是，对于稀疏拉格朗日数据，结果很快恶化。

图形摘要