Abstract

Understanding the onset of clustering is an essential consideration in many engineering and natural systems. High operating costs and sophisticated methods associated with the existing spatial measurement techniques in clustering analysis call upon the use of low-dimensional measurements. In this experimental study, we study the 1D temporal droplet diameter data in a polydisperse droplet field with a background turbulence, to directly demonstrate a viable relationship between inertial clustering and the droplet arrival pattern. Upon the onset of clustering, a substantial increase is observed in the occurrence of ordinal patterns containing monotonically increasing sub-patterns, where smaller droplets are followed by bigger droplets, and a clear bifurcation in the occurrence of these patterns with downstream location is observed. These trends are understood in terms of droplet settling velocity. The mean flow dominated background turbulence enhances the settling velocity of clustered droplets, which induces a size–velocity correlation of the droplets and, as a result, increases the occurrence of ordinal patterns containing monotonically increasing sub-patterns. This study shows the potential of using low-dimensional measurements for the qualitative understanding of complex flows in different natural and engineering systems.

中文翻译：

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多分散液滴场中的液滴到达模式、聚集和沉降速度

摘要

了解聚类的开始是许多工程和自然系统中的一个重要考虑因素。聚类分析中现有空间测量技术的高运营成本和复杂方法要求使用低维测量。在这项实验研究中，我们研究了具有背景湍流的多分散液滴场中的一维时间液滴直径数据，以直接证明惯性聚类与液滴到达模式之间的可行关系。聚类开始后，观察到包含单调增加子模式的序数模式的出现显着增加，其中较小的液滴后面跟着较大的液滴，并且观察到这些模式与下游位置的出现存在明显的分叉。这些趋势可以通过液滴沉降速度来理解。平均流主导的背景湍流增强了簇状液滴的沉降速度，从而引起液滴的尺寸-速度相关性，从而增加了包含单调增加子模式的有序模式的出现。这项研究显示了使用低维测量来定性理解不同自然和工程系统中复杂流动的潜力。