Abstract

Particle Tracking Velocimetry (PTV) is a popular optical method to measure the velocity field of complex flow at high spatial resolution. One of the practical limitations of this technique is that in general, the accuracy of matching individual particles between a pair of images is limited by the particle image displacement. To deal with this limitation, the present work proposed a hybrid ant colony optimization (ACO) algorithm for particle matching in three-dimensional (3D) scenarios . It can be regarded as an update of the conventional ACO PTV algorithm (Takagi J Vis 27:89–90. https://doi.org/10.3154/jvs.27.Supplement2_89, 2007, Ohmi et al. Exp Fluids 48(4):589–605. https://doi.org/10.1007/s00348-009-0815-2, 2010). The key concept is to seek a global solution of the minimization of a displacement-pattern function (DPF) via improved ant colony optimization (ACO). The object function, i.e., DPF, hybrids the measure of particle image displacement and the measure of pattern similarity as the particle matching criterion, the latter of which is constructed as the similarity level of Voronoï polygons (VPs) of paired particles. Performance evaluation was based on both the standard particle image database of Visualization Society of Japan (Okamoto et al. Meas Sci Technol 11(6):685–691. https://doi.org/10.1088/0957-0233/11/6/311, 2000) and the laboratory-made synthetic flow. It was shown that this hybrid ACO algorithm has higher matching accuracy than those of exiting ACO methods based on either minimum displacement function or relaxation function. Its credibility in dealing with the scenarios of large relative particle displacement, i.e., the cases where particle image displacement is comparable to or even larger than the mean spacing of neighboring particles was also empirically demonstrated. Other features including fast convergence speed and regular pattern of outliers were also seen. All these make this algorithm a suitable candidate for 3D particle matching in PTV.

Graphic abstract

中文翻译：

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基于蚁群优化的混合3D粒子匹配算法

摘要

粒子跟踪测速（PTV）是一种流行的光学方法，可以在高空间分辨率下测量复杂流的速度场。该技术的实际限制之一是，通常，在一对图像之间匹配单个粒子的精度受到粒子图像位移的限制。为了解决这一限制，本工作提出了一种用于三维（3D）场景中粒子匹配的混合蚁群优化（ACO）算法。它可以被视为对传统ACO PTV算法的更新（Takagi J Vis 27：89–90。https://doi.org/10.3154/jvs.27.Supplement2_89，2007，Ohmi等人，Exp Fluids 48（4） ）：589–605。https：//doi.org/10.1007/s00348-009-0815-2，2010年）。关键概念是寻求使位移模式函数（DPF）最小化的全局解决方案）通过改进的蚁群优化（ACO）。对象函数，即DPF，将粒子图像位移的量度和图案相似性的量度作为粒子匹配标准进行混合，后者被构造为配对粒子的Voronoï多边形（VPs）的相似度。性能评估基于日本可视化协会的标准粒子图像数据库（冈本等人，Meas Sci Technol 11（6）：685–691。https://doi.org/10.1088/0957-0233/11/6 / 311，2000）和实验室合成流。结果表明，与基于最小位移函数或松弛函数的现有ACO方法相比，该混合ACO算法具有更高的匹配精度。它在处理较大的相对粒子位移的情况下的可信度，即 经验还证明了粒子图像位移与相邻粒子的平均间距相当甚至更大的情况。还看到了其他特征，包括快速收敛速度和异常值的规则模式。所有这些使该算法成为PTV中3D粒子匹配的合适候选者。

图形摘要