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首页> 外文期刊>Experiments in Fluids: Experimental Methods and Their Applications to Fluid Flow >Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements
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Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements

机译:基于SOM神经网络的杆状颗粒匹配算法在分散两相流测量中的应用

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摘要

A matching algorithm based on self-organizing map (SOM) neural network is proposed for tracking rodlike particles in 2D optical measurements of dispersed twophase flows. It is verified by both synthetic images of elongated particles mimicking 2D suspension flows and direct numerical simulations-based results of prolate particles dispersed in a turbulent channel flow. Furthermore, the potential benefit of this algorithm is evaluated by applying it to the experimental data of rod-like fibers tracking in wall turbulence. The study of the behavior of elongated particles suspended in turbulent flows has a practical importance and covers a wide range of applications in engineering and science. In experimental approach, particle tracking velocimetry of the dispersed phase has a key role together with particle image velocimetry of the carrier phase to obtain the velocities of both phases. The essential parts of particle tracking are to identify and match corresponding particles correctly in consecutive images. The present study is focused on the development of an algorithm for pairing non-spherical particles that have one major symmetry axis. The novel idea in the algorithm is to take the orientation of the particles into account for matching in addition to their positions. The method used is based on the SOM neural network that finds the most likely matching link in images on the basis of feature extraction and clustering. The fundamental concept is finding corresponding particles in the images with the nearest characteristics: position and orientation. The most effective aspect of this two-frame matching algorithm is that it does not require any preliminary knowledge of neither the flow field nor the particle behavior. Furthermore, using one additional characteristic of the non-spherical particles, namely their orientation, in addition to its coordinate vector, the pairing is improved both for more reliable matching at higher concentrations of dispersed particles and for higher robustness against loss of particle pairs between image frames.
机译:提出了一种基于自组织映射(SOM)神经网络的匹配算法,用于在分散的两相流的二维光学测量中跟踪棒状粒子。通过模拟2D悬浮液流的细长颗粒的合成图像和分散在湍流通道流中的长颗粒的直接基于数值模拟的结果进行了验证。此外,通过将该算法应用于在壁湍流中跟踪棒状纤维的实验数据,可以评估该算法的潜在优势。对湍流中悬浮的细长颗粒行为的研究具有实际意义,并且涵盖了工程和科学领域的广泛应用。在实验方法中,分散相的粒子跟踪测速法与载体相的粒子图像测速法一起具有关键作用,以获取两相的速度。粒子跟踪的基本部分是在连续图像中正确识别和匹配相应的粒子。本研究集中于一种算法的发展,该算法用于配对具有一个主要对称轴的非球形粒子。该算法中的新颖思想是除了考虑粒子的位置外,还要考虑粒子的方向。所使用的方法基于SOM神经网络,该SOM神经网络基于特征提取和聚类在图像中找到最可能的匹配链接。基本概念是在图像中找到具有最接近特征(位置和方向)的相应粒子。这种两帧匹配算法最有效的方面是,它不需要流场或粒子行为的任何初步知识。此外,除了使用其非球面坐标矢量以外,还使用非球形粒子的另一个特征,即它们的方向,可以改善配对,以在更高浓度的分散粒子上实现更可靠的匹配,并提高图像间粒子对丢失的稳定性框架。

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