Recognition of analogous oil droplet attached to transparent pipe wall

IF 2.3 3区工程技术 Q2 ENGINEERING, MECHANICAL

Flow Measurement and Instrumentation Pub Date : 2025-02-20 DOI:10.1016/j.flowmeasinst.2025.102852

Han Lian-fu , Zhang Yin-hao , Wang Hai-xia , Gu Jian-fei , Liu Xingbin , Fu Chang-feng

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引用次数: 0

Abstract

PTV is an active study method of oil-water two-phase flow characteristic based on photogrammetry. It has advantages of undisturbed, no-contact and high measurement accuracy which directly related to the image quality. However, analogous oil droplet attachments on inner transparent pipe wall are often recorded as part of image, thus reducing measurement accuracy. To overcome the obstacle, it is necessary to identify and locate the outline of analogous oil droplet attachments. Extracting color and motion characters of oil-water two-phase flow images as features for clustering and applying K-means algorithm to identify and locate the outline of the analogous oil droplet attachment. K-means algorithm's clustering result is greatly affected by initial clustering centers and outlier data in practical applications, so Isolation Forest is adopted to improve K-means algorithm. The new algorithm proposed in this paper is called ILF-Kmeans. Simulation and experiment verification are carried out on ILF-Kmeans algorithm. Simulation results show that ILF-Kmeans algorithm has better clustering effect and higher identification accuracy than K-means algorithm; Experiment results show that measurement accuracy of PTV based on ILF-Kmeans to measure the oil phase velocity of oil-water two-phase flow increases by 4.25 %.

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来源期刊

Flow Measurement and Instrumentation 工程技术-工程：机械

CiteScore

4.30

自引率

13.60%

发文量

123

审稿时长

6 months

期刊介绍： Flow Measurement and Instrumentation is dedicated to disseminating the latest research results on all aspects of flow measurement, in both closed conduits and open channels. The design of flow measurement systems involves a wide variety of multidisciplinary activities including modelling the flow sensor, the fluid flow and the sensor/fluid interactions through the use of computation techniques; the development of advanced transducer systems and their associated signal processing and the laboratory and field assessment of the overall system under ideal and disturbed conditions. FMI is the essential forum for critical information exchange, and contributions are particularly encouraged in the following areas of interest: Modelling: the application of mathematical and computational modelling to the interaction of fluid dynamics with flowmeters, including flowmeter behaviour, improved flowmeter design and installation problems. Application of CAD/CAE techniques to flowmeter modelling are eligible. Design and development: the detailed design of the flowmeter head and/or signal processing aspects of novel flowmeters. Emphasis is given to papers identifying new sensor configurations, multisensor flow measurement systems, non-intrusive flow metering techniques and the application of microelectronic techniques in smart or intelligent systems. Calibration techniques: including descriptions of new or existing calibration facilities and techniques, calibration data from different flowmeter types, and calibration intercomparison data from different laboratories. Installation effect data: dealing with the effects of non-ideal flow conditions on flowmeters. Papers combining a theoretical understanding of flowmeter behaviour with experimental work are particularly welcome.