• Proceedings of the KSME Conference
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• 2000.04b
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• pp.650-654
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• 2000

Particle Imaging Velocimetry (PIV) is becoming one of essential methods to measure velocity fields of fluid flows. In this paper, a genetic algorithm capable of tracking same particle pairs on two separated images is introduced. The fundamental of the developed technique is based on that on-to-one correspondence is found between two tracer particles selected in two image planes by taking advantage of combinatorial optimization of the genetic algorithm. The fitness function controlling reproductive success in the genetic algorithm is expressed by physical distances between the selected tracer particles. The capability of the developed genetic algorithm is verified by a computer simulation on a farced vortex flow.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.170-177
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• 2020

A mixer with a new concept design has been adapted into water treatment plants. It reportedly cuts down the energy consumption of the mixer by the new mixer, which moves vertically and creates internal flows toward its bottom. However, no experimental observations have been made on the internal flow caused by a vertical impeller. In this study, a radiotracer experiment, radioactive particle tracking (RPT) technique, and particle image velocimetry (PIV) were carried out to visualize the flow in the mixer, and compared to each other. The results show that the flow patterns from these techniques are very similar to each other, and the performance of the mixer was good enough to mix the inner materials.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.153-165
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• 2010

Large-Scale Particle Image Velocimetry (LSPIV) is an extension of particle image velocimetry (PIV) for measurement of flows spanning large areas in laboratory or field conditions. LSPIV is composed of six elements - seeding, illumination, recording, image transformation, image processing, postprocessing - based on PIV. Possible error elements at each step of Mobile LSPIV (MLSPIV), which is a mobile version of LSPIV, in field applications are identified and summarized the effect of the errors which were quantified in the previous studies. The total number of elemental errors is 27, and five error sources were evaluated previously, seven elemental errors are not effective to the current MLSPIV system. Among 15 elemental errors, four errors - sampling time, image resolution, tracer, and wind - are investigated through an experiment at a laboratory to figure out how those errors affect to velocity calculation. The analysis to figure out the effect of the number of images used for image processing on the velocity calculation error shows that if over 50 images or more are used, the error due to it goes below 1 %. The effect of the image resolution on velocity calculation was investigated through various image resolution using digital camera. Low resolution image set made 3 % of velocity calculation error comparing with high resolution image set as a reference. For the effect of tracers and wind, the wind effect on tracer is decreasing remarkably with increasing the flume bulk velocity. To minimize the velocity evaluation error due to wind, tracers with high specific gravity is favorable.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.821-831
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• 2013

Recently image velocimetries, including particle image velocimetry (PIV) and surface image velocimetry (SIV), are often used to measure flow velocities in laboratories and rivers. The most difficult point in using image velocimetries may be how to determine the sizes of the interrogation areas and the measurement uncertainties. Especially, it is a little hard for unskilled users to use these instruments, since any standardized measuring techniques or measurement uncertainties are not well evaluated. Sometimes the user's skill and understanding on the instruments may make a wide gap between velocity measurement results. The present study aims to evaluate image velocimetry's uncertainties due to the changes in the sizes of interrogation areas and searching areas with the error analyses. For the purpose, we generated 12 series of artificial images with known velocity fields and various numbers and sizes of particles. The analysis results showed that the accuracy of velocity measurements of the image velocimetry was significantly affected by the change of the size of interrogation area. Generally speaking, the error was reduced as the size of interrogation areas became small. For the same sizes of interrogation areas, the larger particle sizes and the larger number of particles resulted smaller errors. Especially, the errors of the image velocimetries were more affected by the number of particles rather than the sizes of them. As the sizes of interrogation areas were increased, the differences between the maximum and the minimum errors seemed to be reduced. For the size of the interrogation area whose average errors were less than 5%, the differences between the maximum and the minimum errors seemed a little large. For the case, in other words, the uncertainty of the velocity measurements of the image velocimetry was large. In the viewpoint of the particle density, the size of the interrogation area was small for large particle density cases. For the cases of large number of particle and small particle density, however, the minimum size of interrogation area became smaller.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.211-221
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• 1998

A digital image processing technique which is able to get the velocity vector distribution of a surface of the spilled oil in the ocean without contacting the flow itself. This technique is based upon the PIV(Particle Imaging Velocimetry) technique and its system mainly consists of a high sensitive camera, a CCD camera, an image grabber, and a host computer in which an image processing algorithm is adopted for velocity vector acquisition. For the acquisition of the advective velocity vector of floating matters on the ocean, a new multi-frame tracking algorithm is proposed, and for the acquisition of the diffusion velocity vector distribution of the spilt oil onto the water surface, a high sensitive gray-level cross-correlation algorithm is proposed.

• Journal of Power System Engineering
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• v.20 no.6
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• pp.51-57
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• 2016

Experiment is carried out to investigate the mixed convective flow in three-dimensional horizontal rectangular channels filled with high viscous fluid. The particle image velocimetry(PIV) with thermo-sensitive liquid crystal tracers is used for visualizing and analysis. Quantitative data of temperature and velocity are obtained by applying the color-image processing to a visualized image, and neural network is applied to the color-to-temperature calibration. In this study, the fluid used is silicon oil(Pr=909), the aspect ratio(channel width to heigh) is 4 and Reynolds number is $2{\times}10^{-2}$. From the present study, we can visualize the quantitative temperature and velocity of mixed convective flow in three-dimensional horizontal rectangular channels simultaneously.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.291-302
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• 1998

A digital image processing technique which is able to be used for getting the velocity vector distribution of a surface of the spilt oil in the ocean without contacting the flow itself. This technique is based upon the PIV(Particle Imaging Velocimetry) technique and its system mainly consists of a high sensitive camera, a CCD camera, an image grabber, and a host computer in which an image processing algorithm is adopted for velocity vector acquisition. For the acquisition of the advective velocity vector of floating matters on the ocean, a new multi-frame tracking algorithm is proposed, and for the acquisition of the diffusion velocity vector distribution of the spilt oil onto the water surface, a high sensitive gray-level cross-correlation algorithm is proposed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.6
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• pp.603-608
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• 2011

Since PTV (particle tracking velocimetry) provides velocity vectors by tracking each particle in a fluid flow, it has significant benefits when used for nano- and bio-fluid flows. However, PTV has only been used for limited flow fields because interpolation data loss is inevitable in PTV in principle. In this paper, a hybrid particle image velocimetry (PIV) algorithm that eliminates interpolation data loss was constructed by using an affine transformation. For the evaluation of the performance of the constructed hybrid PIV algorithm, an artificial image test was performed using Green-Taylor vortex data. The constructed algorithm was tested on experimental images of the wake flow (Re = 5,300) of a rectangular body ($6cm\;{\times}3cm$), and was demonstrated to provide excellent results.

• Journal of the Korean Society of Visualization
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• v.15 no.2
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• pp.10-15
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• 2017

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.982-988
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• 2002