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하이브리드 PTV-PIV 알고리듬에 의한 고정밀 와도 추정
조경래(Gyong Rae Cho),도덕희(Deog Hee Doh),권성용(Seong Ryong Kwon),김형준(Heong Jon Kim),이재민(Jae Min Lee) 한국가시화정보학회 2010 한국가시화정보학회 학술발표대회 논문집 Vol.2010 No.11
In this paper, an algorithm which does not require an interpolation is newly constructed by using an affine transformation algorithm. Since PTV(Particle Tracking Velocimetry) has strong benefits when it is used for Nano- and Bio- fluid flows and shows better performances in probing the turbulences than that of PIV(particle image velocimetry), a hybrid algorithm based upon PTV is introduced through PIV calculating processes. Since PTV results are from tracking each particle in the fluid flows, the obtained raw vectors should be interpolated on grids to evaluate the physics of the fluid flows, which produces inevitably large interpolation errors in principle. The developed algorithm has been tested for the calculation of vorticities for the cylinder wake(Re=300).
하이브리드 PTV-PIV알고리듬에 의한 고정밀 와도 추정
도덕희(Deog Hee Doh),조경래(Gyong Rae Cho),이재민(Jae Min Lee) 한국가시화정보학회 2010 한국가시화정보학회지 Vol.8 No.4
A PTV algorithm was constructed using a linear transformation, in which the merits of the conventional PIV and PTV were adopted. In PIV calculations, the obtained velocity vectors are affected by the filtering effects by its calculation principle. PTV techniques are widely used for their excellences of measuring small scaled flows, such as nano and bio flows. However, PTVs produce vector errors due to interpolation process. To overcome these problems, a hybrid PTV algorithm was constructed by combining PTVs' and PIVs' benefits using a linear transformation. The Taylor-Green vortex flows were generated for the tests of vorticity calculations. The conventional gray-level cross-correlation PIV technique and 2-Frame PTV technique were tested for the same flows for comparisons with those obtained by the constructed hybrid algorithm. The excellence of the constructed hybrid algorithm was validated through an actual experiment on the cylinder wake.
가스배관망 작동상태 실시간 진단용 인공신경망 기반 모니터링 시스템
전민규(Min Gyu Jeon),조경래(Gyong Rae Cho),이강기(Kang Ki Lee),도덕희(Deog Hee Doh) 대한기계학회 2015 大韓機械學會論文集B Vol.39 No.2
본 연구에서는 인공신경망을 이용하여 배관이나 배관요소의 작동상태를 예측할 수 있는 진단방법을 제안한다. 입자영상유속계 기술을 이용하여 얻어진 배관의 검사부위의 진동에 의한 이동량을 인공신경망의 학습용으로 사용한다. 측정시스템은 카메라, 조명, 인공신경망이 탑재된 호스트컴퓨터로 구성된다. 구축된 모니터링시스템이 제대로 작동하는지 이미 알고 있는 진동원(2개의 휴대폰)에 대하여 적용하였다. 진동가속도의 최소값, 최대값, 평균값을 인공신경망의 학습에 사용해 본 결과, 평균값이 진동 상태의 실시간 모니터링에 적합함을 확인하였다. 구축된 진단시스템은 실제 가스배관의 작동상태에 대하여 모니터링 가능함이 확인되었다. In this study, a new diagnosis method which can predict the working states of a pipe or its element in realtime is proposed by using an artificial neural network. The displacement data of an inspection element of a piping system are obtained by the use of PIV (particle image velocimetry), and are used for teaching a neural network. The measurement system consists of a camera, a light source and a host computer in which the artificial neural network is installed. In order to validate the constructed monitoring system, performance test was attempted for two kinds of mobile phone of which vibration modes are known. Three values of acceleration (minimum, maximum, mean) were tested for teaching the neural network. It was verified that mean values were appropriate to be used for monitoring data. The constructed diagnosis system could monitor the operation condition of a gas pipe.
도덕희(Deog Hee Doh),조경래(Gyong Rae Cho),이재민(Jae Min Lee) 대한기계학회 2011 大韓機械學會論文集B Vol.35 No.6
입자추적유속계(PTV)는 나노 및 바이오 분야의 유체유동장에서는 각 입자들을 추적하여 속도측정을 하는 관계로 많은 강점이 있다. 그러나 측정원리상 보간에 의한 속도장 측정오차를 피할 수 없는 관계로 PTV기술을 사용함에 있어서 제한적이었다. 본 연구에서는 어파인변환 알고리듬을 PIV 및 PTV측정에 도입함으로써 보간에 의한 오차를 줄일 수 있는 어파인변환 기반 하이브리드 PIV알고리듬을 구축하였다. 구축된 알고리듬에 대한 성능평가를 위하여 Green-Taylor와유동의 수치적 데이터를 이용한 가상영상에 대한 시험을 실시하였으며, 이로부터 입자수가 2000개 이상일 때 최적의 측정성능임을 확인하였으며 상호상관PIV법 및 확률일치PTV법보다 우수한 측정성능임을 확인하였다. 나아가 길이비 2:1(6㎝ × 3㎝)인 장방형 물체후류(Re=5,300)에 대한 실험 영상에 대한 실제 계산을 통하여 구축된 알고리듬에 대한 측정성능의 우수성을 확인하였다. 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 (6 ㎝ × 3 ㎝), and was demonstrated to provide excellent results.
이창제(Chang Je Lee),조경래(Gyong Rae Cho),김의간(Uei Kan Kim),김동혁(Dong Hyuk Kim),도덕희(Deog Hee Doh) 한국가시화정보학회 2016 한국가시화정보학회지 Vol.14 No.2
In this study, a Masked Omni-Directional Integration(MODI) method for pressure calculation is proposed using the Particle Image Velocimetry (PIV) data. To obtain the velocity field, the Affine PIV method was adopted. Synthetic images were generated for a solid body rotation. Calculation on the pressure was based on the Navier-Stokes equation. The results obtained by the MODI were compared with those obtained by theoretical pressure and by the Omni-Directional Integration(ODI) method. It was shown that the minimum error by the proposed MODI method was attained when the mask size was 1.