• Title/Summary/Keyword: 추종입자

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Study on Quantitative Visualization Using Bubble Tracer in a Cavitation Tunnel (공동수조에서 추종입자로서 기포를 이용한 정량적 가시화에 대한 연구)

  • Paik, Bu-Geun;Kim, Kyung-Youl;Cho, Seong-Rak;Ahn, Jong-Woo
    • Journal of the Korean Society of Visualization
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    • v.5 no.1
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    • pp.22-29
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    • 2007
  • In the present study, naturally generated bubbles were investigated to be sure if they could be adopted as the tracer for PIV techniques. The bubble can be grown from the nuclei melted in the water of tunnel and the size of bubbles is changed through the variation of tunnel pressure. Since the trace ability and appropriate size of tracer are so important for PIV techniques, the characteristics of bubbles as tracer are revealed in terms of trace ability and bubble size with the variation of flow speed and tunnel pressure in this study. In addition, PIV measurements of (low behind a rotating propeller are conducted to confirm the trace ability of bubbles even in a highly vortical flow.

PIV를 이용한 만곡형 전개판의 가시화 실험

  • 박경현;이주희;배재현;현범수
    • Proceedings of the Korean Society of Fisheries Technology Conference
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    • 2001.05a
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    • pp.45-46
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    • 2001
  • PIV(Particle Image Velocimetry : 입자영상유속계)는 유동장에 분포된 추종입자의 위치를 영상처리에 의해 자동추적 함으로써 속도벡터를 전유동영역에 걸쳐 동시에 구할 수 있는 계측기법이다. 따라서, CFD와 같이 정량적 및 정성적으로 수치해석된 결과와 바로 비교 검토가 가능한 유일한 실험기법으로 인식되고 있다. 본 실험에서는 CFD에 의한 모형의 유체유동 특성을 분석하고 이를 회류수조에서 PIV를 이용해 모형 전개판 주위의 유체흐름을 분석하여 각 전개판 모형의 유체유동 특성을 파악하였다. (중략)

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RBFNN Based Decentralized Adaptive Tracking Control Using PSO for an Uncertain Electrically Driven Robot System with Input Saturation (입력 포화를 가지는 불확실한 전기 구동 로봇 시스템에 대해 PSO를 이용한 RBFNN 기반 분산 적응 추종 제어)

  • Shin, Jin-Ho;Han, Dae-Hyun
    • Journal of the Institute of Convergence Signal Processing
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    • v.19 no.2
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    • pp.77-88
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    • 2018
  • This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

The Simulation for the Organization of Fishing Vessel Control System in Fishing Ground (어장에 있어서의 어선관제시스템 구축을 위한 모의실험)

  • 배문기;신형일
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.36 no.3
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    • pp.175-185
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    • 2000
  • This paper described on a basic study to organize fishing vessel control system in order to control efficiently fishing vessel in Korean offshore. It was digitalized ARPA image on the fishing processing of a fleet of purse seiner in conducting fishing operation at Cheju offshore in Korea as a digital camera and then simulated by used VTMS. Futhermore, it was investigated on the application of FVTMS which can control efficiently fishing vessels in fishing ground. The results obtained were as follows ; (1) It was taken 16 minutes and 35 minutes to casting and hauling net in fishing processing respectively. The length of rope pulled by scout boat was 200m, tactical diameter in casting net was 340.8m, turning speed was 6kts as well. (2) The processing of casting and hauling net was moved to SW, NE as results of simulation when the current direction and speed set into NE, 2kts and SW, 2kts respectively. Such as these results suggest that can predict to control the fishing vessel previously with information of fishing ground, fishery and ship's maneuvering, etc. (3) The control range of VTMS radar used in simulation was about 16 miles. Although converting from a radar of the control vessel to another one, it was continuously acquired for the vector and the target data. The optimum control position could be determined by measuring and analyzing to distance and direction between the control vessel and the fleet of fishing vessel. (4) The FVTMS(fishing vessel traffic management services) model was suggested that fishing vessels received fishing conditions and safety navigation information can operate safely and efficiently.

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