• Title/Summary/Keyword: 중성자 영상법

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Visualization of Water Distribution in Cathode Side of a Direct Methanol Fuel Cell Using Neutron Radiography (중성자 라디오그래피 방법을 이용한 직접 메탄올 연료전지 공기극의 내부 물 분포 가시화)

  • Je, Jun-Ho;Doh, Sung-Woo;Kim, Tae-Joo;Kim, Jong-Rok;Xie, Xiaofeng;Kim, Moo-Hwan
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.36 no.10
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    • pp.965-970
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    • 2012
  • In this study, the water distribution in the cathode side of a direct methanol fuel cell (DMFC) is visualized using a neutron imaging technique at the Neutron Radiography Facility (NRF), KAERI. It is difficult to quantify the water content in the cathode side because of $CO_2$ gas. A compared open circuit voltage (OCV) image, relative $CO_2$, and water distribution can be visualized by the neutron imaging technique. This means that the neutron imaging technique is useful for the optimization of the flow field design and the establishment of water management, and, in turn, for the improvement of the cell performance.

A Study on the Metallurgical Characteristics for Sand Iron Ingot Reproduced by the Traditional Iron-making Method on Ancient Period under the Neutron Imaging Analysis (중성자 영상 분석을 활용한 고대 제철법 재현 사철강괴의 금속학적 특성 연구)

  • Cho, Sung Mo;Kim, Jong Yul;Sato, Hirotaka;Kim, TaeJoo;Cho, Nam Chul
    • Journal of Conservation Science
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    • v.35 no.6
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    • pp.631-640
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    • 2019
  • The purpose of this study was to compare analytical results of sand iron bars reproduced by the traditional iron-making method through a destructive analysis and a non-destructive analysis. For these studies, we produced two types of samples. One was sample(SI-A), a part of the sand iron bar for destructive analysis. The other was SI-B(9 ㎠) for non-destructive analysis. A metallurgical microscope and scanning electron microscope were used for the destructive analysis, and neutron imaging analysis with the Hokkaido University Neutron Source (HUNS) at Hokkaido University, Japan, was used for the non-destructive analysis. The results obtained by destructive analysis showed that there was ferrite and pearlite of fine crystallite size, and some of these showed Widmanstätten ferrite microstructure grown within the pearlite and coarse ferrite at the edge of the specimen. The results from the neutron imaging analysis showed that there was also ferrite and pearlite with 3 ㎛ α-Fe of BCC structure. Based on these results, neutron imaging analysis is capable of identifying material characteristics without destroying the object and obtaining optimal research results when applying it to objects of cultural heritage.

Visualization of 2-Phase Flow at Heat Pipe using Neutron Imaging Technique (중성자 영상법을 이용한 Heat Pipe 내의 이상유동 가시화)

  • Kim, TaeJoo;Park, SuJi;Kim, JongYul;Doh, SeungWoo
    • Journal of the Korean Society of Visualization
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    • v.14 no.3
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    • pp.15-21
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    • 2016
  • The circular and flat heat pipe were experimentally investigated by using neutron imaging technique. This experimental study was performed at the DINGO of OPAL research reactor, Australia. The diameter of the circular heat pipe is 10 mm and the dimension of flat is $10(width){\times}3(thickness)mm2$, respectively. We used the distilled water as a coolant. The coolant distributions and 2-phase flow patterns were measured under heating conditions. Experimental results show that neutron imaging technique is a good tool to visualize the 2-phase flow and phenomena in the heat pipe. The coolant distributions and 2-phase flow patterns depend on installation posture of the heat pipe and volume ratio of the coolant. Finally, it was discussed to calculate the void fraction by neutron imaging technique.

Evaluation of Machine Learning Methods to Reduce Stripe Artifacts in the Phase Contrast Image due to Line-Integration Process (선적분에 의한 위상차 영상의 줄무늬 아티팩트 감소를 위한 기계학습법에 대한 평가)

  • Kim, Myungkeun;Oh, Ohsung;Lee, Seho;Lee, Seung Wook
    • Journal of the Korean Society of Radiology
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    • v.14 no.7
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    • pp.937-946
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    • 2020
  • The grating interferometer provides the differential phase contrast image of an phase object due to refraction of the wavefront by the object, and it needs to be converted to the phase contrast image. The line-integration process to obtain the phase contrast image from a differential phase contrast image accumulates noise and generate stripe artifacts. The stripe artifacts have noise and distortion increases to the integration direction in the line-integrated phase contrast image. In this study, we have configured and compared several machine learning methods to reduce the artifacts. The machine learning methods have been applied to simulated numerical phantoms as well as experimental data from the X-ray and neutron grating interferometer for comparison. As a result, the combination of the wavelet preprocessing and machine learning method (WCNN) has shown to be the most effective.