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X선 위상차 가시화 기법을 이용한 GDI 인젝터 노즐 근방의 분무 간 상호간섭 해석
배규한 ( Gyuhan Bae ),문석수 ( Seoksu Moon ) 한국분무공학회지 2020 한국액체미립화학회지 Vol.25 No.2
Despite its benefit in engine thermal efficiency, gasoline-direct-injection (GDI) engines generate substantial particulate matter (PM) emissions compared to conventional port-fuel-injection (PFI) engines. One of the reasons for this is that the spray collapse caused by the spray-to-spray interaction forms the locally rich fuel-air mixture and increases the fuel wall film. Previous studies have investigated the spray collapse phenomenon through the macroscopic observation of spray behavior using laser optical techniques, but it is somewhat difficult to understand the interaction between sprays that is initiated in the nearnozzle region within 10 mm from the nozzle exit. In this study, the spray structure, droplet size and velocity data were obtained using an X-ray imaging technique from the near-nozzle to the downstream of the spray to investigate the spray-tospray interaction and discuss the effects of spray collapse on local droplet size and velocity distribution. It was found that as the ambient density increases, the spray collapse was promoted due to the intensified spray-to-spray interaction, thereby increasing the local droplet size and velocity from the near-nozzle region as a result of droplet collision/coalescence.
배규한(Gyuhan Bae),정용한(Yonghan Jung),유환희(Hwanhee Yoo) 대한공간정보학회 2015 한국지형공간정보학회 학술대회 Vol.2015 No.9
본 연구는 경상남도 진주시의 화재 발생의 추이를 분석하기 위하여 진주소방서와 국가화재 정보시스템에서 2007년부터 2014년까지 화재발생데이터를 수집하여 연구를 진행하였다. 진주시는 경남 서부권에 위치하고 있으며 대상기간 내 발생한 30,086건의 화재 중 2,441건의 화재가 발생하여 경상남도 20개 시, 군, 구 중 3순위에 기록 되고 있으며 화재의 위험이 높은 것으로 나타났다. 이에 본 연구에서는 대상지의 2007년부터 2014년까지 화재발생데이터를 바탕으로 화재발생 관련 각종 정보와 위치정보를 연계하여 트렌드 분석을 하여 화재피해저감 대책 수립에 활용될 수 있는 방안을 마련하는데 활용되고자 한다.
이상권 ( Sanggwon Lee ),배규한 ( Gyuhan Bae ),( Omer Faruk Atac ),문석수 ( Seoksu Moon ),강진석 ( Jinsuk Kang ) 한국분무공학회 2020 한국액체미립화학회지 Vol.25 No.4
To meet stringent emission regulations of automotive engines, fuel injection control techniques have advanced based on reliable and fast computing prediction models. This study aims to develop a reliable lightweight prediction model of fuel injection rates using a small number of input parameters and based on simple fluid dynamic theories. The prediction model uses the geometry of the injector nozzle, needle motion data, injection conditions and the fuel properties. A commercial diesel injector and US No. 2 diesel were used as the test injector and fuel, respectively. The needle motion data were measured using X-ray phase-contrast imaging technique under various fuel injection pressures and injection pulse durations. The actual injector rate profiles were measured using an injection rate meter for the validation of the model prediction results. In the case of long injection durations with the steady-state operation, the model prediction results showed over 99 % consistency with the measurement results. However, in the case of short injection cases with the transient operation, the prediction model overestimated the injection rate that needs to be further improved.