• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.558-564
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• 2021

The water purifier market has increased rapidly in recent years. The welding technology of the evaporator is a key component that determines the level of ice production and the cold water performance of an ice purifier. The finger type evaporator of an ice purifier can remove ice and is divided largely into an instant heat method and a hot gas method. In the hot gas type evaporator, particularly during the production process, the pinhole phenomenon inside the copper pipe and clogging problems occur intermittently when welding high-pressure pipes due to the high-temperature oxygen welding. Its use in a water purifier can cause a problem in that ice and cold water do not form, and repairs cannot be made on site. To solve this problem, in this study, a cap jig was applied to improve the welding defect of the hot gas evaporator. In addition, the oxygen welding flame size was adjusted so that the heat source could be well supplied to the cap jig, and the effectiveness was confirmed through a wave pressure test, a test, and a thermal shock test.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.2
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• pp.139-145
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• 1998

Filament winding method is widely used for composite fabrications using low viscosity liquid for-mation and processing asymmetrical structures of pressure vessel pipe rocket motor case etc. The filament winding method is affected by several parameters such as pot life of process time viscosi-ty of resin filament winding temperature and schedules curing condition and post curing condi-tion of resin. To develope high functional composite materials the rotation(5, 15, 20, 30rpm) of the winding machine was controlled by D.C motor. And the wiper to give proper tension was equipped between strand and resin bath. The resin is hooked by the design wiper. The adequate cure schedule was found by DSC. NOL ring test is carried out to investigate the basic physical properties such as design technology. The void contents in filament winding is generally higher than that of the prepreg laminated plate. These high contents of void can make a crack in resin in spite of low deformation. These problem was solved by giving tension in processing. To improve the characteristics of fiber volume fraction void contents resin/fiber bonding the winding speedc is changed under constant tension. It was found that resin impregnation was not different from in fiber contents void contents at the range of 0.5~1kg tension but it was found that resin was not impregnated at the above of 1.5kg tension. In burst test a pure PE liner was failed at a nozzle part under the $14kg/\textrm{cm}^2$ pressure but a pressure vessel of CNG was failed at a cylinder part under the $200kg/\textrm{cm}^2$ pressure.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.521-521
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• 2023

지방상수도의 관 파손사고 감지 및 누수관리 방법에는 블록시스템 구축을 통한 소블록별 야간최소유량 감시방법이 가장 대표적이다. 야간최소유량은 새벽 2시와 4시 사이의 인구 활동 비율이 가장 낮은 새벽 시간대에 소블록에 공급된 유량을 의미하며, 대부분 유량 성분은 누수량일 것이라는 가정에서 출발한다. 그러나 아파트 중심의 주거 형태를 보이는 도심지의 경우, 새벽 시간대에도 다량의 물수요가 비정기적으로 발생하고 있어 관망의 이상 여부를 감시하기 위한 관리기준으로서 야간최소유량을 이용하기에는 높은 일간 변동성에 따른 한계가 있다고 할 수 있다. 즉, 야간최소유량은 관 파손사고 발생의 감시보다는 관로 연결 또는 급수전 분기 부위에서 발생하는 미량의 누수가 수개월에 걸쳐 누적되는 장기추세를 분석하여 누수탐사반의 투입 시점을 결정하기 위한 근거를 제시하기 위한 목적으로 사용되며, 아직까지 관 파손사고의 발생은 자체적인 감지보다는 민원에 의해 인지되는 경우가 많다. 최근, 스마트관망 구축사업(SWM) 등을 통해 관 파손 및 누수 감지를 위한 청음식 누수감지센서가 소블록 내 도입되고 있으나, 초기 시설투자에 큰 비용이 수반되며 주변 소음과 배터리 전원방식의 한계로 인하여 새벽 시간대에만 분석이 제한적으로 적용되는 경우가 많아 이 역시도 상시적인 관 파손사고의 감시기술이라 보기는 어렵다. 본 연구에서는 소블록 유입점에서의 유량·압력과 소블록 내에 설치된 대수용가 스마트미터, 그리고 사고감지를 위한 수압계 사이의 평상시 수리적 균형을 학습한 DNN(Deep Neural Network) 모델을 이용하여 관 파손사고를 실시간 감지하는 모델 개발연구를 수행하였다. 모델은 관 파손사고 감지를 위한 수압계의 최적 위치와 대수를 결정하기 위한 모듈과 관 파손사고 감지모듈로 구성되며, 1개 소블록 Test-Bed를 구축하여 모델을 생성하고 PDD 관망해석 모델을 통해 생성된 가상의 사고에 대한 감지 여부로서 개발 모델의 감지성능을 평가하였다.

• Korean Chemical Engineering Research
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• v.50 no.6
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• pp.980-987
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• 2012

Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.

• Fire Science and Engineering
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• v.25 no.6
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• pp.168-177
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• 2011

The combustion test for real box of AC outdoor unit has been performed in this study in order to estimate the fire hazard in multi-system type of AC outdoor unit which is currently used for commercial use. The result showed that in test, there was explosion inside of AC outdoor unit, and flame erupted and fire spread through upper side grill. And then this fire burnt the combustibles such as wires, electronic control board, heat exchange copper plate and plastics etc inside the unit, refrigerant gas pipe was burst due to fire, and accelerated the explosion and flame eruption to outside while the refrigerant was erupting. It is found in this test that the maximum heat release rate of AC outdoor unit is 5,830 kW, the maximum internal temperature measured with infrared camera and thermocouple is $1,201^{\circ}C$, maximum ambient temperature is $881^{\circ}C$, and flame rose higher than about 5 m. It is concluded that the fire in AC outdoor unit cause fire to combustibles around the unit, and may give big damage by generating the secondary fire. It is expected that the result obtained from the test on the real object may be applied to fire realization of AC outdoor unit and estimation of fire spreading to the combustibles around in the future computer simulation.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.