• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.3
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• pp.134-142
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• 2018

Developing model to predict the characteristic of vented gas was vented through an orifice is presented. Simulations with models which were developed with assumptions considering heat transfer inside the vessel were conducted. Also, representative pressure and temperature were measured from experiments with the pressure vessel which is applicable to a propulsion system. Developed model were verified with comparison between calculations and experiments.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.268-276
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• 2017

Developing a model was carried out to predict the characteristic of a pressure vessel from which the gas was vented through an orifice. An experimental test was conducted on a pressure vessel applicable to a propulsion system so that representative pressure and temperature were measured. Simulations were conducted with models using assumptions considering heat transfer inside the vessel, and the results were compared to those from the experiment. As a result, it was found out that a proposed heat transfer model was proper to predict pressure and temperature of the vented gas comparable to the measured data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.963-971
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• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.33-47
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• 2018

City gas pipelines are buried underground. Because of this, pipeline is hard to manage, and can be easily damaged. This research proposes a real time prediction system that helps experts can make decision about pressure anomalies. The gas pipline pressure data of Jungbu City Gas Company, which is one of the domestic city gas suppliers, time variables and environment variables are analysed. In this research, regression models that predicts pipeline pressure in minutes are proposed. Random forest, support vector regression (SVR), long-short term memory (LSTM) algorithms are used to build pressure prediction models. A comparison of pressure prediction models' preformances shows that the LSTM model was the best. LSTM model for Asan-si have root mean square error (RMSE) 0.011, mean absolute percentage error (MAPE) 0.494. LSTM model for Cheonan-si have RMSE 0.015, MAPE 0.668.

• Plant Journal
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• v.10 no.3
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• pp.39-44
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• 2014

The gas turbine thermal efficiency has been predicted when the compressor pressure ratio increases from the previously set 13.5. Thermal efficiency has been predicted from 14.2 up to 18.2 at which the turbine work reaches its maximum value on the assumption that isentropic efficiency of the compressor and the turbine are constant using the operating data at the pressure ratio of 13.5. 35.11% of thermal efficiency has been acquired by the performance test when the pressure ratio increased to 16.2 since replacing the compressor low pressure stages. It's been approved that predicting thermal efficiency using the operating data at the pressure ratio of 13.5 is useful within 7.86% of tolerance as the figure measured by the performance test.

• Journal of Energy Engineering
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• v.4 no.2
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• pp.288-296
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• 1995

촉매변환기용 모노리스에서의 속도변화에 따른 압력강하를 알아보기 위하여 풍동을 제작하여 실험하였다. 200 cpsi, 300 cpsi와 400 cpsi의 모노리스 담체에 대한 압력강하를 측정하였고, 듀얼베드 형태에서의 압력강하를 알아보기 위하여 200 cpsi, 300 cpsi와 400 cpsi들 중 두 개씩 조합하여 두 모노리스 담체의 사이 간격을 변화시켜가면서 압력강하를 측정하였다. 또한 많이 사용되고 있는 촉매가 담지된 400cpsi의 모노리스를 이용하여 촉매 담지에 대한 유동의 영향을 살표보았다. 모노리스 상·하류간의 압력강하는 공극율에 상관없이 공기와 유로벽과의 접촉면적에 따라 증가한다. 실험 결과로부터 제안된 상관관계를 상용하여 모노리스 형상에 따른 압력강하를 근사적으로 예측 할 수 있다. 듀얼베드 형태에서의 압력강하는 상류부와 하류부의 개별적인 모노리스의 압력강하와 두 모노리스 사이에서의 압력강하의 합으로 볼 수 있는데, 두 모노리스 사이에서의 압력강하는 무시할 만 하였다. 따라서 듀얼베드 형태의 전체적인 압력강하는 상류부와 하류부의 개별적인 모노리스에서 생기는 압력강하만의 합으로 구할 수 있다. 촉매가 담지되지 않은 모노리스의 측정결과로부터 제안된 상관관계를 촉매가 담지된 모노리스의 압력강하를 예측하는데 사용하기 위해서는 모노리스 길이를 원래길이의 1.25배로 수정하여 사용하여야 한다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2017.11a
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• pp.360-361
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• 2017

도시가스 배관의 안전을 위해 다양한 시스템이 가동되고 있지만 대부분 현장점검에 의존하는 한계점을 가지고 있다. 본 연구에서는 국내 도시가스 공급업체들 중 하나인 중부도시가스사의 실시간 배관운영 데이터를 분석해 배관의 위험을 예측한다. 배관의 압력, 출력전압, 출력전류, 방식전위, 전위값 데이터와 기타 도시가스 관련요인 데이터를 통합해 상관분석을 진행한다. 그리고 특정 공급권역의 실시간 배관 압력 데이터를 분석해 압력 수치를 예측한다. Random forest regression과 support vector regression(SVR) 알고리즘을 사용해 모델을 구성한 결과 배관 데이터의 시계열 정보를 추가한 데이터 셋과 random forest regression을 사용한 모델에서 가장 우수한 예측 성능을 보인다.

• The Korean Journal of Rheology
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• v.11 no.2
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• pp.153-158
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• 1999

An improved method to predict preferred direction of gas in gas assisted injection molding processes is introduced. Resistance of resin flow is defined and this resistance of resin flow is not directly related to the resistance of gas flow. Pressure drop requirement was believed to be proportional to the resistance to gas flow in our previous work. Instead of using the pressure drop requirement, velocity of resin should be compared to predict the gas flow direction. This method predicts the gas flow direction from the knowledge of process variables such as resin flow length, cross section area of cavity, melt temperature, and short shot. A simulation package was used to confirm the method.

• Applied Chemistry for Engineering
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• v.21 no.3
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• pp.258-264
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• 2010

Most of fluid systems, such as P&ID in ships, power plants, and chemical plants, consist of various components. The components such as bends, tees, sudden-expansions, sudden-contractions, and orifices contribute to overall pressure loss of the system. The local pressure losses across such components are determined using a pressure loss coefficient, k-factor, in lumped parameter models. In many engineering problems Idelchik's k-factor models have been used to estimate them. The present work compares the k-factor based on CFD calculation against Idelchik's model in order to confirm whether a commercial CFD package can be used for pressure loss coefficient estimation of complex geometries. The results show that RSM is the best appropriate for evaluating pressure loss coefficient. Commercial CFD package can be used as a tool evaluating k-factor even though the accuracy is influenced by a turbulence model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.5763-5768
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• 2015

A relief valve is a mechanical element to keep safety by controlling high pressure. Usually, the high pressure is relieved by using the spring force and letting the fluid to flow from another way out of system. When its normal pressure is reached, the relief valve can return to initial state. The relief valve should be designed for smooth operation and should satisfy the structural safety requirement under operating condition. The commercial software ANSYS/WORKBENCH is utilized for flow and structural analysis. Very high pressure may cause structural problem due to severe stress. The study suggests the design satisfying the structural design requirement