• Tribology and Lubricants
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• v.21 no.6
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• pp.278-282
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• 2005

Deep groove ball bearing is installed in a control element of an integral nuclear reactor, where water is used as coolant and lubricant. This bearing is made of STS440C stainless steel for the raceways and the balls to use in radioactive environment and water. It is known that the retainer design affects ball bearing operability and endurance life, however there is no verified retainer design and material for water lubricated ball bearing. Four kinds of retainers are manufactured for the endurance test of water lubricated deep groove ball bearing. Three of them are commercially developed types and the other is designed for this research. It is verified that ball bearings with steel pressed and general plastic retainer can not survive to required life in the water, however bearings with machined type and cylinder type retainer can survive. This proves that one of the major design parameters for water lubricated ball bearing is retainer type and material. In this paper, experimental research of endurance test for water-lubricated ball bearing are reported.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.471-476
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• 2007

Effluent ground water overflow in deep and broad ground space building. Temperature of effluent ground water is in $12{\sim}20^{\circ}C$ annually and the quality of that water is as good as living water. Therefore if the flow rate of effluent ground water is sufficient as source of heat pump, that is good heat source and heat sink of heat pump. Effuent ground water contain the thermal energy of surrounding ground. So this is a new application of ground source heat pump. In this study open type and c lose type heat pump system using effluent ground water was installed and tested for it church building with large and deep ground space. The effluent flow rate of this building is $800{\sim}1000$ ton/day. The heat pump capacity is 5RT each. The heat pump cooling COP is $4.9{\sim}5.2$ for the open type and $4.9{\sim}5.7$ for close type system. The system cooling COP is $3.2{\sim}4.5$ for open type and $3.8{\sim}4.2$for close type system. This performance is up to that of BHE type ground source heat pump.

• New & Renewable Energy
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• v.3 no.2 s.10
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• pp.40-46
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• 2007

Effluent ground water overflow in deep and broad ground space building. Temperature of effluent ground water is in $12{\sim}20^{\circ}...$ annually and the quality of that water is as good as well water. Therefore if the flow rate of effluent ground water is sufficient as source of heat pump, that is good heat source and heat sink of heat pump. Effuent ground water contain the thermal energy of surrounding ground. So this is a new application of ground source heat pump. In this study open type and close type heat pump system using effluent ground water was installed and tested for a church building with large and deep ground space. The effluent flow rate of this building is $800{\sim}1000\;ton/day$. The heat pump capacity is 5RT. The heat pump heating COP was $3.85{\sim}4.68$ for the open type and $3.82{\sim}4.69$ for the close type system. The system heating COP including pump power is $3.0{\sim}3.32$ for the open type and $3.32{\sim}3.84$ for close type system. This performance is up to that of BHE type ground source heat pump.

• 수도
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• s.37
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• pp.54-61
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• 1986

• The Korean Journal of Food And Nutrition
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• v.29 no.2
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• pp.237-245
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• 2016

In this study, the physicochemical and sensory characteristics of low-salt Myungran jeotkal (Alaskan pollock roe) were evaluated after fermentation at $4^{\circ}C$ and $20^{\circ}C$ with or without the addition of deep sea water, salt from deep sea water, and vegetable-origin lactic acid bacteria (Lactobacillus fermentum JS, LBF). When fermented at $20^{\circ}C$, the addition of LBF to Myungran jeotkal resulted in a slow increase in lactic acid content, followed by an abrupt increase after five days of fermentation. However, when fermented at $4^{\circ}C$, the lactic acid content did not change significantly. Further, when Myungran jeotkal fermented at $4^{\circ}C$, the pH decreased as lactic acid production increased. The salinity of Myungran jeotkal fermented at $4^{\circ}C$ and $20^{\circ}C$ was 7% and was not affected by fermentation period. When fermented at $20^{\circ}C$, volatile basic nitrogen and amino nitrogen contents increased with increasing duration of fermentation. Further, volatile acid content decreased, however, the content of amino nitrogen increased after 11 days of fermentation with LBF and no salt effects were observed. When fermented at $20^{\circ}C$ for 13 days, preference (sensory evaluation) was the highest in all experimental groups after 9 days of fermentation, and then decreased as the fermentation period increased. The free amino acid content was highest (1,648.8 mg/100 g) in Myungran jeotkal when sun-dried salt and LBF were added, 2.3 times higher than in the control.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.14 no.1
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• pp.34-40
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• 2002

In order to estimate the height of long period wave from character of deep water wave, field observation is carried out three wave gauge are arranged by a straight line from the seashore to offshore direction and the result is analyzed. In addition, the existing theory of the mechanism for long period wave producer is verified by field observation, and the relation between deep water wave and long period wave of shallow area is examined. Observed long period wave is coincided with the existing theory for the most part. In order to add the change of time and space of long period wave, the height of long period wave is calculated by the composition of long period wave in each position. As a result, the relation of long period wave and deep water wave is presented more clear. Estimate formula is drew through them.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.77-84
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• 2000

In this paper, the stress analysis of the cast iron insert of spent nuclear fuel disposal canister in a deep repository at 500m underground is done for the underground pressure variation. Since the nuclear fuel disposal usually emits much heat and radiation, its careful treatment is required. And so a long term safe repository at a deep bedrock is used. Under this situation, the canister experiences some mechanical external loads such as hydrostatic pressue of underground water, swelling pressure of bentonite, sudden rock movement etc.. Hence, the canister should be designed to withstand these loads. The cast iron insert of the canister mainly supports these loads. Therefore, the stress analysis of the cast iron insert is done to determine the design variables such as the diameter versus length of canister and the number and array type of inner baskets in this paper, The linear static structural analysis is done using the finite element analysis method. And the finite element analysis code, NISA, is used for the computation.

• Proceedings of the Korean Geotechical Society Conference
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• 1990.10a
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• pp.97-110
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• 1990

This paper presents a case history of a deep open cut excavation of Nakagawa section for Futuoka Subway construction which adopted observational mettled against boiling failure and completed with success by modifying construction based upon field monitoring. One of the difficult conditions for the excavation was sandy layer with high water pressure which was anticipated boiling failure. The boiling was generally considered as one of the difficult phenomena to work with the observational method because of its unpredictable catastrophic nature. Laboratory experiments showed the existence of the prefailure movements of the ground and the possibility of the application of the observational method against the boiling failure. Construction step was planned to be modified, if necessary, based upon field monitoring and was completed with success.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.150-150
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• 2023

기작기반의 역학적 모델과 자료기반의 딥러닝 모델은 수질예측에 다양하게 적용되고 있으나, 각각의 모델은 고유한 구조와 가정으로 인해 장·단점을 가지고 있다. 특히, 딥러닝 모델은 우수한 예측 성능에도 불구하고 훈련자료가 부족한 경우 오차와 과적합에 따른 분산(variance) 문제를 야기하며, 기작기반 모델과 달리 물리법칙이 결여된 예측 결과를 생산할 수 있다. 본 연구의 목적은 주요 상수원인 댐 저수지를 대상으로 수심별 수온과 탁도를 예측하기 위해 기작기반과 자료기반 모델의 장점을 융합한 PGDL(Process-Guided Deep Learninig) 모델을 개발하고, 물리적 법칙 만족도와 예측 성능을 평가하는데 있다. PGDL 모델 개발에 사용된 기작기반 및 자료기반 모델은 각각 CE-QUAL-W2와 순환 신경망 딥러닝 모델인 LSTM(Long Short-Term Memory) 모델이다. 각 모델은 2020년 1월부터 12월까지 소양강댐 댐 앞의 K-water 자동측정망 지점에서 실측한 수온과 탁도 자료를 이용하여 각각 보정하고 훈련하였다. 수온 및 탁도 예측을 위한 PGDL 모델의 주요 알고리즘은 LSTM 모델의 목적함수(또는 손실함수)에 실측값과 예측값의 오차항 이외에 역학적 모델의 에너지 및 질량 수지 항을 제약 조건에 추가하여 예측결과가 물리적 보존법칙을 만족하지 않는 경우 penalty를 부가하여 매개변수를 최적화시켰다. 또한, 자료 부족에 따른 LSTM 모델의 예측성능 저하 문제를 극복하기 위해 보정되지 않은 역학적 모델의 모의 결과를 모델의 훈련자료로 사용하는 pre-training 기법을 활용하여 실측자료 비율에 따른 모델의 예측성능을 평가하였다. 연구결과, PGDL 모델은 저수지 수온과 탁도 예측에 있어서 경계조건을 통한 에너지와 질량 변화와 저수지 내 수온 및 탁도 증감에 따른 공간적 에너지와 질량 변화의 일치도에 있어서 LSTM보다 우수하였다. 또한 역학적 모델 결과를 LSTM 모델의 훈련자료의 일부로 사용한 PGDL 모델은 적은 양의 실측자료를 사용하여도 CE-QUAL-W2와 LSTM 보다 우수한 예측 성능을 보였다. 연구결과는 다차원의 역학적 수리수질 모델과 자료기반 딥러닝 모델의 장점을 결합한 새로운 모델링 기술의 적용 가능성을 보여주며, 자료기반 모델의 훈련자료 부족에 따른 예측 성능 저하 문제를 극복하기 위해 역학적 모델이 유용하게 활용될 수 있음을 시사한다.