• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.17 no.1
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• pp.56-63
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• 2021

The feedwater ring is an assembly in steam generator internal piping, which distributes feedwater into the secondary side of the steam generator. It consists of an assembly of carbon steel piping, pipe fittings and J-nozzles which are inserted into the top of the feedwater ring and welded to the diameter of the ring. The feedwater ring at the attachment region of the J-nozzle may be susceptible to flow accelerated corrosion (FAC) due to flow turbulence which increases local fluid velocities. If a J-nozzle becomes a loose part, it can cause damage to tubing near the tube sheet. In this paper, the structural stress analysis for a wall thinned feedwater ring and integrity evaluations under assumed loading conditions are carried out in compliance with ASME B&PV SecIII, NB-3200.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.18 no.1
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• pp.11-18
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• 2022

When wall-thinning in a pipe occurs during operation of nuclear power plant, reinforcement of the pipe needs to be performed. Accordingly, the structural response of the piping system due to introduction of the reinforcement may be re-evaluated. For elastic structural analysis of the piping system with the reinforced pipe using finite element (FE) analysis, the stiffness of the reinforced pipe is needed. In this study, the stiffness matrix of wall-thinned pipe with pad reinforcement or composite reinforcement is analytically derived. The validity of the proposed equations is checked by comparing with systematic finite element (FE) analysis results.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.11-14
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• 2022

본 논문에서는 회귀 문제에서 예측값들의 분산을 줄이기 위한 딥뉴럴 네트워크 구조를 제안한다. 일반적인 회귀 문제에서 딥뉴럴 네트워크 학습 시, 하나의 입력에 대한 레이블 값을 이용하여 학습한다. 본 눈문에서는 하나의 입력에 대한 레이블 값뿐만 아니라 두 입력에 대한 레이블 값들의 차이를 학습시키는 딥뉴럴 네트워크 구조를 제안한다. 통계학 이론을 통하여 예측값들의 분산이 줄어든다는 것을 증명한다. 또한, 배관 곡관의 감육두께를 예측하는 문제를 통해 제안된 네트워크의 성능을 검증한다. 일반적인 딥뉴럴 네트워크 구조를 이용하였을 때에 비하여 제안한 네트워크 구조를 이용하였을 때, 회귀 문제의 예측값들의 분산이 감소함을 확인한다.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.127.2-127
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• 2001

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.460.1-460
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• 2001

• Nuclear industry
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• v.7 no.4 s.50
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• pp.86-87
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• 1987

미국원자력규제위원회(NRC)의 조사에 의하면 배관파단의 원인은 파단부 부근의 배관내면이 심하게 감육하고 있으며 원자로 트?후의 2차계의 압력변동에 견디지 못했기 때문이라고 보여지고 있다.

• Proceedings of the Korean Nuclear Society Conference
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• 2002.10a
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• pp.401.1-401
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• 2002

• Proceedings of the Korean Nuclear Society Conference
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• 1999.10a
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• pp.228.2-228
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• 1999

• Proceedings of the Korean Magnestics Society Conference
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• 2016.11a
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• pp.90-91
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• 2016

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.4
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• pp.437-442
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• 2010

Flow-accelerated corrosion (FAC) leads to thinning of steel pipe walls that are exposed to flowing water or wet steam. From experience, it is seen that FAC damage to piping at fossil and nuclear plants can result in outages that require expensive repairs and can affect plant reliability and safety. CHECWORKS have been utilized in domestic nuclear plants as a predictive tool to assist FAC engineers in planning inspections and evaluating the inspection data so that piping failures caused by FAC can be prevented. However, CHECWORKS may be occasionally ignore local susceptible portions when predicting FAC damage in a group of pipelines after constructing a database for all the secondary side piping in nuclear plants. This paper describes the methodologies that can complement CHECWORKS and the verifications of CHECWORKS prediction results using numerical analysis. FAC susceptible locations determined using CHECWORKS for two pipeline groups of a nuclear plant was compared with determined using the numerical-analysis-based FLUENT.