통합 검색 | Korea Science

Park, Young-Hwan;Kang, Suk-Chull
- Nuclear Engineering and Technology
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- 제32권1호
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- pp.67-76
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- 2000
Significant wall thinning due to flow accelerated corrosion(FAC）was recently reported in main feedwater pipes in 3 Korean pressurized water reactor(PWR) plants. The main feedwater pipes in one plant were repaired using overlay weld method at the outside of pipe, while those in 2 other plants were replaced with new pipes. In this study, the effect of the wall thinning in the main feedwater pipes on piping integrity was evaluated using finite element method. Especially, the effects of both the overlay weld repair and the stress concentration in notch-type thinned area on the piping integrity were investigated. The results are as follows : (1) The piping load carrying capacity may significantly decrease due to FAC. In special, the load carrying capacity of the main feedwater pipe was reduced by about 40% during about 140 months operation in Korean PWR plants. (2) By performing overlay weld repair at the outside of pipe, the piping load carrying capacity can increase and the stress concentration level in the thinned area can be reduced.
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송석윤;김형남
- 한국유체기계학회 논문집
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- 제12권4호
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- pp.54-62
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- 2009
The feedwater heaters are Critical components in a nuclear power plant. As the operation years of heaters go by, the maintenance costs required for continuous operation increase. When the carbon steel components in nuclear make contact with running fluid, the wall thinning caused by FAC (flow accelerated corrosion) can be generated. Local wall thinning is inevitable at the area around wet steam entrance to be attacked due to the long term operation. Sometimes the shell with thinned wall is eventually ruptured. To identify the relationship between the local wall thinning and fluid behavior of the feedwater heater, the practical data of a plant, which were based on ultrasonic thickness measurement tests, were analyzed and CFD(Computed Fluid Dynamics) analyses were performed.
https://doi.org/10.5293/KFMA.2009.12.4.054 인용 PDF KSCI

서혁기;박상훈;김형준;김경훈;황경모
- 대한기계학회:학술대회논문집
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- 대한기계학회 2008년도 추계학술대회B
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- pp.2631-2636
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- 2008
The most components and piping of the secondary side of domestic nuclear power plants were manufactured carbon-steel and low-alloy steel. Flow accelerated corrosion leads to wall thinning (metal loss) of carbon steel components and piping exposed to the flowing water or wet steam of high temperature, pressure, and velocity. The feedwater heaters of many nuclear power plants have recently experienced sever wall thinning damage, which increases as operating time progress. Several nuclear power plants in Korea have also experienced wall thinning damage in the shell wall around the impingement baffle. This paper describes the comparisons between the numerical analysis results using the FLUENT code and the experimental results based on down-scaled experimental facility. The experiments were performed based on several types of impingement baffle plates which are installed in low pressure feedwater heater.
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Moon, Seongin;Kim, Kyungmo;Lee, Gyeong-Geun;Yu, Yongkyun;Kim, Dong-Jin
- Nuclear Engineering and Technology
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- 제53권12호
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- pp.4060-4066
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- 2021
Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.
https://doi.org/10.1016/j.net.2021.06.040 인용 PDF KSCI