• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2001년도 춘계학술발표회요약집
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• pp.132.1-132
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• 2001

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1999년도 추계학술발표회요약집
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• pp.74-74
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• 1999

• Nuclear Engineering and Technology
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• 제36권6호
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• pp.497-511
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• 2004

A PSV (pressurizer safety valve) popping test carried out in the early phases of a refueling outage may trigger a test-induced LOCA(loss of coolant accident) if a PSV fails to fully close and is stuck in a partially open position. According to a KSNP (Korea standard nuclear power plant) low power and shutdown PSA (probabilistic safety assessment), the failure of a high pressure safety injection (HPSI) accompanied by the failure of a PSV to fully close was identified as a dominant accident sequence with a significant impact on low power and shutdown risks (LPSR). In this study, we aim to investigate and verify a new means for mitigating this type of accident using a thermal-hydraulic analysis. In particular, we explore the applicability of an aggressive cool-down combined with operator actions. The results of the various sensitivity studies performed there will help reduce LPSR and improve Refueling outage safety.

• Nuclear Engineering and Technology
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• 제43권2호
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• pp.167-174
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• 2011

A pressurizer in a small integral type pressurized water reactor is located inside the upper region of the reactor vessel, and uses a space between the upper head of the reactor vessel and the upper region of the upper guide structure which is partially filled with fluid depending on the operating power. This new design requires a comprehensive investigation of vibration characteristics. This study investigates the modal characteristics of a pressurizer which uses a simplified cylindrical shell model, focusing on how having fluid in the shell affects vibration and response characteristics. In addition, an analysis of sloshing is performed and the response characteristics are addressed.

• 대한기계학회논문집A
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• 제33권10호
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• pp.1151-1157
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• 2009

Nuclear power plants applying for the continued operation over design life are required to address the effects of reactor water environment in fatigue design requirement of the ASME Code. Reactor water environmental effects are generally evaluated by calculating fatigue correction factors on fatigue usage. This paper describes the application for pressurizer surge line of environmental fatigue correction factors and the strain rate impact in the application. From this paper, the environmental fatigue correction factors resulted from the assumption of a step change in temperature are especially compared with those calculated from the data measured during plant startup. As a conclusion of this paper, the design transient conditions applied to the fatigue design may be conservative in case of the environmental fatigue evaluation.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 춘계학술발표회논문집(2)
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• pp.657-662
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• 1996

Venting capability to prevent excess pressurization caused by loss of Residual Heat Removal System (RHRS) during mid-loop operation hat been evaluated analytically and the peak Reactor Coolant System (RCS) pressure was compared with the results of the MIDLOOP computer code. Even though analytical method if relatively simple, the results are in a good agreement with those of the computer code. For both methods, the peak pressures have not, exceeded the nozzle dam design pressure, if the vent paths such as pressurizer safety valves or a pressurizer manway are available in a closed RCS configuration with the nozzle dam installed.

• Nuclear Engineering and Technology
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• 제33권5호
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• 한국압력기기공학회 논문집
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• 제8권1호
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• pp.19-24
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• 2012

This paper presents the results of finite element analysis for the effects of hydrostatic test and operating condition on the weld residual stress at dissimilar metal weld of a pressurizer safety nozzle in a nuclear power plant. For the study, the weld residual stress at ambient condition was analyzed using ABAQUS in the first place. After the weld residual stress analysis, the hydrostatic test condition and operating condition was applied to the same model one after another. The weld residual stress was observed to change due to the successive hydrostatic test and operating condition. The axial residual stresses on inner surface of the dissimilar metal weld and HAZ region were decreased by hydrostatic test and operating condition, which gives beneficial effect on preventing primary water stress corrosion cracking.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1429-1435
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• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1999년도 추계학술발표회요약집
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• pp.75.1-75
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• 1999