• Journal of the Korean Society of Safety
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• v.29 no.5
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• pp.160-165
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• 2014

The realistic cases causing severe core damage should be analyzed and arranged systematically for preparing an accident management of the specific nuclear power plant. The objective of this paper is to establish basic technical information for reactor safety and reactor building integrity management strategies in CANDU reactor severe accident. For the development of severe accident management strategies, plant specific features and behaviors must be studied by detailed analysis works. This analysis scope will serve to cover overall methods and analyzing results to understand the reactor building integrity status in the most likely severe accident sequences that could occur at CANDU reactor. Also analysis results could help prevent or mitigate severe accidents for the identification of any plant specific vulnerabilities to severe accidents using the probabilistic safety assessment (PSA) quantified results.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.311-322
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• 2012

Since the crisis at the Fukushima plants, severe accident progression during a station blackout accident in nuclear power plants is recognized as a very important area for accident management and emergency planning. The purpose of this study is to investigate the comparative characteristics of anticipated severe accident progression among the three typical types of nuclear reactors. A station blackout scenario, where all off-site power is lost and the diesel generators fail, is simulated as an initiating event of a severe accident sequence. In this study a comparative analysis was performed for typical pressurized water reactor (PWR), boiling water reactor (BWR), and pressurized heavy water reactor (PHWR). The study includes the summarization of design differences that would impact severe accident progressions, thermal hydraulic/severe accident phenomenological analysis during a station blackout initiated-severe accident; and an investigation of the core damage process, both within the reactor vessel before it fails and in the containment afterwards, and the resultant impact on the containment.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.66-73
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• 2015

Background: A numerical simulation was carried out to investigate the difference between internal and external heat-flux distributions at the reactor vessel wall under in-vessel retention through external reactor vessel cooling (IVR-ERVC). Methods: Total loss of feed water, station blackout, and large break loss of coolant accidents were selected as the severe accident scenarios, and a transient analysis using the element-birth-and-death technique was conducted to reflect the vessel erosion (vessel wall thickness change) effect. Results: It was found that the maximum heat flux at the focusing region was decreased at least 10% when considering the two-dimensional heat conduction at the reactor vessel wall. Conclusion: The results show that a higher thermal margin for the IVR-ERVC strategy can be achieved in the focusing region. In addition, sensitivity studies revealed that the heat flux and reactor vessel thickness are dominantly affected by the molten corium pool formation according to the accident scenario.

• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.921-928
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• 2009

As part of an evaluation for an in-vessel severe accident management strategy, a coolant injection into the reactor vessel under depressurization of the reactor coolant system (RCS) has been evaluated in detail using the SCDAP/RELAP5 computer code. A high-pressure sequence of a small break loss of coolant accident (SBLOCA) has been analyzed in the Optimized Power Reactor (OPR) 1000. The SCDAP/RELAP5 results have shown that safety injection timing and capacity with RCS depressurization timing and capacity are very effective on the reactor vessel failure during a severe accident. Only one train operation of the high pressure safety injection (HPSI) for 30,000 seconds with RCS depressurization prevents failure of the reactor vessel. In this case, the operation of only the low pressure safety injection (LPSI) without a HPSI does not prevent failure of the reactor vessel.

• Nuclear Engineering and Technology
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• v.46 no.2
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• pp.207-216
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• 2014

This paper revisits the Fukushima accident to draw lessons in the aspect of nuclear safety considering the fact that the Fukushima accident resulted in core damage for three nuclear power plants simultaneously and that there is a high possibility of a failure of the integrity of reactor vessel and primary containment vessel. A brief review on the accident progression at Fukushima nuclear power plants is discussed to highlight the nature and characteristic of the event. As the severe accident management measures at the Fukushima Daiich nuclear power plants seem to be not fully effective, limitations of current severe accident management strategy are discussed to identify the areas for the potential improvements including core cooling strategy, containment venting, hydrogen control, depressurization of primary system, and proper indication of event progression. The gap between the Fukushima accident event progression and current understanding of severe accident phenomenology including the core damage, reactor vessel failure, containment failure, and hydrogen explosion are discussed. Adequacy of current safety goals are also discussed in view of the socio-economic impact of the Fukushima accident. As a conclusion, it is suggested that an investigation on a coherent integrated safety principle for the severe accident and development of innovative mitigation features is necessary for robust and resilient nuclear power system.

• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.702-710
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• 2016

Global concern and interest in the safety of nuclear power plants have increased considerably since the Fukushima accident. In the event of a severe accident, the reactor vessel water level cannot be measured. The reactor vessel water level has a direct impact on confirming the safety of reactor core cooling. However, in the event of a severe accident, it may be possible to estimate the reactor vessel water level by employing other information. The cascaded fuzzy neural network (CFNN) model can be used to estimate the reactor vessel water level through the process of repeatedly adding fuzzy neural networks. The developed CFNN model was found to be sufficiently accurate for estimating the reactor vessel water level when the sensor performance had deteriorated. Therefore, the developed CFNN model can help provide effective information to operators in the event of a severe accident.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.782-787
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• 2001

This paper is concerned with the global rupture of a nuclear reactor pressure vessel(RPV) in a severe accident. During the severe reactor accident of molten core, the temperature and the pressure in the nuclear reactor rise to a certain level depending on the initial and subsequent condition of a severe accident. While the rise of the temperature cause the thermal softening of RPV material, the rise of the internal pressure could cause failure of the RPV lower head. The global rupture of an RPV is simulated by finite element limit analysis for the collapse pressure and mode and this analysis results have been compared with a variation of the internal pressure of RPV. The finite element limit method is a systematic tool to secure the safety criteria of a nuclear reactor and to evaluate the in-vessel corium retention.

• Nuclear Engineering and Technology
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• v.41 no.1
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• pp.63-70
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• 2009

A steam generator tube rupture (SGTR) accident, which is a partial reactor building bypass scenario, has a low probability and high consequences. SAMG has been used to manage the progression of severe accidents and the release of fission products induced by an SGTR at the Wolsong plants. Four of the six SAGs in the SAMG are used to manage the progression of a severe accident induced by an SGTR at the Wolsong plants. The results of the ISAAC code calculation have shown that the proper use the SAMG can stop a severe accident from progressing and keep the reactor building intact during a severe accident. These results confirm that the SAMG is an effective means of managing the progression of severe accidents initiated by an SGTR at the Wolsong plants.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.