• 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.

• 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.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.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3990-4002
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• 2021

CSPACE (Core meltdown, Safety and Performance Analysis CodE for nuclear power plants) for a simulation of severe accident progression in a Pressurized Water Reactor (PWR) is developed by coupling of verified system thermal hydraulic code of SPACE (Safety and Performance Analysis CodE for nuclear power plants) and core damage progression code of COMPASS (Core Meltdown Progression Accident Simulation Software). SPACE is responsible for the description of fluid state in nuclear system nodes, while COMPASS is responsible for the prediction of thermal and mechanical responses of core fuels and reactor vessel heat structures. New heat transfer models to each phase of the fluid, flow blockage, corium behavior in the lower head are added to COMPASS. Then, an interface module for the data transfer between two codes was developed to enable coupling. An implicit coupling scheme of wall heat transfer was applied to prevent fluid temperature oscillation. To validate the performance of newly developed code CSPACE, we analyzed typical severe accident scenarios for OPR1000 (Optimized Power Reactor 1000), which were initiated from large break loss of coolant accident, small break loss of coolant accident, and station black out accident. The results including thermal hydraulic behavior of RCS, core damage progression, hydrogen generation, corium behavior in the lower head, reactor vessel failure were reasonable and consistent. We demonstrate that CSPACE provides a good platform for the prediction of severe accident progression by detailed review of analysis results and a qualitative comparison with the results of previous MELCOR analysis.

• Journal of the Korean Society of Safety
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• v.16 no.1
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• pp.84-87
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• 2001

Severe accident management can be defined as the use of existing and alternative resources, systems, and actions to prevent or mitigate a core-melt accident in nuclear power plants. TRAIN (Training pRogram for AMP In NPP), developed for training control room staff and the technical group, is introduced in this paper. The TRAIN composes of phenomenological knowledge base (KB), accident sequence KB and accident management procedures with AM strategy control diagrams and information needs. This TRAIN might contribute to training them by obtaining phenomenological knowledge of severe accidents, understanding plant vulnerabilities, and solving problems under high stress.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.1-10
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• 2015

After the Fukushima Daiichi nuclear power plant (NPP) accident, new regulatory requirements were enforced in July 2013 and a backfit was required for all existing nuclear power plants. It is required to take measures to prevent severe accidents and mitigate their radiological consequences. The Regulatory Standard and Research Department, Secretariat of Nuclear Regulation Authority (S/NRA/R) has been conducting numerical studies and experimental studies on relevant severe accident phenomena and countermeasures. This article highlights fission product (FP) release and hydrogen risk as two major areas. Relevant activities in the S/NRA/R are briefly introduced, as follows: 1. For FP release: Identifying the source terms and leak mechanisms is a key issue from the viewpoint of understanding the progression of accident phenomena and planning effective countermeasures that take into account vulnerabilities of containment under severe accident conditions. To resolve these issues, the activities focus on wet well venting, pool scrubbing, iodine chemistry (in-vessel and ex-vessel), containment failure mode, and treatment of radioactive liquid effluent. 2. For hydrogen risk: because of three incidents of hydrogen explosion in reactor buildings, a comprehensive reinforcement of the hydrogen risk management has been a high priority topic. Therefore, the activities in evaluation methods focus on hydrogen generation, hydrogen distribution, and hydrogen combustion.

• Nuclear Engineering and Technology
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• v.45 no.5
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• pp.597-604
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• 2013

Containment Filtered Vent Systems (CFVSs) have been mainly equipped in nuclear power plants in Europe and Canada for the controlled depressurization of the containment atmosphere under severe accident conditions. This is to keep the containment integrity against overpressure during the course of a severe accident, in which the radioactive gas-steam mixture from the containment is discharged into a system designed to remove the radionuclides. In Korea, a CFVS was first introduced in the Wolsong unit-1 nuclear power plant as a mitigation measure to deal with the threat of over pressurization, following post-Fukushima action items. In this paper, the overall features of a CFVS installation such as risk assessments, an evaluation of the performance requirements, and a determination of the optimal operating strategies are analyzed for the Wolsong unit 1 nuclear power plant using a severe accident analysis computer code, ISAAC.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.139-147
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• 2015

Recently, severe accidents in nuclear power plants (NPPs) have become a global concern. The aim of this paper is to predict the hydrogen buildup within containment resulting from severe accidents. The prediction was based on NPPs of an optimized power reactor 1,000. The increase in the hydrogen concentration in severe accidents is one of the major factors that threaten the integrity of the containment. A method using a fuzzy neural network (FNN) was applied to predict the hydrogen concentration in the containment. The FNN model was developed and verified based on simulation data acquired by simulating MAAP4 code for optimized power reactor 1,000. The FNN model is expected to assist operators to prevent a hydrogen explosion in severe accident situations and manage the accident properly because they are able to predict the changes in the trend of hydrogen concentration at the beginning of real accidents by using the developed FNN model.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.116-129
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• 2020

Resilience is defined as the intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations or functions with the related systems under both expected and unexpected conditions. Resilience engineering is a relatively new paradigm for safety management that focuses on how to cope with complexity under pressure or disturbance to achieve successful functioning. This study aims to develop a quantitative resilience model for severe accident response organizations of nuclear power plants using the Analytic Hierarchy Process (AHP) method. First, we investigated severe accident response organizations based on a radiation emergency plan in the Korean case and developed a qualitative resilience model for the organizations with resilience-influencing factors, which have been identified in the author's previous studies. Then, a quantitative model for entire severe accident response organizations was developed by using the Analytic Hierarchy Process (AHP) method with a tool for System Dynamics. For applying the AHP method, several experts who are working on implementing, regulating or researching the severe accident response participated in collecting their expertise on the relative importance between all the possible relations in the model. Finally, a sensitivity analysis was carried out to discuss which factors have the most influenceable on resilience.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1537-1546
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• 2017

Many thermal-hydraulic tests have been conducted at the Korea Atomic Energy Research Institute for verification of the SMART (System-integrated Modular Advanced ReacTor) design, the standard design approval of which was issued by the Korean regulatory body. In this paper, the contributions of these tests to the standard design approval of SMART are discussed. First, an integral effect test facility named VISTA-ITL (Experimental Verification by Integral Simulation of Transients and Accidents-Integral Test Loop) has been utilized to assess the TASS/SMR-S (Transient and Set-point Simulation/Small and Medium) safety analysis code and confirm its conservatism, to support standard design approval, and to construct a database for the SMART design optimization. In addition, many separate effect tests have been performed. The reactor internal flow test has been conducted using the SCOP (SMART COre flow distribution and Pressure drop test) facility to evaluate the reactor internal flow and pressure distributions. An ECC (Emergency Core Coolant) performance test has been carried out using the SWAT (SMART ECC Water Asymmetric Two-phase choking test) facility to evaluate the safety injection performance and to validate the thermal-hydraulic model used in the safety analysis code. The Freon CHF (Critical Heat Flux) test has been performed using the FTHEL (Freon Thermal Hydraulic Experimental Loop) facility to construct a database from the $5{\times}5$ rod bundle Freon CHF tests and to evaluate the DNBR (Departure from Nucleate Boiling Ratio) model in the safety analysis and core design codes. These test results were used for standard design approval of SMART to verify its design bases, design tools, and analysis methodology.