• Journal of the Korea Safety Management & Science
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• v.24 no.1
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• pp.61-72
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• 2022

This study aims to propose a conceptual design of information displays for supporting responsive actions under severe accidents in Nuclear Power Plants (NPPs). Severe accidents in NPPs can be defined as accident conditions that are more severe than a design basis accident and involving significant core degradation. Since the Fukushima accident in 2011, the management of severe accidents is increasing important in nuclear industry. Dealing with severe accidents involves several cognitively complex activities, such as situation assessment; accordingly, it is significant to provide human operators with appropriate knowledge support in their cognitive activities. Currently, severe accident management guidelines (SAMG) have been developed for this purpose. However, it is also inevitable to develop information displays for supporting the management of severe accidents, with which human operators can monitor, control, and diagnose the states of NPPs under severe accident situations. It has been reported that Ecological Interface Design (EID) framework can be a viable approach for developing information displays used in complex socio-technical systems such as NPPs. Considering the design principles underlying the EID, we can say that EID-based information displays can be useful for dealing with severe accidents effectively. This study developed a conceptual design of information displays to be used in severe accidents, following the stipulated design process and principles of the EID framework. We particularly attempted to develop a conceptual design to make visible the principle knowledge to be used for coping with dynamically changing situations of NPPs under severe accidents.

• Nuclear Engineering and Technology
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• v.38 no.8
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• pp.733-752
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• 2006

Severe accidents in PWRs are characterized by a continuously changing geometry of the core due to chemical reactions, melting and mechanical failure of the rods and other structures. These local variations of the porosity and other parameters lead to multi-dimensionnal flows and heat transfers. In this paper, a comprehensive set of multi-dimensionnal models describing heat transfers, thermal-hydraulics and melt relocation in a reactor vessel is presented. Those models are suitable for the core description during a severe accident transient. A series of applications at the reactor scale shows the benefits of using such models.

• 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.

• 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.44 no.3
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• pp.225-236
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• 2012

The Fukushima-Daiichi accident shook the world, as a well-known plant design, the General Electric BWR Mark I, was heavily damaged in the tsunami, which followed the Great Japanese Earthquake of 11 March 2011. Plant safety functions were lost and, as both AC and DC failed, manoeuvrability of the plants at the site virtually came to a full stop. The traditional system of Emergency Operating Procedures (EOPs) and Severe Accident Management Guidelines (SAMG) failed to protect core and containment, and severe core damage resulted, followed by devastating hydrogen explosions and, finally, considerable radioactive releases. The root cause may not only have been that the design against tsunamis was incorrect, but that the defence against accidents in most power plants is based on traditional assumptions, such as Large Break LOCA as the limiting event, whereas there is no engineered design against severe accidents in most plants. Accidents beyond the licensed design basis have hardly been considered in the various designs, and if they were included, they often were not classified for their safety role, as most system safety classifications considered only design basis accidents. It is, hence, time to again consider the Design Basis Accident, and ask ourselves whether the time has not come to consider engineered safety functions to mitigate core damage accidents. Associated is a proper classification of those systems that do the job. Also associated are safety criteria, which so far are only related to 'public health and safety'; in reality, nuclear accidents cause few casualties, but create immense economical and societal effects-for which there are no criteria to be met. Severe accidents create an environment far surpassing the imagination of those who developed EOPs and SAMG, most of which was developed after Three Mile Island - an accident where all was still in place, except the insight in the event was lost. It requires fundamental changes in our present safety approach and safety thinking and, hence, also in our EOPs and SAMG, in order to prevent future 'Fukushimas'.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.469-474
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• 1997

To provide tile information ell severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore was performed to investigate the effect of thermal hydraulic design parameters ell severe accident progression of pressurized water reactors (PWRs), Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among mile parameters. For training. different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3&4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout(SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to tile other six parameters.

• Nuclear Engineering and Technology
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• v.32 no.5
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• pp.425-432
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• 2000

It has been an essential part for the safety assessment of nuclear power plants to understand various phenomena associated with the molten core-concrete interaction(MCCI) under severe accidents. In this study, the severe accident analysis code MELCOR was used to simulate the MCCI experiments such as SWISS and SURC test series which had been performed in Sandia National Laboratories(SNL). The calculation results were compared with corresponding experimental data such as melt temperature, concrete ablation distance, gas generation rate, and aerosol release rate. Good agreements were observed between MELCOR calculation and experimental data. The melt pool was sustained within the range of high temperature and the concrete ablation occurred continuously. The gas generation and aerosol release were under the influence of melt temperature and overlying water pool, respectively.

• 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.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.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.91-98
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• 2011

We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.