• 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 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.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.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.55 no.1
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• pp.144-155
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• 2023

The core catcher is used as a passive safety system in new generation nuclear power plants to create a space in the containment for the placing and cooling of the molten corium under various severe accidents. This research investigates the role of the core catcher in the VVER-1000 reactor containment system in mitigating the effects of core meltdown under various severe accidents within the context of the Ex-vessel Melt Retention (EVMR) strategy. Hence, a comparison study of three severe accidents is conducted, including Station Black-Out (SBO), SBO combined with the Large Break Loss of Coolant Accident (LB-LOCA), and SBO combined with the Small Break Loss of Coolant Accident (SB-LOCA). Numerical comparative simulations are performed for the aforementioned scenario with and without the EX-vessel core-catcher. The results showed that considering the EX-Vessel core catcher reduces the amount of hydrogen by about 18.2 percent in the case of SBO + LB-LOCA, and hydrogen production decreases by 12.4 percent in the case of SBO + SB-LOCA. Furthermore, in the presence of an EX-Vessel core-catcher, the production of gases such as CO and CO2 for the SBO accident is negligible. It was revealed that the greatest decrease in pressure and temperature of the containment is related to the SBO accident.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• 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.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2670-2677
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• 2023

In the nuclear safety studies, a new trend refers to the evaluation of uncertainties as a mandatory component of best-estimate safety analysis which is a modern and technically consistent approach being known as BEPU (Best Estimate Plus Uncertainty). The major objectives of this study consist in performing a study of uncertainties/sensitivities of the major analysis results for a generic CANDU 6 Nuclear Power Plant during Station Blackout (SBO) progression to understand and characterize the sources of uncertainties and their effects on the key figure-of-merits (FOMs) predictions in severe accidents (SA). The FOMs of interest are hydrogen mass generation and event timings such as the first fuel channel failure time, beginning of the core disassembly time, core collapse time and calandria vessel failure time. The outcomes of the study, will allow an improvement of capabilities and expertise to perform uncertainty and sensitivity analysis with severe accident codes for CANDU 6 Nuclear Power Plant.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.434-441
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• 2017

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.