• 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.

• 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.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.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3086-3091
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• 2007

When a molten corium is relocated in a lower head of a reactor vessel, the ERVC (External Reactor Vessel Cooling) system is actuated as coolant is supplied into a reactor cavity to remove a decay heat from the molten corium during a severe accident. To achieve this severe accident mitigation strategy, the two-phase natural circulation flow in the annular gap between the external reactor vessel and the insulation should be formed sufficiently by designing the coolant inlet/outlet area and gap size adequately on the insulation device. For this reason, one-dimensional natural circulation flow tests were conducted to estimate the natural circulation flow under the ERVC condition of APR1400. The experimental facility is one-dimensional and scaled-down as the half height and 1/238 rectangular channel area of the APR1400 reactor vessel. As the water inlet area increased, the natural circulation mass flow rate asymptotically increased, that is, it converged at a specific value. And the circulation mass flow rate also increased as the outlet area, injected air flow rate, and outlet height increased. But the circulation mass flow rate was not changed along with the external water level variation if the water level was higher than the outlet height.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4551-4559
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• 2022

The aim of this research is to analyze the characteristics of a core melt discharged from the reactor vessel and the spreading behavior the core melt in the reactor cavity of the SMART. First, a severe accident sequence under conservative conditions is simulated by the MELCOR code to obtain the conditions for an analysis of the spreading behavior and coolability of the ex-vessel melt. Second, the spreading behavior and coolability of the ex-vessel melt are analyzed by the MELTSPREAD code. The level, temperature, and pressure of the water in the cavity as well as the temperature, mass, composition, and discharge velocity of the melt were utilized to construct the ex-vessel analysis. The melt spread only to part of the cavity, and that the height of the corium in a static state was less than 25 cm. The characteristics of a small modular reactor on the spreading behavior and coolability of melt were analyzed. In the SMART, the amount of melt discharged into the cavity is relatively small and the area of the cavity is sufficiently large when compared to a high-power pressurized water reactor. It was found that the coolability of an ex-vessel core melt can be sufficiently secured.

• Nuclear Engineering and Technology
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• v.45 no.6
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• pp.767-790
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• 2013

MAAP4 is a computer code that can simulate the response of a light water reactor power plant during severe accident sequences, including actions taken as part of accident management. The code quantitatively predicts the evolution of a severe accident starting from full power conditions given a set of system faults and initiating events through events such as core melt, reactor vessel failure, and containment failure. Furthermore, models are included in the code to represent the actions that could mitigate the accident by in-vessel cooling, external cooling of the reactor pressure vessel, or cooling the debris in containment. A key element tied to using a code like MAAP4 is an uncertainty analysis. The purpose of this paper is to present a MAAP4 based analysis to examine the sensitivity of a key parameter, in this case hydrogen production, to a set of model parameters that are related to a Level 2 PRA analysis. The Level 2 analysis examines those sequences that result in core melting and subsequent reactor pressure vessel failure and its impact on the containment. This paper identifies individual contributors and MAAP4 model parameters that statistically influence hydrogen production. Hydrogen generation was chosen because of its direct relationship to oxidation. With greater oxidation, more heat is added to the core region and relocation (core slump) should occur faster. This, in theory, would lead to shorter failure times and subsequent "hotter" debris pool on the containment floor.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.103-110
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• 2019

The domestic innovative power reactor named iPOWER will employ the passive molten corium cooling system(PMCCS) to cool down and stabilize the core melt in the severe accident. The final design concept of the PMCCS is yet to be determined, but the in-vessel retention through external reactor vessel cooling has been also considered as a viable strategy to cope with the severe accident. In this study, the two-phase natural circulation flow established between the reactor vessel and the insulation was simulated using a thermal-hydraulic system code, MARS-KS. The flow path of cooling water was modeled with one-dimensional nodes, and the boundary condition of the heat load from the molten core was defined to estimate the naturally-driven flow rate. The evolution of major thermal-hydraulic parameters were also evaluated, including the temperature and the level of cooling water, the void fraction around the lower head of the reactor vessel, and the heat transfer mode on its external surface.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1412-1420
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• 2018

An experiment was performed using the large-scale test facility (LSTF), which simulated a 1% vessel upper head small-break loss-of-coolant accident with an accident management (AM) measure under an assumption of total-failure of high-pressure injection (HPI) system in a pressurized water reactor (PWR). In the LSTF test, liquid level in the upper head affected break flow rate. Coolant was manually injected from the HPI system into cold legs as the AM measure when the maximum core exit temperature reached 623 K. The cladding surface temperature largely increased due to late and slow response of the core exit thermocouples. The AM measure was confirmed to be effective for the core cooling. The RELAP5/MOD3.3 code indicated insufficient prediction of primary coolant distribution. The author conducted uncertainty analysis for the LSTF test employing created phenomena identification and ranking table for each component. The author clarified that peak cladding temperature was largely dependent on the combination of multiple uncertain parameters within the defined uncertain ranges.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.283-288
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• 1996

Direct safety injection into the reactor vessel downcomer annulus(DVI) is a fundamental feature of the KNGR(Korean Next Generation Reactor) four-train safety injection system. The numerical analysis of thermal mixing of ECC(Emergency Core Cooling) water through DVI with the water in the RVDC(Reactor Vessel Downcomer) annulus has been performed, in order to study the impact of nozzle location on the pressurized thermal shock and safety analysis. The results of this study show that the thermal mixing due to the natural circulation induced by the limiting accident conditions is sufficient to prevent temperature in the RVDC from dropping to the level of concern for PTS. When the DVI nozzle is located right above the cold leg, the temperature distribution at the outlet of flow field is most uniform. The tool used for numerical analysis is CFDS-FLOW3D.

• Nuclear Engineering and Technology
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• v.38 no.1
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• pp.99-108
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• 2006

SNU-RCCS is a water pool type RCCS (Reactor Cavity Cooling System) developed for VHTR (Very High Temperature Reactor) application by SNU (Seoul National University). Since radiation heat transfer is the major process of passive heat removal in a RCCS, it is important to determine the precise emissivity of the reactor vessel. Review studies have used a constant emissivity in the passive heat removal analysis, even though the emissivity depends on many factors such as temperature, surface roughness, oxidation level, wavelength, direction, atmosphere conditions, etc. Therefore, information on the emissivity of a given material in a real RCCS is essential in order to properly analyze the radiation heat transfer in a VHTR. The objectives of this study are to develop a method for compensation of the factors affecting the emissivity measurement using an infrared thermometer and to estimate the true emissivity from the measured emissivity via the developed method, especially in the SNU-RCCS environment. From this viewpoint, we investigated factors such as the attenuation effect of the window, filling gas, and the effect of background radiation on the emissivity measurements. The emissivity of the vessel surface of the SNU-RCCS facility was then measured using a sight tube. The background radiation was subsequently removed from the measured emissivity by solving a simultaneous equation. Finally, the calculated emissivity was compared with the measured emissivity in a separate emissivity measurement device, yielding good agreement with the emissivity increase with vessel temperature in a range of 0.82 to 0.88.