• 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 the Korean Society of Safety
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• v.27 no.5
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• pp.229-234
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• 2012

Operator's action time is evaluated from MAAP4 analysis used in conventional probabilistic safety assessment(PSA) of a nuclear power plant. MAAP4 code which was developed for severe accident analysis is too conservative to perform a realistic PSA. A best-estimate code such as RELAP5/MOD3, MARS has been used to reduce the conservatism of thermal hydraulic analysis. In this study, operator's action time of core cooling recovery operation is evaluated by using the MARS code, which its Fussell-Vessely(F-V) value was evaluated as highly important in a small break loss of coolant(SBLOCA) event and loss of component cooling water(LOCCW) event in previous PSA. The main conclusions were elicited : (1) MARS analysis provides larger time window for operator's action time than MAAP4 analysis and gives the more realistic time window in PSA (2) Sufficient operator's action time can reduce human error probability and core damage frequency in PSA.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1516-1525
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• 2022

Interfacing the output of severe accident analysis with the input of radiological consequence analysis is an important and mandatory procedure at the beginning of Level 3 PSA. Such interfacing between the severe accident analysis code MELCOR and MACCS, one of the most commonly used consequence analysis codes, is relatively tractable since they share the same chemical groups, and the related interfacing software, MelMACCS, has already been developed. However, the linking between MAAP, another frequently used code for severe accident analyses, and MACCS has difficulties because MAAP employs a different chemical grouping method than MACCS historically did. More specifically, MAAP groups by chemical compound, while MACCS groups by chemical element. An appropriate interfacing method between MAAP and MACCS has therefore long been requested by users. This study suggests a way of extracting relevant information from MAAP results and providing proper source term information to MACCS by an appropriate treatment. Various parameters are covered in terms of magnitude and manner of release in this study, and special treatment is made for a bypass scenario. It is expected that the suggested approach will provide an important contribution as a guide to interface MAAP and MACCS when performing radiological consequence analyses.

• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.945-952
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• 2009

The Three Mile Island Unit 2 (TMI-2) accident provides unique full scale data, thus providing opportunities to check the capability of codes to model overall plant behavior and to perform a spectrum of sensitivity and uncertainty calculations. As part of the TMI-2 analysis benchmark exercise sponsored by the Organization for Economic Cooperation and Development Nuclear Energy Agency (OECD NEA), several member countries are continuing to improve their system analysis codes using the TMI-2 data. The Republic of Korea joined this benchmark exercise in November 2005. Seoul National University has analyzed the TMI-2 accident as well as the currently proposed alternative scenario along with a sensitivity study using the Modular Accident Analysis Program Version 4.03 (MAAP4.03) code in collaboration with the Korea Hydro and Nuclear Power Company. Two input files are required to simulate the TMI-2 accident with MAAP4: the parameter file and an input deck. The user inputs various parameters, such as volumes or masses, for each component. The parameter file contains the information on TMI-2 relevant to the plant geometry, system performance, controls, and initial conditions used to perform these benchmark calculations. The input deck defines the operator actions and boundary conditions during the course of the accident. The TMI-2 accident analysis provided good estimates of the accident output data compared with the OECD TMI-2 standard reference. The alternative scenario has proposed the initial event as a loss of main feed water and a small break on the hot leg. Analysis is in progress along with a sensitivity study concerning the break size and elevation.

• 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.

• Nuclear Engineering and Technology
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• v.45 no.5
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• pp.589-596
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• 2013

In a pressurized heavy water reactor, following loss of the primary coolant, severe core damage would begin with the depletion of the liquid moderator, exposing the top row of internally-voided fuel channels to steam cooling conditions on the inside and outside. The uncovered fuel channels would heat up, deform and disassemble into core debris. Large inventories of water passively reduce the rate of progression of the accident, prolonging the time for complete loss of engineered heat sinks. The efficacy of available backup and ultimate heat sinks, available in a CANDU 6 reactor, in mitigating the consequences of a prolonged station blackout scenario was analysed using the MAAP4-CANDU code. The analysis indicated that the steam generator secondary side water inventory is the most effective heat sink during the accident. Additional heat sinks such as the primary coolant, moderator, calandria vault water and end shield water are also able to remove decay heat; however, a gradually increasing mismatch between heat generation and heat removal occurs over the course of the postulated event. This mismatch is equivalent to an additional water inventory estimated to be 350,000 kg at the time of calandria vessel failure. In the Enhanced CANDU 6 reactor ~2,040,000 kg of water in the reserve water tank is available for prolonged emergencies requiring heat sinks.

• 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.33 no.5
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• pp.468-482
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• 2001

As a process of Direct Containment Heating (DCH) issue resolution for Korean Standard Nuclear Power Plants (KSNPs), a containment load/strength assessment with two different approaches, the probabilistic and the deterministic, was performed with all plant-specific and phenomena-specific data. In case of the probabilistic approach, the framework developed to support the Zion DCH study, Two-Cell Equilibrium (TCE) coupled with Latin Hypercubic Sampling (LHS), provided a very efficient tool to resolve DCH issue. In case of the deterministic approach, the evaluation methodology using the sophisticated mechanistic computer code, CONTAIN 2.0 was developed, based on findings from DCH-related experiments or analyses. For three bounding scenarios designated as Scenarios V, Va, and VI, the calculation results of TCE/LHS and CONTAIN 2.0 with the conservatism or typical estimation for uncertain parameters, showed that the containment failure resulted from DCH loads was not likely to occur. To verify that these two approaches might be conservative , the containment loads resulting from typical high-pressure accident scenarios (SBO and SBLOCA) for KSNPs were also predicted. The CONTAIN 2.0 calculations with boundary and initial conditions from the MAAP4 predictions, including the sensitivity calculations for DCH phenomenological parameters, have confirmed that the predicted containment pressure and temperature were much below those from these two approaches, and, therefore, DCH issue for KSNPS might be not a problem.

• 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.

• 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.