• 제목/요약/키워드: MAAP

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A Study on the Implementation Effect of Accident Management Strategies on Safety

  • Moosung Jae;Kim, Dong-Ha;Jin, Young-Ho
    • Nuclear Engineering and Technology
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    • 제28권3호
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    • pp.247-256
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    • 1996
  • This paper presents a new approach for assessing accident management strategies using containment event trees (CETs) developed during an individual plant examination (IPE) for a reference plant (CE type, 950 MWe PWR). Various accident management strategies to reduce risk have been proposed through IPE. Three strategies for the station blackout sequence are used as an example : 1) reactor cavity flooding only, 2) primary system depressurization only, and 3) doing both. These strategies are assumed to be initiated at about the time of core uncovery. The station blackout (SBO) sequence is selected in this paper since it is identified as one of the most threatening sequences to safety of the reference plant. The effectiveness and adverse effects of each accident management strategy are considered synthetically in the CETs. A best estimate assessment for the developed CETs using data obtained from NUREG-1150, other PRA results, and the MAAP code calculations is performed. The strategies are ranked with respect to minimizing the frequencies of Various containment failure modes. The proposed approach is demonstrated to be very flexible in that it can be applied to any kind of accident management strategy for any sequence.

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Assessing the Feasibility of an Accident Management Strategy Using Dynamic Reliability Methods

  • Moosung Jae;Kim, Jae-Hwan
    • Nuclear Engineering and Technology
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    • 제29권1호
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    • pp.1-6
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    • 1997
  • This paper presents a new dynamic approach for assessing feasibility associated with the implementation of accident management strategies by the operators. This approach includes the combined use of both the concept of reliability physics and a dynamic event tree generation scheme. The reliability physics is based on the concept of a comparison between two competing variables, i.e., the requirement and the achievement parameter, while the dynamic event tree generation scheme on the continuous generation of the possible event sequences at every branch point up to the desired solution. This approach is applied to a cavity flooding strategy in a reference plant, which is to supply water into the reactor cavity using emergency fire systems in the station blackout sequence. The MAAP code and Latin Hypercube sampling technique are used to determine the uncertainty of the requirement parameter. It has been demonstrated that this combined methodology may contribute to assessing the success likelihood of the operator actions required during accidents and therefore to developing the accident management procedures.

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A New Dynamic HRA Method and Its Application

  • Jae, Moosung
    • International Journal of Reliability and Applications
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    • 제2권1호
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    • pp.37-48
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    • 2001
  • This paper presents a new dynamic human reliability analysis method and its application for quantifying the human error probabilities in implementing management action. For comparisons of current HRA methods with the new method, the characteristics of THERP, HCR, and SLIM-MAUD, which are most frequency used method in PSAs, are discussed. The action associated with implementation of the cavity flooding during a station blackout sequence is considered for its application. This method is based on the concepts of the quantified correlation between the performance requirement and performance achievement. The MAAP 3.0B code and Latin Hypercube sampling technique are used to determine the uncertainty of the performance achievement parameter. Meanwhile, the value of the performance requirement parameter is obtained from interviews. Based on these stochastic obtained, human error probabilities are calculated with respect to the various means and variances of the things. It is shown that this method is very flexible in that it can be applied to any kind of the operator actions, including the actions associated with the implementation of accident management strategies.

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Use of Dynamic Reliability Method in Assessing Accident Management Strategy

  • Jae, Moosung
    • International Journal of Reliability and Applications
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    • 제2권1호
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    • pp.27-36
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    • 2001
  • This Paper proposes a new methodology for assessing the reliability of an accident management, which Is based on the reliability physics and the scheme to generate dynamic event tree. The methodology consists of 3 main steps: screening; uncertainty propagation; and probability estimation. Sensitivity analysis is used for screening the variables of significance. Latin Hypercube sampling technique and MAAP code are used for uncertainty propagation, and the dynamic event tree generation method is used for the estimation of non-success probability of implementing an accident management strategy. This approach is applied in assessing the non-success probability of implementing a cavity flooding strategy, which is to supply water into the reactor cavity using emergency fire systems during the sequence of station blackout at the reference plant.

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The detection and diagnosis model for small scale MSLB accident

  • Wang, Meng;Chen, Wenzhen
    • Nuclear Engineering and Technology
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    • 제53권10호
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    • pp.3256-3263
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    • 2021
  • The main steam line break accident is an essential initiating event of the pressurized water reactor. In present work, the fuzzy set theory and the signal-based fault detection method has been used to detect the occurrence and diagnosis of the location and break area for the small scale MSLB. The models are validated by the AP1000 accident simulator based on MAAP5. From the test results it can be seen that the proposed approach has a rapid and proper response on accident detection and location diagnosis. The method proposed to evaluate the break area shows good performances for small scale MSLB with the relative deviation within ±3%.

PREDICTION OF THE REACTOR VESSEL WATER LEVEL USING FUZZY NEURAL NETWORKS IN SEVERE ACCIDENT CIRCUMSTANCES OF NPPS

  • Park, Soon Ho;Kim, Dae Seop;Kim, Jae Hwan;Na, Man Gyun
    • Nuclear Engineering and Technology
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    • 제46권3호
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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.

An Assessment on the Containment Integrity of Korean Standard Nuclear Power Plants Against Direct Containment Heating Loads

  • Seo, Kyung-Woo;Kim, Moo-Hwan;Lee, Byung-Chul;Jeun, Gyoo-Dong
    • Nuclear Engineering and Technology
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    • 제33권5호
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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.

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PREDICTION OF HYDROGEN CONCENTRATION IN CONTAINMENT DURING SEVERE ACCIDENTS USING FUZZY NEURAL NETWORK

  • KIM, DONG YEONG;KIM, JU HYUN;YOO, KWAE HWAN;NA, MAN GYUN
    • Nuclear Engineering and Technology
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    • 제47권2호
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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.

Thermal Hydraulic Design Parameters Study for Severe Accidents Using Neural Networks

  • Roh, Chang-Hyun;Chang, Soon-Heung
    • 한국원자력학회:학술대회논문집
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    • 한국원자력학회 1997년도 추계학술발표회논문집(1)
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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.

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Time uncertainty analysis method for level 2 human reliability analysis of severe accident management strategies

  • Suh, Young A;Kim, Jaewhan;Park, Soo Yong
    • Nuclear Engineering and Technology
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    • 제53권2호
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    • pp.484-497
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    • 2021
  • This paper proposes an extended time uncertainty analysis approach in Level 2 human reliability analysis (HRA) considering severe accident management (SAM) strategies. The method is a time-based model that classifies two time distribution functions-time required and time available-to calculate human failure probabilities from delayed action when implementing SAM strategies. The time required function can be obtained by the combination of four time factors: 1) time for diagnosis and decision by the technical support center (TSC) for a given strategy, 2) time for strategy implementation mainly by the local emergency response organization (ERO), 3) time to verify the effectiveness of the strategy and 4) time for portable equipment transport and installation. This function can vary depending on the given scenario and includes a summation of lognormal distributions and a choice regarding shifting the distribution. The time available function can be obtained via thermal-hydraulic code simulation (MAAP 5.03). The proposed approach was applied to assess SAM strategies that use portable equipment and safety depressurization system valves in a total loss of component cooling water event that could cause reactor vessel failure. The results from the proposed method are more realistic (i.e., not conservative) than other existing methods in evaluating SAM strategies involving the use of portable equipment.