• Journal of Mechanical Science and Technology
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• v.17 no.9
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• pp.1380-1387
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• 2003

When a cold HPSI (High pressure Safety Injection) fluid associated with an overcooling transient, such as SGTR (Steam Generator Tube Rupture), MSLB (Main Steam Line Break) etc., enters the cold legs of a stagnated primary coolant loop, thermal stratification phenomena will arise due to incomplete mixing. If the stratified flow enters the downcomer of the reactor pressure vessel, severe thermal stresses are created in a radiation embrittled vessel wall by local overcooling. As general thermal-hydraulic system analysis codes cannot properly predict the thermal stratification phenomena, RG 1.154 requires that a detailed thermal-mixing analysis of PTS (pressurized Thermal Shock) evaluation be performed. Also. previous PTS studies have assumed that the thermal stratification phenomena generated in the stagnated loop side of a partially stagnated primary coolant loop are neutralized in the vessel downcomer by the strong flow from the unstagnated loop. On the basis of these reasons, this paper focuses on the development of a 3-dimensional thermal-mixing analysis model using PHOENICS code which can be applied to both partial and total loop stagnated cases. In addition, this paper verifies the fact that, for partial loop stagnated cases, the cold plume generated in the vessel downcomer due to the thermal stratification phenomena of the stagnated loop is almost neutralized by the strong flow of the unstagnated loop but is not fully eliminated.

• Nuclear Engineering and Technology
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• v.41 no.10
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• pp.1361-1374
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• 2009

Due to the need of data for a Human Reliability Analysis (HRA), a number of data collection efforts have been undertaken in several different organizations. As a part of this effort, a human error analysis that focused on a set of simulator records on a Steam Generator Tube Rupture (SGTR) scenario was performed by using the Human Event Repository and Analysis (HERA) system. This paper summarizes the process and results of the HERA analysis, including discussions about the usability of the HERA system for a human error analysis of simulator data. Five simulated records of an SGTR scenario were analyzed with the HERA analysis process in order to scrutinize the causes and mechanisms of the human related events. From this study, the authors confirmed that the HERA was a serviceable system that can analyze human performance qualitatively from simulator data. It was possible to identify the human related events in the simulator data that affected the system safety not only negatively but also positively. It was also possible to scrutinize the Performance Shaping Factors (PSFs) and the relevant contributory factors with regard to each identified human event.

• 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.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.51 no.2
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• pp.402-409
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• 2019

When a loss of coolant accident (LOCA) occurs in a nuclear power plant, accident scenarios which can prevent core damage are defined based on break size. Current probabilistic safety assessment evaluates that core damage can be prevented under small-break LOCA (SBLOCA) and steam generator tube rupture (SGTR) with rapid cool down (RCD) strategy when all safety injection systems are unavailable. However, previous research has pointed out a limitation of RCD in terms of initiation time. Therefore, RCD success criteria estimation based on allowable coping time under a SBLOCA or SGTR when all safety injection systems are unavailable was performed based on time-line and thermal-hydraulic analyses. The time line analysis assumed a single emergency operating procedure flow, and the thermal hydraulic analysis utilized MARS-KS code with variables of break size, cooling rate, and operator allowable time. Results show while RCD is possible under SGTR, it is impossible under SBLOCA at the APR1400's current cooling rate limitation of 55 K/hr. A success criteria map for RCD under SBLOCA is suggested without cooling rate limitation.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.1
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• pp.51-58
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• 2021

Chemical decontamination of primary systems in a nuclear power plant (NPP) prior to commencing the main decommissioning activities is required to reduce radiation exposure during its process. The entire process is repeated until the desired decontamination factor is obtained. To achieve improved decontamination factors over a shorter time with fewer cycles, the appropriate flow characteristics are required. In addition, to prepare an operating procedure that is adaptable to various conditions and situations, the transient analysis results would be required for operator action and system impact assessment. In this study, the flow characteristics in the steady-state and transient conditions for the chemical decontamination operations of the Kori-1 NPP were analyzed and compared via the MARS-KS code simulation. Loss of residual heat removal (RHR) and steam generator tube rupture (SGTR) simulations were conducted for the postulated abnormal events. Loss of RHR results showed the reactor coolant system (RCS) temperature increase, which can damage the reactor coolant pump (RCP)s by its cavitation. The SGTR results indicated a void formation in the RCS interior by the decrease in pressurizer (PZR) pressure, which can cause surface exposure and tripping of the RCPs unless proper actions are taken before the required pressure limit is achieved.

• Nuclear Engineering and Technology
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• v.27 no.1
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• pp.53-66
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• 1995

Since the current licensed system codes for Non-LOCA safety analysis are applicable only for a specific type PWR, it is necessary to develope a new system analysis code applicable for all apes of PWRs. As a R&D program, KAERI is developing TASS code as an interactive and faster-than-real-time code for the NSSS transient simulation of both CE and Westinghouse plane. It is flexible tool for PWR analysis which gives the user complete control over the simulation through convenient input and output options. In this paper the code applicability to Westinghouse ape plants was verified by comparing the TASS prediction to plant data of loss of AC power and loss of load transients, and comparing to the prediction of RELAP5/MOD3 for feedline break, locked rotor, steam generator tube rupture and steam line break accidents.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.204-211
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• 2015

Probabilistic safety assessment (PSA) has had a significant role in quantitative decision-making by finding design and operational vulnerabilities and evaluating cost-benefit in improving such weak points. In particular, it has been widely used as the core methodology for risk-informed applications (RIAs). Even though the nature of PSA seeks realistic results, there are still "conservative" aspects. One of the sources for the conservatism is the assumptions of safety analysis and the estimation of failure frequency. Surveillance, diagnosis, and prognosis (SDP), utilizing massive databases and information technology, is worth highlighting in terms of its capability for alleviating the conservatism in conventional PSA. This article provides enabling techniques to solidify a method to provide time- and condition-dependent risks by integrating a conventional PSA model with condition monitoring and prognostics techniques. We will discuss how to integrate the results with frequency of initiating events (IEs) and probability of basic events (BEs). Two illustrative examples will be introduced: (1) how the failure probability of a passive system can be evaluated under different plant conditions and (2) how the IE frequency for a steam generator tube rupture (SGTR) can be updated in terms of operating time. We expect that the proposed model can take a role of annunciator to show the variation of core damage frequency (CDF) depending on operational conditions.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.74-80
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• 2021

Korea Atomic Energy Research Institute (KAERI) has been operating an integral effects test facility, the Advanced Thermal-Hydraulic Test Loop for Accident Simulation (ATLAS), according to APR1400 for transient experimental and design basis accident simulation. Moreover, based on the experimental data, the domestic standard problem (DSP) program has been conducted in Korea to validate system codes. Recently, through DSP-05, the performance of the passive auxiliary feedwater system (PAFS) in the event of multiple steam generator tube rupture (MSGTR) has been analyzed. However, some errors exist in the reference input model distributed for DSP-05. Furthermore, the calculation results of the heat loss correlation for the secondary system presented in the technical report of the reference indicate that a large difference is present in heat loss from the target value. Thus, in this study, the reference model is corrected using the geometric information from the design report and drawings of ATLAS. Additionally, a new heat loss correlation is suggested by fitting the results of the heat loss tests. Herein, MSGTR-PAFS accident analysis is performed using MARS-KS 1.5 with the improved model. The steady-state calculation results do not significantly differ from the experimental values, and the overall physical behavior of the transient state is properly predicted. Particularly, the predicted operating time of PAFS is similar to the experimental results obtained by the modified model. Furthermore, the operating time of PAFS varies according to the heat loss of the secondary system, and the sensitivity analysis results for the heat loss of the secondary system are presented.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.