• Nuclear Engineering and Technology
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• v.49 no.3
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• pp.459-465
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• 2017

The goal of this study is to evaluate the effective thermal conductivity and diffusivity of containment walls as heat sinks or passive cooling systems during nuclear power plant (NPP) accidents. Containment walls consist of steel reinforced concrete, steel liners, and tendons, and provide the main thermal resistance of the heat sinks, which varies with the volume fraction and geometric alignment of the rebar and tendons, as well as the temperature and chemical composition. The target geometry for the containment walls of this work is the standard Korean NPP OPR1000. Sample tests and numerical simulations are conducted to verify the correlations for models with different densities of concrete, volume fractions, and alignments of steel. Estimation of the effective thermal conductivity and diffusivity of the containment wall models is proposed. The Maxwell model and modified Rayleigh volume fraction model employed in the present work predict the experiment and finite volume method (FVM) results well. The effective thermal conductivity and diffusivity of the containment walls are summarized as functions of density, temperature, and the volume fraction of steel for the analysis of the NPP 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.

• Journal of ILASS-Korea
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• v.8 no.1
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• pp.23-28
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• 2003

When a cold HPSI (High Pressure Safety Injection) fluid associated with a design basis accident, such as LOCA (Loss of Coolant Accident), enters the cold legs of a stagnated primary coolant loop, thermal stratification phenomena may arise due to incomplete mixing. If the stratified flow enters a reactor pressure vessel downcomer, severe thermal stresses are created in a radiation embrittled vessel wall by local overcooling. Previous thermal-mixing analyses have assumed that the thermal stratification phenomena generated in stagnated loop of a partially stagnated collant loop are neutralized in the vessel downcomer by strong flow from unstagnated loop. On the basis of these reasons, this paper presents the thermal-mixing analysis results in order to identify the fact that the cold plume generated in the vessel downcomer due to the thermal stratification phenomena of the stagnated loop is affected by the strong flow of the unstagnated loop.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.261-269
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• 2023

During a nuclear power plant (NPP) accident, radioactive material may be released into the surrounding environment in the form of a radioactive plume. The behavior of the radioactive plume is influenced by meteorological factors such as wind direction and speed. If the residents are evacuated to a shelter in the direction of the flow of the radioactive plume, the radiation exposure of the residents may increase, contrary to the purpose of the evacuation. To avoid such an undesirable outcome, this paper applies a big data analysis to evaluate the suitability of the shelter locations near 5 NPPs in the Republic of Korea in terms of the seasonal wind direction frequency in those areas. To this end, the wind data measured around the NPPs from 2016 to 2020 were analyzed to derive the seasonal wind direction frequency using a big data analysis. These analyses results were then used to determine how many shelters around NPPs locate in areas with prevailing wind direction per season. Then, suggestions were made on the direction for residents not to evacuate, if possible, that is, the prevailing seasonal wind directions for 5 NPPs, depending on the season in which the accident occurs.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.335-341
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• 2017

The main control room (MCR) in advanced nuclear power plants (NPPs) has changed from analog to digital control system (DCS). Operation and control have become more automated, centralized, and accurate due to the digitalization of NPPs, which has improved the efficiency and security of the system. New issues associated with human reliability inevitably arise due to the adoption of new accident procedures and digitalization of main control rooms in NPPs. The LingAo II NPP is the first digital NPP in China to apply the state-oriented procedure. In order to address issues related to human reliability analysis for DCS and DCS + state-oriented procedure, the Hunan Institute of Technology conducted a research project based on a cooperative agreement with the LingDong Nuclear Power Co. Ltd. This paper is a brief introduction to the project.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05b
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• pp.579-585
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• 1996

A CATHENA model for the reactor regulating system is developed and tested independently. A CATHENA plant model is created by combining this model with the reference CATHENA model which has been developed to analyze a loss-of-coolant accident (LOCA) for the Wolsong 2 generating station. This model is intended to provide a trip coverage analysis capability. The CATHENA reactor regulating system model includes the demand power routine. the light water zone control absorbers, mechanical control absorbers and adjusters. The CATHENA model is tested for steady state at 103% full power. A postulated accident transient (small LOCA) was also tested. The results show that the control routines in CATHENA were set up properly.

• Nuclear Engineering and Technology
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• v.38 no.1
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• pp.11-32
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• 2006

System thermal-hydraulic codes have been used in the past decades in the areas of design, operation, licensing and safety of Nuclear Power Plants (NPPs). The development and validation of these codes have reached a high degree of maturity, through the consideration of huge experiments and advanced numerical models. Nowadays, the analyses are based upon realistic approaches rather than the conservative evaluation models. However the applications of these computational tools require preliminary qualification issues. Although huge amounts of financial and human resources have been invested for the development and improvement of codes, the calculation results are still affected by errors. In the sophisticated nuclear technology, design and safety of NPP, these errors must be quantified. An overview of the state of the art of the current thermal-hydraulic system code is developed and the need of uncertainty analysis in code calculations is emphasized. Several sources of uncertainty have been classified and commented, and typical applications of such methods are shown.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.24-35
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• 2004

In order to analyze the hydrogen distribution during a severe accident in the APR1400 containment, GASFLOW II was used. For the APR1400 NPP, a hydrogen mitigation system is considered from the design stage, but a fully time-dependent, three-dimensional analysis has not been performed yet. In this study GASFLOW code II is used for the three-dimensional analysis. The first step to analysis involving hydrogen behavior in a full containment with the GASLOW code is to generate a realistic geometry model, which includes nodalization and modeling of the internal structures such as walls, ceilings and equipment. Geometry modeling of the APR1400 is conducted using GUI program by overlapping the containment cut drawings in a graphical file format on the mesh view. The total number of mesh cells generated is 49,476. And the calculated free volume of the APR1400 containment by GASFLOW is almost the same as the value from the GOTHIC modeling. A hypothetical SB-LOCA scenario beyond design base accident was selected to analyze the hydrogen behavior with the hydrogen mitigation system. The source of hydrogen and steam for the GASFLOW II analysis is obtained from a MAAP calculation. Combustion pressure and temperature load possibilities within the compartments used in the GOTHIC analysis are studied based on the Sigma-Lambda criteria. Finally the effectiveness of HMS installed in the APR1400 containment is evaluated from the point of severe accident management

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2821-2835
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• 2020

The phenomenon identification and ranking table (PIRT) is an important basis in the nuclear power plant (NPP) thermal-hydraulic analysis. This study focuses on the importance ranking of the input parameters when lacking the PIRT, and the target scenario is the small break loss of coolant accident (SBLOCA) in a pressurized water reactor (PWR) CPR1000. A total of 54 input parameters which might have influence on the figure of merit (FOM) were identified, and the sensitivity measure of each input on the FOM was calculated through an optimized moment-independent global sensitivity analysis method. The importance ranking orders of the parameters were transformed into the Savage scores, and the parameters were categorized based on the Savage scores. A parameter importance ranking table for the SBLOCA scenario of the CPR1000 reactor was obtained, and the influences of some important parameters at different break sizes and different accident stages were analyzed.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.