• Nuclear Engineering and Technology
• /
• v.42 no.4
• /
• pp.460-467
• /
• 2010

In this study, the parameters of the feedwater control system (FWCS) of the OPR1000 type nuclear power plant (NPP) are optimized by response surface methodology (RSM) in order to acquire better level control performance from the FWCS. The objective of the optimization is to minimize the steam generator (SG) water level deviation from the reference level during transients. The objective functions for this optimization are relationships between the SG level deviation and the parameters of the FWCS. However, in this case of FWCS parameter optimization, the objective functions are not available in the form of analytic equations and the responses (the SG level at plant transients) to inputs (FWCS parameters) can be evaluated by computer simulations only. Classical optimization methods cannot be used because the objective function value cannot be calculated directely. Therefore, the simulation optimization methodology is used and the RSM is adopted as the simulation optimization algorithm. Objective functions are evaluated with several typical transients in NPPs using a system simulation computer code that has been utilized for the system performance analysis of actual NPPs. The results show that the optimized parameters have better SG level control performance. The degree of the SG level deviation from the reference level during transients is minimized and consequently the control performance of the FWCS is remarkably improved.

• Nuclear Engineering and Technology
• /
• v.55 no.9
• /
• pp.3342-3351
• /
• 2023

High-density polyethylene (HDPE) pipelines in nuclear power plants (NPPs) have to meet high requirements for seismic performance. HDPE pipes have been proved to have good seismic performance, but joints are the weak links in the pipelines, and pipeline failures usually initiate from the defects inside the joints. Limited data are available on the seismic performance of butt-fusion joints of HDPE pipelines in NPPs, especially in terms of defects changes inside the joints after earthquakes. In this paper, full-scale shaking table tests were performed on a test section of suspended HDPE pipelines in an NPP, which included straight pipes, elbows, and 10 butt-fusion joints. During the tests, the seismic load-induced strain of the joints was analyzed by strain gauges, and it was much smaller than the internal pressure and self-weight-induced strain. Before and after the shaking table tests, phased array ultrasonic testing (PA-UT) was conducted to detect defects inside the joints. The locations, numbers, and dimensions of the defects were analyzed. It was found that defects were more likely to occur in elbows joints. No new defect was observed after the shaking table tests, and the defects showed no significant growth, indicating the satisfactory seismic performance of the butt-fusion joints.

• Nuclear Engineering and Technology
• /
• v.42 no.2
• /
• pp.219-230
• /
• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.25 no.2
• /
• pp.363-374
• /
• 2015

Instrumentation and control(I&C) system has been mainly designed and operated based on analog technologies in existing Nuclear Power Plants(NPPs). However, As the development of Information Technology(IT), digital technologies are gradually being adopted in newly built NPPs. I&C System based on digital technologies has many advantages but it is vulnerable to cyber threat. For this reason, cyber threat adversely affects on safety and reliability of I&C system as well as the entire NPPs. Therefore, the software equipped to NPPs should be developed with cyber security attributes from the initiation phase of software development life cycle. Moreover through cyber security assessment, the degree of confidence concerning cyber security should be measured and if managerial, technical and operational work measures are implemented as intended should be reviewed in order to protect the I&C systems and information. Currently the overall cyber security program, including cyber security assessment, is not established on I&C systems. In this paper, we propose cyber security assessment methods in the Software Development Life Cycle by drawing cyber security activities and assessment items based on regulatory guides and standard technologies concerned with NPPs.

• Nuclear Engineering and Technology
• /
• v.54 no.8
• /
• pp.2859-2870
• /
• 2022

Because nuclear power plants (NPPs) are safety-critical infrastructure, it is essential to increase their safety and minimize risk. To reduce human error and support decision-making by operators, several artificial-intelligence-based diagnosis methods have been proposed. However, because of the nature of data-driven methods, conventional artificial intelligence requires large amount of measurement values to train and achieve enough diagnosis resolution. We propose a graph neural network (GNN) based accident diagnosis algorithm to achieve high diagnosis resolution with limited measurements. The proposed algorithm is trained with both the knowledge about physical correlation between components and measurement values. To validate the proposed methodology has a sufficiently high diagnostic resolution with limited measurement values, the diagnosis of multiple accidents was performed with limited measurement values and also, the performance was compared with convolution neural network (CNN). In case of the experiment that requires low diagnostic resolution, both CNN and GNN showed good results. However, for the tests that requires high diagnostic resolution, GNN greatly outperformed the CNN.

• Journal of the Ergonomics Society of Korea
• /
• v.25 no.3
• /
• pp.85-96
• /
• 2006

Main control room(MCR) man-machine interface(MMI) design of advanced nuclear power plants(NPPs) such as APR(advanced power reactor)-1400 can be validated through performance-based tests to determine whether it acceptably supports safe operation of the plant. In this work, plant performance, personnel task, situation awareness, workload, teamwork, and anthropometric/physiological factor are considered as factors for the human performance evaluation. For development of measures in each of the factors, techniques generally used in various industries and empirically proven to be useful are adopted as main measures and some helpful techniques are developed in order to complement the main measures. Also the development of the measures is addressed based on the theoretical background. Finally we discuss the way in which the measures can be effectively integrated and then HUPESS(HUman Performance Evaluation Support System) which is in development based on the integrated way is briefly introduced.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1999.04a
• /
• pp.17-24
• /
• 1999

The porvision against earthquakes and aseismic design of nuclear power plants (NPPs) in Korea are composed of four stages: site-selection, design, construction, and operation stages, Since regulatory criteria are strictly applied in each stage, the NPPs in Korea are believed to have a sufficient safety against maximum potential earthquakes. However, it has been recognized that those regulatory criteria borrowed from U.S. should be replace by Korea-specific ones by using earthquake data obtained from earthquake observation at and around NPP sites. Also, the government made a plan after the Yongwol and th Kyongju earthquakes that the regulatory body operates an independent earthquake network in order to reinforce the earthquake preparedness of NPPs. In compliance with the government's plan, this project is aiming at deployment of an earthquake motoring network composed of four seismic stations at NPP sites to record earthquake ground motions at NPP sites, to derive attenuation formulas of various ground motions and site-specific response spectra, and to develop structural intergrity assesment program.

• Nuclear Engineering and Technology
• /
• v.51 no.2
• /
• pp.453-462
• /
• 2019

Mental workload (MWL) is a major consideration for the user interface design in nuclear power plants (NPPs). However, each MWL evaluation method has its advantages and limitations, thus the evaluation and control methods based on multi-index methods are needed. In this study, fuzzy comprehensive evaluation (FCE) theory was adopted for assessment of interface designs in NPP based on operators' MWL. An evaluation index system and membership functions were established, and the weights were given using the combination of the variation coefficient and the entropy method. The results showed that multi-index methods such as performance measures (speed of task and error rate), subjective rating (NASA-TLX) and physiological measure (eye response) can be successfully integrated in FCE for user interface design assessment. The FCE method has a correlation coefficient compared with most of the original evaluation indices. Thus, this method might be applied for developing the tool to quickly and accurately assess the different display interfaces when considering the aspect of the operators' MWL.

• Journal of the Korean Society of Systems Engineering
• /
• v.16 no.2
• /
• pp.47-58
• /
• 2020

Most of the reactor trips in Korean NPPs related to core protection systems were caused not because of proximity of boiling crisis and, consequently, a damage in the core, but due to particular miscalculations or component failures related to the core protection system. The most common core protection system applied in Korean NPPs is the Core Protection Calculator System (CPCS), which is installed in OPR1000 and APR1400 plants. It generates a trip signal to scram the reactor in case of low Departure from Nucleate Boiling Ratio (DNBR) or high Local Power Density (LPD). However, is a reactor trip necessary to protect the core? Or could a fast power reduction be enough to recover the DNBR/LPD without a scram? In order to analyze the online calculation of DNBR/LPD, and the use of fast power reduction as trip avoidance methodology, a concept of CPCS with fast power reduction function was developed in Matlab® Simulink using systems engineering approach. The system was validated with maximum of 0.2% deviation from the reference and the dynamic deviation was maximum of 12.65% for DNBR and 6.72% for LPD during a transient of 16,000 seconds.

• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1336-1342
• /
• 2022

Risk-informed safety classification of structures, systems and components (SSCs) is very important for ensuring the safety and economic efficiency of nuclear power plants (NPPs). However, previous methods for safety classification of SSCs do not take the plant operating modes or the operational process of SSCs into consideration, thus cannot concentrate on the safety and economic efficiency accurately. In this contribution, a new method for safety classification of SSCs based on the categorization of plant operating modes is proposed, which considers the NPPs operating history to improve the economic efficiencies while maintaining the safety. According to the time duration of plant configurations in plant operating modes, average importances of SSCs are accessed for an NPP considering the operational process, and then safety classification of SSCs is performed for plant operating modes. The correctness and effectiveness of the proposed method is demonstrated by application in an NPP's safety classification of SSCs.