Kwae Hwan Yoo

ON-POWER DETECTION OF PIPE WALL-THINNED DEFECTS USING IR THERMOGRAPHY IN NPPS

Wall-thinned defects caused by accelerated corrosion due to fluid flow in the inner pipe appear in many structures of the secondary systems in nuclear power plants (NPPs) and are a major factor in degrading the integrity of pipes. Wall-thinned defects need to be managed not only when the NPP is under maintenance but also when the NPP is in normal operation. To this end, a test technique was developed in this study to detect such wall-thinned defects based on the temperature difference on the surface of a hot pipe using infrared (IR) thermography and a cooling device. Finite element analysis (FEA) was conducted to examine the tendency and experimental conditions for the cooling experiment. Based on the FEA results, the equipment was configured before the cooling experiment was conducted. The IR camera was then used to detect defects in the inner pipe of the pipe specimen that had artificially induced defects. The IR thermography developed in this study is expected to help resolve the issues related to the limitations of non-destructive inspection techniques that are currently conducted for NPP secondary systems and is expected to be very useful on the NPPs site.

Smart Sensing of the RPV Water Level in NPP Severe Accidents Using a GMDH Algorithm

The reactor pressure vessel (RPV) water level is critical information for confirming the condition of core cooling in severe accident situations. However, the measured RPV water level signal cannot be trusted during severe accidents due to the unknown integrity of the sensor. In this study, the RPV water level was predicted under severe accident conditions using a group method of data handling (GMDH) algorithm. The prediction model was developed using data obtained from numerical simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code and validated using independent test data. The developed GMDH model performed very well. In addition, to investigate the effect of uncertainties in the input variables, the model was tested using input data with an artificially added random error. It was accurate enough to predict the RPV water level in severe accident situations when the RPV water level sensor cannot be trusted. Therefore, the developed GMDH model will be helpful for providing effective information for operators in severe accident situations.

Estimation of Minimum DNBR Using Cascaded Fuzzy Neural Networks

It is very important for plant operators to be informed of the departure from nucleate boiling ratio (DNBR) to prevent the fuel cladding from melting and a boiling crisis in a nuclear reactor. The reactor core monitoring and protection systems require a minimum DNBR value to monitor reactor coolant conditions. In this study, in order to estimate the minimum DNBR value, a cascaded fuzzy neural network (CFNN) method was used. The CFNN model can be used to estimate the minimum DNBR value through the process of adding fuzzy neural networks (FNNs) repeatedly. The proposed DNBR estimation algorithm was verified by applying the nuclear and thermal data acquired from many numerical simulations of the optimized power reactor 1000 (OPR1000). The CFNN model was compared to previously developed models and was found to be superior to them. Therefore, this model can be used to effectively monitor and predict the minimum DNBR in the reactor core.

Prediction of Leak Flow Rate Using Fuzzy Neural Networks in Severe Post-LOCA Circumstances

Providing information about the leak flow rate caused by a loss-of-coolant accident (LOCA) to nuclear power plant (NPP) operation personnel is a key to the management and mitigation of severe post-LOCA circumstances at NPPs where active safety injection systems do not actuate. The leak flow rate is a function of break size, differential pressure (i.e., difference between internal and external reactor vessel pressure), temperature, and so on. In this study, the break position and size were first identified and predicted, and then, the leak flow rate was predicted using a fuzzy neural network (FNN). The FNN was developed using training data and validated using independent test data. The data were generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The data for training the FNN model were selected among the acquired data using the subtractive clustering method, and FNN performance was improved. The developed FNN model was sufficiently accurate to be used for predicting leak flow rate, which is useful information for managing severe post-LOCA situations.

Identification of LOCA and Estimation of Its Break Size by Multiconnected Support Vector Machines

Nuclear power plants (NPPs) are composed of very large complex systems. During transient occurrences in NPPs, operators determine the transients of the NPP through information acquired from various measuring instruments. A support vector machine (SVM) based on serial and parallel connections, termed as a multiconnected SVM, is introduced in this paper. The loss of coolant accidents (LOCAs) was identified and their break sizes are estimated using the multiconnected SVM model. The optimal parameter values of the multiconnected SVM models are obtained using a genetic algorithm. In this paper, the modular accident analysis program code was used to simulate the severe accidents occurring due to a variety of design basis accidents. The proposed algorithm uses the short time-integrated simulated sensor signals just after the reactor trip. The results show that the multiconnected SVM model can identify LOCAs and estimate their break sizes accurately. It is expected that the LOCA identification and the accurate estimation of the break size are useful for NPP operators when they try to manage severe accidents.