Prediction of golden time using SVR for recovering SIS under severe accidents
| Autor: | Man Gyun Na, Seop Hur, Chang Hwoi Kim, Jae Hwan Kim, Ju Hyun Back, Kwae Hwan Yoo |
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| Rok vydání: | 2016 |
| Předmět: |
Operations research
010308 nuclear & particles physics Computer science business.industry 020209 energy 02 engineering and technology Power reactor Accident analysis Modular design 01 natural sciences Reliability engineering Support vector machine Nuclear Energy and Engineering Nuclear reactor core 0103 physical sciences Genetic algorithm 0202 electrical engineering electronic engineering information engineering business Reactor pressure vessel |
| Zdroj: | Annals of Nuclear Energy. 94:102-108 |
| ISSN: | 0306-4549 |
| Popis: | Nuclear power plants (NPPs) are designed in consideration of design basis accidents (DBAs). However, if the safety injection system (SIS) is not working properly in a loss-of-coolant-accident (LOCA) situation, it can induce a severe accident that exceeds DBAs. Therefore, it is important to properly actuate the SIS before a DBA becomes a severe accident. If the SIS is not working in time, the reactor core may be uncovered and the reactor vessel (RV) may be damaged. In this paper, we defined the golden time as the available time from an initial SIS malfunction for actuating the SIS to prevent reactor core uncovery and RV failure. A support vector regression (SVR) model was applied to predict the golden time. The input variables and parameters of the SVR model were selected and optimized by using a genetic algorithm. The data set of severe accident scenarios was obtained by using the Modular Accident Analysis Program (MAAP) code. An optimized power reactor (OPR1000) was used for the simulations. It was shown that that the proposed SVR model could predict the golden time accurately. |
| Databáze: | OpenAIRE |
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