DocumentCode :
687264
Title :
Prediction of reactor vessel water level using GMDH in severe accidents due to LOCA
Author :
Soon Ho Park ; Jae Hwan Kim ; Man Gyun Na
Author_Institution :
Chosun Univ., Gwangju, South Korea
fYear :
2013
fDate :
Oct. 27 2013-Nov. 2 2013
Firstpage :
1
Lastpage :
4
Abstract :
In certain circumstances of the severe accident, it is essential to confirm major parameters of a nuclear power plant. The reason of confirmation is to check the status of a nuclear power plant and to respond appropriately to each situation. Particularly, the reactor vessel water level is important data in order to confirm the reactor core condition. Therefore, in preparation for the uncertainty of a sensor in severe accident situations, the reactor vessel water level was predicted using a group method of data handling (GMDH) algorithm. The prediction model of a reactor vessel water level was developed based upon numerical simulation data such as development data and test data. These data were generated by simulating the severe accidents of a total of 810 cases using MAAP4 code about the OPR1000 nuclear power plant. As a result of predictions, the prediction performance of the developed GMDH model was quite satisfactory. Therefore, the developed GMDH model could be successfully applied for providing effective information for operators in severe accident situations.
Keywords :
fission reactor accidents; fission reactor coolants; fission reactor core control; forecasting theory; identification; nuclear engineering computing; nuclear power stations; numerical analysis; GMDH algorithm; GMDH model prediction performance; LOCA; MAAP4 code; OPR1000 nuclear power plant; development data; group method of data handling algorithm; major nuclear power plant parameters; nuclear power plant status check; numerical simulation data; reactor core condition; reactor vessel water level prediction model; sensor uncertainty preparation; severe accident simulation; severe accident situation; severe accident situations; test data; Accidents; Data models; Inductors; Polynomials; Power generation; Prediction algorithms; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE
Conference_Location :
Seoul
Print_ISBN :
978-1-4799-0533-1
Type :
conf
DOI :
10.1109/NSSMIC.2013.6829711
Filename :
6829711
Link To Document :
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