Estimation of LOCA Break Size Using Cascaded Fuzzy Neural Networks

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict theprogressionof LOCAs innuclear power plants. Therefore, accurate informationon theLOCAbreakpositionandsize shouldbeprovided toefficientlymanage theaccident. In this paper, the LOCAbreak size is predicted using a cascaded fuzzy neural network (CFNN)model. The input data of theCFNNmodel are the time-integratedvalues of eachmeasurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model. Copyright © 2016, Published by Elsevier Korea LLC on behalf of Korean Nuclear Society. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).