PRESSURIZED WATER-REACTOR CORE PARAMETER PREDICTION USING AN ARTIFICIAL NEURAL NETWORK

摘要: In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economics. The local power peaking factor must be kept lower than a predetermined value during cycle, effective multiplication maximized to extract maximum energy. If these core parameters could obtained very short time, optimal patterns would found more effectively quickly. A fast parameter prediction system is developed using back propagation neural network. This predicts several hundred times as reference numerical code, within an error few percent. effects variation training rate coefficients, momentum, hidden layer units are also discussed.