A two-step neutron spectrum unfolding method for fission reactors based on artificial neural network

Abstract Comprehensive knowledge of neutron spectrum is significant in reactor design. Online wide-range neutron spectrum unfolding technology still requires improvement in accuracy and efficiency. In the work, a “two-step” neutron spectrum unfolding method based on artificial neural network (ANN) was developed to unfold spectrum with wide energy range. First, a default spectrum was reconstructed by using the ANN model which had been trained with a large amount of neutron spectra generated from Monte Carlo transport calculation. Second, the default spectrum was optimized by using iteration algorithm. The two-step method was verified with a thermal neutron reactor VENUS-3 and a fast neutron reactor BN-600. Comparison of mean square error (MSE) between this method and the traditional unfolding method showed reduction of 83.4% and 85.6% on VENUS-3 and BN-600 respectively, and average relative deviation (ARD) reduction of 89.3% and 86.1% respectively. Also, comparison of spectrum quality (Qs) showed reduction of 83.4% and 86.0% respectively for the two cases. This work demonstrated that the developed two-step method could obtain the better accuracy than traditional method.