Prediction Accuracy Improvement of Neutronic Characteristics of a Breeding Light Water Reactor Core by Extended Bias Factor Methods with Use of FCA-XXII-1 Critical Experiments

Abstract

Two extended bias factor methods, the LC and PE methods, were applied to the prediction accuracy evaluation of neutronic characteristics of a breeding light water reactor, using data of FCA-XXII-1 critical experiments, in order to investigate the features and effectiveness of these methods on the basis of an actual core design and existing experimental results. The present study confirms the following features of these methods. Both the LC and PE methods can improve the prediction accuracy the most when all the experimental results are used. The prediction accuracy improvement is achieved mainly by reducing uncertainty due to errors in cross sections. This is done by realizing a profile of sensitivity coefficients closer to that of the target core and suppressing the influence of errors in experiments and experimental analysis methods. The PE method always improves the prediction accuracy with the use of any combination of experimental results. It is always superior to the LC method in the improvement of the prediction accuracy. Concerning the effectiveness of using the extended bias factor methods with the data of FCA XXII-1 critical experiments, it is concluded that the experimental results regarding multiplication factor are more effective than the other experimental results, namely, reaction rate ratios of 238U capture to 239Pu fission (C28/F49) and void reactivity, in reducing prediction uncertainties of all the neutronic characteristics of the target core investigated: the multiplication factor, the C28/F49, and the void reactivity of the target core. This is due to the fact that the extended bias factor methods cannot fully utilize the potential that these experimental results have for the reduction of the uncertainties due to the errors in cross sections because of their strong correlations to the target core characteristics. This failure is due to large errors in the experiments and/or the experimental analysis methods.