Study on Uncertainty Analysis Method of Fast Reactor Based on Covariance Matrix Sampling

The uncertainty analysis methods based on traditional statistical sampling have received widespread attention in China and other countries due to their simple algorithms, easy realization of codes, and consideration of high-order effects. However, these methods usually require a large number of samples to ensure the calculation accuracy of response variables. As found in the study, this phenomenon occurs because of the poor quality of the samples. After a covariance matrix sampling is used instead of the traditional sampling method, a small sample size can also ensure a high calculation accuracy. This paper firstly demonstrates theoretically the feasibility of the covariance matrix sampling method, and verifies it with simple tests. On this basis, this paper, using the self-developed fast spectrum reactor sensitivity and uncertainty analysis code - SUFR and the international reference configuration for fast reactor ZPR-6/7, calculates the uncertainty of effective multiplication factor (keff) caused by the nuclear cross sections of different reaction types of multiple nuclides, and compares the calculation results with the uncertainty calculated using the deterministic method. As demonstrated by the results, if the covariance matrix sampling is used, with a sample size of 50, the uncertainty deviation calculated in the two methods each is below 1.3%. This indicates that the use of the covariance matrix sampling method can solve the problems present in the use of the traditional sampling method to calculate uncertainty, and that it is appropriate to develop the SUFR code function against the covariance matrix sampling. This method represents a further development of the traditional sampling method. © 2021, Editorial Board of Journal of Nuclear Power Engineering. All right reserved.