Uncertainty quantification of the SPND dynamic model based on non-intrusive polynomial chaos method

Abstract

Self-Powered Neutron Detectors (SPNDs) are widely used in nuclear reactors to monitor the core neutron flux distribution and to provide meaningful information for regulation and protection of the reactor. In this paper we present the implementation and the application of Non-Intrusive Polynomial Chaos (NIPC) method for uncertainty analysis of the SPND dynamic model. The stochastic responses of the SPND model can be expanded as a series of orthogonal polynomial basis, and the corresponding coefficients are obtained using the regression-based approach. The variances of the SPND responses are determined using these spectral coefficients and are compared to those obtained by Monte Carlo Random Sampling (RS) method. The results show that for SPND dynamic response model NIPC method achieves the accuracy of RS method with a lower computational cost.