Bayesian inference of multi-group nuclear data by Monte Carlo sampling method

Abstract

The multi-group nuclear data adjustment combines integral experiments with covariance data to reduce the crucial parameter uncertainties in the code verification and data validation. In this paper, the Monte Carlo sampling-based multi-group nuclear data adjustment methods, namely the unified Bayesian inference (UBI) method, the Unified Monte Carlo (UMC) method, and the Bayesian Monte Carlo (BMC) method, have been developed. These methods resort to statistical sampling to adjust the multi-group nuclear data and avoid sensitivity analysis. Besides, the sampling-based similarity analysis has been proposed to evaluate the neutronics similarity between experiments and industrial applications. Two simple pin-cells, regarded as an experiment and an industrial application respectively, are constructed to test the correctness and effectiveness of these methods. Besides, the B&W-1810 experiment with measurement values is chosen for the method verification. The results show that the posterior deviation and uncertainty see a dramatic decrease after the application of these adjustment methods.