Reactor core power mapping based on Bayesian inference

Abstract

Bayesian inference provides a coherent probabilistic approach for combining information from measurements of in-core neutron detectors and numerical neutronics simulation results, and thus is an appropriate framework for reactor core power mapping which has been implemented in this paper. Measurements from DayaBay Unit 1 PWR are used to verify the accuracy of the Bayesian inference method, and comparisons are made among the Bayesian inference method, the Kalman filter method and the very-often-used coupling coefficients method. The root mean square errors (RMSE), the maximum relative errors (MRE), and the power peak reconstruction error (PPRE) of the Bayesian inference method are less than 0.31%, 1.64% and 0.07% for the entire operating cycle separately. The reconstructed assembly power distribution results and the calculation speed show that the Bayesian inference method is a promising candidate for on-line core power distribution monitoring system.