Reactor Core Power Mapping Based on Bayesian Inference

The reactor core power mapping method based on Bayesian inference has been implemented, and it provides an effective way to combine the information from the measurements of in-core neutron detectors and the numerical neutronics simulation results. Measurements from Unit 1 of Daya Bay Nuclear Power Plant 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 coupling coefficients method. The root mean square errors, the maximum relative errors, and the power peak reconstruction error of the Bayesian inference method are less than 0.31%, 1.64% and 0.07% for the entire operating cycle, respectively, and the Bayesian inference method outperforms the Kalman filter method and the coupling coefficients method in terms of accuracy. The reconstructed assembly power distribution results and the calculation speed show that the Bayesian inference method is a promising candidate for the on-line core power distribution monitoring system.