Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference

TL;DR

This paper introduces a Monte Carlo-based Bayesian inference approach to improve PWR core simulation accuracy by integrating nuclear data uncertainties with reactor measurements, significantly reducing prediction errors.

Contribution

It presents a novel non-perturbative Bayesian framework that combines high-dimensional nuclear data covariance with operational data for enhanced reactor parameter predictions.

Findings

Prediction uncertainty of boron letdown curve reduced by tenfold.

Bayesian inference improves cycle-to-cycle prediction accuracy.

Updated nuclear data libraries yield consistent results with Bayesian predictions.

Abstract

A Monte Carlo-based Bayesian inference model is applied to the prediction of reactor operation parameters of a PWR nuclear power plant. In this non-perturbative framework, high-dimensional covariance information describing the uncertainty of microscopic nuclear data is combined with measured reactor operation data in order to provide statistically sound, well founded uncertainty estimates of integral parameters, such as the boron letdown curve and the burnup-dependent reactor power distribution. The performance of this methodology is assessed in a blind test approach, where we use measurements of a given reactor cycle to improve the prediction of the subsequent cycle. As it turns out, the resulting improvement of the prediction quality is impressive. In particular, the prediction uncertainty of the boron letdown curve, which is of utmost importance for the planning of the reactor cycle length, can be reduced by one order of magnitude by including the boron concentration measurement information of the previous cycle in the analysis. Additionally, we present first results of non-perturbative nuclear-data updating and show that predictions obtained with the updated libraries are consistent with those induced by Bayesian inference applied directly to the integral observables.