Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix

Abstract

An uncertainty reduction method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize that there exist some correlations among the prediction errors of core safety parameters, e.g., a correlation between the control rod worth and the assembly relative power at corresponding position. Correlations of errors among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients of core parameters. The estimated correlations of errors among core safety parameters are verified through the direct Monte Carlo sampling method. Once the correlation of errors among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained.

Uncertainty quantification of neutronic parameters of light water reactor fuel cells with JENDL-4.0 covariance data

“…Furthermore, cross-correlations have also been evaluated for each fuel cell, and strong correlations among the same parameters at BOC and EOC and among different parameters have been observed. This result is quite optimistic to introduce the uncertainty reduction technique, such as one proposed by Yamamoto [12], for unmeasurable parameters using other measurable parameters.…”

“…As well as the uncertainty of each reactor neutronic parameter, cross-correlations among different reactor neutronic parameters are also important as pointed out by Yamamoto [12]. Thus, we evaluate the crosscorrelations among different reactor neutronic parameters for each fuel cell.…”

Recent activities in the field of reactor physics

“…Those subjects have been well studied, especially for fast reactors (FRs) [20] followed by recent research works on light water reactors [21][22][23] and accelerator-driven systems (ADSs) [24]. With recent improvements in the computing environment, new approaches for sensitivity analysis and cross section adjustments have been studied; stochastic methods such as the "Total Monte Carlo" or "Random Sampling" approach have been proposed for adjustment of cross section data [25,26] and uncertainty analysis [27].…”

Confidence interval estimation by bootstrap method for uncertainty quantification using random sampling method

“…In the core analysis, the safety parameters should satisfy their limitations. In this context, prediction accuracy or uncertainty of the core analysis is crucial for the reactor safety [1,2]. There are various factors for the prediction errors in the core analysis, e.g.…”