Incorporating Experimental Information in the Total Monte Carlo Methodology Using File Weights

Abstract

Some criticism has been directed towards the Total Monte Carlo method because experimental information has not been taken into account in a statistically well-founded manner. In this work, a Bayesian calibration method is implemented by assigning weights to the random nuclear data files and the method is illustratively applied to a few applications. In most of the considered cases, the estimated nuclear data uncertainty is reduced and the central values are significantly shifted. The study suggests that the method can be applied both to estimate uncertainties in a more justified way and in the search for better central values. Some improvements are however necessary; for example, the treatment of outliers and cross-experimental correlations should be more rigorous and random files that are intended to be prior files should be generated.

Propagation of nuclear data uncertainties for fusion power measurements

“…The reason for this should be investigated further, but one possible explanation is that there are stronger correlations in the model based TMC method than what is available in the IRDFF covariance files used in [2] and [7]. Better ways to include both differential [9] and integral [10] experimental data are currently being developed. Since [2] uses the standard cross-section and its experimental uncertainty directly we would suggest, to use these as the best estimate of the nuclear data uncertainty, i.e., 2.2% in DD and 1.4% in DT.…”

The TENDL library: Hope, reality and future

“…The idea is relatively simple to understand and follows the logic of sampling model parameters as well as experimental data, as presented in Refs. [17,18]. In the iteration number 0, all model parameters are sampled uniformly with large standard deviations, large enough to cover all experimental data.…”

Selecting benchmarks for reactor simulations: An application to a lead fast reactor

“…The TALYS code is run several times, each time with a different set of model parameters and a large set of unique random nuclear data files are produced for the isotope of interest. Differential experimental data are taken into account by using Bayesian statistical inference (Koning, 2015;Helgesson et al, 2015). The TMC method has been presented extensively elsewhere (Koning and Rochman, 2008; and applied to criticality benchmark experiments and reactor applications (Alhassan et al, 2014a;Rochman et al, 2009;Alhassan et al, 2015;Helgesson et al, 2014), and to dose calculations .…”