Uncertainty and correlation analysis of lead nuclear data on reactor parameters for the European Lead Cooled Training Reactor

Abstract

The Total Monte Carlo (TMC) method was used in this study to assess the impact of 204,206,207,208 Pb nuclear data uncertainties on reactor safety parameters for the ELECTRA reactor. Relatively large uncertainties were observed in the k eff and the coolant void worth (CV W ) for all isotopes except for 204 Pb with significant contribution coming from 208 Pb nuclear data; the dominant effect came from uncertainties in the resonance parameters; however, elastic scattering cross section and the angular distributions also had significant impact. It was also observed that the k eff distribution for 206,207,208 Pb deviates from a Gaussian distribution with tails in the high k eff region. An uncertainty of 0.9% on the k ef f and 3.3% for the CV W due to lead nuclear data were obtained. As part of the work, cross section-reactor parameter correlations were also studied using a Monte Carlo sensitivity method. Strong correlations were observed between the k eff and (n, el) cross section for all the lead isotopes. The correlation between the (n, inl) and the k eff was also found to be significant.

XGPT: Extending Monte Carlo Generalized Perturbation Theory capabilities to continuous-energy sensitivity functions

“…This result is rather unexpected to the authors, taking into account that a single, non-fissile isotope was considered in the uncertainty propagation study. Nonetheless the result is consistent with uncertainty quantification studies that used similar TMC input uncertainties for lead (Alhassan et al, 2015). Despite larger differences between XGPT and reference results compared to the PMF-35 benchmark, also this case study confirms a good agreement between the proposed perturbation-based approach and the TMC method, especially considering the large reduction in computational time requirements.…”

“…As reported in previous works related to the TENDL-based TMC analysis of lead isotopes (e.g., see Alhassan et al, 2015), the k eff distribution appears to be highly skewed. The sample standard deviation and higher moments of the distribution are presented in Table 2.…”

“…Previously published TMC results showed highly skewed k eff distribution for cases involving this nuclide (e.g., see Alhassan et al, 2015).…”

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

“…To infer differential experimental information from the resonance region into the tendl random files will thus need some special treatment. This is however of high importance since the resonance parameters seem to dominate the propagated uncertainty in some applications [22].…”

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

“…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 . In Fig.…”