Nuclear Data Uncertainty Quantification and Propagation for Safety Analysis of Lead-Cooled Fast Reactors

Abstract

In this study, the Best Estimate Plus Uncertainty (BEPU) approach is developed for the systematic quantification and propagation of uncertainties in the modelling and simulation of lead-cooled fast reactors (LFRs) and applied to the demonstration LFR (DLFR) initially investigated by Westinghouse. The impact of nuclear data uncertainties based on ENDF/B-VII.0 covariances is quantified on lattice level using the generalized perturbation theory implemented with the Monte Carlo code Serpent and the deterministic code PERSENT of the Argonne Reactor Computational (ARC) suite. The quantities of interest are the main eigenvalue and selected reactivity coefficients such as Doppler, radial expansion, and fuel/clad/coolant density coefficients. These uncertainties are then propagated through safety analysis, carried out using the MiniSAS code, following the stochastic sampling approach in DAKOTA. An unprotected transient overpower (UTOP) scenario is considered to assess the effect of input uncertainties on safety parameters such as peak fuel and clad temperatures. It is found that in steady state, the multiplication factor shows the most sensitivity to perturbations in 235U fission, 235U ν, and 238U capture cross sections. The uncertainties of 239Pu and 238U capture cross sections become more significant as the fuel is irradiated. The covariance of various reactivity feedback coefficients is constructed by tracing back to common uncertainty contributors (i.e., nuclide-reaction pairs), including 238U inelastic, 238U capture, and 239Pu capture cross sections. It is also observed that nuclear data uncertainty propagates to uncertainty on peak clad and fuel temperatures of 28.5 K and 70.0 K, respectively. Such uncertainties do not impose per se threat to the integrity of the fuel rod; however, they sum to other sources of uncertainties in verifying the compliance of the assumed safety margins, suggesting the developed BEPU method necessary to provide one of the required insights on the impact of uncertainties on core safety characteristics.

Digital Twin Concepts with Uncertainty for Nuclear Power Applications

“…Computationally, forward UQ is typically tackled by Monte Carlo (MC) sampling [93], from which useful statistics such as the mean, covariance, probabilities of rare/failure events, and expectations of performance and health metrics may be obtained. For example, one major area of forward UQ in nuclear science involves propagating uncertainty from the cross section data of nuclear isotopes through reactor analysis calculations, with some recent examples found in [94,95]. However, MC sampling converges slowly and is considered prohibitively expensive.…”

Impact of H in H2O thermal scattering data on criticality calculation: uncertainty and adjustment

“…Examples of recently evaluated nuclear data are the Nuclear Energy Agency JEFF-3.3 library [1], the US ENDF/B-VIII.0 library [2], the Japanese JENDL-4.0 library [3] and finally the TENDL libraries [4]. The need from users might concern better estimations of current reactor quantities (boron letdown curves, power maps) [5][6][7], spent fuel quantities (decay heat, source terms) [8][9][10][11][12], or quantities for advanced systems [13][14][15]. A commonly-used neutronics parameter is the neutron multiplication factor, or k eff , which plays for instance a crucial role in spent fuel management and in burnup credit [16][17][18].…”