Data-Driven and Precursor-Group Uncertainty Propagation of Lattice Kinetic Parameters in UAM Benchmark

Abstract

A new data-driven sampling-based framework was developed for uncertainty quantification (UQ) of the homogenized kinetic parameters calculated by lattice physics codes such as TRITON and Polaris. In this study, extension of the database for the delayed neutron data (DND) is performed by exploring more delayed neutron experiments and adding additional isotopes/actinides to the data libraries. Afterwards, the framework is utilized to obtain a deeper knowledge of the kinetic parameters’ sensitivity and uncertainty. The kinetic parameters include precursor-group-wise delayed neutron fraction (DNF) and decay constant. Input uncertainties include nuclear data (i.e., cross-sections) and DND (i.e., precursor group parameters and fractional delayed neutron yield). It is found that kinetic parameters, especially DNFs, have large uncertainties. The DNF uncertainty is driven by the cross-section uncertainties for LWR designs, while decay constant uncertainty is dominated by the DND uncertainties. The usage of correlated U-235 thermal DND in the UQ process significantly reduces the DND uncertainty contribution on the kinetic parameters. Large void fraction and presence of neutron absorber (e.g., control rod) increase the DNF uncertainty due to the hardening of neutron spectrum. High correlation between the DNF groups (β1,..,β6) is observed, while the decay constant groups (λ1,..,λ6) show weak correlation to each other and also to DNF groups. The DNF uncertainties of the dominant precursor group 4 for PWR, BWR, and VVER are about 7.5%, 9.4%, and 7.6%, respectively. The DNF uncertainty grows to larger values after fuel burnup. Kinetic parameters’ values and uncertainties provided here can be efficiently used in subsequent core calculations, point reactor kinetics, and other applications.

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

“…For example, neutrons are likely to have I number of interactions (eg, fission, capture, and scatter-ing) with isotope X (eg, U-235 and Pu-239), and the probability of these interactions changes with the incident neutron energy E i (ie, which is discretized into energy groups). [48][49][50] It is worth mentioning that nuclear and delayed neutron data uncertainties are classified as parametric uncertainty according to the hierarchy shown later in Figure 4. The process is done by Monte Carlo sampling according to the flowchart in Figure 3 (top left of the figure shows the nuclear data category).…”

“…Detailed applications and investigation of the kinetic parameters' sensitivity and uncertainty were demonstrated in previous studies. [48][49][50] It is worth mentioning that nuclear and delayed neutron data uncertainties are classified as parametric uncertainty according to the hierarchy shown later in…”

“…The analysis is done on the basis of the delayed neutron dataset developed by Radaideh et al 48 and the 56-energy-group nuclear data library in the SCALE code system. 7 The geometry is modeled in 2D with reflective boundary conditions in all directions.…”