Document Type
Grant
Publication Date
10-9-2007
First page number:
1
Last page number:
2
Abstract
Monte Carlo methods are beginning to be used for three-dimensional fuel depletion analyses to compute various quantities of interest, including isotopic compositions of used fuel.1 The TRITON control module, available in the SCALE 5.1 code system, can perform three dimensional (3-D) depletion calculations using either the KENO V.a or KENO-VI Monte Carlo transport codes, as well as the two-dimensional (2- D) NEWT discrete ordinates code. For typical reactor systems, the neutron flux is not spatially uniform. For Monte Carlo simulations, this results in non-uniform statistical uncertainties in the computed reaction rates. For spatial regions where the flux is low, e.g., axial fuel ends, computed quantities, such as isotopic compositions, may have large statistical uncertainties. However, in currently available Monte Carlo depletion codes these statistical uncertainties are not calculated or reported to the user. Consequently, users have no indication of the fidelity of their results in such regions, which can be a significant impediment to the effective use of Monte Carlo methods for design and optimization studies of advanced fuel designs. Additionally, for applications such as criticality safety of used nuclear fuel, the lower depleted end regions tend to dominate the reactivity, and hence must be accurately and/or conservatively represented.
Keywords
Nuclear reactors; Monte Carlo method; Spent reactor fuels; Stochastic processes
Controlled Subject
Monte Carlo method; Nuclear reactors; Spent reactor fuels; Stochastic processes
Disciplines
Nuclear | Nuclear Engineering | Oil, Gas, and Energy | Statistical Methodology | Statistical Models
File Format
File Size
66 KB
Language
English
Repository Citation
Sanders, C.,
Beller, D.
(2007).
Implementation of Uncertainty Propagation in TRITON/KENO: To Support the Global Nuclear Energy Partnership.
1-2.
Available at:
https://digitalscholarship.unlv.edu/hrc_trp_reactor/22
Included in
Nuclear Commons, Nuclear Engineering Commons, Oil, Gas, and Energy Commons, Statistical Methodology Commons, Statistical Models Commons