EPJ Web Conf.
Volume 146, 2017ND 2016: International Conference on Nuclear Data for Science and Technology
|Number of page(s)||4|
|Section||Nuclear Data for Applications|
|Published online||13 September 2017|
Update and evaluation of decay data for spent nuclear fuel analyses
1 Studsvik Scandpower Inc., Waltham, Massachusetts, USA
2 Studsvik Scandpower Inc., Idaho Falls, Idaho, USA
a e-mail: firstname.lastname@example.org
Published online: 13 September 2017
Studsvik’s approach to spent nuclear fuel analyses combines isotopic concentrations and multi-group cross-sections, calculated by the CASMO5 or HELIOS2 lattice transport codes, with core irradiation history data from the SIMULATE5 reactor core simulator and tabulated isotopic decay data. These data sources are used and processed by the code SNF to predict spent nuclear fuel characteristics. Recent advances in the generation procedure for the SNF decay data are presented. The SNF decay data includes basic data, such as decay constants, atomic masses and nuclide transmutation chains; radiation emission spectra for photons from radioactive decay, alpha-n reactions, bremsstrahlung, and spontaneous fission, electrons and alpha particles from radioactive decay, and neutrons from radioactive decay, spontaneous fission, and alpha-n reactions; decay heat production; and electro-atomic interaction data for bremsstrahlung production. These data are compiled from fundamental (ENDF, ENSDF, TENDL) and processed (ESTAR) sources for nearly 3700 nuclides. A rigorous evaluation procedure of internal consistency checks and comparisons to measurements and benchmarks, and code-to-code verifications is performed at the individual isotope level and using integral characteristics on a fuel assembly level (e.g., decay heat, radioactivity, neutron and gamma sources). Significant challenges are presented by the scope and complexity of the data processing, a dearth of relevant detailed measurements, and reliance on theoretical models for some data.
© The Authors, published by EDP Sciences, 2017
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