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Spent fuel recycling and conditioning topic of U.S.-Japan meeting
Officials with the Department of Energy’s Office of Environmental Management discussed spent nuclear fuel recycling and conditioning with counterparts from Japan during the 13th U.S.-Japan Technical Meeting of the Civil Nuclear Energy Research and Development Working Group, held recently in Santa Fe, N.M.
Ketaki Joshi, Nicholas Branam, Isaac Meyer, Ben Forget, Abdulla Alhajri, Vladimir Sobes
Nuclear Science and Engineering | Volume 197 | Number 7 | July 2023 | Pages 1356-1363
Technical Paper | doi.org/10.1080/00295639.2022.2159268
Articles are hosted by Taylor and Francis Online.
An analytic benchmark for nuclear data uncertainty propagation in k-eigenvalue calculations is demonstrated. Flat-flux-weighted cross-section covariance matrices are available in the ENDF/B library for many isotopes. For application-specific purposes, flux-weighted multigroup cross sections with carefully constructed energy group boundaries are desired. In this paper, we use the covariance information from ENDF/B-VII.1 for the defined continuous-energy cross section and an artificially inflated variance version of the same covariance matrix for first-order and Monte Carlo propagation of uncertainty calculations. A flat-flux weighting function is used for the continuous-energy cross-section uncertainty collapse resulting in a higher propagated uncertainty on the k-eigenvalue as the group structure becomes coarser. The results of this analytic benchmark suggest that the reporting of flat-flux-weighted multigroup cross-section covariance matrices at the ENDF level may lead to inaccurate predictions of the uncertainty on the k-eigenvalue for certain applications. This work implies that not only should the resonance parameter uncertainties that go into the calculation of the continuous-energy cross sections be published, but the parameter uncertainties should also be processed into continuous-energy cross-section uncertainties that can be collapsed to application-specific multigroup cross-section covariance matrices.