Deterministically estimated fission source distributions for Monte Carlo keigenvalue problems
The standard Monte Carlo (MC) keigenvalue algorithm involves iteratively converging the fission source distribution using a series of potentially timeconsuming inactive cycles before quantities of interest can be tallied. One strategy for reducing the computational time requirements of these inactive cycles is the Sourcerer method, in which a deterministic eigenvalue calculation is performed to obtain an improved initial guess for the fission source distribution. This method has been implemented in the Exnihilo software suite within SCALE using the SPNSPN or SNSN solvers in Denovo and the Shift MC code. The efficacy of this method is assessed with different Denovo solution parameters for a series of typical keigenvalue problems including small criticality benchmarks, fullcore reactors, and a fuel cask. Here it is found that, in most cases, when a large number of histories per cycle are required to obtain a detailed flux distribution, the Sourcerer method can be used to reduce the computational time requirements of the inactive cycles.
 Authors:

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 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Publication Date:
 Grant/Contract Number:
 AC0500OR22725
 Type:
 Accepted Manuscript
 Journal Name:
 Annals of Nuclear Energy (Oxford)
 Additional Journal Information:
 Journal Name: Annals of Nuclear Energy (Oxford); Journal Volume: 119; Journal Issue: C; Journal ID: ISSN 03064549
 Publisher:
 Elsevier
 Research Org:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org:
 USDOE Office of Science (SC); USNRC
 Country of Publication:
 United States
 Language:
 English
 Subject:
 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Fission source convergence; Monte Carlo transport; Hybrid methods
 OSTI Identifier:
 1435255
Biondo, Elliott D., Davidson, Gregory G., Pandya, Tara M., Hamilton, Steven P., and Evans, Thomas M.. Deterministically estimated fission source distributions for Monte Carlo keigenvalue problems. United States: N. p.,
Web. doi:10.1016/j.anucene.2018.04.039.
Biondo, Elliott D., Davidson, Gregory G., Pandya, Tara M., Hamilton, Steven P., & Evans, Thomas M.. Deterministically estimated fission source distributions for Monte Carlo keigenvalue problems. United States. doi:10.1016/j.anucene.2018.04.039.
Biondo, Elliott D., Davidson, Gregory G., Pandya, Tara M., Hamilton, Steven P., and Evans, Thomas M.. 2018.
"Deterministically estimated fission source distributions for Monte Carlo keigenvalue problems". United States.
doi:10.1016/j.anucene.2018.04.039.
@article{osti_1435255,
title = {Deterministically estimated fission source distributions for Monte Carlo keigenvalue problems},
author = {Biondo, Elliott D. and Davidson, Gregory G. and Pandya, Tara M. and Hamilton, Steven P. and Evans, Thomas M.},
abstractNote = {The standard Monte Carlo (MC) keigenvalue algorithm involves iteratively converging the fission source distribution using a series of potentially timeconsuming inactive cycles before quantities of interest can be tallied. One strategy for reducing the computational time requirements of these inactive cycles is the Sourcerer method, in which a deterministic eigenvalue calculation is performed to obtain an improved initial guess for the fission source distribution. This method has been implemented in the Exnihilo software suite within SCALE using the SPNSPN or SNSN solvers in Denovo and the Shift MC code. The efficacy of this method is assessed with different Denovo solution parameters for a series of typical keigenvalue problems including small criticality benchmarks, fullcore reactors, and a fuel cask. Here it is found that, in most cases, when a large number of histories per cycle are required to obtain a detailed flux distribution, the Sourcerer method can be used to reduce the computational time requirements of the inactive cycles.},
doi = {10.1016/j.anucene.2018.04.039},
journal = {Annals of Nuclear Energy (Oxford)},
number = C,
volume = 119,
place = {United States},
year = {2018},
month = {4}
}