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Title: Eigenvalue sensitivity analysis capabilities with the differential operator method in the superhistory Monte Carlo method
Authors: Yamamoto, Toshihiro  kyouindb  KAKEN_id
Author's alias: 山本, 俊弘
Keywords: Monte Carlo
Sensitivity coefficient
Differential operator
Superhistory
Issue Date: Feb-2018
Publisher: Elsevier BV
Journal title: Annals of Nuclear Energy
Volume: 112
Start page: 150
End page: 157
Abstract: This paper applies the first-order differential operator method to the Monte Carlo keff-eigenvalue sensitivity analyses. The effect of the perturbed fission source distribution due to the change of a cross section on the sensitivity coefficients can be accurately estimated by introducing the source perturbation iteration method. However, a prohibitively huge memory is required for the source perturbation iteration method if a large number of sensitivity coefficients are calculated at the same time. For a reduction of the memory requirements, the superhistory method is applied to incorporate the effect of the source perturbation into the differential operator method for sensitivity analyses. In the superhistory method, one source particle and its progenies are followed over super-generations within one cycle calculation. It is not necessary to wait or store a large amount of information until all histories in each cycle are terminated. Although the superhistory method increases the variance of the sensitivity coefficients with the super-generation, the memory requirement can be dramatically reduced by introducing the superhistory method. The first-order differential operator method combined with the superhistory method is verified through some numerical examples where a localized cross section change significantly affects the sensitivity coefficients.
Rights: © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
The full-text file will be made open to the public on 01 February 2020 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
URI: http://hdl.handle.net/2433/234645
DOI(Published Version): 10.1016/j.anucene.2017.10.002
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