An estimation of cross-section covariance data suitable for predicting neutronics parameters uncertainty

• Material Type: 文書・図像類
• Title: An estimation of cross-section covariance data suitable for predicting neutronics parameters uncertainty
• Author/Editor: Takeda, Toshikazu; Takeda, Satoshi; Koike, Hiroki; Kitada, Takanori; Sato, Daisuke
• Author Heading: Takeda, Toshikazu; Takeda, Satoshi; Koike, Hiroki; Kitada, Takanori; Sato, Daisuke
• Publication Date: 2020
• Publication Date (W3CDTF): 2020
• Alternative Title: An estimation of cross-section covariance data suitable for predicting neutronics parameters uncertainty
• Periodical title: Annals of Nuclear Energy
• No. or year of volume/issue: 145
• Volume: 145
• Pages: 107534-107534
• ISSN (Periodical Title): ISSN : 0306-4549
• Text Language Code: eng
• Subject Heading: Cross section; Covariance data; Criticality; Uncertainty; Estimation methods
• Target Audience: 一般
• Note (General): A covariance data which represents the uncertainty of the difference between the evaluated cross section data and the true data has been discussed and estimated. The two methods of estimating the covariance data suitable for predicting neutronics parameters uncertainty is proposed. One is the method to use the scatter of bias factors, the ratio of the measured neutronics parameters to the calculated ones, among different critical assemblies and the other is the method to use the difference between the bias factors and the true value (unity). The features of the methods is discussed. As a preliminary application the two methods have been used to the criticality problem of thermal reactors with uranium fuel. The covariance data suitable for k-eff uncertainty prediction has been estimated using the data of 65 thermal critical assemblies. From the numerical results, it has been found that the conventional covariance data should be reduced by a factor of 4–16 for predicting k-eff uncertainty. Though the result is preliminary because the covariance data for individual nuclides is not explicitly considered, one can conceive how the proposed methods could be generalized to obtain a new covariance data which could be used for practical uncertainty prediction.
• Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=28698&item_no=1&attribute_id=22&file_no=1
• Access Restrictions: 限定公開
• Rights (production): copyright 2020 Elsevier B.V.
• Related Material (DOI): 10.1016/j.anucene.2020.107534
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ