Estimation of Flow Field in Natural Convection with Density Stratification by Local Ensemble Transform Kalman Filter

Material type: 文書・図像類

Author: Ishigaki, Masahiroほか

Publisher: MDPI

Publication date: 2022-07-12

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Note (General)：: : For estimating thermal flow in a nuclear reactor during an accident accurately, it is important to improve the accuracy of computational fluid dynam...

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Material Type: 文書・図像類
Title: Estimation of Flow Field in Natural Convection with Density Stratification by Local Ensemble Transform Kalman Filter
Author/Editor: Ishigaki, Masahiro
Hirose, Yoshiyasu
Abe, Satoshi
Nagai, Toru
Watanabe, Tadashi
Author Heading: Ishigaki, Masahiro
Hirose, Yoshiyasu
Abe, Satoshi
Nagai, Toru
Watanabe, Tadashi
Publication, Distribution, etc.: MDPI
Publication Date: 2022-07-12
Publication Date (W3CDTF): 2022-07-12
Alternative Title: Estimation of Flow Field in Natural Convection with Density Stratification by Local Ensemble Transform Kalman Filter
Periodical title: fluids
No. or year of volume/issue: 2022 7(7)237
Volume: 2022
Issue: 7(7)237
Pages: 1-18
ISSN (Periodical Title): ISSN : 2311-5521
Text Language Code: eng
Subject Heading: data assimilation
local ensemble transform Kalman filter
natural convection
density stratification
Target Audience: 一般
Note (General): : For estimating thermal flow in a nuclear reactor during an accident accurately, it is important to improve the accuracy of computational fluid dynamics simulations. The temperature and flow velocity are not homogeneous and have large variations in a reactor containment vessel because of its very large volume. In addition, Kelm’s work pointed out that the influence of variations of initial and boundary conditions was important. Therefore, it is necessary to set the initial and boundary conditions taking into account the variations of these physical quantities. However, it is a difficult subject to set such complicated initial and boundary conditions. Then, we can obtain realistic initial and boundary conditions and an accurate flow field by data assimilation, and we can improve the accuracy of the simulation result. In this study, we applied data assimilation by a local ensemble transform Kalman filter to a simulation of natural convection behavior in density stratification, and we performed a twin model experiment. We succeeded in estimating the flow fields and improving the simulation accuracy by the data assimilation, even if we applied the boundary condition with error for the true condition.
source:https://www.mdpi.com/2311-5521/7/7/237
Source: https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=29359&item_no=1&attribute_id=22&file_no=1
https://u-fukui.repo.nii.ac.jp/?action=repository_action_common_download&item_id=29359&item_no=1&attribute_id=22&file_no=1
Access Restrictions: 限定公開
Rights (production): cc-by 4.0
CC BY
Related Material (DOI): 10.3390/fluids7070237
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