このエントリーをはてなブックマークに追加
ID 60636
フルテキストURL
fulltext.pdf 1.21 MB
著者
Yanase, Shinichiro Department of Mechanical and Systems Engineering, Okayama University Kaken ID publons researchmap
Yamasaki, Ryo Technical Division, Tsurumi Manufacturing Co.
Kouchi, Toshinori Department of Mechanical and Systems Engineering, Okayama University
Hosoda, Shunsuke Department of Mechanical and Systems Engineering, Okayama University
Nagata, Yasunori Department of Mechanical and Systems Engineering, Okayama University
Shunji, Higuchi Technical Division, Tsurumi Manufacturing Co.
Kawabe, Toshihiko Technical Division, Tsurumi Manufacturing Co.
Takami, Toshihiro Department of Mechanical and Systems Engineering, Okayama University of Science
抄録
Numerical detection of harmful vortices in pump sumps, such as an air-entraining vortex (AEV) and a submerged vortex (SMV), is crucially important to develop the drain pump machinery. We performed numerical simulations of the benchmark experiments of the pump sump conducted by Matsui et al. (2006 and 2016) using the OpenFOAM and compared the simulation results with the experimental data considering the effects of turbulence model, grid density and detection method of the vortices. We studied the threshold of the gas-liquid volume fraction of the VOF method and the second invariant of velocity gradient tensor to identify AEV and SMV. The methods proposed in the present paper were found to be very effective for the detection of the vortices, and the simulation results by RANS with the SST k-omega model successfully reproduced the experimental data. LES with the Smagorinsky model, however, was sensitive to the grid system and difficult to reproduce the experimental data even for the finest grid system having 3.7 million cells in the present study.
備考
29th IAHR Symposium on Hydraulic Machinery and Systems
発行日
2019
出版物タイトル
IOP Conference Series: Earth and Environmental Science
240巻
3号
出版者
IOP Publishing
開始ページ
032001
ISSN
1755-1307
資料タイプ
学術雑誌論文
言語
English
OAI-PMH Set
岡山大学
著作権者
©authors
論文のバージョン
publisher
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.1088/1755-1315/240/3/032001
ライセンス
https://creativecommons.org/licenses/by/3.0/