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A DEM-CFD numerical model for the prediction of the effective thermal conductivity of pebble beds with contact conduction
Wang C(王聪)1,2; Chen L(陈磊)1; Liu SL(刘松林)1
2019-07-02
Source PublicationFusion Engineering and Design
Abstract

Pebble beds are main schemes for tritium breeding zones in the solid blanket for fusion reactors. The effective thermal conductivity of pebble beds (keff) is an important thermal property used in the thermal hydraulic design of solid blankets. In this study, a 3D DEM-CFD one-way coupling method considering contact conduction is applied to analyze the influence of solid contact conduction and solid-fluid-solid conduction on the effective thermal conductivity of pebble beds. The current numerical model gives a keff consistent with experimental data in a wide range of solid-to-fluid conductivity ratio ks/kf from 1 to 10,000. It can be concluded that when the ks/kf is more than 20, contact conduction should be taken into account to make a good prediction for keff.

DOI10.1016/j.fusengdes.2019.111257
Indexed BySCI
Language英语
WOS IDWOS:000487765500033
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.hfcas.ac.cn:8080/handle/334002/42701
Collection中科院等离子体物理研究所
Corresponding AuthorChen L(陈磊)
Affiliation1.Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui, 230031, China
2.University of Science and Technology of China, Hefei, Anhui, 230026, China
Recommended Citation
GB/T 7714
Wang C,Chen L,Liu SL. A DEM-CFD numerical model for the prediction of the effective thermal conductivity of pebble beds with contact conduction[J]. Fusion Engineering and Design,2019.
APA Wang C,Chen L,&Liu SL.(2019).A DEM-CFD numerical model for the prediction of the effective thermal conductivity of pebble beds with contact conduction.Fusion Engineering and Design.
MLA Wang C,et al."A DEM-CFD numerical model for the prediction of the effective thermal conductivity of pebble beds with contact conduction".Fusion Engineering and Design (2019).
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