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Home / Publications / Journals / Nuclear Technology / Volume 205 / Number 1-2

Application of the Improved Spacer Grid Model in Subchannel Analysis Code

Siying Dong, Wei Liu, Yang Liu, Jianqiang Shan

Nuclear Technology / Volume 205 / Number 1-2 / January-February 2019 / Pages 352-363

Technical Paper /

Received:April 27, 2018
Accepted:June 15, 2018
Published:December 12, 2018

In most subchannel analysis codes, spacer grids are simulated using an effective loss coefficient that can account only for the spacer grid’s mean axial effect on the pressure drop. Since the mixing vane spacer grid (MVG) in a rod bundle has great influence on local flow fields, neglecting the effect of mixing vanes will degrade fidelity and resolution in thermal-hydraulic calculation. This paper focuses on improving the spacer grid model in subchannel analysis. First, cross-flow mixing effects of MVGs are accounted for by applying the distributed resistance method. By choosing resistance correlation appropriately and considering the geometric dimensions of mixing vanes, the source term of mixing vanes can be represented quantitatively in the axial and lateral momentum equations of a subchannel analysis code. Second, the Carlucci model is used to calculate mixing rates, and obstruction factor Fobs is introduced to consider turbulent mixing effects caused by spacer grids. The improved MVG cross-flow model and turbulent mixing model are implemented in the subchannel code ATHAS. Validation is provided for the 5 × 5 rod bundle experiments provided by Karoutas et al. [Proc. 7th Int. Mtg. Nuclear Reactor Thermal-Hydraulics (NURETH-7), Saratoga, New York (1995)] and high-quality experimental data provided by the Organisation for Economic Co-operation and Development/U.S. Nuclear Regulatory Commission Pressurized Water Reactor Subchannel and Bundle Test (PSBT) benchmark to demonstrate their effects and accuracy. From the validation, it can be concluded that the calculated lateral velocities agree well with those provided by the experimental data. In addition, the improved cross-flow and turbulent mixing models significantly increase the accuracy of predictions of exit subchannel coolant temperatures, with reduction in root-mean-square error to be 2.27 K.

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