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Author:

Liu, Wei (Liu, Wei.) | Shan, Jianqiang (Shan, Jianqiang.)

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Abstract:

There are two ways in the development of CHF correlation in rod bundles: one is based on round tube CHF correlations, considering the effects of spacers and CHF promoters, effects of cold walls, etc. The other one is to develop the correlation directly using the rod bundles CHF experimental data which includes two methods: me minimum DNBR point method and the BO point method. In this study, the local thermal-hydraulic parameters at DNB occurrence point of each set of bundle CHF data were obtained by using the subchannel code ATHAS. Both of the minimum DNBR point and the BO point methods were then applied to develop a new CHF correlation for rod bundles. The so-called 'three-step method' was used to determine the shape of me correlation and the non-linear regression analysis method was applied to determine the coefficients of the correlation. Based on the large database of CHF tests, a new CHF correlation named the ACC correlation has been developed. The analysis and assessment results indicate that me ACC correlation can fit the experimental data well with high prediction accuracy and correct parametric trends. Compared with the minimum DNBR point method, me BO point method is recommended to predict me risk of DNB because it is more reasonable and conservative with better predication rate. Coupled with subchannel code ATHAS, this correlation can simulate the thermalhydraulics performances of rod bundles exactly.

Keyword:

Codes (symbols) Forecasting Hydraulics Nuclear reactors Regression analysis

Author Community:

  • [ 1 ] [Liu, Wei]Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu; 610041, China
  • [ 2 ] [Shan, Jianqiang]School of Nuclear Science and Technology, Xi'an Jiao Tong University, Xi'an; 710049, China

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Year: 2015

Volume: 8

Page: 6285-6296

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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