Implementation of a phenomenological DNB prediction model based on macroscale boiling flow processes in PWR fuel bundles
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- 作者：Maryam Mohitpour ; Gholamreza Jahanfarnia ; Mehrzad Shams
- 关键词：Departure from nucleate boiling ; Mechanistic model ; Two-phase flow ; Two-fluid model ; PWR subchannel analysis
- 刊名：Annals of Nuclear Energy
- 出版年：February, 2014
- 年：2014
- 卷：64
- 期：Complete
- 页码：176-190
- 全文大小：4754 K
文摘
In this study, a numerical framework, comprising of a two-phase flow subchannel solver module and a Departure from Nucleate Boiling (DNB) evaluation module, was developed to mechanistically predict DNB in rod bundles of Pressurized Water Reactor (PWR). In this regard, the liquid sublayer dryout model was adapted as the Critical Heat Flux (CHF) triggering mechanism to reduce the dependency of the model on empirical correlations in the DNB evaluation module. To predict local flow boiling processes, a three-dimensional two-fluid formalism coupled with heat conduction was selected as the basic tool for the development of the two-phase flow subchannel analysis solver. Evaluation of the DNB modeling approach was performed against OECD/NRC NUPEC PWR Bundle tests (PSBT Benchmark) which supplied an extensive database for the development of truly mechanistic and consistent models for boiling transition and CHF. The results of the analyses demonstrated the need for additional assessment of the subcooled boiling model and the bulk condensation model implemented in the two-phase flow solver module. The proposed model slightly under-predicts the DNB power in comparison with the ones obtained from steady-state benchmark measurements. However, this prediction is acceptable compared with other codes. Another point about the DNB prediction model is that it has a conservative behavior. Examination of the axial and radial position of the first detected DNB using code-to-code comparisons on the basis of PSBT data indicated that the our simulation is reasonably accurate. DNB prediction in the power increase test case showed that the proposed model predicts the appearance of DNB before it happens; nevertheless, better agreement was obtained in comparison with the data from other codes.