Aluminum cladding oxide growth prediction for high flux research reactors

Research output: Contribution to journalArticle

Authors

Institutes & Expert groups

  • Argonne National Laboratory
  • KAERI - Korea Atomic Energy Research Institute - Korea

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Abstract

Aluminum cladding oxidation of research-reactor fuel elements at high power conditions has a disadvantageous effect on fuel performance due to the lower thermal conductivity of the oxide. The oxide growth prediction models available in the literature were mostly developed for low power conditions. To examine the applicability of the models to high power and high temperature test conditions, the models were studied by coupling with the most frequently employed heat transfer coefficient (HTC) correlations including the Dittus-Boelter correlation, the Colburn correlation, the Sieder-Tate correlation, and KAERIdeveloped correlation. The Griess model over-predicted the oxide growth while the KAERI-Griess model under-predicted the oxide growth for high power tests. The Kim model, coupled with the Colburn correlation, gave most consistent results with the measured data from two BR2 experiments. However, the Kim model was found to be inapplicable to the EUHFRR conditions at the peak power locations if it was coupled with the Dittus-Boelter correlation. A revision of the prediction models to more closely agree with the measured data was recommended. AG3NE and AlFeNi cladding types were tested in the EFUTURE experiment, and a noticeable (although small) reduction in oxide thickness on the AlFeNi cladding was observed. However, this difference was believed to be only a secondary effect considering other uncertainties in model predictions, so no attempt was made to model the alloying effect.

Details

Original languageEnglish
Article number151926
Pages (from-to)1-13
Number of pages13
JournalJournal of Nuclear Materials
Volume529
DOIs
Publication statusPublished - 24 Nov 2019

Keywords

  • Aluminum alloy cladding, Research reactor fuel plate, In-pile oxide data, Oxide growth prediction model

ID: 5874527