Optimization of Conductive Fins to Minimize UO 2 Fuel Temperature and Radial Temperature Gradient

Kyle M. Paaren (), Pavel Medvedev and Robert Mariani
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Kyle M. Paaren: Idaho National Laboratory, 2525 Fremont Ave., Idaho Falls, ID 83415, USA
Pavel Medvedev: Idaho National Laboratory, 2525 Fremont Ave., Idaho Falls, ID 83415, USA
Robert Mariani: Idaho National Laboratory, 2525 Fremont Ave., Idaho Falls, ID 83415, USA

Energies, 2023, vol. 16, issue 6, 1-16

Abstract: To further the development of low-enriched uranium fuels, precedence has been placed on delivering the same amount of power while lowering the fuel temperature and radial temperature gradient. To address this, modeling efforts have resulted in a novel design featuring conductive fins of varying thermal conductivities and geometries inserted into the fuel matrix. These conductive inserts were not allowed to exceed 6% of the original fuel volume. This constraint was imposed due to other designs displacing 10% of fuel volume. A parametric study was performed that consisted of 2.56 million BISON simulations involving varying fin characteristics (i.e., fin thermal conductivity, number, and geometry) to determine the optimal geometric configuration for a desired amount of fuel volume displaced. The results from this study show that the thickness and length of each fin affect the fuel temperature and temperature gradient more than varying the number and thermal conductivity of the fins. The parametric study resulted in the development of an optimized combination to produce the lowest peak fuel temperature, lowest radial temperature gradient, and highest temperature reduction for the amount of original fuel volume displaced. The simulations presented in this work will eventually be compared with irradiation experiments of similar fuel designs at Idaho National Laboratory’s Advanced Test Reactor.

Keywords: BISON; simulation; finite element methods; fuel performance; UO 2 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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