Depletion benchmarks calculation of random media using explicit modeling approach of RMC. (January 2016)

• Record Type: Journal Article
• Authors: Liu, Shichang; She, Ding; Liang, Jin-gang; Wang, Kan
• Is Part Of: Annals of nuclear energy. Volume 87:Part 2(2016:Jan.)
• Page Start: 167
• Page End: 175
• Publication Date: 2016-01
• Subjects: Stochastic media -- Dispersed fuel -- RMC -- Explicit modeling -- Depletion benchmark -- Burnup regions combination; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2015.09.003
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Explicit modeling of RMC is applied to depletion benchmark for HTGR fuel element. Explicit modeling can provide detailed burnup distribution and burnup heterogeneity. The results would serve as a supplement for the HTGR fuel depletion benchmark. The method of adjacent burnup regions combination is proposed for full-core problems. The combination method can reduce memory footprint, keeping the computing accuracy. Abstract: Monte Carlo method plays an important role in accurate simulation of random media, owing to its advantages of the flexible geometry modeling and the use of continuous-energy nuclear cross sections. Three stochastic geometry modeling methods including Random Lattice Method, Chord Length Sampling and explicit modeling approach with mesh acceleration technique, have been implemented in RMC to simulate the particle transport in the dispersed fuels, in which the explicit modeling method is regarded as the best choice. In this paper, the explicit modeling method is applied to the depletion benchmark for HTGR fuel element, and the method of combination of adjacent burnup regions has been proposed and investigated. The results show that the explicit modeling can provide detailed burnup distribution of individual TRISO particles, and this work would serve as a supplement for the HTGR fuel depletion benchmark calculations. The combination of adjacent burnup regions can effectively reduce the memory footprint while keeping the computational accuracy.