Particulate Fuel Modeling of MC2-3 using Iterative Local Spatial Self-shielding Method. (July 2022)

• Record Type: Journal Article
• Authors: Park, H.; Yang, W.S.; Lee, C.H.
• Is Part Of: Annals of nuclear energy. Volume 172(2022)
• Publication Date: 2022-07
• Subjects: Double heterogeneity -- TRISO -- MC2-3 -- Iterative local spatial self-shielding; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2022.109060
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: A new method named the iterative local spatial self-shielding (ILSS) method was developed and implemented in MC 2 -3. The Williams method and the Sanchez-Pomraning method were also implemented in MC 2 -3 for consistent comparison. In the ILSS method, stochastic dispersion of particles is accounted for in the spatial self-shielding factor calculation model of particles. The ILSS method showed much smaller error tendency on particle size and packing fraction than the Williams and S-P methods. The ILSS method agreed well with Serpent-2 solutions for various double heterogeneity problems for graphite moderated system. Abstract: A new spatial self-shielding method for particulate fuels has been developed based on disadvantage factors and implemented in the MC 2 -3 code. This method named the iterative local spatial self-shielding (ILSS) method considers the shadowing effect of randomly distributed particles on spatial self-shielding in particles through a homogenized composition region added outside the particle of interest at the center. The self-shielded cross sections of the central particle are determined iteratively since they are used in determining the cross sections of the homogenized composition region. The ILSS method was verified for infinite stochastic medium problems of single and multiple types of particles, VHTR unit-cell problems, and HTTR assembly problems. The verification test results show that the ILSS method accurately predicts the stochastic particle shadowing effect and reaction rates in particles, whereas the regular array model and the stochastic collision probability method underpredict the particle shadowing effect and overestimate reaction rates in particles.