Reactivity Feedback Effect on Supercritical Transient Analysis of Fuel Debris

Fukuda, Kei; Nishiyama, Jun; Obara, Toru

doi:10.1080/00295639.2020.1743580

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…They varied the size and porosity of fuel debris particles to determine the best model in terms of computational accuracy, geometry complexity, and computational power requirements. Fukuda et al studied effect of reactivity feedback on supercritical transient analysis of fuel debris [4] and radiation dose in criticality accident of fuel debris in water [5]. Takezawa et al studied passive measure for preventing recriticality of fuel debris dust for defueling at Fukushima Daiichi NPS [6].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo codes benchmarking on sub-critical fuel debris particles system for neutronic analysis

Smirnov

Bogdanova

Pugachev

et al. 2021

Journal of Nuclear Science and Technology

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Fuel debris removal is the most challenging part of damaged nuclear power station decommissioning. It is important to carry out nuclear safety calculations accurately and quickly enough. Here, it was clarified that modern codes based on the Monte Carlo method were capable of performing neutronic analysis with the same accuracy and without significant differences in the results. The benchmark calculations were performed using three codes: MVP, Serpent, and MCU. In this study, the comparison focused on multiplication factor, neutron fluxes and reaction rates relative difference, and calculation time of many fuel debris particles system. Then the calculation results were used when codes comparing. It was shown that the calculation results showed good agreement between all codes. It was assumed that minor differences in the thermal range of neutron fluxes can be caused by different thermal neutrons scattering treatment for all codes. The study also showed that solving such problems requires significant computing power and time. It has been proven that the statistical geometry model in the MVP and the explicit stochastic geometry model in the Serpent have the possibility to provide solutions with the same accuracy, but much faster.

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Section: Introductionmentioning

confidence: 99%