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Lee, Deokjung
Computational Reactor physics & Experiment lab (CORE Lab)
Research Interests
  • Reactor Analysis computer codes development
  • Methodology development of reactor physics
  • Nuclear reactor design(SM-SFR,PWR and MSR)

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Validation of nuclide depletion capabilities in Monte Carlo code MCS

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Title
Validation of nuclide depletion capabilities in Monte Carlo code MCS
Author
Ebiwonjumi, BamideleLee, HyunsukKim, WonkyeongLee, Deokjung
Issue Date
2020-09
Publisher
KOREAN NUCLEAR SOC
Citation
NUCLEAR ENGINEERING AND TECHNOLOGY, v.52, no.9, pp.1907 - 1916
Abstract
In this work, the depletion capability implemented in Monte Carlo code MCS is investigated to predict the isotopic compositions of spent nuclear fuel (SNF). By comparison of MCS calculation results to post irradiation examination (PIE) data obtained from one pressurized water reactor (PWR), the validation of this capability is conducted. The depletion analysis is performed with the ENDF/B-VII.1 library and a fuel assembly model. The transmutation equation is solved by the Chebyshev Rational Approximation Method (CRAM) with a depletion chain of 3820 isotopes. 18 actinides and 19 fission products are analyzed in 14 SNF samples. The effect of statistical uncertainties on the calculated number densities is discussed. On average, most of the actinides and fission products analyzed are predicted within +/- 6% of the experiment. MCS depletion results are also compared to other depletion codes based on publicly reported information in literature. The code-to-code analysis shows comparable accuracy. Overall, it is demonstrated that the depletion capability in MCS can be reliably applied in the prediction of SNF isotopic inventory. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
URI
https://scholarworks.unist.ac.kr/handle/201301/47846
URL
https://www.sciencedirect.com/science/article/pii/S1738573319302165?via%3Dihub
DOI
10.1016/j.net.2020.02.017
ISSN
1738-5733
Appears in Collections:
MNE_Journal Papers
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