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Decay Branch Ratio Sampling Method with Dirichlet Distribution

Author

Listed:
  • Yizhen Wang

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advance Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Menglei Cui

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advance Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Jiong Guo

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advance Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Han Zhang

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advance Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Yingjie Wu

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advance Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

  • Fu Li

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advance Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

Abstract

The decay branch ratio is evaluated nuclear data related to the decay heat calculation in reactor safety analysis. Decay branch ratio data are inherently subjected to the “sum-to-one” constraint, making it difficult to generate perturbed samples while preserving their suggested statistics in a library of evaluated nuclear data. Therefore, a stochastic-sampling-based uncertainty analysis method is hindered in quantifying the uncertainty contribution of the decay branch ratio to the decay heat calculation. In the present work, two alternative sampling methods are introduced, based on Dirichlet and generalized Dirichlet distribution, to tackle the decay branch ratio sampling issue. The performance of the introduced methods is justified by three-branch decay data retrieved from ENDF/B-VIII.0. The results show that the introduced sampling methods are capable of generating branch ratio samples and preserving their suggested statistics in an evaluated nuclear data library while satisfying their inherent “sum-to-one” constraint. These decay-branch-ratio sampling methods are expected to be alternative procedures in conducting stochastic-sampling-based uncertainty analyses of the decay branch ratio in reactor simulations.

Suggested Citation

  • Yizhen Wang & Menglei Cui & Jiong Guo & Han Zhang & Yingjie Wu & Fu Li, 2023. "Decay Branch Ratio Sampling Method with Dirichlet Distribution," Energies, MDPI, vol. 16(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1962-:d:1070426
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    References listed on IDEAS

    as
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