IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p1962-d1070426.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/1962/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/1962/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rameshwar D. Gupta & Donald St. P. Richards, 2001. "The History of the Dirichlet and Liouville Distributions," International Statistical Review, International Statistical Institute, vol. 69(3), pages 433-446, December.
    2. Ronald L. Iman & Jon C. Helton, 1988. "An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models," Risk Analysis, John Wiley & Sons, vol. 8(1), pages 71-90, March.
    3. Dan Gabriel Cacuci, 2021. "On the Need to Determine Accurately the Impact of Higher-Order Sensitivities on Model Sensitivity Analysis, Uncertainty Quantification and Best-Estimate Predictions," Energies, MDPI, vol. 14(19), pages 1-38, October.
    4. Wong, Tzu-Tsung, 2010. "Parameter estimation for generalized Dirichlet distributions from the sample estimates of the first and the second moments of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1756-1765, July.
    5. Jerzy Cetnar & Przemysław Stanisz & Mikołaj Oettingen, 2021. "Linear Chain Method for Numerical Modelling of Burnup Systems," Energies, MDPI, vol. 14(6), pages 1-19, March.
    6. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    2. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    3. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
    4. Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    5. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    6. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    7. Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.
    8. Edward Boone & Jan Hannig & Ryad Ghanam & Sujit Ghosh & Fabrizio Ruggeri & Serge Prudhomme, 2022. "Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study," Stats, MDPI, vol. 5(4), pages 1-17, November.
    9. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    10. Campbell, Katherine, 2006. "Statistical calibration of computer simulations," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1358-1363.
    11. Michał Górkiewicz & Jerzy Cetnar, 2021. "Flattening of the Power Distribution in the HTGR Core with Structured Control Rods," Energies, MDPI, vol. 14(21), pages 1-14, November.
    12. Perrin, G., 2016. "Active learning surrogate models for the conception of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 130-136.
    13. Shabbir Ahmed Osmani & Foysol Mahmud, 2021. "An integrated approach of machine algorithms with multi-objective optimization in performance analysis of event detection," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1976-1993, February.
    14. Antony M. Overstall & David C. Woods, 2016. "Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 483-505, August.
    15. Na, Wei & Wang, Mingming, 2022. "A Bayesian approach with urban-scale energy model to calibrate building energy consumption for space heating: A case study of application in Beijing," Energy, Elsevier, vol. 247(C).
    16. Andrew White & Malachi Tolman & Howard D Thames & Hubert Rodney Withers & Kathy A Mason & Mark K Transtrum, 2016. "The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
    17. MacKenzie, Cameron A. & Hu, Chao, 2019. "Decision making under uncertainty for design of resilient engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    18. Girma Tadesse Chala & Berihun Mamo Negash, 2022. "Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines," Energies, MDPI, vol. 15(5), pages 1-14, February.
    19. Pasanisi, Alberto & Keller, Merlin & Parent, Eric, 2012. "Estimation of a quantity of interest in uncertainty analysis: Some help from Bayesian decision theory," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 93-101.
    20. White, Staci A. & Herbei, Radu, 2015. "A Monte Carlo approach to quantifying model error in Bayesian parameter estimation," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 168-181.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1962-:d:1070426. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.