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Analysis of the Prior Nuclear Data Correlation and Its Effect on the Adjustment in Bayesian Inference
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0002-7595-8024
French Alternative Energies and Atomic Energy Commission, Cadarache, France.
French Alternative Energies and Atomic Energy Commission, Cadarache, France.
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2019 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

High-quality nuclear data is of prime importance while considering the design of advanced fast reactors. The quantification of nuclear data uncertainties is always required for the safety calculations of nuclear power plants. Using reliable nuclear data and its correlation are of great importance for the quality of uncertainty analysis. Nuclear data is usually evaluated using mathematical models and is improved by using integral experiments. To utilize the past critical experimental data to the reactor design work, a typical procedure for the nuclear data adjustment is based on the Bayesian theory (least-square technique or Monte-Carlo). Using Bayesian theorem, the nuclear model parameters are optimized by the inclusion of experimental information. The posterior data and its covariance are evaluated from the optimized model parameters. In this process, the prior data, its covariance and the integral experimental data are provided to the optimizer as input. The selection of integral experiments is based on the availability of well-documented specifications and experimental uncertainties. The prior covariance matrix used for the adjustment is usually very large as it contains correlation information between energy groups, nuclear reactions and isotopes considered for the adjustment. Often, due to the choices made for adjustment; the prior correlation matrix is encountered as negative definite or singular. These situations affect the accuracy of the adjustment heavily. Hence, in the adjustment process, it is very important to avoid the prior matrix as singular or negative definite. In this work, using principal component analysis, the size of the prior nuclear data (and its correlation) is first reduced to ensure the non-singularity in the prior. Further, the reduced parameters are used in Bayesian inference for the posterior calculation. Furthermore, the influence of each individual ingredient (experiment/nuclear data) will be analyzed using the concept of Cook’s distance. First, JEZEBEL (239Pu, 240Pu and 241Pu) will be considered and the transposition of the result on ASTRID Fast reactor concept will be discussed.

Place, publisher, year, edition, pages
2019.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-396768OAI: oai:DiVA.org:uu-396768DiVA, id: diva2:1368924
Conference
International Conference on Nuclear Data for Science and Technology, Beijing, China, May 19-24, 2019
Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2019-11-11Bibliographically approved

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Kumar, DineshSjöstrand, Henrik

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