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Influence of nuclear data parameters on integral experiment assimilation using Cook's distance
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. CEA, DEN, Cadarache, F-13108 Saint Paul les Durance, France. (Nuclear Reactions Group)
CEA, DEN, Cadarache, F-13108 Saint Paul les Durance, France.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics. (Nuclear Reactions Group)ORCID iD: 0000-0002-7595-8024
CEA, DEN, Cadarache, F-13108 Saint Paul les Durance, France.
2019 (English)In: EPJ Web of Conferences, ISSN 2101-6275, E-ISSN 2100-014X, Vol. 211, article id 07001Article in journal (Other academic) Published
Abstract [en]

Nuclear data used in designing of various nuclear applications (e.g., core design of reactors) 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). In this method, the nuclear data parameters are optimized by the inclusion of the experimental information using a Bayesian inference. The selection of integral experiments is based on the availability of well-documented specifications and experimental data. Data points with large uncertainties or large residuals (outliers) may aect the accuracy of the adjustment. Hence, in the adjustment process, it is very important to study the influence of experiments as well as of the priori nuclear data on the adjusted results. In this work, the influence of each individual ingredient (related to nuclear data) is analyzed using the concept of Cook’s distance. First, JEZEBEL (Pu239, Pu240 and Pu241) integral experiment is considered for data assimilation and then the transposition of results on ASTRID fast reactor concept is discussed.

Place, publisher, year, edition, pages
EDP Sciences, 2019. Vol. 211, article id 07001
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-378672DOI: 10.1051/epjconf/201921107001ISI: 000501343100028OAI: oai:DiVA.org:uu-378672DiVA, id: diva2:1294672
Conference
5th edition of the International Workshop On Nuclear Data Evaluation for Reactor Applications (WONDER-2018), Aix en Provence, France, October 8-12, 2018
Funder
Swedish Research CouncilAvailable from: 2019-03-08 Created: 2019-03-08 Last updated: 2020-01-14Bibliographically approved

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