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Fission yield covariances for JEFF: A Bayesian Monte Carlo method
Paul Scherrer Inst, Villigen, Switzerland.
Paul Scherrer Inst, Villigen, Switzerland.
United Kingdom Atom Energy Author, Abingdon, Oxon, England.
United Kingdom Atom Energy Author, Abingdon, Oxon, England.
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2017 (English)In: ND 2016: INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY / [ed] Plompen, A Hambsch, FJ Schillebeeckx, P Mondelaers, W Heyse, J Kopecky, S Siegler, P Oberstedt, S, Les Ulis: EDP Sciences, 2017, article id 09023Conference paper, Published paper (Refereed)
Abstract [en]

The JEFF library does not contain fission yield covariances, but simply best estimates and uncertainties. This situation is not unique as all libraries are facing this deficiency, firstly due to the lack of a defined format. An alternative approach is to provide a set of random fission yields, themselves reflecting covariance information. In this work, these random files are obtained combining the information from the JEFF library (fission yields and uncertainties) and the theoretical knowledge from the GEF code. Examples of this method are presented for the main actinides together with their impacts on simple burn-up and decay heat calculations.

Place, publisher, year, edition, pages
Les Ulis: EDP Sciences, 2017. article id 09023
Series
EPJ Web of Conferences, ISSN 2100-014X ; 146
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-354402DOI: 10.1051/epjconf/201714609023ISI: 000426429500250ISBN: 978-2-7598-9020-0 OAI: oai:DiVA.org:uu-354402DiVA, id: diva2:1229374
Conference
ND 2016: International Conference on Nuclear Data for Science and Technology, SEP 11-16, 2016, Bruges, BELGIUM.
Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-06-29Bibliographically approved

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