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Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes
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: 0000-0001-7370-6539
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0002-5133-6829
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
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2018 (English)In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 885, p. 67-78Article in journal (Refereed) Published
Abstract [en]

This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope  134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

Place, publisher, year, edition, pages
2018. Vol. 885, p. 67-78
Keywords [en]
Spent nuclear fuel, MOX, Gamma spectroscopy, Multivariate analysis, Classification
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-337676DOI: 10.1016/j.nima.2017.12.020ISI: 000424740800009OAI: oai:DiVA.org:uu-337676DiVA, id: diva2:1170532
Funder
Swedish Research Council, VR 621-2009-3991Swedish Radiation Safety Authority, SSM2016-661Available from: 2018-01-03 Created: 2018-01-03 Last updated: 2018-04-19Bibliographically approved
In thesis
1. Nuclear safeguards evaluation and analysis techniques for application to nuclear fuel material in Generation IV nuclear energy systems
Open this publication in new window or tab >>Nuclear safeguards evaluation and analysis techniques for application to nuclear fuel material in Generation IV nuclear energy systems
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A new generation of nuclear energy systems called Generation IV is under development to ensure that nuclear power will be a safe, reliable and sustainable energy source for the future. This thesis addresses the challenge of making future nuclear energy systems increasingly resistant to nuclear material diversion attempts.

Several tools have been developed for structured evaluation of a system's resistance to nuclear proliferation, in order to identify areas where nuclear energy systems are the most inherently vulnerable. In this thesis, the TOPS methodology has been applied to three different fuel cycles involving a fast reactor with fuel recycling and fuel fabrication capabilities. The recycling facility, where the fuel is dissolved and undergoes chemical separation, is identified as being particularly vulnerable. Nondestructive measurements for verification of fuel assemblies in the receipt area of the recycling facility are essential, since it is the last opportunity to verify intact fuel items. Moreover, iterative evaluation of proliferation resistance by using two different assessment methodologies – TOPS and PR&PP – as suggested in this thesis, may act as an aid in facility design and for proposing safeguards implementation.

Based on the identified need to measure irradiated fuel assemblies prior to dissolution in the recycling facility, new methods used for analyzing gamma-ray spectroscopy data using multivariate analysis methods have been investigated. Fuel parameters of modeled nuclear fuel have been determined without any reliance on operator-declared data. Nonlinear classifiers, e.g. support vector machines (SVM), have successfully been used for discrimination between uranium oxide fuels and mixed oxide fuels. Cooling time, burnup and initial fissile content have been determined using decision tree and SVM regression. The results are promising and indicate that the nuclear safeguards regime may benefit from using multivariate techniques for data analysis. It must be emphasized, however, that experimental verification of the multivariate analysis techniques is necessary.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 72
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1617
National Category
Physical Sciences
Research subject
Physics
Identifiers
urn:nbn:se:uu:diva-337699 (URN)978-91-513-0202-7 (ISBN)
Public defence
2018-02-23, Å4001, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2018-01-31 Created: 2018-01-09 Last updated: 2018-03-08

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Åberg Lindell, MatildaAndersson, PeterGrape, SophieHellesen, CarlHåkansson, AneEriksson, Måns

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