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Improved Cherenkov Light Prediction Model for Enhanced DCVD Performance
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0001-8207-3462
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.ORCID iD: 0000-0002-3136-5665
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.ORCID iD: 0000-0003-3411-7058
2018 (English)Conference paper, Published paper (Other academic)
Abstract [en]

The Digital Cherenkov Viewing Device (DCVD) is an instrument used to verify irradiated nuclear fuel assemblies in wet storage based on the fuel’s Cherenkov light emissions. The DCVD is frequently used for partial defect verification, verifying that 50% or more of an assembly has not been diverted. The verification methodology is based on comparison of the measured Cherenkov light intensity to a predicted intensity, based on operator declarations.

For the last five years, a dedicated PhD project at Uppsala University has been aiming at enhancing and improving the verification capabilities when using the DCVD. The project is now approaching its end, and this paper summarizes the comprehensive work performed regarding improving the prediction capabilities.

A new prediction model has been developed, considering more fuel assembly details to ensure more accurate predictions. With the new model, the irradiation history of an assembly, the assembly design and the contributions from gamma and beta decays are taken into account. The model has also been extended to account for the radiation from neighbouring fuel assemblies, which can enter the assembly being measured and contribute to the measured Cherenkov light. The performance of the prediction model and the neighbour intensity prediction model has been validated against fuel measurements by the IAEA at a PWR facility with short-cooled fuel. The results show that the new model offers an improved prediction capability, allowing the fuel inventory to be verified with no fuel assemblies being identified as outliers requiring additional investigation. A simplified version of the prediction model will be implemented in the next DCVD software version, making it available to IAEA inspectors.

This development of the DCVD capabilities are in line with the fourth theme of the IAEA safeguards symposium, “Shaping the future of safeguards implementation”, by resolving challenges related to the DCVD and by extending the capabilities of the instrument.

Place, publisher, year, edition, pages
2018.
Keywords [en]
DCVD, Nuclear Fuel, Nuclear Safeguards
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:uu:diva-367334OAI: oai:DiVA.org:uu-367334DiVA, id: diva2:1266991
Conference
IAEA Symposium on International Safeguards: Building Future Safeguards Capabilities 5–8 November 2018, Vienna, Austria
Funder
Swedish Radiation Safety Authority, SSM2012-2750Swedish National Infrastructure for Computing (SNIC), p2007011Available from: 2018-11-30 Created: 2018-11-30 Last updated: 2019-03-07Bibliographically approved

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https://media.superevent.com/documents/20181030/4ee8692e43eddb18f259e362a313887b/id058_branger_paper.pdf

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Branger, ErikGrape, SophieJansson, PeterJacobsson, Staffan

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