Fuel rod classification from Passive Gamma Emission Tomography (PGET) of spent nuclear fuel assemblies

Riina Virta, Rasmus Backholm, Tatiana Bubba, Tapio Helin, Mikael Moring, Samuli Siltanen, Peter Dendooven, Tapani Honkamaa

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)


Safeguarding the disposal of spent nuclear fuel in a geological repository needs an effective, efficient, reliable and robust non-destructive assay (NDA) system to
ensure the integrity of the fuel prior to disposal. In the context of the Finnish geological repository, Passive Gamma Emission Tomography (PGET) will be a part of such an NDA system. We report here on the results of PGET measurements at the Finnish nuclear power plants during the years 2017-2020. The PGET prototype device developed by IAEA and partners was used during 2017-2019, whereas an updated device was used in 2020. The
PGET device contains two linear arrays of collimated CdZnTe (CZT) gamma ray detectors installed opposite each other inside a torus. Gamma activity profiles are
recorded from all angles by rotating the detector arrays around the fuel assembly that has been inserted into the center of the torus. Image reconstruction from the resulting tomographic data is defined as a constrained minimization problem with the function being minimized containing a data fidelity term and regularization terms. The activity
and attenuation maps, as well as detector sensitivity corrections, are the variables in the minimization process.
The regularization terms ensure that prior information on the (possible) locations of fuel rods and their diameter are taken into account. Fuel rod classification, the main purpose of the PGET method, is based on the difference of the activity of a fuel rod from its immediate neighbors, taking into account its distance from the assembly center.
The classification is carried out by a support vector machine. We report on the results for 10 different fuel
types with burnups between 5.72 and 55.0 GWd/tU, cooling times between 1.87 and 34.6 years and initial enrichments between 1.9 and 4.4%. For the 77 fuel assemblies measured, the total misclassification rate including misclassifications of missing fuel rods, present rods and water channels, was 0.94% for the Olkiluoto campaigns and 0.66% for the Loviisa campaigns. Further
development of the image reconstruction method is
discussed. We conclude that the combination of the PGET
device and our image reconstruction method provides
a reliable base for fuel rod classification. The method is
well-suited for nuclear safeguards verification of BWR fuel
assemblies in Finland prior to geological disposal. For
VVER-440 assemblies, some further work is needed to
investigate the ability to detect missing rods near the
center of the assembly.
Original languageEnglish
Pages (from-to)10-21
Number of pages12
JournalEsarda Bulletin
Issue number2
Publication statusPublished - 1 Dec 2020


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