Improved Passive Gamma Emission Tomography image quality in the central region of spent nuclear fuel

Riina Virta, Tatiana A. Bubba, Mikael Moring, Samuli Siltanen, Tapani Honkamaa, Peter Dendooven

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)

Abstract

Reliable non-destructive methods for verifying spent nuclear fuel are essential to draw credible nuclear safeguards conclusions from spent fuel. In Finland, spent fuel items are verified prior to the soon starting disposal in a geological repository with Passive Gamma Emission Tomography (PGET), a uniquely accurate method capable of rod-level detection of missing active material. The PGET device consists of two highly collimated detector banks, collecting gamma emission data from a 360° rotation around a fuel assembly. 2D cross-sectional activity and attenuation images are simultaneously computed. We present methods for improving reconstructed image quality in the central parts of the fuel. The results are based on data collected from 2017 to 2021 at the Finnish nuclear power plants with 10 fuel assembly types of varying characteristics, for example burnups from 5.7 to 55 GWd/tU and cooling times from 1.9 to 37 years. Data is acquired in different gamma energy windows, capturing the peaks of Cs-137 (at 662 keV) and Eu-154 (at 1274 keV), abundant isotopes in long-cooled spent nuclear fuel. Data from these gamma energy windows at well-chosen angles are used for higher-quality images, resulting in more accurate detection of empty rod positions. The method is shown to detect partial diversion of nuclear material also in the axial direction, demonstrated with a novel measurement series scanning over the edge of partial-length rods.

Original languageEnglish
Article number12473
JournalScientific Reports
Volume12
Issue number1
Early online date21 Jul 2022
DOIs
Publication statusPublished - 31 Dec 2022

Bibliographical note

Funding Information:
TAB and SS acknowledge partial support by the Academy of Finland through the Finnish Centre of Excellence in Inverse Modelling and Imaging 2018–2025, decision number 312339. Open access funded by Helsinki University Library.

ASJC Scopus subject areas

  • General

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