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Neutron Tomography of Spent Fuel Casks

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Abstract

Dry casks for spent nuclear fuel (SNF) ensure the safe storage of SNF and provide radiation shielding. However, the presence of the thick casks encompassing several layers of steel and concrete makes inspection of the SNF a challenging task. Fast neutron interrogation is a viable method for the nondestructive assay of dry storage casks. In this study, we performed a Monte Carlo simulation-based study associated with a machine-learning-based image reconstruction method to verify the content of SNF dry storage casks. We studied the use of neutron transmission and back-scattered measurements to assess the potential damage to fuel assemblies or fuel pin diversion during transportation of dry casks. We used Geant4 to model a realistic HI-STAR 100 cask, MPC-68 canister and basket, and GE-14 fuel assembly irradiated by a D-T neutron generator. Several bundle diversion scenarios were simulated. The angular distribution of the neutrons scattered by the cask was used to identify the diversions inside the fuel cask. A fuel bundle with at least 75% of its pins removed can be identified with a drop in the back-scattered signature larger than \(2\sigma\) compared with a fully loaded scenario. We combined an iterative reconstruction algorithm with a convolutional neural network (CNN) to obtain a cross-sectional image of the fuel inside the cask. The proposed imaging approach allows locating the position of a missing fuel bundle with at least 75% of the pins removed when performing tomographic imaging of a canister with an overall scan time of less than two hours, when using a commercial neutron generator with a source strength of \(10^{10}\) n/s in the 4\(\pi\) solid angle.

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Funding

This work is funded in part by the Department of Energy (DOE) under contract number 000128931. The Argonne Leadership Computing Facility (ALCF) provided the access to super-computing resources.

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Authors and Affiliations

  1. Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois, Urbana-Champaign, 104 South Wright Street, Urbana, IL, 61801, USA

    Zhihua Liu, Ming Fang, Ling-Jian Meng & Angela Di Fulvio

  2. Department of Radiation and Cellular Oncology, University of Chicago Medicine, 5758 S Maryland Ave, Chicago, Urbana, IL, 60637, USA

    Jon George

  3. Beckman Institute for Advance Science and Technology, University of Illinois, Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, USA

    Ling-Jian Meng

  4. Department of Bioengineering, University of Illinois, Urbana-Champaign, 1406 W Green St, Urbana, IL, 61801, USA

    Ling-Jian Meng

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  1. Zhihua Liu
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  2. Ming Fang
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  5. Angela Di Fulvio
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Correspondence to Zhihua Liu.

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Liu, Z., Fang, M., George, J. et al. Neutron Tomography of Spent Fuel Casks. J Sign Process Syst 94, 399–409 (2022). https://doi.org/10.1007/s11265-021-01706-7

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  • DOI: https://doi.org/10.1007/s11265-021-01706-7

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