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Title: Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data

Abstract

Spent fuel monitoring and characterization has been a pillar of safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting of large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also in for nuclear power facilities, as accounting can be a long and arduous process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel, or a partial defect detection. A second method, Gamma Emission Tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. This work identifies deficiencies in both methods, and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%respectively for DCVD and GET data on their own. Finally, these classification results are obtained withmore » algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real-time while also maintaining the information barrier between output and measurement desired in safeguards.« less

Authors:
 [1];  [1];  [2];  [2]
  1. North Carolina State Univ., Raleigh, NC (United States). Mathematics Dept.
  2. North Carolina State Univ., Raleigh, NC (United States). Nuclear Engineering Dept.
Publication Date:
Research Org.:
North Carolina State Univ., Raleigh, NC (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation (NA-20)
OSTI Identifier:
1440901
Grant/Contract Number:  
NA0002576
Resource Type:
Accepted Manuscript
Journal Name:
Nuclear Technology
Additional Journal Information:
Journal Volume: 204; Journal Issue: 3; Journal ID: ISSN 0029-5450
Publisher:
Taylor & Francis - formerly American Nuclear Society (ANS)
Country of Publication:
United States
Language:
English
Subject:
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS; DCVD; tomography; image analysis; spent fuel characterization

Citation Formats

Brayfindley, Eva, Smith, Ralph C., Mattingly, John, and Brigantic, Robert. Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data. United States: N. p., 2018. Web. doi:10.1080/00295450.2018.1490123.
Brayfindley, Eva, Smith, Ralph C., Mattingly, John, & Brigantic, Robert. Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data. United States. doi:10.1080/00295450.2018.1490123.
Brayfindley, Eva, Smith, Ralph C., Mattingly, John, and Brigantic, Robert. Wed . "Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data". United States. doi:10.1080/00295450.2018.1490123. https://www.osti.gov/servlets/purl/1440901.
@article{osti_1440901,
title = {Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data},
author = {Brayfindley, Eva and Smith, Ralph C. and Mattingly, John and Brigantic, Robert},
abstractNote = {Spent fuel monitoring and characterization has been a pillar of safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting of large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also in for nuclear power facilities, as accounting can be a long and arduous process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel, or a partial defect detection. A second method, Gamma Emission Tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. This work identifies deficiencies in both methods, and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%respectively for DCVD and GET data on their own. Finally, these classification results are obtained with algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real-time while also maintaining the information barrier between output and measurement desired in safeguards.},
doi = {10.1080/00295450.2018.1490123},
journal = {Nuclear Technology},
number = 3,
volume = 204,
place = {United States},
year = {2018},
month = {8}
}

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