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Title: Automated Defect Detection in Spent Nuclear Fuel Using Combined Cerenkov Radiation and Gamma Emission Tomography Data

Abstract

Spent fuel monitoring and characterization has been central to 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 for 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 for nuclear power facilities, as accounting can be a long, arduous and costly 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 identies deciencies in both methods, and proposes a combination of data gathered from each method to address these deciencies 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. These classication results are obtained with algorithmsmore » 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:
ORCiD logo [1];  [1];  [2];  [3]
  1. North Carolina State University, Department of Mathematics, Raleigh, North Carolina 27695
  2. North Carolina State University, Department of Nuclear Engineering, Raleigh, North Carolina 27695
  3. Pacific Northwest National Laboratory, Applied Statistics and Computational Modeling, 902 Battelle Boulevard, P.O. Box 999, MSIN: K7-20, Richland, Washington 99352
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1495346
Report Number(s):
PNNL-SA-132748
Journal ID: ISSN 0029-5450
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
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

Citation Formats

Brayfindley, Eva, Smith, Ralph C., Mattingly, John, and Brigantic, Robert. Automated Defect Detection in Spent Nuclear Fuel Using Combined Cerenkov Radiation 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 Combined Cerenkov Radiation and Gamma Emission Tomography Data. United States. doi:10.1080/00295450.2018.1490123.
Brayfindley, Eva, Smith, Ralph C., Mattingly, John, and Brigantic, Robert. Thu . "Automated Defect Detection in Spent Nuclear Fuel Using Combined Cerenkov Radiation and Gamma Emission Tomography Data". United States. doi:10.1080/00295450.2018.1490123.
@article{osti_1495346,
title = {Automated Defect Detection in Spent Nuclear Fuel Using Combined Cerenkov Radiation 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 central to 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 for 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 for nuclear power facilities, as accounting can be a long, arduous and costly 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 identies deciencies in both methods, and proposes a combination of data gathered from each method to address these deciencies 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. These classication 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},
issn = {0029-5450},
number = 3,
volume = 204,
place = {United States},
year = {2018},
month = {7}
}

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