Nuclear Engineering and Technology

  • 제55권5호
  • /
  • Pages.1533-1540
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  • 2023
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Sorted compressive sensing for reconstruction of failed in-core detector signals

  • Gyu-ri Bae (Department of Quantum and Nuclear Engineering, Sejong University) ;
  • Moon-Ghu Park (Department of Quantum and Nuclear Engineering, Sejong University) ;
  • Youngchul Cho (Department of Quantum and Nuclear Engineering, Sejong University) ;
  • Jung-Uk Sohn (ZettaCognition)
  • 투고 : 2022.07.03
  • 심사 : 2023.02.03
  • 발행 : 2023.05.25
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초록

Self-Powered Neutron Detectors(SPNDs) are used to calculate core power distributions, an essential factor in the safe operation of nuclear power plants. Some detectors may fail during normal operation, and signals from failed detectors are isolated from intact signals. The calculated detailed power distribution accuracy depends on the number of available detector signals. Failed detectors decrease the operating margin by enlarging the power distribution measurement error. Therefore, a thorough reconstruction of the failed detector signals is critical. This note suggests a compressive sensing based methodology that rationally reconstructs the readings of failed detectors. The methodology significantly improves reconstruction accuracy by sorting signals and removing high-frequency components from conventional compressive sensing methodology.

키워드

과제정보

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2016R1A5A1013919) and by the KETEP funded by the Korea government Ministry of Trade, Industry and Energy (20206510100040).

참고문헌

  1. R.K. Endter, R.G. Foster, Effect of inoperable in-core detectors on core monitoring system accuracy, Nucl. Technol. 54 (1981) 145-154. https://doi.org/10.13182/NT81-A32731
  2. M.G. Park, H.C. Shin, Reactor power shape synthesis using group method of data handling, Ann. Nucl. Energy 72 (2014) 467-470. https://doi.org/10.1016/j.anucene.2014.06.010
  3. USNRC Technical Training Center, CE technology cross training, USNRC HRTD, Rev 10/08.," [Online]. Available: https://www.nrc.gov/docs/ML1125/ML11251A026.pdf.
  4. E.K. Lee, Y.H. Kim, K.H. Cha, M.G. Park, Reconstruction of core axial power shapes using the alternating conditional expectation algorithm, Ann. Nucl. Energy 26 (11) (1999) 983-1002, 26(11). https://doi.org/10.1016/S0306-4549(98)00099-1
  5. KEPCO/KHNP, Status Report - APR1400, IAEA Advanced Reactors Information System, 2015 [Online]. Available: https://aris.iaea.org/sites/overview.html.
  6. J. Ma, J. Jiang, Applications of fault detection and diagnosis methods in nuclear power plants: a review, Prog. Nucl. Energy 53 (2011) 255-266. https://doi.org/10.1016/j.pnucene.2010.12.001
  7. D.L. Donoho, Compressed sensing, IEEE Trans. Inf. Theor. 52 (4) (2006) 1289-1306. https://doi.org/10.1109/TIT.2006.871582
  8. J. Kim, A. Basarab, P. Hill, D. Bull, D. Kouame, A. Achim, Ultrasound image reconstruction from compressed measurements using approximate message passing, in: 2016 24th European Signal Processing Conference (EUSIPCO), 2016, pp. 557-561. Budapest, Hungary.
  9. X. Zhang, J. Wen, Y. Han, J. Villasenor, An improved compressive sensing reconstruction algorithm using linear/non-linear mapping, 2011 Information Theory and Appl. Workshop (2011). La Jolla, CA, USA.