Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

In-situ Blockage Monitoring of Sensing Line

  • Mangi, Aijaz Ahmed (Research Center for Modeling and Simulation, National University of Sciences and Technology) ;
  • Shahid, Syed Salman (Research Center for Modeling and Simulation, National University of Sciences and Technology) ;
  • Mirza, Sikander Hayat (Research Center for Modeling and Simulation, National University of Sciences and Technology)
  • Received : 2015.06.29
  • Accepted : 2015.08.17
  • Published : 2016.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

A reactor vessel level monitoring system measures the water level in a reactor during normal operation and abnormal conditions. A drop in the water level can expose nuclear fuel, which may lead to fuel meltdown and radiation spread in accident conditions. A level monitoring system mainly consists of a sensing line and pressure transmitter. Over a period of time boron sediments or other impurities can clog the line which may degrade the accuracy of the monitoring system. The aim of this study is to determine blockage in a sensing line using the energy of the composite signal. An equivalent Pi circuit model is used to simulate blockages in the sensing line and the system's response is examined under different blockage levels. Composite signals obtained from the model and plant's unblocked and blocked channels are decomposed into six levels of details and approximations using a wavelet filter bank. The percentage of energy is calculated at each level for approximations. It is observed that the percentage of energy reduces as the blockage level in the sensing line increases. The results of the model and operational data are well correlated. Thus, in our opinion variation in the energy levels of approximations can be used as an index to determine the presence and degree of blockage in a sensing line.

Keywords

References

  1. IAEA-TECDOC-1402, Management of Life Cycle and Aging at Nuclear Power Plants: Improved I&C Maintenance, International Atomic Energy Agency, Vienna, Austria, 2014.
  2. M.H. Hashemian, On-line monitoring applications in nuclear power plants, Prog. Nucl. Energy 53 (2011) 167-181. https://doi.org/10.1016/j.pnucene.2010.08.003
  3. K. Lin, K.E. Holbert, Void diagnostics in liquid-filled pressure sensing lines, Prog. Nucl. Energy 52 (2010) 503-511. https://doi.org/10.1016/j.pnucene.2009.12.002
  4. H.M. Hashemian, J.A. Thie, B.R. Upadhyaya, K.E. Holbert, Sensor response time monitoring using noise analysis, Prog. Nucl. Energy 21 (1998) 583-592.
  5. H.M. Hashemian, J. Jiang, Using the noise analysis technique to detect response time problems in the sensing lines of nuclear plant pressure transmitters, Prog. Nucl. Energy 52 (2010) 367-373. https://doi.org/10.1016/j.pnucene.2009.08.001
  6. J. Barbero, J. Blazquez, O. Vela, Bubbles in the sensing line of nuclear power plant pressure transmitters: the shift of spectrum resonances, Nucl. Eng. Design 199 (2000) 327-334. https://doi.org/10.1016/S0029-5493(00)00236-3
  7. H.M. Hashemian, J. Jiang, A practical review of methods for measuring the dynamic characteristics of industrial pressure transmitters, ISA Trans. 49 (2010) 2-9. https://doi.org/10.1016/j.isatra.2009.09.004
  8. K. Lin, K.E. Holbert, Blockage diagnostics for nuclear power plant pressure transmitter sensing lines, Nucl. Eng. Design 239 (2009) 365-372. https://doi.org/10.1016/j.nucengdes.2008.10.012
  9. D. Ozevin, J. Harding, Novel leak localization in pressurized pipeline networks using acoustic emission and geometric connectivity, Int. J. Press. Vessel. Pip. 92 (2012) 63-69. https://doi.org/10.1016/j.ijpvp.2012.01.001
  10. M.H. Hashemian, Aging management of instrumentation & control sensors in nuclear power plants, Nucl. Eng. Design 240 (2010) 3781-3790. https://doi.org/10.1016/j.nucengdes.2010.08.014
  11. D. Matko, G. Geiger, W. Gregoritza, Pipeline simulation techniques, Math. Comput. Simul. 52 (2000) 211-230. https://doi.org/10.1016/S0378-4754(00)00152-X
  12. J. Grainger, W. Stevenson, Power System Analysis, first ed., McGraw Hill, New York, 1994.
  13. Y. Zhang, S. Chen, J. Li, S. Jin, Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter, Measurement 49 (2014) 382-389. https://doi.org/10.1016/j.measurement.2013.12.009
  14. P.S. Addison, The Illustrated Wavelet Transform Handbook, first ed., CRC Press, London, 2002.
  15. S. Mallat, A Wavelet Tour of Signal Processing, third ed., Academic Press, Burlington, 2009.
  16. M. Weeks, Digital Signal Processing using Matlab and Wavelets, second ed., Jones & Bartlett Learning, USA, 2011.
  17. M. Misiti, Y. Misiti, Wavelet Toolbox, User Guide, fifth ed., MathWorks Inc., USA, 2015.
  18. A. Mertins, Signal Analysis Wavelets, Filter Banks, Time-Frequency Transforms and Applications, Wiley, Chichester, England, 1999.
  19. M. Behbahani-Nejad, A. Bagheri, The accuracy and efficiency of a MATLAB-Simulink library for transient flow simulation of gas pipelines and networks, J. Pet. Sci. Eng. 70 (2010) 256-265. https://doi.org/10.1016/j.petrol.2009.11.018
  20. J. Maa, 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
  21. G. Geiger, D. Matko, Models of pipelines in transient mode, Math. Comput. Model. Dyn. Syst. 8 (2002) 117-136. https://doi.org/10.1076/mcmd.8.1.117.8341
  22. M.Y. Gokhale, Time domain signal analysis using wavelet packet decomposition approach, Int. J. Commun. Netw. Syst. Sci. 3 (2010) 321-329.