DOI QR코드

DOI QR Code

Experimental investigation of a method for diagnosing wall thinning in an artificially thinned carbon steel elbow based on changes in modal characteristics

  • Received : 2022.06.26
  • Accepted : 2022.11.01
  • Published : 2023.03.25

Abstract

Curved cylindrical structures such as elbows have a non-uniform thickness distribution due to their fabrication process, and as a result have a number of complex mode shapes, including circumferential and axial nodal patterns. In nuclear power plants, material degradation is induced in pipes by flow accelerated erosion and corrosion, causing the wall thickness of carbon steel elbows to gradually thin. The corresponding frequencies of each mode shape vary according to the wall thinning state. Therefore, the thinning state can be estimated by monitoring the varying modal characteristics of the elbow. This study investigated the varying modal characteristics of artificially thinned carbon steel elbows for each thinning state using numerical simulation and experimental methods (MRIT, Multiple Reference Impact Test). The natural frequencies of specified mode shapes were extracted, and results confirmed they linearly decreased with increasing thinning. In addition, by comparing single FRF (Frequency Response Function) data with the results of MRIT, a concise and cost effective thinning estimation method was suggested.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (Ministry of Science and ICT) (No. RS-2022-00144206).

References

  1. C.E. Rossi, Thinning of pipe walls in nuclear power plants, NRC Bulletin 87-01 (1987). 
  2. Nuclear Regulatory commission (NRC), NRC IFORMATION NOTICE 2006-08: Secondary Piping Rupture at the Mihama Power Station in Japan, 2006. 
  3. V. Kain, Flow accelerated corrosion: forms, mechanisms and case studies, Procedia Engineering 86 (2014) 576-588.  https://doi.org/10.1016/j.proeng.2014.11.083
  4. Electric Power Research Institute (EPRI), TR-3002000563, Recommendations for an Effective Flow-Accelerated Corrosion Program, NSAC-202L-R4, 2013. 
  5. C. SCHEFSKI, T. HAZELTON, CHECWORKSTM integrated software for corrosion control, in: 4th International Conference on CANDU Maintenance, 1997. Toronto, Canada, November 16-18. 
  6. ASME, Requirements for Analytical Evaluation of Pipe Wall Thinning, ASME Code Case N-597-2, ASME B&PV Sec. XI, Div. 1, 2003. 
  7. H. Yun, S.J. Moon, Y.J. Oh, Development of wall-thinning evaluation procedure for nuclear power plant piping - Part 1: quantification of thickness measurement deviation, Nuclear Engineering and Technology 48 (2016) 820-830.  https://doi.org/10.1016/j.net.2016.01.020
  8. H. Yun, S.J. Moon, Y.J. Oh, Development of wall-thinning evaluation procedure for nuclear power plant piping - Part 2: local wall-thinning estimation method, Nuclear Engineering and Technology 52 (2020) 2119-2129.  https://doi.org/10.1016/j.net.2020.03.001
  9. C.S. Angani, D.G. Park, G.D. Kim, C.G. Kim, Y.M. Cheong, Differential pulsed eddy current sensor for the detection of wall thinning in an insulated stainless steel pipe, Journal of Applied Physics 107 (2010), 09E720. 
  10. J.H. Lee, S.J. Lee, Application of laser-generated guided wave for evaluation of corrosion in carbon steel pipe, NDT & E International 42 (3) (2009) 222-227.  https://doi.org/10.1016/j.ndteint.2008.09.011
  11. Yavuz Ege, Mustafa Coramik, A new measurement systems using magnetic flux leakage method in pipeline inspection, Measurement 123 (2018) 163-174.  https://doi.org/10.1016/j.measurement.2018.03.064
  12. Wei Fan, Pizhong Qiao, Vibration-based damage identification methods: a review and comparative study, Structural Health Monitoring 10 (1) (2011), 83-29. 
  13. M. El-Gebeily, Y.A. Khulief, Identification of wall-thinning and cracks in pipes utilizing vibration modes and wavelets, Applied Mathematical Modeling 40 (9-10) (2016) 5335-5348.  https://doi.org/10.1016/j.apm.2015.12.031
  14. S.W. Han, J.H. Park, T. Kang, Detection of pipe wall-thinning based on change of natural frequencies of shell vibration modes, in: 19th World Conference on Non-destructive Testing, 2016. Munich, Germany, June 13-17. 
  15. R. Zhou, Monitoring Flow-Accelerated Corrosion in a Bent Pipe Using Vibrational Methods, University of New Brunswick., 2018. 
  16. Obukho E Esu, Ying Wang, K. Marios, Chryssanthopoulos, A Baseline-free Method for Damage Identification in Pipes from Local Vibration Mode Pair Frequencies, Structural Health Monitoring, 2021, https://doi.org/10.1177/14759217211052335. 
  17. Y.H. Chae, S.G. Kim, H. Kim, J.T. Kim, P.H. Seong, A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models, Annals of Nuclear Energy 143 (2020), 107501. 
  18. J.H. Kim, B.Y. Jung, Y.C. Choi, J.H. Park, Estimation of pipe wall thinning using a convolutional neural network for regression, Nuclear Technology (2021), https://doi.org/10.1080/00295450.2021.2018271. 
  19. J. Bridgeman, R. Shankar, Erosion/corrosion data handling for reliable NDE, Nuclear Engineering and Design 131 (1991) 285-297. https://doi.org/10.1016/0029-5493(91)90302-X