Structural Monitoring and Maintenance

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EPC method for delamination assessment of basalt FRP pipe: electrodes number effect

  • Altabey, Wael A. (International Institute for Urban Systems Engineering, Southeast University)
  • Received : 2017.01.22
  • Accepted : 2017.02.28
  • Published : 2017.03.25
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Abstract

Delamination is the most common failure mode in layered composite materials. The author have found that the electrical potential change (EPC) technique using response surfaces method is very effective in assessment delamination in basalt fiber reinforced polymer (FRP) laminate composite pipe by using electrical capacitance sensor (ECS). In the present study, the effect of the electrodes number on the method is investigated using FEM analyses for delamination location/size detection by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Three cases of electrodes number are analyzed here are eight, twelve and sixteen electrodes, afterwards, the delamination is introduced into between the three layers [$0^{\circ}/90^{\circ}/0^{\circ}$]s laminates pipe, split into eight, twelve and sixteen scenarios for cases of eight, twelve and sixteen electrodes respectively. Response surfaces are adopted as a tool for solving inverse problems to estimate delamination location/size from the measured EPC of all segments between electrodes. As a result, it was revealed that the estimation performances of delamination location/size depends on the electrodes number. For ECS, the high number of electrodes is required to obtain high estimation performances of delamination location/size. The illustrated results are in excellent agreement with solutions available in the literature, thus validating the accuracy and reliability of the proposed technique.

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References

  1. Al-Tabey, W.A. (2010), "Effect of pipeline filling material on electrical capacitance tomography", Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010), Perlis, Malaysia, October.
  2. Al-Tabey, W.A. (2012), Finite Element Analysis in Mechanical Design Using ANSYS: Finite Element Analysis (FEA) Hand Book For Mechanical Engineers With ANSYS Tutorials, LAP Lambert Academic Publishing, Germany, ISBN 978-3-8454-0479-0.
  3. Altabey, W.A. (2016), "Detecting and predicting the crude oil type inside composite pipes using ECS and ANN", Struct. Monit. Mainten., 3(4), 377-393. https://doi.org/10.12989/smm.2016.3.4.377
  4. Altabey, W.A. (2016), "FE and ANN model of ECS to simulate the pipelines suffer from internal corrosion", Struct. Monit. Mainten., 3(3), 297-314. https://doi.org/10.12989/SMM.2016.3.3.297
  5. Altabey, W.A. (2016), "The thermal effect on electrical capacitance sensor for two-phase flow monitoring", Struct. Monit. Mainten., 3(4), 335-347. https://doi.org/10.12989/smm.2016.3.4.335
  6. Amaro, A.M., Santos, J.B. and Cirne, J.S. (2011), "Delamination depth in composites laminates with interface elements and ultrasound analysis", Strain, 47(2), 138-145. https://doi.org/10.1111/j.1475-1305.2008.00491.x
  7. ANSYS Low-Frequency Electromagnetic analysis Guide, The Electrostatic Module in the Electromagnetic subsection of ANSYS, (2015), ANSYS, inc. Southpointe 275 Technology Drive Canonsburg, PA 15317, Published in the USA.
  8. Asencio, K., Bramer-Escamilla, W., Gutierrez, G. and Sanchez, I. (2015), "Electrical capacitance sensor array to measure density profiles of a vibrated granular bed", J. Pow. Technol., 270, 10-19. https://doi.org/10.1016/j.powtec.2014.10.003
  9. Cunedioglu, Y. and Beylergil, B. (2015), "Free vibration analysis of damaged composite beams", Struct. Eng. Mech., 55(1), 79-92. https://doi.org/10.12989/sem.2015.55.1.079
  10. Daoye, Y., Bin, Z., Chuanlong, X., Guanghua, T. and Shimin, W. (2009), "Effect of pipeline thickness on electrical capacitance tomography", Proceedings of the 6th International Symposium on Measurement Techniques for Multiphase Flows, Journal of Physics: Conference Series, 147, 1-13.
  11. Davijani, A.A.B., Hajikhani, M. and Ahmadi, M. (2011), "Acoustic Emission based on sentry function to monitor the initiation of delamination in composite materials", J. Mater. Des., 32(5), 3059-3065. https://doi.org/10.1016/j.matdes.2011.01.010
  12. De Albuquerque, V.C., Tavares, J.R.S. and Durao, L.M.P. (2010), "Evaluation of delamination damage on composite plates using an artificial neural network for the radiographic image analysis", J. Compos. Mater., 44(9), 1139-1159. https://doi.org/10.1177/0021998309351244
  13. Fasching, G.E. and Smith, N.S. (1988), "High Resolution Capacitance Imaging System", US Dept. Energy, 37, DOE/METC-88/4083.
  14. Fasching, G.E. and Smith, N.S. (1991) "A capacitive system for 3-Dimensional imaging of fluidized-beds", Rev. Sci. Instr., 62(9), 2243-2251. https://doi.org/10.1063/1.1142343
  15. Garcia, D., Palazzetti, R., Trendafilova, I., Fiorini, C. and Zucchelli, A. (2015), "Vibration-based delamination diagnosis and modelling for composite laminate plates", J. Compos. Struct., 130, 155-162. https://doi.org/10.1016/j.compstruct.2015.04.021
  16. Heuer, H., Schulze, M.H. and Meyendorf, N. (2013), "Non-destructive evaluation (NDE) of composites: eddy current techniques", Non-destructive evaluation (NDE) of polymer matrix composites: Techniques and applications, 33-55.
  17. Hu, N., Liu, Y., Li, Y., Peng, X. and Yan, B. (2010), "Optimal excitation frequency of lamb waves for delamination detection in CFRP laminates", J. Compos. Mater., 44(13), 1643-1663. https://doi.org/10.1177/0021998309353965
  18. Huang, S.M., Plaskowski, A.B., Xie, C.G. and Beck, M.S. (1989), "Tomographic imaging of two-flow component flow using capacitance sensor", J. Phys. E:Sci. Instrum., 22, 173-177. https://doi.org/10.1088/0022-3735/22/3/009
  19. Jaworski, A.J. and Bolton, G.T. (2000), "The design of an electrical capacitance tomography sensor for use with media of high dielectric permittivity", Measurement Sci. Technol., 11(6), 743-757. https://doi.org/10.1088/0957-0233/11/6/318
  20. Jiang, S., Li, D., Zhou, C. and Zhang, L. (2014), "Capabilities of stochastic response surface method and response surface method in reliability analysis", Struct. Eng. Mech., 49(1), 111-128. https://doi.org/10.12989/sem.2014.49.1.111
  21. Li, H. and Huang, Z. (2000), "Special measurement technology and application", Zhejiang University Press, Hangzhou.
  22. Liu, Z., Yu, H., He, C. and Wu, B. (2013), "Delamination damage detection of laminated composite beams using air-coupled ultrasonic transducers", Sci. China Phys., Mech. Astronomy, 56(7), 1269-1279. https://doi.org/10.1007/s11433-013-5092-7
  23. Liu, Z., Yu, H., He, C. and Wu, B. (2014), "Delamination detection in composite beams using pure Lamb mode generated by air-coupled ultrasonic transducer", J. Intel. Mater. Syst. Struct., 25(5), 541-550. https://doi.org/10.1177/1045389X13493339
  24. Mohamad, E.J., Rahim, R.A., Leow, P.L., Fazalul, Rahiman, M.H., Marwah, O.M.F., Nor Ayob, N.M., Rahim, H.A. and Mohd Yunus, F.R. (2012), "An introduction of two differential excitation potentials technique in electrical capacitance tomography", J. Sens. Actuat. A, 180, 1-10. https://doi.org/10.1016/j.sna.2012.03.025
  25. Mohamad, E.J., Rahim, R.A., Rahiman, M.H.F., Ameran, H.L.M., Muji, S.Z.M. and Marwah, O.M.F. (2016), "Measurement and analysis of water/oil multiphase flow using Electrical Capacitance Tomography sensor", J. Flow Measure. Instrument., 47, 62-70. https://doi.org/10.1016/j.flowmeasinst.2015.12.004
  26. Myers, R. and Montgomery, D.C. (2002), "Response surface methodology process and product optimization using designed experiments", 2nd ed. New York: Wiley-Interscience.
  27. Nguyen, K., Ho, D. and Kim, J. (2013), "Damage detection in beam-type structures via PZT", Smart Struct. Syst., 11(2), 217-240. https://doi.org/10.12989/sss.2013.11.2.217
  28. Pei, T. and Wang, W. (2009), "Simulation analysis of sensitivity for electrical capacitance tomography", Proceedings of Ninth International Conference on Electronic Measurement & Instruments (ICEMI 2009).
  29. Peng, Q., Zhang, X., Huang, C., Carter, E.A. and Lu, G. (2012), "Hierarchical fiber-optic delamination detection system for carbon fiber reinforced plastic structures", J. Model. Simulat. Mater. Sci. Eng., 18, 1-14.
  30. Saeedifar, M., Fotouhi, M., Najafabadi, M.A. and Toudeshky, H.H. (2015), "Prediction of delamination growth in laminated composites using acoustic emission and Cohesive Zone Modeling techniques", J. Compos. Struct., 124, 120-127. https://doi.org/10.1016/j.compstruct.2015.01.003
  31. Saeedifar, M., Fotouhi, M., Najafabadi, M.A., Toudeshky, H.H. and Minak, G. (2016), "Prediction of quasistatic delamination onset and growth in laminated composites by acoustic emission", J. Compos. Part B: Eng., 85, 113-122.
  32. Sardeshpande, M.V., Harinarayan, S. and Ranade, V.V. (2015), "Void fraction measurement using electrical capacitance tomography and high speed photography", J. Chem. Eng. Res. Des., 9(4), 1-11.
  33. Spiegel, M.D. (2014), "Damage detection in composite materials using PZT actuators and sensors for structural health monitoring", Master, Department of Electrical and Computer Engineering, University of Alabama.
  34. Todoroki, A., Tanaka, Y. and Shimamura, Y. (2004), "Multi-prove electric potential change method for delamination monitoring of graphite/epoxy composite plates using normalized response surfaces", J. Compos. Sci. Technol., 64, 749-758. https://doi.org/10.1016/j.compscitech.2003.08.004
  35. Tompson, C.G. and Johnson, W.S. (2011), "Determination of the nontraditional lay-up influence and loading configuration on fatigue damage development under bearing-bypass loading conditions using radiography", J. Compos. Mater., 45(22), 2259-2269. https://doi.org/10.1177/0021998311401078
  36. Toscano, C., Riccio, A., Camerlingo, F. and Meola, C. (2012), "Lock in thermography to monitor propagation of delamination in CFRP composites during compression tests", 11th International Conference on Quantitative InfraRed Thermography, Naples, Italy, June.
  37. Yang, W.Q. (1997), "Modelling of capacitance sensor", IEEE proceedings: Measurement Science and Technology, 144(5), 203-208. https://doi.org/10.1049/ip-smt:19971425
  38. Yang, W.Q. and York, T.A. (1999), "New AC-based capacitance tomography system", IEEE proceedings: Measurement Science and Technology, 146(1), 47-53. https://doi.org/10.1049/ip-smt:19990008
  39. Yang, W.Q., Beck, M.S. and Byars, M. (1995b), "Electrical capacitance tomography -from design to applications", Measure. Control, 28(9), 261-266. https://doi.org/10.1177/002029409502800901
  40. Yang, W.Q., Stott, A.L., Beck, M.S. and Xie, C.G. (1995a), "Development of capacitance tomographic imaging systems for oil pipeline measurements", Rev. Sci. Instr., 66(8), 4326. https://doi.org/10.1063/1.1145322
  41. Yeum, C.M., Sohn, H., Ihn, J.B. and Lim, H.J. (2012), "Delamination detection in a composite plate using a dual piezoelectric transducer network", J. Compos. Struct., 94, 3490-3499. https://doi.org/10.1016/j.compstruct.2012.06.003
  42. Zhang, W., Wang, C., Yang, W. and Wang, C. (2014), "Application of electrical capacitance tomography in particulate process measurement - A review", J. Adv. Pow. Technol., 25(1), 174-188. https://doi.org/10.1016/j.apt.2013.12.003

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