Advances in aircraft and spacecraft science

  • 제4권5호
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  • Pages.515-528
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  • 2017
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  • 2287-528X(pISSN)
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  • 2287-5271(eISSN)
테크노프레스 (Techno-Press)
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Delamination evaluation on basalt FRP composite pipe by electrical potential change

  • Altabey, Wael A. (International Institute for Urban Systems Engineering, Southeast University)
  • 투고 : 2017.03.02
  • 심사 : 2017.04.26
  • 발행 : 2017.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Since composite structures are widely used in structural engineering, delamination in such structures is an important issue of research. Delamination is one of a principal cause of failure in composites. In This study the electrical potential (EP) technique is applied to detect and locate delamination in basalt fiber reinforced polymer (FRP) laminate composite pipe by using electrical capacitance sensor (ECS). The proposed EP method is able to identify and localize hidden delamination inside composite layers without overlapping with other method data accumulated to achieve an overall identification of the delamination location/size in a composite, with high accuracy, easy and low-cost. Twelve electrodes are mounted on the outer surface of the pipe. Afterwards, the delamination is introduced into between the three layers (0º/90º/0º)s laminates pipe, split into twelve scenarios. The dielectric properties change in basalt FRP pipe is measured before and after delamination occurred using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to delamination, a finite element simulation model for delamination location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Response surfaces method (RSM) are adopted as a tool for solving inverse problems to estimate delamination location/size from the measured electrical potential changes of all segments between electrodes. The results show good convergence between the finite element model (FEM) and estimated results. Also the results indicate that the proposed method successfully assesses the delamination location/size for basalt FRP laminate composite pipes. The illustrated results are in excellent agreement with the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.

키워드

참고문헌

  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.
  3. Altabey, W.A. (2016), "Detecting and predicting the crude oil type inside composite pipes using ECS and ANN", J. Struct. Monitor. 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", J. Struct. Monitor. 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", J. Struct. Monitor. Mainten., 3(4), 335-347. https://doi.org/10.12989/smm.2016.3.4.335
  6. 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, U.S.A.
  7. 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. Powd. Technol., 270, 10-19. https://doi.org/10.1016/j.powtec.2014.10.003
  8. 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, J. Phys.: Conf. Ser., 147, 1-13.
  9. Jaworski, A.J. and Bolton, G.T. (2000), "The design of an electrical capacitance tomography sensor for use with media of high dielectric permittivity", Measure. Sci. Technol., 11(6), 743-757. https://doi.org/10.1088/0957-0233/11/6/318
  10. 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
  11. Li, H. and Huang, Z. (2000), Special Measurement Technology and Application, Zhejiang University Press, Hangzhou, China.
  12. 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. Sensor. Actuat. A, 180, 1-10. https://doi.org/10.1016/j.sna.2012.03.025
  13. 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
  14. Myers, R. and Montgomery, D.C. (2002), Response Surface Methodology Process and Product Optimization Using Designed Experiments, 2nd Edition, Wiley-Interscience, New York, U.S.A.
  15. Pei, T. and Wang, W. (2009), "Simulation analysis of sensitivity for electrical capacitance tomography", Proceedings of the 9th International Conference on Electronic Measurement & Instruments.
  16. 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.
  17. 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
  18. 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
  19. Yang, W.Q., Beck, M.S. and Byars, M., (1995b), "Electrical capacitance tomography-from design to applications", Measure. Contr., 28(9), 261-266. https://doi.org/10.1177/002029409502800901
  20. Yang, W.Q. and York, T.A. (1999), "New AC-based capacitance tomography system", IEEE Proc.: Measure. Sci. Technol., 146(1), 47-53. https://doi.org/10.1049/ip-smt:19990008
  21. Yang, W.Q. (1997), "Modelling of capacitance sensor", IEEE Proc.: Measure. Sci. Technol., 144(5), 203-208. https://doi.org/10.1049/ip-smt:19971425
  22. Zhang, W., Wang, C., Yang, W. and Wang C. (2014), "Application of electrical capacitance tomography in particulate process measurement-a review", J. Adv. Powd. Technol., 25, 174-188. https://doi.org/10.1016/j.apt.2013.12.003

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