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Model-Based Optimization of MFL Testing of Ferromagnetic Steam Generator Tubes

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Abstract

This paper proposes finite element modelling based optimization of the magnetic flux leakage (MFL) technique to detect outer surface defects in ferromagnetic steam generator tubes. It features optimized design of core, coil, coil current and location of leakage detection sensor as well as prediction of MFL signals. The 3D modelling, simulation and optimization of MFL signals have been performed using COMSOL 4.3a for prediction of leakage fields from defects in tubes. The influence of length, width and depth of defects on the MFL signal is analysed for enhancing the detection reliability and defect sizing.

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Acknowledgments

The authors thank the Board of Research in Nuclear Science (BRNS), Department of Atomic Energy, Mumbai for funding the work. Authors also thank Dr. W. Sharat Chandra Singh, NDE Division, Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkamand Mr. T. SenthilKumar, Junior Research Fellow, National Engineering College, Kovilpattifor their help in the experiments and for technical discussions.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Syed Ammal Engineering College, Ramanathapuram, TN, India

    P. Karuppasamy

  2. Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, TN, India

    A. Abudhahir

  3. Department of Control and Instrumentation Engineering, Sri Ramakrishna Engineering College, Coimbatore, TN, India

    M. Prabhakaran

  4. Non-Destructive Evaluation Division, Indira Gandhi Centre for Atomic Research, Kalpakkam, TN, India

    S. Thirunavukkarasu, B. P. C. Rao & T. Jayakumar

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  1. P. Karuppasamy
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  2. A. Abudhahir
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  3. M. Prabhakaran
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  4. S. Thirunavukkarasu
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  5. B. P. C. Rao
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  6. T. Jayakumar
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Correspondence to P. Karuppasamy.

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Karuppasamy, P., Abudhahir, A., Prabhakaran, M. et al. Model-Based Optimization of MFL Testing of Ferromagnetic Steam Generator Tubes. J Nondestruct Eval 35, 5 (2016). https://doi.org/10.1007/s10921-015-0320-x

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  • DOI: https://doi.org/10.1007/s10921-015-0320-x

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