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Optimal Axial-Probe Design for Foucault-Current Tomography: A Global Optimization Approach Based on Linear Sampling Method

Author

Listed:
  • Brahim Benaissa

    (Department of Mechanical Systems Engineering, Design Engineering Lab, Toyota Technological Institute, 2 Chome-12-1, Hisakata, Tempaku Ward, Nagoya 468-8511, Japan)

  • Samir Khatir

    (Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 903, B-9052 Zwijnaarde, Belgium)

  • Mohamed Soufiane Jouini

    (Department of Mathematics, Khalifa University of Sciences and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Mohamed Kamel Riahi

    (Department of Mathematics, Khalifa University of Sciences and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
    Emirates Nuclear Technology Center (ENTC), Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
    Current address: Zarkuh Building, Khalifa University, SAN Campus, Abu Dhabi P.O. Box 127788, United Arab Emirates.)

Abstract

This paper is concerned with the optimal design of axial probes, commonly used in the Non-Destructive Testing (NDT) of tube boiling in steam generators. The goal is to improve the low-frequency Foucault-current imaging of these deposits by designing a novel probe. The approach uses a combination of an inverse problem solver with global optimization to find the optimal probe characteristics by minimizing a function of merit defined using image processing techniques. The evaluation of the function of merit is computationally intensive and a surrogate optimization approach is used, incorporating a multi-particle search algorithm. The proposed design is validated through numerical experiments and aims to improve the accuracy and efficiency of identifying deposits in steam generator tubes.

Suggested Citation

  • Brahim Benaissa & Samir Khatir & Mohamed Soufiane Jouini & Mohamed Kamel Riahi, 2023. "Optimal Axial-Probe Design for Foucault-Current Tomography: A Global Optimization Approach Based on Linear Sampling Method," Energies, MDPI, vol. 16(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2448-:d:1087505
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    References listed on IDEAS

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    1. Rommel G. Regis & Christine A. Shoemaker, 2007. "A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions," INFORMS Journal on Computing, INFORMS, vol. 19(4), pages 497-509, November.
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