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

Brahim Benaissa, Samir Khatir, Mohamed Soufiane Jouini and Mohamed Kamel Riahi ()
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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

Energies, 2023, vol. 16, issue 5, 1-15

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.

Keywords: NDT; optimal design; eddy-current; inverse problem; linear sampling method; finite element method; surrogate optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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