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SHM of Thin and Small Diameter Tubes Operating at High Temperature for Pitting Corrosion

ADITYA CHILUKURI, NISHANTH RAJA, KRISHNAN BALASUBRAMANIAM

Abstract


The use of ultrasonic guided waves has been identified as a promising technology for continuous monitoring of pipe structures to detect small defects of cross-sectional area ratio less than 5%. This paper presents an effective approach to use torsional guided wave mode T(0,1) in thin and small diameter stainless steel tubes operating at elevated temperatures in the range of 150 oC. for detecting small pitting defects using magnetostriction sensors. The phenomenon of scattering of the fundamental torsional mode T(0,1) from complex-shaped discontinuities in thin tubes is closely examined, and its excitation parameters are optimized to an extent using finite element (FE) simulations. From the numerical and experimental results, the optimized design for transmitting/receiving pure torsional modes are investigated. Then, the reflection from three dimensional (3D) notches with varying depth profiles in thin tubes is studied. The results show that the reflection coefficient of torsional guided wave mode T(0,1) is a stronger function of the through-thickness depth and, axial and circumferential extent of the notch. As a result, plots of reflection coefficient as a function of the circumferential extent, axial extent and depth of the defect are shown for particular pipe size. The finite element results are then compared with the experimental data. The sensitivity of the reflected signal from defects up to 2% of the cross-sectional area ratio was examined, and it was found that defects smaller than 2% of the cross-sectional area ratio were detectable. The results from these analyses have been used to propose a practical approach to determine the maximum depth of a pitting defect from the reflection coefficient behaviour, provided that the external circumferential extent or axial extent of the defect is known. This work is of great interest to many process industries including semiconductor manufacturing industries.


DOI
10.12783/shm2019/32323

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