RESUMO
There is a constant drive within the nuclear power industry to improve upon the characterization capabilities of current ultrasonic inspection techniques in order to improve safety and reduce costs. Particular emphasis has been placed on the ability to characterize very small defects which could result in extended component lifespan and help reduce the frequency of in-service inspections. Super-resolution (SR) algorithms, also known as sampling methods, have been shown to demonstrate the capability to resolve scatterers separated by less than the diffraction limit when deployed in representative inspections and therefore could be used to tackle this issue. In this paper, the factorization method (FM) and the Time Reversal Multiple-Signal-Classification (TR-MUSIC) algorithms are applied to the simulated ultrasonic array inspection of small rough embedded planar defects to establish their characterization capabilities. Their performance was compared to the conventional total focusing method (TFM). A full 2-D finite-element (FE) Monte Carlo modeling study was conducted for defects with a range of sizes, orientations, and magnitude of surface roughness. The results presented show that for subwavelength defects, both the FM and TR-MUSIC algorithms were able to size and estimate defect orientation accurately for smooth cases and, for rough defects, up to a roughness of 100 [Formula: see text]. This level of roughness is representative of the thermal fatigue defects encountered in the nuclear power sector. This contrasted with the relatively poor performance of TFM in these cases which consistently oversized these defects and could not be used to estimate the defect orientation, making through-wall sizing with this method impossible.