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Thickness reconstruction of nuclear power plant pipes with flow-accelerated corrosion damage using laser ultrasonic wavenumber imaging

The pipeline is an essential part of nuclear and other power plants. Designed to operate for decades, the pipeline is susceptible to multiple types of damage due to working in a high pressure, high temperature, and highly corrosive environment. Therefore, it requires regular inspection. The issue of...

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Bibliographic Details
Published in:Structural health monitoring 2018-03, Vol.17 (2), p.255-265
Main Authors: Truong, Thanh Chung, Lee, Jung-Ryul
Format: Article
Language:English
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Summary:The pipeline is an essential part of nuclear and other power plants. Designed to operate for decades, the pipeline is susceptible to multiple types of damage due to working in a high pressure, high temperature, and highly corrosive environment. Therefore, it requires regular inspection. The issue of many current nondestructive testing systems for pipelines is the use of a one-dimensional scan mechanism along the longitudinal direction of the pipe, which is incompatible with the two-dimensional curvature of elbow pipes, yet it is at these places in which the flow changes directions inside the elbow pipes that a common type of damage occurs: flow-accelerated corrosion damage. In this article, flow-accelerated corrosion damage is visualized using the Ultrasonic Wavenumber Imaging algorithm, which maps the dominant local spectroscopic wavenumber of the wavefield at a particular mode and a particular frequency. In previous studies, the mode and frequency were chosen empirically. In this article, a novel theoretical study is presented to optimize the two input parameters of Ultrasonic Wavenumber Imaging based on a sensitivity analysis of the spectroscopic wavenumber with respect to the change in thickness of the structural base. Additionally, an inverse method is proposed to estimate the thickness of the structure based on the Ultrasonic Wavenumber Imaging map.
ISSN:1475-9217
1741-3168
DOI:10.1177/1475921716689733