Development of wall-thinning evaluation procedure for nuclear power plant piping - Part 2: Local wall-thinning estimation method

Flow-accelerated corrosion (FAC), liquid droplet impingement erosion (LDIE), cavitation and flashing can cause continuous wall-thinning in nuclear secondary pipes. In order to prevent pipe rupture events resulting from the wall-thinning, most NPPs (nuclear power plants) implement their management pr...

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Veröffentlicht in:Nuclear engineering and technology 2020, 52(9), , pp.2119-2129
Hauptverfasser: Yun, Hun, Moon, Seung-Jae, Oh, Young-Jin
Format: Artikel
Sprache:eng
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Zusammenfassung:Flow-accelerated corrosion (FAC), liquid droplet impingement erosion (LDIE), cavitation and flashing can cause continuous wall-thinning in nuclear secondary pipes. In order to prevent pipe rupture events resulting from the wall-thinning, most NPPs (nuclear power plants) implement their management programs, which include periodic thickness inspection using UT (ultrasonic test). Meanwhile, it is well known in field experiences that the thickness measurement errors (or deviations) are often comparable with the amount of thickness reduction. Because of these errors, it is difficult to estimate wall-thinning exactly whether the significant thinning has occurred in the inspected components or not. In the previous study, the authors presented an approximate estimation procedure as the first step for thickness measurement deviations at each inspected component and the statistical & quantitative characteristics of the measurement deviations using plant experience data. In this study, statistical significance was quantified for the current methods used for wall-thinning determination. Also, the authors proposed new estimation procedures for determining local wall-thinning to overcome the weakness of the current methods, in which the proposed procedure is based on analysis of variance (ANOVA) method using sub-grouping of measured thinning values at all measurement grids. The new procedures were also quantified for their statistical significance. As the results, it is confirmed that the new methods have better estimation confidence than the methods having used until now.
ISSN:1738-5733
2234-358X
DOI:10.1016/j.net.2020.03.001