Probability analysis of PIT distribution on corroded ballast tank

Nugroho, Firman A.; Braun, Moritz; Ehlers, Sören

doi:10.1016/j.oceaneng.2021.108958

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…Also, the kurtosis value demonstrated that the data distribution peaked more due to pit formations. These values agreed with similar results in previous studies where corrosion pits were characterized 55,56 . This is critical information, as the depth and distribution of pits significantly impact the corrosion's structural effect.…”

Section: Resultssupporting

confidence: 91%

“…These values agreed with similar results in previous studies where corrosion pits were characterized. 55,56 This is critical information, as the depth and distribution of pits significantly impact the corrosion's structural effect. Thus, the probability density distribution of variable Z (x, y) for the SSC precorroded specimen is presented in Figure 9.…”

Section: Resultsmentioning

confidence: 99%

“…Deep pits show higher stress concentrations, and the characteristics of corrosion pits or localized corrosion behavior can be fundamentally understood through skewness and kurtosis assessments. 56,57 Statistical analysis of the probability distributions have been employed as a valuable tool in understanding corrosion pits, which also involved time. This research establishes a connection between the pit characterization, the localized stresses, and its relationship to cyclic loading under corrosive conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Also, significant volumetric changes are observed over time, signifying the transition from pits to more uniform corrosion. Deep pits show higher stress concentrations, and the characteristics of corrosion pits or localized corrosion behavior can be fundamentally understood through skewness and kurtosis assessments 56,57 …”

Section: Introductionmentioning

confidence: 99%

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Corrosion surface morphology‐based methodology for fatigue assessment of offshore welded structures

Okenyi,

Afazov,

Mansfield

et al. 2023

Fatigue Fract Eng Mat Struct

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This work employed a novel corrosion‐based fatigue model to determine the fatigue life of offshore welded structures to enable the fatigue assessment of welds under corrosive conditions. In addition to the material's ultimate strength, endurance limit, and stress ratio (mean stress effect), the model includes a corrosion factor concept to account for the impact of corrosion pits on the fatigue performance of welded S355 steel, which is the novel contribution in this paper. X‐ray computed tomography scans of corroded S355 specimens in a salt spray chamber were characterized. Surface texture characterization was employed to obtain surface roughness, size, and aspect ratio of corrosion pits. The corrosion factor was determined based on notch and surface fatigue theories using the characterized pit size, aspect ratio, and surface roughness. Fatigue S‐N curves were then predicted for critical pits and compared against the fatigue code DNVGL‐RP‐C203 and experimental data from the literature. The novel approach combining corrosion characterization method with corrosion‐based fatigue model for the prediction of fatigue S‐N curves provided a minor deviation of only 2.8% between predicted and measured data. This approach can potentially be integrated into predictive frameworks for the remaining life assessment of offshore structures.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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