Corrosion fatigue life prediction model for 10CrNiCu shipbuilding steel under authentic marine environments
Source abstract
Abstract
Corrosion fatigue is considered as one of the most detrimental threat to steel structures subjected to the coupled effects of cyclic load and corrosive environment, but the scientific data collected in an authentic service environment has been rarely reported. The corrosion fatigue behaviors of 10CrNiCu shipbuilding steel with a prefabricated stress field under authentic marine environments was investigated in this study. The results demonstrate that the shapes of pits in steel specimens can be simplified as conical, trapezoidal and ellipsoidal. Furthermore, the shapes, radius-to-depth ratios and corrosion rates of these pits are not consistent under different wettability conditions. Notably, the specimens corrode the most severely under semi-wettable conditions. A corrosion fatigue life prediction model for 10CrNiCu shipbuilding steel has been proposed by leveraging the profound principles of continuum damage mechanics. When the corrosion duration reaches 10 years, the corrosion fatigue life of steel specimens under wettable conditions will be decreased by 36.3 % in comparison with that of uncorroded specimens. This study provides a constructive insight on the interaction mechanism of mechanical fatigue stress and corrosion environment and the more realistic lifetime estimation of steel structures under authentic marine environments. • The pitting morphology of 10CrNiCu shipbuilding steel can be simplified into cone, trapezium, and ellipsoidal. • Semi-wettable condition represents the most severe corrosion among various wetting conditions. • The corrosion fatigue life prediction model has been verified to be feasible and applicable.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Structural Integrity and Reliability Analysis · Corrosion Behavior and Inhibition · Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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