Abstract
Corrosion and crack defects often exist at the same time in pipelines. The interaction impact between these defects could potentially affect the growth of the fatigue crack. In this paper, a crack propagation method is proposed for pipelines with interacting corrosion and crack defects. The finite element models are built to obtain the Stress Intensity Factors (SIFs) for fatigue crack. SIF interaction impact ratio is introduced to describe the interaction effect of corrosion on fatigue crack. Two approaches based on extreme gradient boosting (XGBoost) are proposed in this paper to predict the SIF interaction impact ratio at the deepest point of the crack defect for pipelines with interacting corrosion and crack defects. Crack size, corrosion size and the axial distance between these two defects are the factors that have an impact on the growth of the fatigue crack, and so they are considered as the input of XGBoost models. Based on the synthetic samples from finite element modeling, it has been proved that the proposed approaches can effectively predict the SIF interaction impact ratio with relatively high accuracy. The crack propagation models are built based on the proposed XGBoost models, Paris’ law and corrosion growth model. Sensitivity analyses regarding corrosion initial depth and axial distance between defects are performed. The proposed method can support pipeline integrity management by linking the crack propagation model with corrosion size, crack size and the axial distance. The problem of how the interaction between corrosion and crack defects impacts crack defect growth is investigated.
Highlights
Received: 10 January 2022Pipelines are widely used to transport oil and gas products over long distances
From all these four figures, it can be found that the Stress Intensity Factors (SIFs) interaction impact ratio is overall decreasing as the axial distance increases
This is because the corrosion defect moves away from the stress concentration zone of the crack defect
Summary
Received: 10 January 2022Pipelines are widely used to transport oil and gas products over long distances. Researchers are committed to constructing more accurate and effective health management models and improving the integrity management system of pipelines. Some researchers use stochastic processes to describe uncertainties associated with the degradation of wall thickness incurred by corrosion defects. Wang et al [4] proposed a stochastic corrosion growth model using the geometric Brownian bridge process. Ossai et al [5] used a non-homogeneous linear growth pure birth Markov model to predict the degradation of internal corrosion defects in oil and gas pipelines. Bazan and Beck [6] employed a Poisson square wave process to describe the corrosion growth rate and compared the proposed non-linear stochastic model with the linear corrosion growth model. Qin et al [7] proposed a corrosion growth model based on Inverse Gaussian process and Markov Chain Monte
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