Growth Of Corrosion Defects Research Articles

Effects of external corrosion defect growth on wall pipeline under internal pressure and various defect sizes with mechano‐electrochemical interaction

AbstractThe assessment of the impact of corrosion defects on the integrity and safe operation of through‐wall pipelines is critical. In this work, a finite element‐based model was developed to study the mechano‐electrochemical (M‐E) effects of external corrosion defects on 10CrMoAl steel pipelines. The effects of parameters such as defect sizes and internal pressures on pipeline conditions are also studied. The results show that increased corrosion depth leads to stress concentration at the center of the defect. The increase in internal pressure causes local plastic deformation and anodic current density concentration at the inner edge of the defect and in the adjacent areas of the defect. M‐E interaction causes the growth of corrosion defects significantly at higher internal pressures. The corrosion defects increase with time. As the pressure increases, the failure pressure of the pipeline gradually decreases. The influence of defect depth on the corrosion rate at the defect is greater than that of defect width. With increasing in internal pressure and corrosion defect size, it will lead to more severe plastic deformation, resulting in accelerated corrosion.

Effects of corrosion defect growth on submarine pipeline under operating pressure and axial displacement

Corrosion defect growth with time seriously threatens the safe operation of submarine pipelines and the study about its effects on strength degradation is essential for pipeline integrity assessment. In this work, a three-dimensional finite element model was developed to simulate the defect growth on pipeline exposed to sea mud environment based on multiphysical field coupling and deformed geometry. The deeper defect and larger operating pressure (OP) enhance local stress concentration and corrosion rate. The corrosion rate decreases first and then increases with the axial displacement (AD) of pipeline. The OP causes pipelines to corrode preferentially on the defect bottom while AD trends to form uniform corrosion on the total defect surface. There is an anti-synergistic effect between OP and AD on pipe thinning. A residual service life estimation method based on the unsteady multi physical field simulation was proposed for the submarine pipeline.

Finite element modeling of corrosion defect growth and failure pressure prediction of pipelines

Corrosion defect on pipelines shows a time-dependent growth in service environments. Prediction of corrosion defect growth and failure pressure of corroded pipelines as a function of time has remained a big challenge to industry. In this work, a finite element (FE)-based model was developed to quantify 3-dimensional (3-D) growth of a corrosion defect on an X100 steel pipe and predict the failure pressure as a function of time by considering a mechano-electrochemical (M-E) interaction. Parametric effects, including internal pressure, axial tensile stress and initial defect length, were investigated. Distributions of von Mises stress and anodic current density (i.e., corrosion rate) at the corrosion defect were determined. Results demonstrated that the growth rate of the corrosion defect followed the order of defect depth > defect length > defect width. With increased stresses resulted from internal pressure and axial tensile loading, the maximum defect depth and defect length increased apparently, but the defect width changed slightly. For example, the defect length increased by 11% and 16% after 10 years of service at internal pressures of 18 MPa and 26 MPa, respectively, and the defect depth increased by 27% and 34% correspondingly. However, the corrosion width increase maintained at about 22% when the internal pressure increased from 18 MPa to 26 MPa. As the corrosion defect grew with time, the failure pressure of the pipeline decreased. It is expected that, upon further validation by substantial data from field and the laboratory, the developed model could contribute to improved pipeline integrity management.

Uncertainty in reliability of thick high strength pipelines with corrosion defects subjected to internal pressure

This paper assesses the uncertainties in the structural reliability levels of thick high strength pipelines with active corrosion defects subjected to internal pressure. A general burst strength model and other applicable to thick high strength pipes are adopted and their predictions uncertainty over a wide range of corrosion defect depths is assessed by Monte Carlo simulation. A 3-parameter lognormal probability model is adopted to describe the uncertainty in the burst pressure. Global and corrosion defect depth dependent model uncertainty factors are derived by comparing the predictions of the burst strength models with burst test data of corroded pipes. Burst failure limit states are formulated in terms of the burst strength models and corresponding model uncertainty factors. Linear and nonlinear corrosion models are adopted to describe the growth of corrosion defects. The uncertainty on the safety levels of pipelines due to the uncertainty on the corrosion defect, corrosion growth model and reliability algorithm is assessed. The results presented for the two strength models provide insights on the adequacy of a thick high strength burst model for probabilistic applications in comparison with a general burst strength model.

Modelling of mechano-electrochemical interaction at overlapped corrosion defects and the implication on pipeline failure prediction

Defect assessment is essential at determination of fitness-for-service of pipelines. While various codes and methods have been developed, none of them focuses on overlapped corrosion defects, especially the investigation of local mechanical-chemical interaction at the overlapped defects. In this work, a finite element-based multi-physics one-way coupling model was developed to simulate and determine the mechano-electrochemical (M-E) interaction at overlapped corrosion defects on an X46 steel pipeline. It is found that the presence of overlapped corrosion defects results in a local stress concentration and enhanced M-E interaction effect on corrosion defect growth. The maximum stress always generates at the corner of the bottom defect. The enhanced M-E interaction causes an accelerated corrosion at the bottom defect, resulting in the rapid defect growth to cause pipeline leaking. The geometry of corrosion defects, especially the defect depth and length, affects local stress concentration and the M-E interaction. With the increasing length and depth of either top or bottom corrosion defects, the overall stress level increases, but the effect is more apparent at the bottom defect. Similarly, the increased M-E interaction due to the increasing length and depth of the corrosion defects results in a more accelerated corrosion at the bottom defect than the top one.

A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study

Understanding the corrosion risk of a pipeline is vital for maintaining health, safety and the environment. This study implemented a data-driven machine learning approach that relied on Principal Component Analysis (PCA), Particle Swarm Optimization (PSO), Feed-Forward Artificial Neural Network (FFANN), Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) to estimate the corrosion defect depth growth of aged pipelines. By modifying the hyperparameters of the FFANN algorithm with PSO and using PCA to transform the operating variables of the pipelines, different Machine Learning (ML) models were developed and tested for the X52 grade of pipeline. A comparative analysis of the computational accuracy of the corrosion defect growth was estimated for the PCA transformed and non-transformed parametric values of the training data to know the influence of the PCA transformation on the accuracy of the models. The result of the analysis showed that the ML modelling with PCA transformed data has an accuracy that is 3.52 to 5.32 times better than those carried out without PCA transformation. Again, the PCA transformed GBM model was found to have the best modeling accuracy amongst the tested algorithms; hence, it was used for computing the future corrosion defect depth growth of the pipelines. This helped to compute the corrosion risks using the failure probabilities at different lifecycle phases of the asset. The excerpts from the results of this study indicate that my technique is vital for the prognostic health monitoring of pipelines because it will provide information for maintenance and inspection planning.

Sensitivity of system reliability of corroding pipelines to modeling of stochastic growth of corrosion defects

The time-dependent system reliability of pressurized pipeline segments containing multiple active corrosion defects is evaluated to investigate the sensitivity of the system reliability to various modeling options regarding the corrosion growth. The gamma and inverse Gaussian processes are employed to model the defect growth, whereas the dependence among the growths of different defects is characterized using the Gaussian copula and sum-of-stochastic-process approach. The analysis results indicate that all else being equal the system reliability is insensitive to using the Gaussian copula or sum-of-stochastic-process approach to model the dependence among the growths of different defects. Furthermore, using the inverse Gaussian process to model the defect growth leads to slightly higher failure probabilities than using the gamma process. Finally, the results suggest that the use of one year as the time increment to simulate Gaussian copula-based dependent defect growths in the reliability analysis is adequate for the relatively slow corrosion growth that is typical for buried pipelines.

A finite element based model for prediction of corrosion defect growth on pipelines

Growth of corrosion defects has been identified as the primary mechanism resulting in pipeline perforation and leaking. In this work, a finite element model was developed to simulate and predict the time-dependent growth of corrosion defects on pipelines in a near-neutral pH bicarbonate solution trapped under disbonded coating. The synergism of stress and local corrosion reaction was determined quantitatively. It is demonstrated that a mechano-electrochemical effect developed at the defect is critical to growth of the defect, resulting in formation of a crack-like flaw at the defect center. The time dependence of the local stress and corrosion current density at the defect is featured with three stages, i.e., a linear increase of local elastic stress and the negligible corrosion enhancement under the testing condition, a slow increase of both local stress and corrosion current density under mild plastic deformation, and a rapid increase of local stress and corrosion current density under a high plastic deformation.

Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data

Stochastic process-based models are developed to characterize the generation and growth of metal-loss corrosion defects on oil and gas steel pipelines. The generation of corrosion defects over time is characterized by the non-homogenous Poisson process, and the growth of depths of individual defects is modeled by the non-homogenous gamma process (NHGP). The defect generation and growth models are formulated in a hierarchical Bayesian framework, whereby the parameters of the models are evaluated from the in-line inspection (ILI) data through the Bayesian updating by accounting for the probability of detection (POD) and measurement errors associated with the ILI data. The Markov Chain Monte Carlo (MCMC) simulation in conjunction with the data augmentation (DA) technique is employed to carry out the Bayesian updating. Numerical examples that involve simulated ILI data are used to illustrate and validate the proposed methodology.

Bayesian dynamic linear model for growth of corrosion defects on energy pipelines

This paper describes the use of the second-order polynomial dynamic linear model (DLM) to characterize the growth of the depth of corrosion defects on energy pipelines using data obtained from multiple high-resolution in-line inspections (ILI). The growth model incorporates the measurement error of the ILI tools and captures the temporal variability of the corrosion growth by allowing the artificially-constructed average growth rate between two successive inspections to vary with time. The Markov Chain Monte Carlo simulation is employed to carry out the Bayesian updating of the growth model and evaluate the posterior distributions of the model parameters. An example involving real ILI data collected from an in-service natural gas pipeline is employed to illustrate and validate the growth model. The analysis results show that the defect depths predicted by the proposed model agree well with the actual depths and are more accurate than those predicted by the Gamma process- and Inverse Gaussian process-based growth models reported in the literature.

Time-Dependent Corrosion Growth Modeling Using Multiple In-Line Inspection Data

This paper describes a nonhomogeneous gamma process-based model to characterize the growth of the depth of corrosion defect on oil and gas pipelines. All the parameters in the growth model are assumed to be uncertain; the probabilistic characteristics of these parameters are evaluated using the hierarchical Bayesian methodology by incorporating the defect information reported by the multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random measurement error associated with the ILI tools as well as the correlation between the measurement errors associated with different ILI tools are taken into account in the analysis. The application of the model is illustrated using an example involving real ILI data on a pipeline that is currently in service. The results suggest that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Probabilistic characterisation of metal-loss corrosion growth on underground pipelines based on geometric Brownian motion process

This article describes a geometric Brownian motion process-based model to characterise the growth rate of the depth of corrosion defects on underground steel pipelines based on inspection data subjected to measurement uncertainties. To account for the uncertainties from different sources, the hierarchical Bayesian method is used to formulate the growth model, and the Markov Chain Monte Carlo simulation techniques are used to numerically evaluate the probabilistic characteristics of the model parameters. The growth model considers the bias and random scattering error associated with the in-line inspection (ILI) tool as well as the correlations between the random scattering errors associated with different ILI tools. The application of the growth model is illustrated through an example involving real ILI data collected from an in-service pipeline in Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines. The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter. The measurement errors associated with the ILI tools are considered in the Bayesian inference. The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta, Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data

This paper describes an inverse Gaussian process-based model to characterize the growth of the depth of corrosion defects on underground energy pipelines based on inspection data. The model is formulated in a hierarchical Bayesian framework, which allows consideration of uncertainties from different sources. The Markov Chain Monte Carlo (MCMC) simulation techniques are used to evaluate the probabilistic characteristics of the model parameters by incorporating the defect depths reported by multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random scattering error associated with the ILI tool as well as the correlation between the random scattering errors associated with different ILI tools are considered in the analysis. An example involving real ILI data collected from an in-service pipeline is employed to illustrate the application of the growth model. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

Markovian model for the growth of corrosion defects and its application for the control of pipeline integrity

A Markovian model for the growth parameters of defects (length, depth, and width) based on the use of in-pipe and verification measurements has been developed. Its consistency and adequacy were verified by an independent method based on the combination of a differential equation describing the growth of defect parameters with the modeling of the initial sizes of defects by the Monte Carlo method. The developed model allows for the determination of the probability of the existence of defect parameters in a determined state at any time and the intensity of the transition from one state to another. The model provides the possibility to obtain an evaluation of the conditional probability for the breakdown of any section of the pipeline by the defect growth parameters obtained as a result of in-pipe crack detection. Based on the developed model, an algorithm for the time optimization of the subsequent inspection/repair of the pipeline was constructed. The methodology was approved on a virtual real pipeline with the use of results of in-pipe and verification measurements.

KINETICS OF CORROSION DEFECT GROWTH IN MO MEDIA

The development of magneto-optic media as a commercially viable product included identification of means to protect the rare earth transition metal (RE-TM) thin film from corrosion. The results of studies used to achieve stability have been analyzed to gain a better scientific understanding of the processes involved in the growth of corrosion defects. Growth rate is exponentially temperature dependent, strongly accelerated by increases in relative humidity and the presence of impurities like Cl, and greatly reduced by the addition of certain alloying elements. Microscopic and microchemical examination showed that if present, Cl was highly concentrated in the corrosion site. Significant diffusion of the thin film constituent elements accompanied corrosion. The growth of individual defects occured by the formation of appendages. The following hypotheses were made concerning growth: 1.) Temperature and humidity dependency are a result of effects on transport rate in the electrolyte, 2.) Impurities like Cl increase rates by impeding the formation of oxide protective films whereas alloying additions like Ta improve the capability to form protective oxide films.