Corrosion Defects Research Articles

Probabilistic burst pressure prediction model for pipelines with single crack-like defect

While several models have been developed to predict the failure pressure of pipelines with corrosion defects, relatively few models are available for pipelines with cracking defects. The goal of this study is to develop an unbiased and accurate probabilistic burst failure pressure model for thin-walled pipelines containing a single, longitudinally-oriented, crack-like defect. A comprehensive database is first established, consisting of experimental and numerical data collected from literature as well as numerical data obtained from the extended finite element method (XFEM) conducted in this study. The failure pressure prediction model is developed by adding a correction factor to the modified Ln-Secant method, a semi-empirical model previously developed. The correction factor is modeled using a multivariate linear regression that considers the pipe properties and defect characteristics as independent variables. The proposed model delivers unbiased and less variable failure pressure predictions. A case study of an example pipeline with a crack like defect shows that the failure pressure model plays a critical role in the risk management of pipelines through a life-cycle cost analysis.

Probabilistic-based burst failure mechanism analysis and risk assessment of pipelines with random non-uniform corrosion defects, considering the interacting effects

Corrosion defects were primary causes for pipeline burst failures. Traditional methodologies simplified the realistic irregular-shaped defects as uniform ones, ignoring the effects of random morphologies on failure behaviours, which caused deviations in remaining strength pb estimation and reliability analysis. Addressing this issue, through coupling random field, non-linear finite element analysis and Monte-Carlo Simulation, an integrated methodology was developed to describe failure behaviours of pipelines with random defects. The failure mechanism and reliability analysis were performed. The pbs for random defects exhibited significant variabilities, of which the probability distribution patterns altered with different corrosion depths attributed to multiple failure modes. The main value of pb for random defects was lower than that of uniform defects, especially for deeper corrosion and interacting defects. At maximum, this reduction attained over 28%; The spatial variation gradient of remaining wall thickness dominated failure developments, based on which three failure modes were revealed; The wall thickness, corrosion degree and interacting effects were influential on failure probabilities Pfs. An average failure risk of Pf over 0.6 would be suffered for employing traditional methodologies. Polynomial chaos expansion models were fitted to estimate pb from corrosion morphologies, showing reasonable accuracies. Besides, the performance variations with different cases were discussed.

Study on Assessment Method of Failure Pressure for Pipelines with Colony Corrosion Defects Based on Failure Location

Evaluating the burst pressure of corrosion cluster defects necessitates considering the interaction of contiguous defects. The importance of this interaction cannot be emphasized enough, as it plays a crucial role in determining the failure pressure of such pipelines. Current methods for assessing the failure pressure of corrosion cluster defects have drawbacks, including complex evaluation procedures and limited applicability. In this research, the failure mode and location of corrosion clusters with two or more defects are studied with a burst experiment on a full-scaled pipeline. Based on the failure position of the corrosion cluster, a “center failure location” method is proposed to estimate the burst pressure of colony corrosion defects. The method takes the defect in the failure position as the center; the influence of contiguous defects that interact with the central defect on its failure pressure is considered, and subsequently, the burst pressure of colony corrosion centered on the failure location is evaluated. In contrast with the current assessment methods, while the proposed approach does not reduce prediction errors, it requires fewer evaluation conditions and is operationally simpler and more versatile.

Framework for Lean Implementation Through Fuzzy AHP-COPRAS Integrated Approach

Manufacturing industries strive hard to improve their productivity to meet the highly demanding business market. Productivity can be improved by effective use of resources, elimination of wastes, process flow enhancement, and continuous improvement. There are strategies and tools to improve productivity but industries face the problem of selecting appropriate strategies. Lean manufacturing philosophy helps the manufacturing industries to enhance their productivity aspects by identifying and eliminating the wastes using specific tools/techniques. This article presents an integrated framework for the lean implementation process in the steel processing industry. The value stream mapping (VSM) tool is used to identify the wastes at each stage of the production process. Lean tools are identified from literature based on the wastes identified from the current state. For prioritizing the lean tools based on the positive correlation on one another, fuzzy integrated analytic hierarchy process and complex proportional assessment of alternatives are used. A future state value stream map is developed to illustrate the improvements achieved due to the implementation of selected lean tools. Comparison of present and future state VSM shows a great reduction of wastes, such as scale waste (50%), miss roll defects (72.7%), size variation defect (62%), corrosion defect, and improvements in talk time and cleanliness.

Novel Prediction Model of Tensile Strain Capacity for Pipelines with Corrosion Defects

Novel Prediction Model of Tensile Strain Capacity for Pipelines with Corrosion Defects

Probabilistic finite element-based reliability of corroded pipelines with interacting corrosion cluster defects

The random distribution of pitting corrosion defects in pipelines normally leads to interacting cluster defects that behave noticeably different from single metal loss defects. Generally, the probabilistic approaches employ explicit burst pressure limit states for corroded pipelines subjected to only internal pressure. However, a significant level of conservatism is typically associated with the probabilistic assessments of corroded pipelines using the closed-form explicit limit state functions, which presents considerable challenges in maintenance planning and risk management. Therefore, this paper proposes a pathway for developing efficient performance functions for the assessment of interacting pipeline corrosion clustering defects using probabilistic finite element-based reliability method. This seven-stage framework combines multiple uncertainty representation schemes to evaluate the probability of failure. The impact of the critical design variables such as the elastic and plastic material properties, corrosion features, and interacting cluster defect characterisations are identified to guide the burst pressure design, operations and maintenance optimisation. The employed surrogate-based active learning reliability approach yielded an efficient probability estimate at a lesser computational cost than the simulation-based reliability methods. The proposed framework reduces the conservatism and computational cost related with explicit burst pressure limit state functions for corroded pipelines and offers informed decision-making on risk and maintenance management.

Effect of Tensile Stress on Self-Magnetic Leakage Field in Carbon Steel Pipe Material

Metal magnetic memory (MMM) technique is a promising non-destructive technology for determining the state of material deformation and structural defect in carbon steel pipe by measuring the self-magnetic leakage field (SMLF). The relationship between SMLF and stress concentration of cracked specimens has been extensively researched, but little is known about corrosion defects in carbon steel pipes, such as circular holes. A static tensile test and MMM measurement were performed on the ASTM A106 Gr. B steel to investigate its applicability in predicting material deformation state and structural defect. The tensile load makes the distribution of self-magnetic leakage field, SMLF intensities in the elastic stage regular. In the defect zone of defective specimens, a peak-trough in the SMLF intensity in the tangential component, Hp(x), and a peak in the normal component, Hp(y), were observed. As yield approached, the SMLF intensities remained unchanged. During plastic deformation, dislocation slip and dislocation interaction rarely change the SMLF intensities and its gradient. The SMLF intensities and its gradient rise as the tensile load approach its ultimate strength. The distribution of SMLF intensities show a peak feature in the Hp(x), and a peak-trough in the Hp(y) as the specimen fractures. Variations in the magnetic and stress fields of carbon steel pipe are caused by structural defect. As a result, the SMLF intensity varies. The method described in this study can be applied to MMM technique to detect material deformation states and defect formation in ASTM A106 Gr. B steel at low cost and with minimal equipment.

Study on burst pressure of subsea pipelines with asymmetrical corrosion defects

As an efficient and inexpensive tool, pipelines are widely used in the transportation of hydrogen, carbon dioxide, oil and gas. The subsea pipelines are inevitably corroded because of the transportation environment and medium. The scholars have studied burst pressure of pipelines with simple corrosion defects, However, there are many research gaps in the burst pressure analysis of pipelines with complex corrosion defects. In this paper, the burst pressure of subsea pipelines with asymmetrical corrosion defects is studied. Firstly, a model of pipelines with asymmetrical and external corrosion defects is established. Then, the finite element method is conducted to analyze the burst capability of subsea pipelines with asymmetrical corrosion defects. Next, a novel method of simplifying asymmetrical corrosion defects is proposed. In the end, a modified DNV equation is presented to predict the burst pressure of subsea pipelines with asymmetrical corrosion defects. This study can provide a novel idea for obtaining the burst pressure of subsea pipelines with complex corrosion defects.

Importance sampling-based probabilistic performance modeling of low-, mid- and high-strength pipelines under coupling effect of hydrogen-induced damage and corrosion

In-service long-distance pipelines are a practical approach to implementing large-scale hydrogen transportation under the context of energy transition. However, the hydrogen-induced damage (HID) and corrosion would deteriorate the material properties and load-bearing capacity of the pipelines. In the present study, the coupling effect of HID and corrosion are probabilistically incorporated into the mechanical characterization models for low-, mid- and high-strength pipelines. Spatiotemporal stochastic growth is modeled using the stochastic gamma processes with significantly affected defect geometric parameters to facilitate the uncertain performance status of pipelines. All unknown parameters of the proposed method can be inferred by inducing in-line inspection (ILI) data. The computational framework for importance sampling (IS) has been developed to estimate the system failure probability of corroded natural gas pipelines considering HID. Apply several programs to conduct probability analysis to demonstrate the applicability and effectiveness of the proposed method. The results demonstrate that HID affects the failure behavior and safety probability state of pipelines containing corrosion defects. The performances of various strength pipelines have significant differences in deterioration paths due to HID, causing additional maintenance or potentially disastrous consequences. The proposed method has been proven to capture the potential uncertainties of HID and corrosion on different-strength steel pipelines, which is beneficial for the lifecycle management of blended hydrogen natural gas pipelines.

An investigation on the effect of widespread internal corrosion defects on the collapse pressure of subsea pipelines

Deepwater subsea pipelines are an essential part of offshore oil and gas infrastructure, and it is a statutory requirement for operators to monitor the degradation of pipelines left-in-place at cessation of production. Collapse is one of the failure modes for subsea pipelines subjected to external pressure. Surface corrosion defects in subsea pipelines, either while still operational or after cessation of production, usually manifest as widespread or localized corrosion pits that are randomly spread on the steel surface. Extensive work has been done on predicting the collapse pressure of pipelines containing single corrosion defects however, the same cannot be said for randomly distributed corrosion defects. In this study, we investigate, for the first time, the effects of the geometry and coverage extent of widespread internal corrosion defects with random spatial distribution on the collapse pressure of pipelines using finite element analysis. The results show that corrosion defect depth and coverage surface area are the main defect geometric parameters that significantly influence the collapse strength of pipelines with randomly distributed internal corrosion. An empirical function for predicting the collapse pressure of a corroded pipeline is developed to reduce the need for recurrent computational effort.

Influence of operating hours of water pipes in aggressive mediums to degradation of structural steel

Ruining the design of water pipelines can lead to negative economic and environmental consequences. The usual scientific, technical and technological developments to ensure reliable corrosion-mechanical resistance and durability of water pipelines are not sufficiently systematic, so there is no need to report the reasons, minds and mechanisms of corrosion-mechanical servicing. Laboratory and experimental investigations were carried out in relation to the operation of structural steels on the structure and instead of schoolhouses. The causes and mechanisms of degradation of metal structures due to routine operation in chemically aggressive technological media were studied. X-ray diffraction studies have established that, with increasing service life of pipes, the surface metal balls adsorb a significant amount of gases. It manifests itself especially strongly in the area of corrosion defects (stains, pittings). During regular operation, the metal walls of the pipes are heavily flooded, which leads to a hardening, increased hardness and decreased ductility. There is a significant change in the mass fraction of cementite in the metal, which indicates a decrease in the value of the material and a decrease in its resistance to corrosion-mechanical damage. Such research is a pioneer in the corrosion-mechanical resistance of metal and its degradation during heavy use in the presence of significant pressures and in the immediate presence of aggressive media.

Exploration and Application of Acoustic Emission Detection Technology in Leakage Detection of the Bottom Plate of Crude Oil Metal Storage Tanks

The use of acoustic emission detection technology can conduct online and non-stop detection and evaluation of crude oil metal storage tanks, achieving the integrity detection and evaluation of storage tanks. In order to explore the applicability of acoustic emission detection technology in the leakage detection of the bottom plate of crude oil metal storage tanks, this project carried out online acoustic emission detection of crude oil metal storage tanks, magnetic flux leakage detection after tank opening and cleaning, and compared and analyzed the results of two detection technologies. The adaptability of online acoustic emission detection technology was analyzed and explored. It was found that acoustic emission technology can qualitatively evaluate the corrosion of tank bottom plates, but cannot accurately locate corrosion defects. For tanks with severe corrosion that require maintenance, it is still necessary to open the tank and use magnetic flux leakage and other detection techniques to determine the location of defects. Through comparative analysis of acoustic emission testing technology and magnetic flux leakage testing technology, the results obtained from acoustic emission testing technology are conservative compared to the actual results. It is recommended to upgrade the evaluation of corrosion degree for the acoustic emission evaluation results of crude oil metal storage tanks.

Dense metal corrosion depth estimation

Introduction: Metal corrosion detection is important for protecting lives and property. X-ray inspection systems are widely used because of their good penetrability and visual presentation capability. They can visually display both external and internal corrosion defects. However, existing X-ray-based defect detection methods cannot present and estimate the dense corrosion depths. To solve this problem, we propose a dense metal corrosion depth estimation method based on image segmentation and inpainting.Methods: The proposed method employs an image segmentation module to segment metal corrosion defects and an image inpainting module to remove these segmented defects. It then calculates the pixel-level dense corrosion depths using the X-ray images before and after inpainting. Moreover, to address the difficulty of acquiring training images with ground-truth dense corrosion depth annotations, we propose a virtual data generation method for creating virtual corroded metal X-ray images and their corresponding ground-truth annotations.Results: Experiments on both virtual and real datasets show that the proposed method successfully achieves accurate dense metal corrosion depth estimation.Discussion: In conclusion, the proposed virtual data generation method can provide effective and sufficient training samples, and the proposed dense metal corrosion depth estimation framework can produce accurate dense corrosion depths.

Experimental investigation for multi-defect detection of industrial pipeline based on passive infrared thermal imaging analysis: A case study

Corrosion of pipe walls is a major hidden hazard for the safety of chemical enterprises; therefore, detection of the depth of pipe wall corrosion defects is extremely important. In this study, passive thermal imaging technology was used to detect and evaluate corrosion defects of different shapes in pipe walls and of different depths for the same shape. Three-dimensional finite element models of defects with different depths were established using COMSOL, and the thermal response mechanisms of defects with different depths and the effects of defect depth and shape on heat transfer were analyzed. The thermal response mechanism of defects with different shapes and depths (1–3 mm) was also analyzed experimentally and compared with that of defect-free parts to analyze the safety of the pipeline and verify the results of the numerical simulation analysis. The results indicated that the level of fluid temperature determined the accuracy of the test, and at the same temperature, the defects at a depth of 3 mm exhibited multiple peaks and a large peak area. However, defects with a depth of 2 mm exhibited the opposite behavior, and the heating rate of 3-mm deep defects during the temperature change was 1.08 times higher than that for 2 mm-deep defects. This study provides a reference for detecting leaks in chemical pipelines.

Corrosion defect segmentation method based on superpixel feature cascade

To solve the segmentation problem caused by the small number of feature points and the change of image brightness on the surface of the storage tank, a corrosion defect segmentation method based on the superpixel feature cascade is proposed in this paper. First, the image is segmented to generate superpixels and color and texture features are extracted in the superpixel region and concatenated with domain superpixel features to form superpixel level context features; Then, a plurality of superpixels are labeled according to the pixel range of corrosion defects and the labeling results are obtained; Then, the relationship between superpixels is modeled by the full connection CRF model to optimize the classification results; Finally, the classification result of the input image is refined to generate a segmented image. The effectiveness of the segmentation method is verified and analyzed by designing comparative experiments.

Estimation of burst pressure of pipelines with interacting corrosion clusters based on machine learning models

Pipeline corrosion defects mostly appear in a colony such that they interact to reduce the failure pressure, which is not defined by features of a single corrosion defect. The huge amount of corrosion defects captured by in-line inspection tools including the variability of defect profile in pipelines and the dependence of the reliability assessment on such data pose significant research challenges in performance assurance. This highlights the need for computationally efficient modelling schemes to estimate the burst pressure of pipelines affected by both longitudinal and circumferential interacting corrosion defects. In the present paper, a novel approach is proposed for this purpose by combining supervised machine learning methods with 25 numerical models of corroded pipelines, validated with experimental results available from literature. Additionally, six improved composite defect shapes are proposed, resulting in 150 models to examine the non-linear behaviour of interacting corrosion defects by capturing the real the defect profiles captured by the In-line Inspection tools. The predicted failure pressures from the developed numerical models produced an absolute mean deviation of not exceeding 2.03% and 2.2% from the experimental burst pressure and the modified Mixed Type Interaction approach respectively, better than published results from the literature. Notably, the predicted failure pressures based on real pipeline data, infused with the generated artificial neural networks and non-linear regression models provide a total mean deviation of 3.1% and 7.3% respectively, thereby providing a path for effective maintenance planning.

THE INFLUENCE OF PIPE STRESS STATE ON THE PITTING CORROSION RATE OF OILFIELD PIPELINES

An analytical review is given on the influence of the stressstrain state on the process of corrosion pitsformation in the process of internal corrosion of the field oil pipelines wall. The views of domestic and foreign researchers on the causes of the corrosion process intensification under the stresses action, as well as on the mechanisms of the stress-strain state influence on the process of pitting formation, are presented. Two main reasons for the intensification of corrosion under the stress-strain state influence are indicated: a decrease in the thermodynamic stability of metals and a deterioration in the stability of protective films formed on the stressed metal surface during corrosion. Existing concepts are considered regarding the effect of increasing the number of pitting formation zones under the action of a stress-strain state, and also put forward their own assumptions on this issue. It was found that a noticeable acceleration of the anode process is observed only when the critical level of stresses from the yield point is reached, under the conditions of field oil pipelines operation. The zones of accumulation of such a level of stresses are surface defects — stress concentrators. The process of deepening corrosion defects is accompanied by an increase in the stress concentration in the metal of their bottom part, which further accelerates the growth of their depth (the synergistic effect of mechanical and electrochemical factors on the growth rate of a surface corrosion defect). Due to the deterioration of the protective properties of passive films under stress-strain conditions, the transition from the activation stage of defect growth (when repassivation is possible) to the diffusion stage is facilitated, which leads to the formation of a larger number of steadily growing corrosion defects. At the same time, the level of accumulated stresses depends on the shape of the formed corrosion defects; with an increase in their depth, it increases, and with an increase in width, it decreases.

Condition assessment of underground corroded pipelines subject to hydrogen damage and combined internal pressure and axial compression

In this work, a 3D finite element (FE) based model was developed to assess the condition of an underground hydrogen transmission pipeline containing a corrosion defect under combined internal pressure and soil movement-induced axial compression. The use of mechanical properties of X100 pipeline steel under different hydrogen charging time models the degree of hydrogen damage in pipelines. Parameter effects, i.e., axial compressive stress, hydrogen damage, defect geometries, and pipeline diameter-to-thickness ratio, were determined. The results demonstrated that the synergistic effect of axial compression, internal pressure, corrosion, and hydrogen damage can lead to a significant decrease in the failure pressure of pipelines. The failure pressure decreased with the wall thickness reduction and increased hydrogen damage, axial compressive stress, defect length, defect depth, and pipe diameter. The competitive effect was observed between the degree of metal loss and hydrogen damage in determining the burst capacity of pipelines. In situations where the pipeline integrity was severely compromised, the failure pressure exhibited minimal reduction despite the increasing severity of hydrogen damage. The stress distribution at the defect zone was influenced by axial compressive stress but remained unaffected by hydrogen damage under normal operating conditions (i.e., an internal pressure of 10 MPa). This work is expected to help operators understand the applicability of elder and in-service pipelines for hydrogen transmission.

Theoretical Evaluation Methods for Remaining Strength of Seawater Pipelines with Corrosion Defects

There are many theoretical evaluation methods for the remaining strength of corroded pipelines. In this paper, we focus on seawater pipelines with corrosion defects, and various theoretical evaluation results under different pipeline parameters and corrosion defect conditions were compared and analyzed by using commonly used theoretical evaluation methods. Meanwhile, the changes in corrosion defect length were studied, and the results of theoretical evaluation methods were compared with those of finite element methods. The use of evaluation methods for the remaining strength of corroded pipelines has certain practical significance.

Magnetic Flux Leakage Testing Method for Pipelines with Stress Corrosion Defects Based on Improved Kernel Extreme Learning Machine

This study aims to study the safety of oil and gas pipelines under stress corrosion conditions and grasp the corrosion damage situation timely and accurately. Consequently, a non-destructive testing method combining magnetic flux leakage testing technology and a kernel function extreme learning machine improved by genetic algorithm (GA-KELM) is proposed. Firstly, the variation of the corrosion defect dimension and profile with time is obtained by numerical simulation. At the same time, the distribution of the magnetic flux leakage signal under different defect conditions is analyzed and studied. Finally, feature selection is carried out on the magnetic flux leakage signal distribution curve, and GA-KELM is used to predict the depth and length of corrosion defects so as to realize the non-destructive testing of the pipeline defects. The results show that different geometric features result in different magnetic flux leakage signal distributions. There is a corresponding relationship between the defect dimension and extreme value, area, and peak width of the magnetic flux leakage signal distribution curve. The GA-KELM prediction model can effectively predict the depth and length of corrosion defects, and the prediction accuracy is better than the traditional extreme learning machine prediction model.