Evolution of the Stress-Induced Residual Magnetic Field of X70 Pipeline Steel With Multiple Corrosion Defects

The aim of this research is to investigate the correlation between the residual magnetic field (RMF) and multiple corrosion defects in ferromagnetic steels. Specimens of X70 pipeline steel were machined into standard bars with a single corrosion defect, double corrosion defects and triple corrosion defects, respectively. Tensile tests were carried out to detect the RMF signals on the surface of these specimens. The variations of abnormal magnetic changes of the RMF signals with the external loads were investigated and the results showed that the tangential component and the normal component of the RMF signals of three defect patterns presented different shapes. It was found that the RMF signals were capable of capturing the location and the number of defects in pipeline steels. The peak values of the tangential component and the slopes of the normal component were not influenced by the defects number. This research will promote the investigation on the corrosion defect cluster of ferromagnetic steels based on the metal magnetic memory (MMM) technique.

Mechanical behavior analysis of gas pipeline with defects under lateral landslide

Landslide is the main factor threatening the operation safety of long-distance gas pipeline, and the internal corrosion of pipeline will also seriously affect its reliability. Using LS-DYNA software, considering the interaction between pipeline and soil, a model of pipeline with defects crossing landslide is established based on the coupling of smoothed particle hydrodynamics and finite element method (SPH-FEM). The effect of the depth, number and spacing of pipeline defects and gas pressure on the mechanical behavior of pipeline is analyzed. The results show that the corrosion defects and gas pressure have little effect on the deformation of the pipeline. It is also found that when the gas pressure of the pipeline increases gradually from zero, the residual strength of the pipeline has a maximum value. Additionally, for the single corrosion defect, the maximum plastic deformation appears in the center of the corrosion defect, but for the double corrosion defect, it appears in junction of the corrosion defects. Furthermore, with the increase of landslide displacement, the plastic strain zone gradually extends along the circumference of the pipeline in these two kinds of defective pipelines. At the same time, the interaction between adjacent corrosion defects is found. The interaction is related to the defect spacing: within a certain range, the interaction increases with the increase of the defect spacing, the maximum equivalent stress appears at the junction of defects, and the stress concentration area expands along the circumferential direction. With the further increase of the spacing, the interaction disappears.

Failure pressure prediction of pipeline with single corrosion defect using artificial neural network

Описываются разработка и применение искусственной нейронной сети (ИНС) для прогнозирования предельного давления трубопровода с точечным коррозионным дефектом, подверженного воздействию только внутреннего давления. Модель ИНС разработана на основе данных, полученных по результатам множественных полномасштабных испытаний на разрыв труб API 5L (класс от X42 до X100). Качество работы модели ИНС проверено в сравнении с данными для обучения, получен коэффициент детерминации R = 0,99. Модель дополнительно протестирована с учетом данных о предельном давлении корродированных труб API 5L X52 и X80. Установлено, что разработанная модель ИНС позволяет прогнозировать предельное давление с приемлемой погрешностью. С использованием данной модели проведена оценка влияния длины и глубины коррозионных дефектов на предельное давление. Выявлено, что глубина коррозии является более значимым фактором разрушения корродированного трубопровода. This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination ( R ) of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error. Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.