A pipeline inspection gauge (PIG) is routinely passed throughout the long‐distance oil and gas pipelines by pipeline operators to clean the pipeline. Sealing performance is a significant evaluation index for PIG's safety operation. To comprehensively evaluate the PIG's seal performance, nonlinear finite element models were developed, and parametric analysis was conducted in this study. The accuracy of simulation model was validated from numerical and experimental results reported in the literature. The results show that comparing with the pigging in straight pipes, the sealing rubber cups of PIGs can be more easily detached from the pipe wall when passing through elbows, causing smaller sealing areas. For a typical elbow (with curvature radius equals to six times of pipe diameter) widely used in pipeline industry, the minimum sealing area of rubber cup is only 8.07% during common operation conditions. The sealing ability of rubber cups can be obviously enhanced by increasing the curvature radius for the pigging operation of small curvature elbow. Elbow radius slightly affects the cup's sealing behavior when the curvature radius is over six times of pipe's outer diameter. An increment in sealing cup interference can increase the contact area between cup and pipe wall, and the blockage risk of PIGs will be reduced due to the good sealing ability and sufficient driving force. The minimum interference required for the cups is 4% under a most common operation condition; that is, the sealing cup thickness, fluid pressure difference, and friction coefficient are 35 mm, 0.02 MPa, and 0.3, respectively. A proper decrease in sealing cup thickness will reduce the stiffness of rubber cups, indicating that the cups with a smaller thickness are more prone to deformation. Thus, an increase in differential pressure over PIG can enhance the sealing performance of cups when the cups are separated from pipe wall. A large friction coefficient is risky for a safe pigging due to the decrease in sealing region with the increase in friction coefficient. In engineering practice, proper measures should be taken to reduce friction force. Above all, the results obtained in this study provide a reference for the structural design of PIGs.
With continued urbanization in China, the construction of urban gas pipelines is increasing, and the safety of gas pipelines are also increasingly affected by urban development and the increased scope of buildings and roads. Pipes with defects are more likely to fail under the surface loads. In this study, uniaxial tensile tests of high-density polyethylene (HDPE) pipes were carried out to obtain the real material parameters of pipe. A pipeline-soil interaction finite element model of HDPE pipeline with defects under surface load was established. The failure mechanism of the urban gas pipeline was studied and the influence of parameters such as internal pressure, defect position, defect depth on the mechanical behavior, and failure of pipelines were analyzed. A failure criterion for HDPE pipes with defects under surface load was proposed based on the limit-state curves obtained under different working conditions. Furthermore, an accurate and efficient fitness-for-service assessment procedure of pipes with defects under surface load was proposed. The results showed that maximum Mises stress of the pipeline gradually increased with increasing surface load and the position of maximum stress changed from the top and bottom of the pipe to the defect position and both sides of the pipe. Finally, when Mises stress of the HDPE pipe exceeds the yield limit, failure will occur. Internal pressure, defect location, and defect depth were found to influence the failure process and critical surface load of the pipeline. Safety evaluation curves of the gas pipeline with defects under surface load were obtained by calculating the critical failure load of the pipeline under various working conditions. Finally, a nonlinear fitting method was used to derive a formula for calculating the critical surface load under different defect parameters. The proposed method provides a useful reference for urban gas pipeline safety management.
Oils are mainly transported by pipe in long distance for its high efficiency. While oil pipe leakage will cause serious social and environmental consequences, e.g. fire even life lost, water and soil pollution. Thus it is important to recognize pipe leakage at initial stage in engineering practice. In this research, a negative pressure wave based detection method was established for pipeline leakage recognition. Suitable parameters of negative pressure wave signals with significant difference for different working conditions were selected. Principal Component Analysis (PCA) method was conducted to reduce the dimensions of the negative pressure wave vector. Self-organizing map (SOM) Neural network was finally adopted to identify the signals for different working conditions. The proposed method was validated by experimental data, which shows that the methodology gives a high recognition rate, which can be referenced in pipe monitoring in engineering practice.
Geohazards have become one of the major threats for pipeline safety as catastrophic consequences can be induced by the ground displacement. To prevent pipe failure, multi-source monitoring technics have been adopted by pipeline operators in engineering practice. While the strain gauge monitored strain results are discretely distributed along the pipeline, which makes the most dangerous pipe section might be not derived directly via sensors. Therefore, it is of great significance to establish an accurate numerical simulation model based on digital twin technology in the geological disaster areas to predict the actual stress and strain status in pipelines. In this paper, an automatically generated parametrical finite element model was established by combining using the general nonlinear finite element software package ABAQUS and the numerical calculation software MATLAB. Numerous numerical strain results were generated as database for a multi-layer backpropagation artificial neural network regressing pipe’s strain state and the geohazard loading conditions (i.e. soil displacement, length of the geohazard areas etc.). Finally, particle swarm optimization algorithm was employed to obtain the most fitting geohazard loading conditions based on monitoring data. An actual case of a buried X65 crude oil pipeline in northeast China was considered as an example, results show that after 5 iterations a relatively accurate strain distribution along the pipe was obtained via the optimization results. The proposed method can be adopted in the integrity management of pipeline crossing geohazard areas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.