Analysis of machine learning models and data sources to forecast burst pressure of petroleum corroded pipelines: A comprehensive review

Engineering Failure Analysis, Journal Year: 2023, Volume and Issue: 155, P. 107747 - 107747

Published: Nov. 3, 2023

Language: Английский

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Finite-Element Modeling of the Dynamic Behavior of a Crack-like Defect in an Internally Pressurized Thin-Walled Steel Cylinder

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 1790 - 1790

Published: Feb. 22, 2024

This article presents one part of a study on the dynamic deformation and fracture sections steel gas pipelines with an external crack-like defect under action internal pressure. work was performed basis finite-element simulations using cylindrical shell model executed by ANSYS-19.2 example section pipeline “Beineu–Bozoy–Shymkent” in Republic Kazakhstan. The propagation incipient along resulting its tip area were investigated. options loading working critical pressure both considered. It found that, within time 1.0 ms, formed crack expanded circumferential direction up to maximum value, which depended value A further growth cracks occurred longitudinal direction. At operating pressure, initial length increased factor 5.6, while equivalent stresses 1.53 3.5 ms. Within 3.75 stopped growing due decompression. Specifically, there stop Vice versa, at did not change qualitatively, process, it decreased results stress–strain state showed distance between walls reached 23 mm free edge. Conversely, periods 2.25 two three times, respectively. elongation 5.8 times time. Together, twice, after Moreover, studies that additional considerations edges led increment 3.6% length. this can be used for development measurements field structural reinforcement.

Language: Английский

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Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines

Sensors, Journal Year: 2024, Volume and Issue: 24(11), P. 3563 - 3563

Published: May 31, 2024

The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, dataset comprising 576,000 images of pipelines with and without was curated. A custom-designed optimized convolutional neural network (CNN) employed binary classification, distinguishing between corroded non-corroded images. This CNN architecture, despite having relatively few parameters compared to existing classifiers, achieved notably high classification accuracy 98.44%. proposed outperformed many contemporary classifiers its efficacy. By leveraging deep learning, this approach effectively eliminates the need manual inspection corrosion, thus streamlining what previously time-consuming cost-ineffective process.

Language: Английский

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Factors affecting extended avalanche destructions on long-distance gas pipe lines: Review

Case Studies in Construction Materials, Journal Year: 2023, Volume and Issue: 19, P. e02376 - e02376

Published: Aug. 7, 2023

Avalanche destructions in long-distance gas pipe lines are of great importance the uninterrupted transport commercial on an international and national scale Republic Kazakhstan. In study, analysis line accidents showed that such incidents can lead to large casualties cause enormous economic damage. The statistics damages a significant part occur due corrosion material, concentration stresses structural defects, as well external actions. work, all these reasons were analyzed and, solution for localization or control avalanche destructions, authors proposed prestressing method, which has been studied by long time various problems. As review showed, problem scale, undoubtedly emphasizes relevance direction. purpose this article is study fill gap issue features propagation development sound methods techniques assessing resistance pipes destructions.

Language: Английский

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Strain-based design and assessment for pipeline integrity management: A review of applications and gaps

International Journal of Pressure Vessels and Piping, Journal Year: 2023, Volume and Issue: 204, P. 104973 - 104973

Published: May 11, 2023

Language: Английский

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Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103233 - 103233

Published: Oct. 1, 2024

Language: Английский

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Probabilistic modeling of hydrogen pipeline failure utilizing limited statistical data

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 95, P. 1052 - 1066

Published: Nov. 23, 2024

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Research progress on crack propagation and failure probability prediction of hydrogen-blended natural gas pipeline

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 134, P. 28 - 43

Published: May 3, 2025

Language: Английский

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Fatigue life characterisation of API X65 steel pipeline for internal vibrational loads under sea water condition

Applied Ocean Research, Journal Year: 2025, Volume and Issue: 155, P. 104454 - 104454

Published: Jan. 29, 2025

Language: Английский

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Random risk assessment model and risk-based maintenance decisions for natural gas pipelines

Journal of Loss Prevention in the Process Industries, Journal Year: 2025, Volume and Issue: unknown, P. 105591 - 105591

Published: Feb. 1, 2025

Language: Английский

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