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Teaching experiment design of non-metallic sealing material performance in a hydrogen environment
Experimental Technology and Management 2026, 43(2): 186-193
Published: 20 February 2026
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Objective

In the context of the “double carbon” goal, hydrogen energy has gradually been promoted as a renewable energy source with the advantages of being green, flexible, having good combustion performance, and possessing high energy density. Hydrogen-doped natural gas pipelines are an effective way to enable large-scale, long-distance, safe, and efficient hydrogen transportation. However, the unique physical properties of hydrogen may affect the performance of pipeline sealing materials and pose a threat to transportation safety. Hydrogen blending in natural gas pipelines may not only degrade the performance of non-metallic sealing materials but also increase pipeline leakage. Because hydrogen is easier to ignite and explode than natural gas, the risk of pipeline transportation is greatly increased. The purpose of this study is to explore the performance evolution of non-metallic sealing materials in a hydrogen environment through systematic experimental teaching, to provide a basis for the optimization design and safety standard formulation of pipeline sealing systems, and to cultivate students' engineering practice ability and scientific research literacy.

Methods

This study designs and constructs an experimental teaching system for evaluating the performance of non-metallic sealing materials in a hydrogen environment, enabling students to gain a deep understanding of the performance degradation behavior of non-metallic materials under hydrogen exposure. The experimental teaching system combines experimental research and numerical calculation. The experimental research component consists mainly of two parts: aging experiments and permeation experiments, which are used to study the aging behavior and permeation characteristics of non-metallic materials in a hydrogen-doped environment. During the experiments, students' participation in the entire process of instrument operation, data recording, and phenomenon analysis was emphasized. In combination with numerical simulation methods, a simulation model of permeation and sealing performance of non-metallic sealing materials in a hydrogen environment was established.

Results

The experimental results show that the volume swelling rates of ethylene propylene rubber and low-nitrile nitrile rubber after hydrogen-induced aging were less than 15%, and the mechanical property retention rates were greater than 85%, indicating the best anti-aging performance, while the performance of fluorine rubber was also favorable. In the permeation experiments, the permeability coefficients of tetrafluoro-propylene rubber and nitrile butadiene rubber were low, demonstrating excellent barrier properties. Among the eight non-metallic materials tested, fluorine rubber exhibited the best comprehensive performance in a hydrogen environment.

Conclusions

By constructing an experimental teaching system for evaluating the performance of non-metallic sealing materials in a hydrogen environment, this study clarified the aging and permeation behaviors of typical non-metallic materials under hydrogen exposure, providing an experimental basis for the selection and safety evaluation of sealing materials for hydrogen-doped natural gas pipelines. At the same time, the experimental system integrates frontier scientific research problems into practical teaching, effectively improving students' comprehensive abilities in high-pressure gas environment material testing, data modeling, and analysis for solving complex engineering problems, and provides an important teaching platform for cultivating engineering and technical talents to meet the needs of energy transformation.

Open Access Original Paper Issue
Analytical model for lateral peak soil resistance based on pipe-soil interaction full-scale test and numerical simulation
Petroleum Science 2026, 23(5): 2846-2862
Published: 25 December 2025
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Pipelines, as critical infrastructure for oil and gas transportation, require precise evaluation of peak loads in displacement-prone zones to ensure operational safety. The current design guidelines for lateral peak soil resistance (ALA-2001 and PRCI-2009) are based on early analytical studies with limited simulations and physical test data. These guidelines fail to adequately account for the coupled effects of soil friction and cohesion while also overlooking asymmetric soil constraints. These limitations raise significant concerns regarding their applicability in practical engineering scenarios, necessitating the development of more accurate analytical methods. The present study combines full-scale lateral pipe-soil interaction tests with finite element modeling via the coupled Eulerian-Lagrangian approach in ABAQUS/Explicit. After validation, parametric studies were conducted to establish a comprehensive database of lateral peak soil resistances. Based on the observed resistance development patterns, the lateral peak resistance calculation equation in ALA-2001 was modified to provide a more accurate analytical model capable of better reflecting real-world pipe-soil interaction behavior. The reliability of the proposed model was confirmed through independent physical tests, demonstrating its significant value for pipeline engineering design and safety assessment.

Open Access Original Paper Issue
A cascaded pipeline defect detection and size estimation method based on the YOLOv11 and physics-informed network using the MFL data
Petroleum Science 2026, 23(3): 1505-1518
Published: 09 August 2025
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Long-distance oil and gas pipelines are crucial in the global energy network. However, due to complex internal and external environments, defects can be formed on a pipelineʼs surface, posing severe threats to structural safety. Aiming to detect surface defects, recent works have used magnetic flux leakage (MFL) inspection data for defect recognition and defect size estimation. Accurately locating and measuring defects based on the MFL data is essential for pipeline integrity assessment and safety maintenance. To obtain effective MFL data on pipeline defects, this study constructs an experimental pipeline at the Daxing pulling-through test site in Beijing. An ultra-high-definition MFL inspection robot is employed to collect defect data, which are then used to construct a defect detection and size estimation database. In addition, to achieve precise defect recognition and quantification, a cascaded method, which integrates a mature computer vision detection model, the YOLOv11 model, with a physics-informed and data-driven prior deep-learning quantification model, is proposed. Validation results show that, even for a limited amount of data, the proposed defect recognition model can achieve an AP50 of 92.1% at a confidence threshold of 0.6, a precision of 100%, a recall of 84.29%, and an F1-score of 91.47 %, indicating high accuracy in identifying surface defects on pipelines. The quantification model can achieve the goodness of fit (Gof) values of 0.987, 0.979, and 0.994 for defect length, width, and depth, with the mean absolute percentage error (MAPE) of 7.97%, 8.52%, and 4.74%, respectively. Comparison analysis with different models confirms the superiority of the proposed cascaded recognition and quantification approach. The results also demonstrate that the proposed method can effectively identify and quantify defects in long-distance pipelines. Finally, it can improve the interpretation efficiency of MFL inspection data and provide reliable support for residual strength assessment and remaining life prediction of pipelines.

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