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Quantitative stress evaluation of hydrogen transmission pipeline based on incremental magnetic-stress coupling model

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  • Miao, Xingyuan
  • Sun, Xianglong
  • Zhao, Hong

Abstract

Long-distance hydrogen transmission based on natural gas pipelines is crucial for hydrogen energy industry. However, stress concentration caused by external loading and defects can result in great hidden risk for safe transportation. Consequently, it is necessary to evaluate the pipeline stress concentration under hydrogen environment. In this paper, we propose a quantitative stress evaluation method for hydrogen transmission pipelines based on incremental magnetic-stress coupling model. Firstly, hydrogen charging experiments for L245 pipeline steels are carried out with varying hydrogen charging times, and then the tensile fracture experiments are conducted. Then, metal magnetic memory (MMM) signals under the coupling of hydrogen-defect-stress are collected. Based on the traditional magnetic-stress coupling model, the quantitative relationship between MMM signals and pipeline stress is established. Finally, the correction factors concerning hydrogen and defects are introduced to modify the quantitative relationship, and the improved pigeon-inspired optimization (IPIO) is utilized to optimize the correction factors based on incremental learning mechanism. The results indicate that the proposed incremental magnetic-stress coupling model can achieve more accurate stress evaluation for different working conditions, compared to other optimization methods, the average relative error is 8.7 %. This study provides a theoretical basis for ensuring the safety and reliability of hydrogen transmission pipelines.

Suggested Citation

  • Miao, Xingyuan & Sun, Xianglong & Zhao, Hong, 2025. "Quantitative stress evaluation of hydrogen transmission pipeline based on incremental magnetic-stress coupling model," Energy, Elsevier, vol. 330(C).
    • Handle: RePEc:eee:energy:v:330:y:2025:i:c:s0360544225026210
    DOI: 10.1016/j.energy.2025.136979
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    References listed on IDEAS

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    1. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Dong, Ze & Jiang, Wei & Wu, Zheng & Zhao, Xinxin & Sun, Ming, 2025. "Prediction of NOx emission from SCR zonal ammonia injection system of boiler based on ensemble incremental learning," Energy, Elsevier, vol. 319(C).
    3. Li, Shulin & Yang, Yan & Huang, Bensheng & Jia, Yanlin, 2025. "Residual strength hybrid prediction of hydrogen-blended natural gas pipelines based on FEM-FC-BP model," Energy, Elsevier, vol. 321(C).
    4. Qin, Guojin & Zhang, Chao & Wang, Bohong & Ni, Pingan & Wang, Yihuan, 2025. "An interpretable machine learning model for failure pressure prediction of blended hydrogen natural gas pipelines containing a crack-in-dent defect," Energy, Elsevier, vol. 320(C).
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