journal article·2025-10-02

Axial stress detection in pipelines based on ultrasonic guided waves

Xiaochuan Niu, Zhenhua Chen, Wenlin Yang, Liqiang Zhu, Xining Xu, Zujun Yu, Junfeng Fan

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Abstract

Oil and gas pipelines are prone to internal axial stress concentration induced by external geological environment change, which can lead to ductile deformation and even fracture. The ultrasonic stress detection with the focus of T(0,1) mode have been studied in recent years, proving the feasibility of ultrasonic guided waves technology. In order to achieve optimal detection performance, the propagation characteristics of guided waves in pipelines are analysed in this paper using semi-analytical finite element (SAFE) theory, and an optimal guided waves mode selection method for axial stress detection is described in detail. Different from the conventional selection of T(0,1) mode in the existing literatures, the L(0,2) mode is confirmed as the optimal guided waves mode for axial stress monitoring by constructing the index model, and the response index is determined as the group velocity of L(0,2) mode. Subsequently, the theoretical calibration curve of group velocity versus axial stress is acquired, which indicates that the group velocity increases with increasing compressive stress and decreases with increasing tensile stress. To verify these findings, a large-scale pipeline experimental platform is established, and validation experiment is conducted. The experimental results correspond to the theoretical analysis results and demonstrate the practicability of the proposed method.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Geophysical Methods and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Beijing Jiaotong University

Pipechina R&D Institute, Pipechina Corporation

Chinese Academy of Sciences

Funding

State Key Laboratory of Advanced Rail Autonomous Operation · Fundamental Research Funds for the Central Universities of China

References (35)

Show source-provided references
1. 10.1016/j.conbuildmat.2025.14274510.1016/j.conbuildmat.2025.142745 ↗
2. 10.1007/s11665-024-09283-z10.1007/s11665-024-09283-z ↗
3. 10.1016/j.tws.2020.10660210.1016/j.tws.2020.106602 ↗
4. 10.1016/j.msea.2017.02.00510.1016/j.msea.2017.02.005 ↗
5. 10.1002/stc.177110.1002/stc.1771 ↗
6. 10.1007/s40948-024-00823-410.1007/s40948-024-00823-4 ↗
7. 10.1016/j.jmmm.2022.16984810.1016/j.jmmm.2022.169848 ↗
8. 10.1016/j.measurement.2019.01.01810.1016/j.measurement.2019.01.018 ↗
9. 10.2478/jee-2018-008510.2478/jee-2018-0085 ↗
10. 10.1063/5.019869410.1063/5.0198694 ↗
11. 10.3390/s2203112810.3390/s22031128 ↗
12. 10.1080/10589759.2023.217037310.1080/10589759.2023.2170373 ↗
13. 10.1016/j.ijpvp.2023.10503310.1016/j.ijpvp.2023.105033 ↗
14. 10.1016/j.ultras.2024.10732510.1016/j.ultras.2024.107325 ↗
15. 10.1007/s11803-024-2238-810.1007/s11803-024-2238-8 ↗
16. 10.3390/coatings1403035810.3390/coatings14030358 ↗
17. 10.1109/JSEN.2023.334394610.1109/JSEN.2023.3343946 ↗
18. Gao W, Zhang DL, Zhang EC. Defect detection in the dead zone of magnetostrictive sensor for pipe monitoring. IEEE Sensors J. 2021;21(3):3420–3428.
19. 10.1016/j.conbuildmat.2023.13361810.1016/j.conbuildmat.2023.133618 ↗
20. 10.1109/JSEN.2020.304338910.1109/JSEN.2020.3043389 ↗
21. 10.3390/s2209311110.3390/s22093111 ↗
22. 10.1016/j.measurement.2023.11333210.1016/j.measurement.2023.113332 ↗
23. 10.1121/10.001746310.1121/10.0017463 ↗
24. 10.1016/j.euromechsol.2025.10558410.1016/j.euromechsol.2025.105584 ↗
25. 10.1109/JSEN.2019.295895310.1109/JSEN.2019.2958953 ↗
26. 10.1109/JSEN.2023.331330810.1109/JSEN.2023.3313308 ↗
27. 10.1016/j.ndteint.2024.10318210.1016/j.ndteint.2024.103182 ↗
28. 10.1063/1.513072310.1063/1.5130723 ↗
29. 10.1016/j.ultras.2008.10.01810.1016/j.ultras.2008.10.018 ↗
30. 10.3390/mi1604036710.3390/mi16040367 ↗
31. 10.1016/j.jsv.2006.01.02110.1016/j.jsv.2006.01.021 ↗
32. 10.1063/5.009802510.1063/5.0098025 ↗
33. 10.1016/j.ymssp.2024.11169310.1016/j.ymssp.2024.111693 ↗
34. 10.1016/j.ultras.2023.10704710.1016/j.ultras.2023.107047 ↗
35. 10.1121/10.001778410.1121/10.0017784 ↗