Bi Haisheng, Zhang Yuhong, Zhang Chen, Ma Chunxun, Li Yuxiang, Miao Jiaxu, Wang Guang, Cheng Haoran
College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
College of Pipeline and Civil Engineering, China University of Petroleum (Huadong), Qingdao 266580, China.
Polymers (Basel). 2024 Aug 10;16(16):2272. doi: 10.3390/polym16162272.
Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues in steel pipes, the demand for glass fiber-reinforced plastic (GFRP) pipes is increasing in oil and gas gathering and transmission systems. However, the medium that is transported through these pipelines contains multiple acid gases such as CO and HS, as well as ions including Cl, Ca, Mg, SO, CO, and HCO. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped defect GFRP pipes with depths of 2 mm and 5 mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.
管道绵延数千公里用于输送和分配石油与天然气。鉴于钢管常面临腐蚀、疲劳及其他问题的挑战，在油气集输和传输系统中，对玻璃纤维增强塑料（GFRP）管道的需求正在增加。然而，通过这些管道输送的介质包含多种酸性气体，如一氧化碳和硫化氢，以及包括氯离子、钙离子、镁离子、硫酸根离子、碳酸根离子和碳酸氢根离子在内的离子。这些物质会引发一系列问题，如老化、脱粘、分层和断裂。在本研究中，对深度为2毫米和5毫米的V形缺陷GFRP管道进行了一系列老化损伤实验。利用声发射（AE）技术研究了在外部力和酸性溶液共同作用下GFRP的老化与失效情况。研究发现，酸性老化溶液在短时间内促进了GFRP管道的基体损伤、纤维/基体脱粘和分层损伤。然而，整体老化效果相对较弱。基于实验数据，提出了SSA-LSSVM算法并将其应用于GFRP的损伤模式识别。实现了高达90%的平均识别率，表明该方法非常适合分析与GFRP损伤相关的声发射信号。
