研究酸性衰老和损伤模式识别玻璃纤维增强塑料石油和天然气采集管道的研究基于声学排放
Haisheng Bi1, Yuhong Zhang1, Chen Zhang1
1College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China.
Polymers
|August 29, 2024
概括
玻璃纤维增强塑料 (GFRP) 管道因酸性环境而面临石油和天然气管道的老化挑战. 声波发射技术和一个新的SSA-LSSVM算法有效地识别了GFRP损伤模式,准确率为90%.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 机械工程 机械工程
背景情况:
- 石油和天然气中的钢管道面临着腐蚀和疲劳.
- 玻璃纤维增强塑料 (GFRP) 管道提供了一个有前途的替代方案.
- 管道中的酸性介质可以通过老化,解,分层和断裂来降解GFRP.
研究的目的:
- 在机械应力和酸性条件下研究具有V形缺陷的GFRP管道的老化和故障机制.
- 评估声辐射 (AE) 技术在监测GFRP降解方面的有效性.
- 开发和验证GFRP中损伤模式识别的新型算法.
主要方法:
- 在GFRP管道上进行了老化损伤实验,其中有2毫米和5毫米的V形缺陷.
- GFRP管道遭受了外力和酸性溶液暴露的联合作用.
- 使用声波发射 (AE) 技术来监测老化和故障.
- 开发并应用了SSA-LSSVM算法用于AE信号分析和损伤模式识别.
主要成果:
- 酸性老化溶液加速了GFRP管道中的矩阵损伤,纤维/矩阵脱吸和分层.
- 在短期内,发现酸性溶液的整体老化效应相对较弱.
- 对于GFRP,SSA-LSSVM算法实现了高达90%的平均损伤模式识别率.
结论:
- 当GFRP管道暴露在酸性环境中时,即使在短期老化时,也会对矩阵损伤和分层产生敏感性.
- 声波发射 (AE) 是检测GFRP降解的一种可行的技术.
- 该SSA-LSSVM算法显示出高适应性和准确性,用于分析AE信号,用于管道中的GFRP损害评估.
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