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Updated: Feb 28, 2026

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一个基于机器学习的新型压力容量预测模型,用于使用LightGBM的高档管道周长接
Xiaoben Liu1, Yanbing Wang1, Yue Yang2
1National Engineering Research Center for Pipeline Safety, MOE Key Laboratory of Petroleum Engineering, Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Beijing 102249, China.
Materials (Basel, Switzerland)
|February 27, 2026
概括
管道环绕接极限状态对于安全至关重要. 本研究使用数值模拟和机器学习来准确预测应变能力,识别影响裂驱动力的关键因素,以提高管道完整性.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 管道的完整性 管道的完整性
背景情况:
- 管道环绕接对于结构完整性至关重要.
- 周边接位置的不均性使极限状态的确定变得复杂.
- 半经验方法难以准确识别极限状态的影响因素.
研究的目的:
- 系统地分析关键因素对管道周围的裂纹驱动力的定量敏感性.
- 开发一个强大的预测模型,用于管道周围的应变能力.
- 为了在先进的接应用中提供可靠的参考来确定圆周接的极限状态.
主要方法:
- 使用数值模拟来分析影响裂纹驱动力的因素的灵敏度规律.
- 使用参数矩阵方法构建了一个关于裂驱动力的综合数据库.
- 使用LightGBM机器学习算法开发了一个全方位的预测模型.
主要成果:
- 强度匹配系数,裂深度和错位被确定为影响裂驱动力的最重要的因素.
- 裂的长度,软化率,产力强度比,内部压力和墙壁厚度也显示出影响.
- 开发的机器学习模型在对文献数据进行验证后,在预测应变能力方面取得了很高的准确性 (6.48%).
结论:
- 这项研究提供了一个非常准确的管道周长应变能力预测模型.
- 影响破裂驱动力的关键因素被定量确定,改善了极限状态的确定.
- 这项研究支持在苛刻的环境和先进的接技术下安全可靠地运行管道.
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