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通过有限元分析和基于机器学习的XGBoost方法优化压力容器的结构完整性
Nagoor Basha Shaik1, Vamsi Aluru2, Kittiphong Jongkittinarukorn3
1Department of Mining and Petroleum Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
Scientific reports
|April 3, 2025
概括
准确预测压力容器爆压对于安全至关重要. 使用XGBoost的机器学习模型,集成有限元分析数据,提供比传统方程更精确和更有效的方法.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 压力容器是关键基础设施,特别是在石油和天然气行业,在苛刻的条件下运行.
- 在加压下压力容器的材料故障是一个重要的安全问题,特别是对于柔性材料.
- 准确的爆破压估计对于确保结构完整性和操作安全至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 方法来估计压力容器的爆压.
- 利用现有的文献数据,并将有限元分析 (FEA) 与机器学习模型集成,以提高准确性.
- 建立一个强大的框架来预测压力容器故障,并提高工程安全.
主要方法:
- 使用XGBoost的机器学习方法,结合了诸如产量强度,最终强度,内径,材料类型和厚度等关键变量.
- 从有限元分析 (FEA) 模拟中集成的数据来训练和测试 XGBoost 模型.
- 使用误差指标计算来评估和比较ML模型的精度和可靠性与FEA和传统方法.
主要成果:
- 与传统的数学方程相比,XGBoost模型在预测各种材料的爆破压力方面表现出卓越的准确性和稳定性.
- 机器学习方法有效地捕捉了多个输入特征之间的复杂相互依赖,从而产生了更可靠的估计.
- 与传统的FEA模拟相比,开发的模型显著降低了计算要求,同时保持了高精度.
结论:
- 基于XGBoost的ML模型为估计压力容器爆压提供了一个高度准确和高效的工具.
- 这种方法提高了压力容器设计和操作的工程实践和安全评估.
- 这些发现为需要及时爆破压力预测的行业提供了现实的,计算效率高的解决方案,例如石油和天然气以及能源行业.
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