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航空发动机叶片盘系统的疲劳可靠性分析使用基于物理的集体学习.
Xue-Qin Li1, Lu-Kai Song2,3, Yat-Sze Choy2
1School of Energy and Power Engineering, Beihang University, Beijing 102206, People's Republic of China.
概括
一种新的基于物理的集体学习 (PIEL) 方法增强了航空发动机叶片盘系统的疲劳可靠性分析. 这种方法比传统方法提高了计算精度和效率.
科学领域:
- 航空航天工程 航空航天工程
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 航空发动机叶片盘疲劳可靠性分析的传统方法的计算效率或准确性较低.
- 复杂的多元件系统对现有的可靠性评估技术构成重大挑战.
研究的目的:
- 为航空发动机叶片盘系统提供高效准确的疲劳可靠性分析提出一种新的基于物理的集体学习 (PIEL) 方法.
- 建立基于PIEL的系统可靠性框架,包括物理特征和故障相关性.
主要方法:
- 将复杂系统可靠性分析分解为基于物理特征的单个组件分析.
- 通过整合构成性响应和多物质物理性质到集体学习中来开发一个PIEL模型.
- 使用Copula函数量化组件故障相关性,以构建系统可靠性框架.
主要成果:
- 与直接的蒙特卡洛相比,拟议的PIEL方法显示出更高的计算精度和效率,支持向量回归,神经网络,集合学习和基于物理的神经网络.
- 在典型的航空发动机高压轮机叶片盘系统上的验证证实了该方法的有效性.
- 该PIEL方法显著提高复杂的工程系统的可靠性分析.
结论:
- 在航空发动机叶片盘系统的疲劳可靠性分析中,PIEL方法提供了显著的进步.
- 这项工作为结构完整性应用的基于物理的建模提供了新的见解.
- 开发的框架被验证为复杂系统可靠性的高效和准确工具.
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