机身
概率逻辑
组分(热力学)
结构工程
有限元法
参数统计
计算机科学
巴黎法
强度因子
边界元法
模拟
算法
工程类
断裂力学
人工智能
数学
航空航天工程
裂缝闭合
物理
统计
热力学
作者
Xuan Zhou,Shuangxin He,Leiting Dong,Satya N. Atluri
出处
期刊:AIAA Journal
[American Institute of Aeronautics and Astronautics]
日期:2022-01-11
卷期号:60 (4): 2555-2567
被引量:1
摘要
To deploy the airframe digital twin or to conduct probabilistic evaluations of the remaining life of a structural component, a (near) real-time crack-growth simulation method is critical. In this paper, a reduced-order simulation approach is developed to achieve this goal by leveraging two methods. On the one hand, the symmetric Galerkin boundary element method - finite element method (SGBEM-FEM) coupling method is combined with parametric modeling to generate the database of computed stress intensity factors for cracks with various sizes/shapes in a complex structural component, by which hundreds of samples are automatically simulated within a day. On the other hand, machine learning methods are applied to establish the relation between crack sizes/shapes and crack-front stress intensity factors. By combining the reduced-order computational model with load inputs and fatigue growth laws, a real-time prediction of probabilistic crack growth in complex structures with minimum computational burden is realized. In an example of a round-robin helicopter component, even though the fatigue crack growth is simulated cycle by cycle, the simulation is faster than real-time (as compared with the physical test). The proposed approach is a key simulation technology toward realizing the digital twin of complex structures, which further requires fusion of model predictions with flight/inspection/monitoring data.
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