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.

翻译：为了部署机体数字双胞胎或对结构部件剩余寿命进行概率评估,必须采用(近距离)实时快成长模拟方法。本文通过两种方法,为实现这一目标而开发了减序模拟方法。一方面,对称加列尔金边界要素法-有限要素法(SGBEM-FEM)结合了参数模型,以生成复杂结构部件中各种大小/形状裂缝的计算压力强度因素数据库,这种结构部件可以自动在一天之内模拟数百个样品。另一方面,采用机器学习方法来确定裂缝大小/形状和裂缝压力强度因素之间的关系。一方面,将减序计算模型与负载输入和疲劳增长法相结合,实现对复杂结构中具有最低计算负担的不稳定性裂痕增长的实时预测。在循环模拟疲劳率增长时,模拟比实时更快(与复杂飞行/模拟方法相比,需要进一步进行数字模拟)。拟议采用的关键飞行/模拟方法,以进一步进行数字模拟。