Real-Time Prediction of Probabilistic Crack Growth with a Helicopter Component Digital Twin

TL;DR

This paper presents a real-time, reduced-order simulation method combining boundary element and finite element techniques with machine learning to predict probabilistic crack growth in helicopter components, enabling digital twin applications.

Contribution

The paper introduces a novel integrated approach that combines advanced numerical methods and machine learning for real-time crack growth prediction in complex structures.

Findings

Simulation faster than real-time in helicopter component example

Hundreds of crack samples generated within a day using the database approach

Effective probabilistic crack growth prediction with minimal computational effort

Abstract

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.