Structural Digital Twin for the Health Monitoring of Aircraft Wings

Intelligent, autonomous, and reliable structural health management for aircraft has been a critical focus in the aerospace industry for a long time. This study proposes a digital twin-based structural health management (SHM) framework for aircraft wing structures, integrating physical modeling and data-driven approaches to enable real-time structural monitoring and fatigue damage prediction. A fatigue crack growth model based on linear elastic fracture mechanics (LEFM) is developed, and stress intensity factors are computed using finite element analysis.with Gaussian process regression employed to construct surrogate models for damage evaluation. Subsequently, a state-space model is introduced for model updating, and short-term load forecasting is realized using an ARIMA-based approach to support fatigue life prediction. The effectiveness of the proposed framework is validated based on a set of numerical studies, showing its potentials for practical engineering.