Digital twin method for predicting the critical load of debonding damage in honeycomb sandwich structures based on distributed optical fiber sensor and Dynamic Bayesian Network

Abstract During the service life of aerospace vehicles, harsh environmental conditions such as vibrations and impacts can easily lead to the partial debonding of honeycomb sandwich structures. Distributed optical fiber sensors (DOFS) based on backscattered Rayleigh scattering are widely used in the field of structural health monitoring due to their advantages of easy embedding and high anti-interference capabilities. How to timely detect debonding damage and predict the structure’s critical safety load to ensure the safety of the vehicle’s structure has always been a concern for researchers. This paper proposes a method combining Optical Frequency Domain Reflectometry (OFDR) DOFS, finite element simulation, and Bayesian probability to predict the critical load of debonding (both current and future). Herein, the optical fiber is used for online length identification of debonding damage, while finite element simulation employs a cohesive zone model (CZM) to provide reliable prediction results. The Bayesian approach utilizes a combination of Dynamic Bayesian networks (DBNs) and improved particle filtering to calibrate parameters of the simulation model based on debonding observation data. To verify the feasibility of the method, model analysis was conducted on data from asymmetric cantilever beam tensile tests on honeycomb sandwich structures. The results demonstrate that the architecture proposed in this paper can accurately predict the critical load of debonding in honeycomb sandwich Structures, providing strong support for predicting the evolution of debonding damage in aerospace structural monitoring.