High-Fidelity Analytical Approaches for Damage Detection and Monitoring in Curved Composite Beams

Abstract

Structural analysis and structural health monitoring (SHM) are essential procedures for ensuring the safety and operational efficiency of aerostructures, particularly composite laminates with complex geometries subjected to static or dynamic loadings. These structures, widely used in aerospace applications such as aircraft nacelles, are often susceptible to impact induced damages which may compromise the performance of structural parts leading to undesirable outcomes. Consequently, characterising the effect of damage on the performance of composite aerostructures becomes inevitable. To mitigate the time-demanding in-service assessment of aerostructure components, this research proposes a high-fidelity analytical-based SHM model for damage detection in curved composite beams. Firstly, a one-dimensional (1D) analytical model which analyses the mechanical response of curved composite beams under arbitrary boundary and loading conditions is developed. The 1D model adopts the kinematical descriptions of refined Timoshenko beam theory enhanced with deterministic continuum damage mechanics principles to functionally achieve stiffness degradation that typifies bulk damages. The ensuing equilibrium equations are then analytically discretised leading to a global system with which the natural and resonant frequencies of damaged structures are simulated. Calibration of the 1D model’s capacity through comparison with 3D Finite Element (FE) solutions and literature benchmarks indicate high-fidelity and significant computational savings in degrees of freedom and runtime. The 1D model is subsequently integrated with swarm intelligence-based optimization algorithms to detect damage in composite laminates. A demonstration of the capability of the analytical-based SHM approaches reveals that rapid damage detection and quantification across various material configurations and boundary conditions can be achieved with satisfactory accuracy emphasising the robustness of the SHM scheme. In view of this outcome, the proposed SHM model offers great potential for real-time SHM applications while contributing to reduced maintenance costs and enhanced structural reliability of composite aerostructures. Keywords: SHM, 1D analytical model, damage detection, swarm intelligence-based optimization