Investigation and optimization of vibration and buckling in aircraft skin panels using constrained layer damping

The analysis of vibration and buckling remains a significant challenge in the field of aviation. Particularly, minimizing the risk of vibration, noise, and buckling issues resulting from external pressures and loads on the shell panels of aircraft passenger cabins is an ongoing concern in the literature. Preventing these structures from resonating and controlling both resonant-induced structural deformation and buckling problems simultaneously is quite challenging. Adding a viscoelastic damping layer to the surface of structures is a traditional, low-budget, lightweight, and easily applicable method. Constrained layer damping (CLD) is one of the most important types of added damping layers. In this study, the characteristics of vibration and structural buckling under the use of CLD in shell panels were investigated, and the effects of design parameters were compared. As a result, a producible shell panel model achieving maximum damping with minimum weight and minimum critical buckling load was obtained. Initially, for the validation of the modeling method in vibration analysis, natural frequencies and mode shapes of a beam element model were obtained by conducting real and complex eigenvalue analyses using CLD. Subsequently, the structure with frequency-based material parameters was simplified using the equivalent modulus method for buckling analysis, and finite element analysis was performed for buckling analysis. The buckling analysis model was validated with boundary conditions, and the results were verified with hand calculations for simple plate buckling analysis. The characteristics of CLD structures under buckling conditions were examined for the first time in this study, resulting in a 30% improvement in critical buckling load compared to the primary structure with the same thickness. To find the desired optimal design, design parameters such as primary layer material, constraint layer material, number of layers, primary layer thickness, and constraint layer thickness were optimized using the Taguchi experimental design with 36 experiments, and the optimization was performed using the TOPSIS method, a multi-criteria decision-making approach.