Failure mechanisms in composite panels subjected to underwater impulsive loads

This work examines the performance of composite panels when subjected to underwater impulsive loads. The scaled fluid–structure experimental methodology developed by Espinosa and co-workers was employed. Failure modes, damage mechanisms and their distributions were identified and quantified for composite monolithic and sandwich panels subjected to typical blast loadings. The temporal evolutions of panel deflection and center deflection histories were obtained from shadow Moiré fringes acquired in real time by means of high speed photography. A linear relationship of zero intercept between peak center deflections versus applied impulse per areal mass was obtained for composite monolithic panels. For composite sandwich panels, the relationship between maximum center deflection versus applied impulse per areal mass was found to be approximately bilinear but with a higher slope. Performance improvement of sandwich versus monolithic composite panels was, therefore, established specially at sufficiently high impulses per areal mass (I0/M¯>170 m s−1). Severe failure was observed in solid panels subjected to impulses per areal mass larger than 300 m s−1. Extensive fiber fracture occurred in the center of the panels, where cracks formed a cross pattern through the plate thickness and delamination was very extensive on the sample edges due to bending effects. Similar levels of damage were observed in sandwich panels but at much higher impulses per areal mass. The experimental work reported in this paper encompasses not only characterization of the dynamic performance of monolithic and sandwich panels but also post-mortem characterization by means of both non-destructive and microscopy techniques. The spatial distribution of delamination and matrix cracking were quantified, as a function of applied impulse, in both monolithic and sandwich panels. The extent of core crushing was also quantified in the case of sandwich panels. The quantified variables represent ideal metrics against which model predictive capabilities can be assessed.