Application of similarity theories to composite materials design

Implementation of similitude theory to constitute similarity among structural systems can save considerable expense and time, provided that the proper scaling laws are established and validated. The importance, complexity and expensiveness of testing of strength scaling in composite structures suggest that a highly reliable, economical and efficient analytical modelling is needed to understand the mechanics involved. The composites are mainly scaled in two methods, Ply- and sublaminate level. The underlying mechanism of variable strength response in ply-level and sublaminate scaling method is not very well understood. Interestingly both have different behaviour towards strength when scaled up or down. Scaling of results from test coupons to macro structures, like aeroplane or wind turbine blade, necessitates an understanding of scaling rules governing strength effect with size variation. Extrapolation of strength results with size thus requires proper inference about the failure mechanism involved. A micro progressive degradation technique and the degradation models at constituent level is developed to predict the strength scale effects of composite laminate in ply-level and sublaminate scaling method. It is found that strength reduces in ply-level scaling method and increase in sublaminate case, in good agreement with the experimental results on the subject issue. The micro progressive damage technique is advantageous tool in finding the underlying effects of variable response in strength scaling which are otherwise difficult to observe in experimentation without very special techniques. Similitude theory is employed to develop necessary similarity parameters. Scaling laws provide a link between a prototype and its scaled-down model and can be used to extrapolate the results easily and economically. It is demonstrated through the prediction of buckling response of thin shell composite laminate cylinder under axial compression. A good correlation was found between the predicted results from the model and analytical results on the prototype.