Alfonso Pagani

Variable-angle-tow (VAT) composite laminates can eventually improve the mechanical performance of lightweight structures by taking advantage of a larger design space compared to straight-fiber counterparts. Here, we provide a scalable low- to high-fidelity methodology to retrieve the tow angles that maximize the buckling load and the fundamental frequency of VAT plates. A genetic algorithm is used to solve the optimization problem in which the objective function is mimicked using a surrogate model. Both unconstrained and manufactured-constrained problems are solved. The surrogates are built with outcomes from numerical models generated by means of the Carrera unified formulation, which enables to obtain straightforwardly different degrees of accuracy by selecting the order of the structural theory employed. The results show both the validity and flexibility of the proposed design approach. It is shown that, although the optimal design fiber angle orientations are consistently similar, discrepancies in the prediction of the buckling load or fundamental frequency can be found between high-fidelity layerwise and low-to-refined equivalent-single-layer models, of which classical laminated plate or first-shear deformation theories are degenerate examples.