Aeroelastic Tailoring of a Composite Forward-Swept Wing Using a Novel Hybrid Pattern Search Method

Aeroelastic tailoring of a composite forward-swept wing model is investigated to achieve a minimum structure weight with increasing divergence speed and flutter speed. A novel hybrid pattern search method is proposed to perform the aeroelastic tailoring of the wing structure subject to multiple constraints including static deformation, strength, buckling, and aeroelastic characteristics. In the new hybrid pattern search method, the sensitivity analysis method and the genetic algorithm are combined to enhance the global convergence rate and obtain a global optimal solution, respectively. A global search is performed by using the genetic algorithm to obtain elite individuals as the initial values for further optimizations, and the sensitivity analysis method is applied to improve the optimization efficiency. The comparative study of the optimized results obtained by other existing modern heuristic optimization methods shows that the present method can achieve the lightest structure weight and cost the least CPU computing time. After optimization of the present method, the structure weight of the initial design can be reduced by 14.21%, whereas the divergence speed and the flutter speed are increased by 40.6 and 14.38%, respectively. This study demonstrates that the proposed hybrid pattern search method is quite suitable for the global optimization of the aeroelastic tailoring of composite forward-swept wings.