Multi objective optimization methodology for airfoil robust design under geometry uncertainty

Traditionally, aerodynamic shape optimization has focused on obtaining the best design given the requirements and flow conditions. However, the manufacturing accuracy of the optimal shape is depends on the available manufacturing technology and other factors, such as manufacturing cost. It is imperative that the performance of the optimal design is retained when the component shape differs from the optimal shape due to manufacturing tolerances and normal wear and tear. These requirements naturally lead to the idea of robust optimal design wherein the concept of robustness to various perturbations is built into the design optimization procedure. Here we demonstrate how both multi-objective evolutionary algorithm and surrogate model can be used to achieve robust optimal designs. Test cases include the deterministic optimization and robust design of airfoils, and the results were compared. It was shown that the present robust aerodynamic shape optimization method is a useful tool to design the more practical airfoil for air vehicles.