Multi objective optimization methodology for airfoil robust design under geometry uncertainty

Traditionally, aerodynamic shape optimization has focusedon obtaining the best design given the requirements and flow conditions.However, the manufacturing accuracy of the optimal shape is depends on theavailable manufacturing technology and other factors, such as manufacturingcost. It is imperative that the performance of the optimal design isretained when the component shape differs from the optimal shape due tomanufacturing tolerances and normal wear and tear. These requirementsnaturally lead to the idea of robust optimal design wherein the concept ofrobustness to various perturbations is built into the design optimizationprocedure. Here we demonstrate how both multi-objective evolutionaryalgorithm and surrogate model can be used to achieve robust optimal designs.Test cases include the deterministic optimization and robust design ofairfoils, and the results were compared. It was shown that the presentrobust aerodynamic shape optimization method is a useful tool to design themore practical airfoil for air vehicles.