An Evolutionary Robust Design Method for the Wing Drag Reduction with Variable Operating Flight Condition Parameters

In Aerospace engineering there are many applications where some values of the design parameters cannot be provided accurately. These values are related either to the geometry (wingspan, length, angles) or to operational flight conditions that vary due to the presence of fluctuations (Mach, angle of attack, air density and temperature, etc....). In this case an alternative strategy is to estimate the variability of input parameters by the mean and variancevalues of the objective function associated to the design optimisation procedure. In this frequent aerodynamic situation the use of the well known Taguchi methods aims to look for design solution with better performance and stability simultaneously within a certain interval where input parameters can randomly vary. In this lecture, a Taguchi robust optimisation concept is implemented with Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEA) to solve numerically wing drag reduction problems with uncertainties and compared to a traditional single point optimization approach . It is shown how the approach can provide robust solutions using game theory in the sense that they are less sensitive to little changes of input parameters. Starting from a statistical definition of stability, the method captures, simultaneously Pareto non-dominated solutions with respect to performance and stability criteria, offering alternative choices to the designer. It is concluded from the preliminary obtained results that a general design framework integrating evolutionary algorithm procedures coupled to a robust optimisation technique can be used advantageously when some of the design parameters, such as operating conditions fluctuate within specified design intervals and that uncertainties in the design have a non-linear effect on the objective functions. CHALLENGES AND SOLUTIONS FOR SIMULATION-ASSISTED ENGINEERING