Aerodynamic rotor blade optimization at Eurocopter - a new way of industrial rotor blade design

Industrial aerodynamic design optimization of helicopter rotor blades requires employment of multi-objective optimization methods to account for the two distinct objectives in hover and forward flight. Genetic algorithms are preferred for finding the Pareto Optimal Front, as they allow the engineer to select out of optimal designs. An optimization loop is created, coupling the Dakota optimization library and two simulation methods for objective function evaluation: comprehensive rotor code HOST and CFD solver elsA. For low-cost rotor simulations by HOST, a genetic algorithm is employed to maximize hover and forward flight rotor performance in single- and two-point optimizations. Twist and chord laws of the 7A blade are optimized separately and simultaneously. As genetic algorithms require too many cost function evaluations for CFD-based optimizations, Surrogate Based Optimization (SBO) is employed. SBO is initialized by a preliminary Design of Experiment (DoE). The results are used to generate a metamodel for estimation of cost function evaluations in the optimization algorithm. The metamodel is updated using information from subsequent simulations. Validation of HOST-based SBO against full genetic algorithm optimizations shows that the Pareto Optimal Front is correctly represented by SBO, while requiring 88% less cost function simulations. Several sizes of initial DoE, number of update cycles and number of simulations added per cycle are tested. Then, a similar SBO optimization is carried out by replacing the HOST code by CFD for hover performance simulation. The results demonstrate the ability of both solvers and both optimization techniques to perform aerodynamic design optimization of helicopter rotor blades.