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

Abstract

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. NomenclatureHOST Helicopter Overall Simulation Tool GA Genetic Algorithm RSM Response Surface Method SBO Surrogate Based Optimization F.M.Figure of Merit (measure for rotor performance in hover) L/D Lift-over-Drag ratio (measure for rotor performance in forward flight)

Rotor-Blade Planform Design Based on an Overset Harmonic-Balance-Adjoint Optimization Framework

“…The design of the Blue-Edge blade [2], developed from the ERATO blade, included parametric studies of the planform shape and for the first time optimisation using high-fidelity CFD in hover. Rotor design optimisation studies in industry [14][15][16], however, are still limited to low/mid-fidelity tools in forward flight. This is primarily due to high computational resources for optimisation of unsteady flows and high turnaround times required in an industrial setting.…”

Aerodynamic blade design with multi-objective optimization for a tiltrotor aircraft

“…Moreover, as the multi-objective optimization procedure produces a final solution in terms of Pareto-optimal solutions (a set of optimal non-dominated solutions), the designer can compare the results and select the best compromised optimal solution for the analyzed problem. A good example of the application of a multi-objective optimization procedure for the aerodynamic design of helicopter rotor has been described by Leusink et al (2013).…”

Multi-fidelity optimization strategy for the industrial aerodynamic design of helicopter rotor blades

“…For complex industrial applications, however, the number of cost-function evaluations required by genetic algorithms remains excessively high, especially when CFD simulation methods are used to compute the quantities of interest [24]. To circumvent this difficulty and drastically reduce the turn-around time of optimization cycles, advanced optimization techniques, such as optimization on a response surface and automatic updating of this surface in Surrogate Based Optimization (SBO) have recently been introduced in rotor optimization loops.…”