Optimising Aspects of Rotor Blades in Forward Flight

Abstract

This work presents a framework for the optimisation of various aspects of rotor blades in forward flight. The proposed method employs CFD in conjunction with artificial neural networks (ANNs) as metamodels, and genetic algorithms (GAs) as optimisation methods. The approach is demonstrated using a relatively well-known case, which is the optimisation of linear twist of rotors in hover, other cases include transonic aerofoils and wing planform. The method was then used to optimise the anhedral and sweep of the UH60-A rotor blade in forward flight. For each case, a parameterisation method is defined, a specific objective function created using the initial CFD data and the ANNs was subsequently used to evaluate this objective function for optimisation using the GAs. The obtained results suggest optima in agreement with engineering intuition but provide precise information about the shape of the final lifting surface and its performance. The results were checked using different optimisation methods and metamodels and were not sensitive to the employed techniques with substantial overlap between the outputs of the selected methods. The main CPU cost was associated with the population of the CFD database necessary for the metamodel.