This work presents a framework for the optimisation of certain aspects of a BERP-like rotor blade in forward flight while constraining hover performance. The proposed method employs a high-fidelity efficient CFD technique that uses the Harmonic Balance method in conjunction with artificial neural networks (ANNs) as metamodels, and genetic algorithms (GAs) for optimisation. The approach has been previously demonstrated for the optimisation of linear twist of rotors in hover (steady case) and the optimisation of rotor sections in forward flight (unsteady case), transonic aerofoils, wing and rotor tip planforms. In this paper, a parameterisation technique was defined for the BERP-like rotor tip and three of the parameters were optimised in forward flight. A specific objective function was created using the initial CFD data and the metamodel was used for evaluating the objective function during the 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 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 using a full factorial method and even that was reduced with the use of the Harmonic Balance method.
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