Optimization of helicopter rotor blade performance by spline-based taper distribution using neural networks based on CFD solutions

Abstract

The taper distribution along the span of a helicopter blade is defined using a novel method applied for the first time and considered as the main contribution of this work. This method uses cubic splines to generate modified blade shapes. The thrust and the torque values, computed by a 3-D Reynolds Average Navier Stokes solver, are used to train a Neural Networks based model. After that a constrained optimization is conducted based on this model for two different rotor speeds under hover condition. The optimization variables are the chord lengths at three different span locations: root, mid-span and tip. The optimization constraints are the torque or thrust values of the original blade and the practical limits for the chord lengths. Two optimum cases are investigated: maximum Figure of Merit with greater thrust and maximum Figure of Merit with less torque than the baseline. The major challenge of this work is to use the taper distribution as the only design parameter to obtain comparable results to other studies in literature in which more than one parameter is used. The results show that the Figure of Merit can improve by around 5% and the torque can be reduced by around 20%.