Optimization of blade tip planform for a quiet helicopter rotor

The blade tip planform for a quiet helicopter rotor is optimized using genetic algorithm. Kriging model built by using Latin Hypercube Sampling (LHS) to produce sample points is used to improve computational efficiency, which makes it possible to finish the optimized process. The aeroacoustic noise for helicopter rotors in hover and forward flight is predicted by a hybrid method, which uses the URANS (Unsteady Reynolds-Averaged Navier-Stokes) equations to calculate the noise level near the rotor blades, and uses FW-Hpds (Ffowcs Willinams-Hawkings equation with Penetrable Data Surface) equations to calculate the far field noise with the near field solutions of URANS equations taken as the input data of FW-Hpds equations' source term. The predicted pressure distribution and acoustic pressure agree well with the experimental data that validate the calculating method. An optimization based on AH-1/OLS rotor in forward flight is accomplished with the time-averaged thrust being kept from changing as an constraint and the minimization of the absolute sound pressure peak value being taken as an objective. The results show that the predicted accuracy of the built Kriging model presented in this paper is very high, and the blade tip planform is optimized for a quiet helicopter rotor.