Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning

To optimize the aerodynamic shape of high-speed helicopter rotor airfoils, a multi-objective optimization framework is proposed based on deep learning. Firstly, a deep neural network is constructed as a surrogate model to predict the aerodynamic coefficients of rotor airfoils. The rotor airfoil SC1095 is selected as the baseline airfoil. The Class function/Shape function Transformation (CST) method is employed to parameterize the airfoil, and the Latin hypercube sampling method is used to generate the airfoil dataset for training deep neural networks. Then, comprehensively considering the aerodynamic performance of multiple design points such as forward flight, maneuvering and hover of the helicopter, a multi-objective aerodynamic shape optimization of the high-speed helicopter rotor airfoil is conducted by combining the deep neural network surrogate model with the multi-island genetic algorithm. The optimization results show that compared with the baseline airfoil, the optimized airfoil can significantly improve its forward flight performance without compromising hover and maneuvering performance. Finally, a rigid coaxial rotor is generated using the baseline and optimized airfoil respectively. The aerodynamic performance of these rotors in forward flight is computed and analyzed. The results indicate that the optimized airfoil significantly enhances the aerodynamic performance of the high-speed helicopter rotor.