Deep-learning-based aerodynamic shape optimization of rotor airfoils to suppress dynamic stall

Abstract

The use of computational fluid dynamics (CFD) to optimize the aerodynamic shape of rotor airfoils with the aim of suppressing dynamic stall is computationally expensive and inefficient. To address this issue, a surrogate model based on deep learning (DL) is employed to replace a CFD module of optimization process in this study. The optimization framework is demonstrated by optimizing a SC1095 rotor airfoil in subsonic flow. The airfoil shapes are parameterized using the class function/shape function transformation method, and an airfoil dataset for a deep neural network (DNN) is generated by Latin hypercube sampling. The surrogate model for predicting the aerodynamic coefficients of the airfoils is established by training the DNN, which can get the predicted results within a second. This surrogate model is then combined with the multi-island genetic algorithm for rotor airfoil optimization. Finally, the aerodynamic performance of a rotor with the optimized airfoil is investigated to verify the dynamic stall suppression effect. The results demonstrate that the peak drag and moment coefficients of the optimized airfoil can be reduced by 82.5% and 88.6%, respectively, compared with the baseline airfoil, while the lift coefficients increase during almost all of the pitching period. This means that the optimized airfoil has significantly improved dynamic stall characteristics. Moreover, by suppressing the dynamic stall, the rotor with the optimized airfoil achieves better aerodynamic performance than the baseline rotor. Statistical data show that our use of a DL-based surrogate model instead of the CFD module will reduce the optimization time by at least one order of magnitude.