Airfoils optimization based on deep reinforcement learning to improve the aerodynamic performance of rotors

Airfoil optimization is the key to improving the aerodynamic performance of a rotor. However, conventional optimization approaches cannot modify the airfoil shape intelligently in the way that an aircraft designer would. Because the optimization process is largely uninterpretable and ungeneralizable, the optimization knowledge and experience cannot be extracted and applied to similar optimization tasks. To address these issues, we propose an optimization framework for rotor airfoils based on deep reinforcement learning (DRL). Our DRL-based framework is capable of learning an interpretable and generalizable optimization strategy for alleviating the dynamic stall of a rotor airfoil. First, to enhance the efficiency of airfoil dynamic stall optimization, a deep neural network is trained to predict the dynamic stall hysteresis loops of rotor airfoils as a surrogate model. The asynchronous advantage actor-critic reinforcement learning algorithm is employed to train and learn the optimization strategy for alleviating the dynamic stall of the rotor airfoil. Next, the OA212 rotor airfoil is optimized using the well-trained optimization strategy. The results show that the dynamic stall characteristics of the airfoil are improved after optimization. The lift coefficients of the optimized airfoil are significantly enhanced, and the drag and moment coefficients peaks are reduced by 46.3% and 73.8%, respectively, compared with the baseline airfoil. Then, 20 airfoils are optimized using the well-trained optimization strategy to evaluate the generalizability of the dynamic stall optimization strategy. The results demonstrate that the optimization strategy learned by DRL for the rotor airfoil optimization is generalizable. Finally, the numerical simulation results comparing a rotor with baseline airfoils to one with optimized airfoils demonstrate that the aerodynamic performance of the optimized rotor is superior to that of the baseline rotor.

Automated summary

This article proposes a deep reinforcement learning-based framework for rotor airfoil optimization, which utilizes a trained neural network as a surrogate model to learn and apply optimization strategies for improving the aerodynamic performance of the rotor. It demonstrates the interpretability and generalizability of the optimization strategy learned through deep reinforcement learning.