A Combined Artificial-Intelligence Aerodynamic Design Method for a Transonic Compressor Rotor Based on Reinforcement Learning and Genetic Algorithm

Abstract

An aircraft engine’s performance depends largely on the compressors’ aerodynamic design, which aims to achieve higher stage pressure, efficiency, and an acceptable stall margin. Existing design methods require substantial prior knowledge and different optimization algorithms to determine the 2D and 3D features of the blades, in which the design policy needs to be more readily systematized. With the development of artificial intelligence (AI), deep reinforcement learning (RL) has been successfully applied to complex design problems in different domains and provides a feasible method for compressor design. In addition, the applications of AI methods in compressor research have progressively developed. This paper described a combined artificial-intelligence aerodynamic design method based on a modified deep deterministic policy gradient algorithm and a genetic algorithm (GA) and integrated the GA into the RL framework. The trained agent learned the design policy and used it to improve the GA optimization result of a single-stage transonic compressor rotor. Consequently, the rotor exhibited a higher pressure ratio and efficiency owing to the sweep feature, lean feature, and 2D airfoil angle changes. The separation near the tip and the secondary flow decreased after the GA process, and at the same time, the shockwave was weakened, providing improved efficiency. Most of these beneficial flow field features remained after agent modification to improve the pressure ratio, showing that the policy learned by the agent was generally universal. The combination of RL and other design optimization methods is expected to benefit the future development of compressor designs by merging the advantages of different methods.

Keywords

• reinforcement learning
• genetic algorithm
• turbomachinery
• design
• optimization