An Intellectual Aerodynamic Design Method for Compressors Based on Deep Reinforcement Learning

Abstract

Aerodynamic compressor designs require considerable prior knowledge and a deep understanding of complex flow fields. With the development of computer science, artificial intelligence (AI) has been widely applied to compressors design. Among the various AI models, deep reinforcement learning (RL) methods have successfully addressed complex problems in different domains. This paper proposes a modified deep deterministic policy gradient algorithm for compressor design and trains several agents, improving the performance of a 3D transonic rotor for the first time. An error reduction process was applied to improve the capability of the surrogate models, and then RL environments were established based on the surrogate models. The rotors generated by the agent were evaluated by computational fluid dynamic methods, and the flow field analysis indicated that the combination of the sweep, lean, and segment angle modifications reduced the loss near the tip, while improving the pressure ratio in the middle section. Different policy combinations were explored, confirming that the combined policy improved the rotor performance more than single policies. The results demonstrate that the proposed RL method can guide future compressor designs.