Explainable Reinforcement Learning for the Initial Design Optimization of Compressors Inspired by the Black-Winged Kite.

Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design.