Fast predesign methodology of centrifugal compressor for PEMFCs combining a physics-based loss model and an interpretable machine learning method

Abstract

For boosted hydrogen fuel cells, the centrifugal compressor greatly affects the system's efficiency, stack power and costs. However, the design of the centrifugal compressor is a lengthy and high-cost process. To tackle this issue, a new method combining a physics-based loss model and a machine learning method was proposed to achieve a fast and more accurate preliminary design step. A high-precision physics-based loss model was developed and validated for data generation. Furthermore, two gradient boosting decision tree (GBDT) models with Bayesian hyperparameter turning were established to construct mapping relationships between geometric parameters and aerodynamic performance. Then, the Sobol sensitivity analysis and Shapley Additive Explanations (SHAP)-based interpretability were used to explore the impact of the geometric parameters on the aerodynamic performances. Results showed that the outlet blade angle, impeller outlet diameter, impeller outlet width, inducer shroud diameter, and inducer blade angle (at the shroud) were the five most sensitive parameters. Additionally, some practical design recommendations were extracted using SHAP-based interpretability. The isentropic efficiencies of two redesign cases with optimal geometric parameters were increased by 2.59% and 1.85% compared with the baseline impellers. The performance prediction was completed within a time of 6 s using the proposed new predesign method. This study represents a significant new attempt to advance the preliminary design methodology of centrifugal compressors.