Application of Surrogate Models for Through-Flow Calculation in an Axial-Flow Compressor

The two dimensional through-flow method can predict the performance of the compressor fast during the preliminary design period. Its accuracy is highly dependent on the empirical models. The prediction error increases if the compressor is far beyond the applicable range of empirical models. In order to improve the prediction accuracy of though-flow method, the feasibility of using surrogate model to replace the traditional empirical was studied. The geometric parameters and aerodynamic experimental data of a two-stage transonic compressor were used as the training database for surrogate models. The most influential features were selected as inputs using sensitivity analysis method. Two supervised machine learning methods, support vector machine regression (SVR) and Gaussian process regression (GPR), respectively, were implemented to build the surrogate models. Bayesian optimization algorithm was applied to search the optimal hyper parameters. The trained surrogate models were integrated into the through-flow program based on the streamline curvature method (SLC) to evaluate the characteristics of the compressor and compare it with the calculation results of the traditional empirical models. The comparison showed that, compared with the traditional empirical model, the SVR and GPR surrogate models reduced the average prediction error of the total pressure characteristics by 62.2% and 55.2%, and the adiabatic efficiency characteristics by 48.4% and 50.1%, respectively. The results indicated that the surrogate model is a reliable alternative when the compressor works beyond the applicable range of traditional empirical models. © 2021, Editorial Department of Journal of Propulsion Technology. All right reserved.