Robust design of compact axial compressor

Abstract

The method of connection weights in neural networks was used to analyze the sensitivity of the compressor rotor, and the Back Propagation Neural Network (BPNN) was used to construct the analysis relationship between the compressor rotor's geometries and the performance based on the training and learning of the data base, and the prediction accuracy can reach more than 99.99%. Then the modified Grason Algorithm based on the neural network connect weights was used to quantify the contribution of the geometrical effects on its performance. The result shows that the tip clearance contributes 11.43% (efficiency sensitivity analysis) and 10.18% (pressure ratio sensitivity analysis) to compressor performance changes. This study focuses mainly on the robust optimization of tip clearance. Non-intrusive probability collection point method (NIPC) was adopted for the uncertainty propagation. The robust optimization method based on BPNN agent model coupled with multi-objective genetic algorithm Non-dominated sorting genetic algorithm-II (NSGA II) was used to perform the optimization. Compared to the design prototype, the variance of robust compressor rotor's efficiency could be reduced by 21.04%.