Research on infilling strategy in surrogate-aided optimization for axial compressor blades

Abstract

The hybrid Voronoi-Latin Hypercube Sampling (Voronoi-LHS) method is proposed for the surrogate-aided optimization of the axial compressor blades. The hybrid method is first applied to the fundamental test cases. The analytical results show that, compared with the Voronoi and LHS strategies, the hybrid method generally improves the robustness and convergency. Then the multi-objective genetic algorithm (MOGA) in conjunction with the artificial neural network (ANN) is applied to optimize the aerodynamic performance of an axial compressor rotor. Before the optimization process, the hybrid Voronoi-LHS sample infilling method is employed to refine the ANN surrogate model. Considering the typical intake distortion, the sweep and lean distributions of this rotor are optimized to pursue the maximum total pressure ratio and adiabatic efficiency. The results show that the optimization significantly improves the pressure ratio, efficiency and surge margin of the compressor with low computing cost.