Throughflow design of multi-stage axial flow compressor still takes the upmost importance throughout the compressor whole design procedure as it generally fixes 80%∼90% of the final design. The incorporation of multi-objective evolutionary algorithms (MOEA) into throughflow design has been proven effective. In the existing works, the MOEA commonly uses thousands of objective evaluations to obtain a representative approximation of the complete Pareto front (PF), which usually consists of tens and hundreds of solutions. Once the representative PF is obtained, several typical solutions on the PF (especially the extreme ones and/or compromised ones) will be chosen and then examined by the decision-makers. But the remained considerable solutions on the PF are left behind, leading to a significant waste of computational resources. The preference-inspired MOEA has been suggested to resolve this problem as its focus on obtaining solutions in the region of interests only.

In this work, a preference-inspired multi-objective axial flow compressor throughflow design optimization approach is developed through integrating the preference-inspired reference vector guided evolutionary algorithm and the open-source axial flow compressor axisymmetric throughflow design code of T-AXI. In terms of the objective definition for the multi-objective throughflow design, efficiency, stall margin, and blade counts are taken as the focuses. Specifically, the equivalent relative velocity ratio is adopted to form an operability parameter to assess designs with better or worse stall margin of the whole compressor. The developed method is used in the design of a 6-stage axial flow compressor at the throughflow level, which is a 3-objective problem with 58 variables. Preference-inspired and non-preference optimization are compared to verify the cost-saving benefit of incorporating the preferences into multi-objective optimization. Designs in the preferred target regions are obtained which demonstrates the practical ability of the proposed preference-inspired throughflow optimization design method for multi-stage axial flow compressors.

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