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. Throughflow design is naturally a multi-objective problem in terms of stringent requirements on efficiency, working range, total axial length and blade counts, etc. An incorporation of multi-objective evolutionary algorithms (MOEA) into throughflow design has been proven effective. In the existing works, the MOEA commonly uses thousands of the 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. To resolve such a problem, an integration of preference-inspired MOEA would be an effective alternative. With preference-incorporated, the multi-objective optimization process can be largely reduced where a relatively small number of optimal designs are obtained merely around the preferred regions, and selection burden of decision-makers can also be relieved since representative solutions are chosen from the small number of optimal solutions around the preferred regions instead of tens or hundreds of solutions.

In this paper, 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 and stall margin are taken as the focus. Specifically, the blade row stall indicator proposed by Aungier, 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 optimization design of a 6-stage axial flow compressor at throughflow level, which is a 2-objective problem with 58 variables. Both non-preference and preference-inspired optimizations are conducted. Compared with baseline design, solutions with significant improvements over efficiency and stall margin are obtained by either non-preference or preference-inspired optimization. For the preference-inspired optimization, well-converged solutions are obtained in the target regions merely using half of the function evaluations of the non-preference optimization, which demonstrated the capacity of the proposed method in accelerating the optimization process and reducing the wasted optimal solutions.


Preference-Inspired Multi-Objective Throughflow-Design Optimization Approach of Axial Flow Compressor and its Application

Division / Sponsor:
International Gas Turbine Institute (IGTI)

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2020/volume/84058

Turbo Expo - Virtual Conference

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.