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<p>This study focused on enhancing the aerodynamic performance of a supercritical carbon dioxide centrifugal compressor through a multi-objective optimization approach. Key geometric parameters of the impeller and vaned diffuser were analyzed using random forest and Sobol sensitivity analysis, revealing distinct regulation mechanisms: adiabatic efficiency was governed by multi-parameter interactions, while pressure ratio was predominantly influenced by first-order parameters, particularly the blade exit angle. A Kriging surrogate model coupled with the NSGA-III algorithm achieved high predictive accuracy, with pressure ratio and adiabatic efficiency errors of only 0.4% and 2%, respectively, compared to CFD results, while significantly reducing optimization time. After optimization, the compressor exhibited a 3.42% increase in pressure ratio and a 4.43% improvement in adiabatic efficiency. The NSGA-III algorithm also demonstrated superior convergence, reducing iteration time by 24.57% in multi-dimensional optimization. Flow field analysis indicated that increasing the impeller outlet inclination angle and the radial passage height effectively reduced tip leakage and vortex intensity, thereby expanding the effective flow area. Moreover, the overall compressor performance was more significantly affected by the internal flow characteristics of the diffuser than by the impeller alone.</p>

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Robust Parameter Screening and Multi-objective Optimization of an SCO2 Centrifugal Compressor

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • ZhengJing Shen,
  • Fengyi Jiang,
  • WeiMing Lin,
  • Liuhao Liu,
  • Jiangbo Wu,
  • Senchun Miao,
  • Xiaoze Du

摘要

This study focused on enhancing the aerodynamic performance of a supercritical carbon dioxide centrifugal compressor through a multi-objective optimization approach. Key geometric parameters of the impeller and vaned diffuser were analyzed using random forest and Sobol sensitivity analysis, revealing distinct regulation mechanisms: adiabatic efficiency was governed by multi-parameter interactions, while pressure ratio was predominantly influenced by first-order parameters, particularly the blade exit angle. A Kriging surrogate model coupled with the NSGA-III algorithm achieved high predictive accuracy, with pressure ratio and adiabatic efficiency errors of only 0.4% and 2%, respectively, compared to CFD results, while significantly reducing optimization time. After optimization, the compressor exhibited a 3.42% increase in pressure ratio and a 4.43% improvement in adiabatic efficiency. The NSGA-III algorithm also demonstrated superior convergence, reducing iteration time by 24.57% in multi-dimensional optimization. Flow field analysis indicated that increasing the impeller outlet inclination angle and the radial passage height effectively reduced tip leakage and vortex intensity, thereby expanding the effective flow area. Moreover, the overall compressor performance was more significantly affected by the internal flow characteristics of the diffuser than by the impeller alone.