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Performance improvement of a transonic centrifugal compressor impeller with splitter blade by three-dimensional optimization

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  • Ekradi, Khalil
  • Madadi, Ali

Abstract

This paper presents a procedure for three-dimensional optimization of a transonic centrifugal compressor impeller with splitter blades by integrating 3D blade parameterization method, a genetic algorithm (GA), an artificial neural network, and a CFD solver. Because computational fluid dynamics (CFD) is a time-consuming method, an artificial neural network is coupled with GA to evaluate the objective function. SRV2-O, a typical high-pressure ratio centrifugal impeller, is selected as the test case. A good understanding of flow characteristics in the passage of SRV2-O is obtained using 3D Reynolds Averaged Navier-Stokes solver. Twenty-eight design variables defining the impeller blade angle distribution are used to parametrize the blade geometry. Isentropic efficiency of the impeller is selected as the objective function while the total pressure ratio and mass flow rate are defined as constraints. The optimization results indicate that the performance of the optimum geometry is improved in comparison with the original impeller at both design and off-design conditions. The isentropic efficiency is increased by 0.97% at the design point, and total pressure ratio and mass flow rate are increased by 0.74%, 0.65%, respectively.

Suggested Citation

  • Ekradi, Khalil & Madadi, Ali, 2020. "Performance improvement of a transonic centrifugal compressor impeller with splitter blade by three-dimensional optimization," Energy, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:energy:v:201:y:2020:i:c:s0360544220306897
    DOI: 10.1016/j.energy.2020.117582
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    References listed on IDEAS

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    1. Moussavi, S. Abolfazl & Hajilouy Benisi, Ali & Durali, Mohammad, 2017. "Effect of splitter leading edge location on performance of an automotive turbocharger compressor," Energy, Elsevier, vol. 123(C), pages 511-520.
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    Cited by:

    1. Marco Bicchi & Michele Marconcini & Ernani Fulvio Bellobuono & Elisabetta Belardini & Lorenzo Toni & Andrea Arnone, 2023. "Multi-Point Surrogate-Based Approach for Assessing Impacts of Geometric Variations on Centrifugal Compressor Performance," Energies, MDPI, vol. 16(4), pages 1-21, February.
    2. Wei Li & Jisheng Liu & Pengcheng Fang & Jinxin Cheng, 2021. "A Novel Surface Parameterization Method for Optimizing Radial Impeller Design in Fuel Cell System," Energies, MDPI, vol. 14(9), pages 1-25, May.
    3. Cheng, Hongzhi & Zhou, Chuangxin & Lu, Xingen & Zhao, Shengfeng & Han, Ge & Yang, Chengwu, 2023. "Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties," Energy, Elsevier, vol. 278(PB).
    4. Cheng, Hongzhi & Li, Ziliang & Duan, Penghao & Lu, Xingen & Zhao, Shengfeng & Zhang, Yanfeng, 2023. "Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles," Applied Energy, Elsevier, vol. 352(C).
    5. Rong Huang & Jimin Ni & Houchuan Fan & Xiuyong Shi & Qiwei Wang, 2023. "Investigating a New Method-Based Internal Joint Operation Law for Optimizing the Performance of a Turbocharger Compressor," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    6. Fu, Jianqin & Wang, Huailin & Sun, Xilei & Bao, Huanhuan & Wang, Xun & Liu, Jingping, 2024. "Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary algorithm," Applied Energy, Elsevier, vol. 363(C).

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