IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v201y2020ics0360544220306897.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220306897
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117582?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tang, Xinzi & Wang, Zhe & Xiao, Peng & Peng, Ruitao & Liu, Xiongwei, 2020. "Uncertainty quantification based optimization of centrifugal compressor impeller for aerodynamic robustness under stochastic operational conditions," Energy, Elsevier, vol. 195(C).
    2. Omer Faruk Atac & Jeong-Eui Yun & Taehyun Noh, 2018. "Aerodynamic Design Optimization of a Micro Radial Compressor of a Turbocharger," Energies, MDPI, vol. 11(7), pages 1-18, July.
    3. Nima Khoshkalam & Mohammad Mojaddam & Keith R. Pullen, 2019. "Characterization of the Performance of a Turbocharger Centrifugal Compressor by Component Loss Contributions," Energies, MDPI, vol. 12(14), pages 1-21, July.
    4. 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.
    5. Nakhchi, M.E. & Naung, S. Win & Rahmati, M., 2022. "Influence of blade vibrations on aerodynamic performance of axial compressor in gas turbine: Direct numerical simulation," Energy, Elsevier, vol. 242(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:201:y:2020:i:c:s0360544220306897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.