IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i21p7301-d1269058.html
   My bibliography  Save this article

Numerical Study on Cavitating Flow-Induced Pressure Fluctuations in a Gerotor Pump

Author

Listed:
  • Peijian Zhou

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
    College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
    Engineering Research Center of High-efficiency and Energy-saving Large Axial Flow Pumping Station, Yangzhou University, Yangzhou 225009, China)

  • Jiayi Cui

    (College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China)

  • Gang Xiao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
    College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China)

  • Chun Xiang

    (School of Mechanical and Automotive Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Jiacheng Dai

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

  • Shuihua Zheng

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)

Abstract

Using the RNG k-ε turbulence model and a full cavitation model, this study numerically simulated cavitating flow-induced pressure fluctuations in a gerotor pump and analyzed the relationship between cavitating flow and pressure fluctuations. The results demonstrate that, as the inlet pressure decreases, the cavitation phenomenon in the gerotor pump intensifies, and the cavitation range in the rotor increases. Some of the vapor even spreads into the oil inlet groove, leading to high vapor content in the chamber that is in contact with the oil inlet groove. The pressure fluctuation characteristics of the flow field in the pump exhibit evident periodic changes. Under different cavitation conditions, the pressure fluctuation amplitude at the monitoring point decreases with increasing inlet pressure, whereas the main frequency of pressure fluctuation remains unaffected by cavitation conditions. The pressure fluctuation amplitude is the strongest at point O 1 of demarcation between the low-pressure and high-pressure zones in the chamber, and the volume between the oil inlet groove and the oil outlet groove serves as the main vibration source in the rotor pump. To ensure the stable and efficient operation of the gerotor pump, it is recommended to operate it at a larger NPSH.

Suggested Citation

  • Peijian Zhou & Jiayi Cui & Gang Xiao & Chun Xiang & Jiacheng Dai & Shuihua Zheng, 2023. "Numerical Study on Cavitating Flow-Induced Pressure Fluctuations in a Gerotor Pump," Energies, MDPI, vol. 16(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7301-:d:1269058
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/21/7301/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/21/7301/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenqiang Zhou & Peijian Zhou & Chun Xiang & Yang Wang & Jiegang Mou & Jiayi Cui, 2023. "A Review of Bionic Structures in Control of Aerodynamic Noise of Centrifugal Fans," Energies, MDPI, vol. 16(11), pages 1-24, May.
    2. Huang, Renfang & Zhang, Zhen & Zhang, Wei & Mou, Jiegang & Zhou, Peijian & Wang, Yiwei, 2020. "Energy performance prediction of the centrifugal pumps by using a hybrid neural network," Energy, Elsevier, vol. 213(C).
    Full references (including those not matched with items on IDEAS)

    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. Dehghan, Amir Arsalan & Shojaeefard, Mohammad Hassan & Roshanaei, Maryam, 2024. "Exploring a new criterion to determine the onset of cavitation in centrifugal pumps from energy-saving standpoint; experimental and numerical investigation," Energy, Elsevier, vol. 293(C).
    2. Wenqiang Zhou & Peijian Zhou & Chun Xiang & Yang Wang & Jiegang Mou & Jiayi Cui, 2023. "A Review of Bionic Structures in Control of Aerodynamic Noise of Centrifugal Fans," Energies, MDPI, vol. 16(11), pages 1-24, May.
    3. Chen Qiao & Xuemin Ye & Yunhao Wu & Chunxi Li, 2025. "Insight into the Impact of Blade Perforation on the Aerodynamics and Acoustics of a Two-Stage Variable-Pitch Axial Fan," Energies, MDPI, vol. 18(8), pages 1-17, April.
    4. Qin, Yonglin & Li, Deyou & Wang, Hongjie & Liu, Zhansheng & Wei, Xianzhu & Wang, Xiaohang, 2022. "Multi-objective optimization design on high pressure side of a pump-turbine runner with high efficiency," Renewable Energy, Elsevier, vol. 190(C), pages 103-120.
    5. Hao Wang & Peijian Zhou & Ting Chen & Jiegang Mou & Jiayi Cui & Huiming Zhang, 2023. "Optimization of Liquid−Liquid Mixing in a Novel Mixer Based on Hybrid SVR-DE Model," Energies, MDPI, vol. 16(4), pages 1-17, February.
    6. Wang, Yuqi & Du, Qiuwan & Li, Yunzhu & Zhang, Di & Xie, Yonghui, 2022. "Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques," Energy, Elsevier, vol. 238(PB).
    7. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).
    8. Xiaoping Chen & Xiaoming Zhang & Xiaojun Li, 2022. "Evolution Characteristics of Energy Change Field in a Centrifugal Pump during Rapid Starting Period," Energies, MDPI, vol. 15(22), pages 1-15, November.
    9. Zhang, Liwen & Wang, Xin & Wu, Peng & Huang, Bin & Wu, Dazhuan, 2023. "Optimization of a centrifugal pump to improve hydraulic efficiency and reduce hydro-induced vibration," Energy, Elsevier, vol. 268(C).
    10. Abrasaldo, Paul Michael B. & Zarrouk, Sadiq J. & Kempa-Liehr, Andreas W., 2024. "A systematic review of data analytics applications in above-ground geothermal energy operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    11. Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
    12. Hanbing Ma & Lukas Gaisser & Stefan Riedelbauch, 2023. "Monitoring Pumping Units by Convolutional Neural Networks for Operating Point Estimations," Energies, MDPI, vol. 16(11), pages 1-12, May.
    13. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    14. Zhang, Yiming & Li, Jingxiang & Fei, Liangyu & Feng, Zhiyan & Gao, Jingzhou & Yan, Wenpeng & Zhao, Shengdun, 2023. "Operational performance estimation of vehicle electric coolant pump based on the ISSA-BP neural network," Energy, Elsevier, vol. 268(C).
    15. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    16. Bai, Ling & Yang, Yang & Zhou, Ling & Li, Yuanzhe & Xiao, Yu & Shi, Weidong, 2022. "Optimal design and performance improvement of an electric submersible pump impeller based on Taguchi approach," Energy, Elsevier, vol. 252(C).
    17. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
    18. Wang, Yuqi & Liu, Tianyuan & Meng, Yue & Zhang, Di & Xie, Yonghui, 2022. "Integrated optimization for design and operation of turbomachinery in a solar-based Brayton cycle based on deep learning techniques," Energy, Elsevier, vol. 252(C).
    19. Lei Wang & Jiayi Cui & Lingfeng Shu & Denghui Jiang & Chun Xiang & Linwei Li & Peijian Zhou, 2022. "Research on the Vortex Rope Control Techniques in Draft Tube of Francis Turbines," Energies, MDPI, vol. 15(24), pages 1-27, December.
    20. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).

    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:gam:jeners:v:16:y:2023:i:21:p:7301-:d:1269058. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.