IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p6970-d663445.html
   My bibliography  Save this article

Research on Performance of Variable-Lead Rotor Twin Screw Compressor

Author

Listed:
  • Huagen Wu

    (Institute of Compressors, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jiankang Liu

    (Institute of Compressors, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yuqi Shen

    (Institute of Compressors, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Mengtao Liang

    (Institute of Compressors, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Beiyu Zhang

    (Institute of Compressors, School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Twin-screw compressors are widely used in aerodynamics, refrigeration and other fields. The screw rotors are the core component of the screw compressor and affect the performance of the compressor. This paper focuses on variable-lead rotors. A thermal process simulation model considering leakage is established to calculate the efficiency of the compressor. Different lead change methods are compared by evaluating the contact line, exhaust port and simulation results. The results show that the compressor obtains better performance when the lead decreases rapidly on the discharge side. Furthermore, the effects of the wrap angle and internal volume ratio on variable-lead rotors are studied. The work provides a reference for the design of the screw compressor rotor.

Suggested Citation

  • Huagen Wu & Jiankang Liu & Yuqi Shen & Mengtao Liang & Beiyu Zhang, 2021. "Research on Performance of Variable-Lead Rotor Twin Screw Compressor," Energies, MDPI, vol. 14(21), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6970-:d:663445
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/6970/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/21/6970/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Tao Wang & Qiang Qi & Wei Zhang & Dengyi Zhan, 2023. "Research on Optimization of Profile Parameters in Screw Compressor Based on BP Neural Network and Genetic Algorithm," Energies, MDPI, vol. 16(9), pages 1-17, April.

    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:14:y:2021:i:21:p:6970-:d:663445. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.