IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i2d10.1007_s10845-021-01824-w.html
   My bibliography  Save this article

Design and application of digital twin system for the blade-rotor test rig

Author

Listed:
  • Jian-Guo Duan

    (Shanghai Maritime University)

  • Tian-Yu Ma

    (Shanghai Maritime University)

  • Qing-Lei Zhang

    (Shanghai Maritime University)

  • Zhen Liu

    (Shanghai Maritime University)

  • Ji-Yun Qin

    (Shanghai Maritime University)

Abstract

Digital twin technology is a key technology to realize cyber-physical system. Owing to the problems of low visual monitoring of the blade-rotor test rig and poor equipment monitoring capabilities, this paper proposes a framework based on the digital twin technology. The digital-twin based architecture and major function implementation have been carried out form five dimensions, i.e. Physical layer, Virtual layer, Data layer, Application layer and User layer. Three key technologies utilized to create the system including underlying equipment real-time communication, virtual space building and virtual reality interaction have been demonstrated in this paper. Based on RS-485 and other communication protocols, the data acquisition of the underlying devices have been successfully implemented, and then the real-time data reading has been achieved. Finally, the rationality of the system has been validated by taking the blade-rotor test rig as the application object, which provides a reference for the monitoring and evaluation of equipment involved in manufacturing and experiment.

Suggested Citation

  • Jian-Guo Duan & Tian-Yu Ma & Qing-Lei Zhang & Zhen Liu & Ji-Yun Qin, 2023. "Design and application of digital twin system for the blade-rotor test rig," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 753-769, February.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01824-w
    DOI: 10.1007/s10845-021-01824-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01824-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01824-w?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    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. Xinzhou Wu & Zhe Cheng & Victor E. Kuzmichev, 2023. "Dynamic Fit Optimization and Effect Evaluation of a Female Wetsuit Based on Virtual Technology," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    2. Sajjad Rahmanzadeh & Mir Saman Pishvaee & Kannan Govindan, 2023. "Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network," Annals of Operations Research, Springer, vol. 329(1), pages 979-1007, October.
    3. Chengjun Li & Liguo Yao & Yao Lu & Songsong Zhang & Taihua Zhang, 2025. "DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network," Future Internet, MDPI, vol. 17(2), pages 1-24, February.
    4. F. H. Abanda & N. Jian & S. Adukpo & V. V. Tuhaise & M. B. Manjia, 2025. "Digital twin for product versus project lifecycles’ development in manufacturing and construction industries," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 801-831, February.
    5. Evangelos Katsamakas, 2024. "Business models for the simulation hypothesis," Papers 2404.08991, arXiv.org.
    6. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
    7. Xueru Zhang & Dennis K. J. Lin & Lin Wang, 2023. "Digital Triplet: A Sequential Methodology for Digital Twin Learning," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    8. Bai, Fan & Quan, Hong-Bing & Yin, Ren-Jie & Zhang, Zhuo & Jin, Shu-Qi & He, Pu & Mu, Yu-Tong & Gong, Xiao-Ming & Tao, Wen-Quan, 2022. "Three-dimensional multi-field digital twin technology for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 324(C).
    9. Muhammad Ali Musarat & Alishba Sadiq & Wesam Salah Alaloul & Mohamed Mubarak Abdul Wahab, 2022. "A Systematic Review on Enhancement in Quality of Life through Digitalization in the Construction Industry," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    10. Kerstens, Andrea & Langley, David J., 2025. "An innovation intermediary’s role in enhancing absorptive capacity for cross-industry digital innovation: Introducing an awareness capability and new intermediary practices," Journal of Business Research, Elsevier, vol. 196(C).
    11. Pin Wu & Lulu Ji & Wenyan Yuan & Zhitao Liu & Tiantian Tang, 2023. "A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    12. Jieyin Lyu & Shouqin Zhou & Jingang Liu & Bingchun Jiang, 2023. "Intelligent-Technology-Empowered Active Emergency Command Strategy for Urban Hazardous Chemical Disaster Management," Sustainability, MDPI, vol. 15(19), pages 1-28, September.
    13. Ruixue Zhang & Huate Zhu & Qinglin Chang & Qirong Mao, 2025. "A Comprehensive Review of Digital Twins Technology in Agriculture," Agriculture, MDPI, vol. 15(9), pages 1-25, April.
    14. Milad Elyasi & Simon Thevenin & Audrey Cerqueus, 2025. "Use of AI in assembly line design and worker and equipment management: review and future directions," Flexible Services and Manufacturing Journal, Springer, vol. 37(2), pages 367-408, June.
    15. Majidi Nezhad, Meysam & Neshat, Mehdi & Sylaios, Georgios & Astiaso Garcia, Davide, 2024. "Marine energy digitalization digital twin's approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    16. Sheng-Wen Zhou & Shun-Sheng Guo & Wen-Xiang Xu & Bai-Gang Du & Jun-Yong Liang & Lei Wang & Yi-Bing Li, 2024. "Digital Twin-Based Pump Station Dynamic Scheduling for Energy-Saving Optimization in Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(8), pages 2773-2789, June.
    17. Yongzhi Wang & Shaoming Liao & Zhiqun Gong & Fei Deng & Shiyou Yin, 2024. "Enhancing Construction Management Digital Twins Through Process Mining of Progress Logs," Sustainability, MDPI, vol. 16(22), pages 1-29, November.
    18. Zhang, Anshan & Wang, Feiliang & Li, Huanyu & Pang, Bo & Yang, Jian, 2024. "Carbon emissions accounting and estimation of carbon reduction potential in the operation phase of residential areas based on digital twin," Applied Energy, Elsevier, vol. 376(PB).
    19. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    20. Jielin Chen & Shuang Li & Hanwei Teng & Xiaolong Leng & Changping Li & Rendi Kurniawan & Tae Jo Ko, 2025. "Digital twin-driven real-time suppression of delamination damage in CFRP drilling," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1459-1476, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01824-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.