IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i5p2495-d505872.html
   My bibliography  Save this article

Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review

Author

Listed:
  • Rong Xie

    (School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China)

  • Muyan Chen

    (School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China)

  • Weihuang Liu

    (School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China)

  • Hongfei Jian

    (School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, China)

  • Yanjun Shi

    (School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.

Suggested Citation

  • Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2495-:d:505872
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/5/2495/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/5/2495/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Jianxi & Liu, Pei & Li, Zheng, 2020. "Hybrid modelling and digital twin development of a steam turbine control stage for online performance monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Roberto Rocca & Paolo Rosa & Claudio Sassanelli & Luca Fumagalli & Sergio Terzi, 2020. "Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case," Sustainability, MDPI, vol. 12(6), pages 1-27, March.
    3. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    4. Kamil Židek & Ján Piteľ & Milan Adámek & Peter Lazorík & Alexander Hošovský, 2020. "Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    5. Hyungjoo Kim & Jungho Kang, 2016. "Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    6. Jinwooung Kim & Sung-Ah Kim, 2020. "Lifespan Prediction Technique for Digital Twin-Based Noise Barrier Tunnels," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    7. Mohanasundaram Anthony & Valsalal Prasad & Kannadasan Raju & Mohammed H. Alsharif & Zong Woo Geem & Junhee Hong, 2020. "Design of Rotor Blades for Vertical Axis Wind Turbine with Wind Flow Modifier for Low Wind Profile Areas," Sustainability, MDPI, vol. 12(19), pages 1-26, September.
    8. Jiman Park & Byungyun Yang, 2020. "GIS-Enabled Digital Twin System for Sustainable Evaluation of Carbon Emissions: A Case Study of Jeonju City, South Korea," Sustainability, MDPI, vol. 12(21), pages 1-21, November.
    9. Michael W. Grieves, 2005. "Product lifecycle management: the new paradigm for enterprises," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 2(1/2), pages 71-84.
    10. Sanguk Park & Sanghoon Lee & Sangmin Park & Sehyun Park, 2019. "AI-Based Physical and Virtual Platform with 5-Layered Architecture for Sustainable Smart Energy City Development," Sustainability, MDPI, vol. 11(16), pages 1-30, August.
    11. Mohammad Omidi & Shu-Jie Liu & Soheil Mohtaram & Hui-Tian Lu & Hong-Chao Zhang, 2019. "Improving Centrifugal Compressor Performance by Optimizing the Design of Impellers Using Genetic Algorithm and Computational Fluid Dynamics Methods," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    12. Muhammad Ateeq & Muhammad Khalil Afzal & Muhammad Naeem & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Deep Learning-Based Multiparametric Predictions for IoT," Sustainability, MDPI, vol. 12(18), pages 1-12, September.
    13. Maurizio Bevilacqua & Eleonora Bottani & Filippo Emanuele Ciarapica & Francesco Costantino & Luciano Di Donato & Alessandra Ferraro & Giovanni Mazzuto & Andrea Monteriù & Giorgia Nardini & Marco Orten, 2020. "Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants," Sustainability, MDPI, vol. 12(3), pages 1-17, February.
    14. Özden Tozanlı & Elif Kongar & Surendra M. Gupta, 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain," Sustainability, MDPI, vol. 12(13), pages 1-33, July.
    15. Junhu Ruan & Felix T. S. Chan & Fangwei Zhu & Xuping Wang & Jing Yang, 2016. "A Visualization Review of Cloud Computing Algorithms in the Last Decade," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    16. Catalina Ferat Toscano & Cecilia Martin-del-Campo & Gabriela Moeller-Chavez & Gabriel Leon de los Santos & Juan-Luis François & Daniel Revollo Fernandez, 2019. "Life Cycle Assessment of a Combined-Cycle Gas Turbine with a Focus on the Chemicals Used in Water Conditioning," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    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. Li, Guolong & Li, Yanjun & Fang, Chengyue & Su, Jian & Wang, Haotong & Sun, Shengdi & Zhang, Guolei & Shi, Jianxin, 2023. "Research on fault diagnosis of supercharged boiler with limited data based on few-shot learning," Energy, Elsevier, vol. 281(C).
    2. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    3. Ágota Bányai & Tamás Bányai, 2022. "Real-Time Maintenance Policy Optimization in Manufacturing Systems: An Energy Efficiency and Emission-Based Approach," Sustainability, MDPI, vol. 14(17), pages 1-15, August.

    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. Özden Tozanlı & Elif Kongar & Surendra M. Gupta, 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain," Sustainability, MDPI, vol. 12(13), pages 1-33, July.
    2. Mezzour Ghita & Benhadou Siham & Medromi Hicham & Mounaam Amine, 2022. "HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring," Energies, MDPI, vol. 15(15), pages 1-38, July.
    3. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
    4. Georgios Falekas & Athanasios Karlis, 2021. "Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-26, September.
    5. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    6. João Vieira & João Poças Martins & Nuno Marques de Almeida & Hugo Patrício & João Gomes Morgado, 2022. "Towards Resilient and Sustainable Rail and Road Networks: A Systematic Literature Review on Digital Twins," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    7. Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    9. Vijayaraja Loganathan & Dhanasekar Ravikumar & Rupa Kesavan & Kanakasri Venkatesan & Raadha Saminathan & Raju Kannadasan & Mahalingam Sudhakaran & Mohammed H. Alsharif & Zong Woo Geem & Junhee Hong, 2022. "A Case Study on Renewable Energy Sources, Power Demand, and Policies in the States of South India—Development of a Thermoelectric Model," Sustainability, MDPI, vol. 14(14), pages 1-29, July.
    10. Maria Mercanti-Guérin, 2021. "From Perceived Creativity To Status Quo Bias The Case Of Digital Twins In The Home," Post-Print hal-03450262, HAL.
    11. Yen Sheng Tsai & Wei-Hsi Hung, 2023. "A low-cost intelligent tracking system for clothing manufacturers," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 473-491, February.
    12. Magdalena Rusch & Josef‐Peter Schöggl & Rupert J. Baumgartner, 2023. "Application of digital technologies for sustainable product management in a circular economy: A review," Business Strategy and the Environment, Wiley Blackwell, vol. 32(3), pages 1159-1174, March.
    13. Chen, Ziyue & Huang, Lizhen, 2021. "Digital twins for information-sharing in remanufacturing supply chain: A review," Energy, Elsevier, vol. 220(C).
    14. Francisco-Javier Ferrández-Pastor & Higinio Mora & Antonio Jimeno-Morenilla & Bruno Volckaert, 2018. "Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services," Sustainability, MDPI, vol. 10(11), pages 1-23, October.
    15. Hassan Alimam & Giovanni Mazzuto & Marco Ortenzi & Filippo Emanuele Ciarapica & Maurizio Bevilacqua, 2023. "Intelligent Retrofitting Paradigm for Conventional Machines towards the Digital Triplet Hierarchy," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    16. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    17. Pengcheng Ni & Zhiyuan Ye & Can Cao & Zhimin Guo & Jian Zhao & Xing He, 2023. "Cooperative Game-Based Collaborative Optimal Regulation-Assisted Digital Twins for Wide-Area Distributed Energy," Energies, MDPI, vol. 16(6), pages 1-17, March.
    18. 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.
    19. Muhammad Saeed & Abdallah S. Berrouk & Burhani M. Burhani & Ahmed M. Alatyar & Yasser F. Al Wahedi, 2021. "Turbine Design and Optimization for a Supercritical CO 2 Cycle Using a Multifaceted Approach Based on Deep Neural Network," Energies, MDPI, vol. 14(22), pages 1-27, November.
    20. Rajeev Rathi & Dattatraya Balasaheb Sabale & Jiju Antony & Mahender Singh Kaswan & Raja Jayaraman, 2022. "An Analysis of Circular Economy Deployment in Developing Nations’ Manufacturing Sector: A Systematic State-of-the-Art Review," Sustainability, MDPI, vol. 14(18), pages 1-23, September.

    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:jsusta:v:13:y:2021:i:5:p:2495-:d:505872. 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.