IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v297y2024ics0360544224011009.html
   My bibliography  Save this article

Development of helium turbine loss model based on knowledge transfer with neural network and its application on aerodynamic design

Author

Listed:
  • Liu, Changxing
  • Zou, Zhengping
  • Xu, Pengcheng
  • Wang, Yifan

Abstract

Helium turbines are widely used in the Closed Brayton Cycle for power generation and aerospace applications. The primary concerns of designing highly loaded helium turbines include choosing between conventional and contra-rotating designs and the guidelines for selecting design parameters. A loss model serving as an evaluation means is the key to addressing this issue. Because of the property disparities between helium and air, turbines utilizing as working fluid experience distinct loss mechanisms. Consequently, directly applying gas turbine experience to the design of helium turbines leads to inherent inaccuracies. A helium turbine loss model is developed by combining knowledge transfer and the Neural Network method to accurately predict performance at design and off-design points. By utilizing the loss model, design parameter selection guidelines for helium turbines are obtained. A comparative analysis is conducted of conventional and contra-rotating helium turbine designs. Results show that the prediction errors of the loss model are below 0.5 % at over 90 % of test samples, surpassing the accuracy achieved by the gas turbine loss model. Design parameter selection guidelines for helium turbines differ significantly from those based on gas turbine experience. The contra-rotating helium turbine design exhibits advantages in size, weight, and aerodynamic performance.

Suggested Citation

  • Liu, Changxing & Zou, Zhengping & Xu, Pengcheng & Wang, Yifan, 2024. "Development of helium turbine loss model based on knowledge transfer with neural network and its application on aerodynamic design," Energy, Elsevier, vol. 297(C).
  • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224011009
    DOI: 10.1016/j.energy.2024.131327
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224011009
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.131327?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. Da Lio, Luca & Manente, Giovanni & Lazzaretto, Andrea, 2014. "New efficiency charts for the optimum design of axial flow turbines for organic Rankine cycles," Energy, Elsevier, vol. 77(C), pages 447-459.
    2. Tian, Zhitao & Zheng, Qun & Jiang, Bin, 2018. "Effect of Reynolds number on supercritical helium axial compressor rotors performance in closed Brayton cycle," Energy, Elsevier, vol. 145(C), pages 217-227.
    3. Son, Seongmin & Jeong, Yongju & Cho, Seong Kuk & Lee, Jeong Ik, 2020. "Development of supercritical CO2 turbomachinery off-design model using 1D mean-line method and Deep Neural Network," Applied Energy, Elsevier, vol. 263(C).
    4. Wang, Tianze & Xu, Jinliang & Wang, Zhaofu & Zheng, Haonan & Qi, Jianhui & Liu, Guanglin, 2023. "Irreversible losses, characteristic sizes and efficiencies of sCO2 axial turbines dependent on power capacities," Energy, Elsevier, vol. 275(C).
    5. Ansari, Mehran & Esfahanian, Vahid & Izadi, Mohammad Javad & Bashi, Hosein & Tavakoli, Alireza & Kordi, Mohammad, 2023. "Implementation of hot steam injection in steam turbine design: A novel mean-line method coupled with multi-objective optimization and neural network," Energy, Elsevier, vol. 283(C).
    6. Li, Xiaoming & Lv, Cui & Yang, Shaoqi & Li, Jian & Deng, Bicai & Li, Qing, 2019. "Preliminary design and performance analysis of a radial inflow turbine for a large-scale helium cryogenic system," Energy, Elsevier, vol. 167(C), pages 106-116.
    7. Witanowski, Łukasz & Klonowicz, Piotr & Lampart, Piotr & Klimaszewski, Piotr & Suchocki, Tomasz & Jędrzejewski, Łukasz & Zaniewski, Dawid & Ziółkowski, Paweł, 2023. "Impact of rotor geometry optimization on the off-design ORC turbine performance," Energy, Elsevier, vol. 265(C).
    8. 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).
    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. Zhang, Weihao & Li, Lele & Li, Ya & Jiang, Chiju & Wang, Yufan, 2023. "A parameterized-loading driven inverse design and multi-objective coupling optimization method for turbine blade based on deep learning," Energy, Elsevier, vol. 281(C).
    2. Li, Jinxing & Li, Yunzhu & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2023. "Multi-fidelity graph neural network for flow field data fusion of turbomachinery," Energy, Elsevier, vol. 285(C).
    3. Thanganadar, Dhinesh & Fornarelli, Francesco & Camporeale, Sergio & Asfand, Faisal & Patchigolla, Kumar, 2021. "Off-design and annual performance analysis of supercritical carbon dioxide cycle with thermal storage for CSP application," Applied Energy, Elsevier, vol. 282(PA).
    4. Sun, Ke & Lu, Huawei & Malik, Adil & Fan, Yingqi & Tian, Zhitao, 2024. "Effect of Reynolds number on the aerodynamic performance of highly loaded helium compressor cascade in high temperature gas-cooled reactor," Energy, Elsevier, vol. 289(C).
    5. Michalski, Sebastian & Hanak, Dawid P. & Manovic, Vasilije, 2020. "Advanced power cycles for coal-fired power plants based on calcium looping combustion: A techno-economic feasibility assessment," Applied Energy, Elsevier, vol. 269(C).
    6. Sun, Lei & Liu, Tianyuan & Wang, Ding & Huang, Chengming & Xie, Yonghui, 2022. "Deep learning method based on graph neural network for performance prediction of supercritical CO2 power systems," Applied Energy, Elsevier, vol. 324(C).
    7. Masi, Massimo & Da Lio, Luca & Lazzaretto, Andrea, 2020. "An insight into the similarity approach to predict the maximum efficiency of organic Rankine cycle turbines," Energy, Elsevier, vol. 198(C).
    8. Łukasz Witanowski, 2024. "Multi-Objective Optimization of a Small-Scale ORC-VCC System Using Low-GWP Refrigerants," Energies, MDPI, vol. 17(21), pages 1-18, October.
    9. Yuhui Xiao & Yuan Zhou & Yuan Yuan & Yanping Huang & Gengyuan Tian, 2023. "Research Advances in the Application of the Supercritical CO 2 Brayton Cycle to Reactor Systems: A Review," Energies, MDPI, vol. 16(21), pages 1-23, October.
    10. Zhang, Chengbin & Wu, Zhe & Wang, Jiadian & Ding, Ce & Gao, Tieyu & Chen, Yongping, 2023. "Thermodynamic performance of a radial-inflow turbine for ocean thermal energy conversion using ammonia," Renewable Energy, Elsevier, vol. 202(C), pages 907-920.
    11. 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).
    12. 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).
    13. D'Amico, F. & Pallis, P. & Leontaritis, A.D. & Karellas, S. & Kakalis, N.M. & Rech, S. & Lazzaretto, A., 2018. "Semi-empirical model of a multi-diaphragm pump in an Organic Rankine Cycle (ORC) experimental unit," Energy, Elsevier, vol. 143(C), pages 1056-1071.
    14. Wang, Zhiqi & Xie, Baoqi & Xia, Xiaoxia & Luo, Lan & Yang, Huya & Li, Xin, 2023. "Entropy production analysis of a radial inflow turbine with variable inlet guide vane for ORC application," Energy, Elsevier, vol. 265(C).
    15. Steven Lecompte & Sanne Lemmens & Henk Huisseune & Martijn Van den Broek & Michel De Paepe, 2015. "Multi-Objective Thermo-Economic Optimization Strategy for ORCs Applied to Subcritical and Transcritical Cycles for Waste Heat Recovery," Energies, MDPI, vol. 8(4), pages 1-28, April.
    16. Zaharil, Hafiz Aman, 2021. "An investigation on the usage of different supercritical fluids in parabolic trough solar collector," Renewable Energy, Elsevier, vol. 168(C), pages 676-691.
    17. Witanowski, Łukasz & Ziółkowski, Paweł & Klonowicz, Piotr & Lampart, Piotr, 2023. "A hybrid approach to optimization of radial inflow turbine with principal component analysis," Energy, Elsevier, vol. 272(C).
    18. Da Lio, Luca & Manente, Giovanni & Lazzaretto, Andrea, 2017. "A mean-line model to predict the design efficiency of radial inflow turbines in organic Rankine cycle (ORC) systems," Applied Energy, Elsevier, vol. 205(C), pages 187-209.
    19. Zhu, Sipeng & Deng, Kangyao & Liu, Sheng, 2015. "Modeling and extrapolating mass flow characteristics of a radial turbocharger turbine," Energy, Elsevier, vol. 87(C), pages 628-637.
    20. Mylena Vieira Pinto Menezes & Icaro Figueiredo Vilasboas & Julio Augusto Mendes da Silva, 2022. "Liquid Air Energy Storage System (LAES) Assisted by Cryogenic Air Rankine Cycle (ARC)," Energies, MDPI, vol. 15(8), pages 1-16, 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:eee:energy:v:297:y:2024:i:c:s0360544224011009. 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.