IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p921-d1591402.html
   My bibliography  Save this article

Investigations on the Aerodynamic Interactions Between Turbine and Diffuser System by Employing the Kriging Method

Author

Listed:
  • Bin Qiu

    (Advanced Gas Turbine Laboratory, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
    National Key Laboratory of Science and Technology on Advanced Light-Duty Gas-Turbine, Beijing 100190, China
    Key Laboratory of Advanced Energy and Power, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China)

  • Jinglun Fu

    (Advanced Gas Turbine Laboratory, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
    National Key Laboratory of Science and Technology on Advanced Light-Duty Gas-Turbine, Beijing 100190, China
    Key Laboratory of Advanced Energy and Power, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China)

  • Xiangling Kong

    (Gas Turbine Digitalization Research Center, Nanjing Institute of Future Energy System, Nanjing 210000, China)

  • Hongwu Zhang

    (Advanced Gas Turbine Laboratory, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China
    National Key Laboratory of Science and Technology on Advanced Light-Duty Gas-Turbine, Beijing 100190, China
    Key Laboratory of Advanced Energy and Power, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China)

  • Qiang Yu

    (Anwise Technology Co., Ltd., Beijing 100024, China)

Abstract

An exhaust diffuser determines the turbine outlet pressure by recovering kinetic energy. Conversely, the distributions of the total pressure and flow directions at the turbine exit affect the aerodynamic performance of the exhaust diffuser. As the output power increases gradually, the structure of the modern gas turbine becomes more compact. Consequently, the coupled effect of the flow in the last-stage turbine and the exhaust diffuser becomes increasingly obvious. Understanding the correlation between the flow field and the performance of the coupled system is of great significance. As a predictive regression algorithm, the Kriging method is widely used due to its high efficiency and unique mathematical characteristics. In this paper, computational fluid dynamics (CFD) numerical simulation is employed to investigate the interactions between the flow fields of the coupled system, and the corresponding datasets are obtained. Accordingly, the Kriging method is successfully employed to reconstruct the complex flow field, and a quantitative model describing the interaction between the two parts is established. This paper provides a detailed summary of the interaction between the flow field in the exhaust diffuser and the flow field at the outlet of the last-stage turbine. Through the prediction of the flow field, the conditions that induce the separation vortex on the casing of the diffuser are determined. Specifically, the slope of the total pressure change along the blade height near the casing is found to be k = −4.37.

Suggested Citation

  • Bin Qiu & Jinglun Fu & Xiangling Kong & Hongwu Zhang & Qiang Yu, 2025. "Investigations on the Aerodynamic Interactions Between Turbine and Diffuser System by Employing the Kriging Method," Energies, MDPI, vol. 18(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:921-:d:1591402
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/921/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/921/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    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. Jesus L. Lobo & Igor Ballesteros & Izaskun Oregi & Javier Del Ser & Sancho Salcedo-Sanz, 2020. "Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants," Energies, MDPI, vol. 13(3), pages 1-28, February.
    2. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    3. Zagorowska, Marta & Schulze Spüntrup, Frederik & Ditlefsen, Arne-Marius & Imsland, Lars & Lunde, Erling & Thornhill, Nina F., 2020. "Adaptive detection and prediction of performance degradation in off-shore turbomachinery," Applied Energy, Elsevier, vol. 268(C).
    4. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    5. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    6. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    7. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).

    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:18:y:2025:i:4:p:921-:d:1591402. 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.