IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v335y2025ics0360544225037545.html

Super-aggressive intermediate turbine duct integrated counter-rotating low-pressure turbine vane: an aerodynamic performance optimization study considering sealing flow

Author

Listed:
  • Li, Minghao
  • Luo, Lei
  • Song, Youfu
  • Du, Wei
  • Yan, Han
  • Jia, Qiankun
  • Li, Yue

Abstract

As advanced aeroengines become increasingly critical to energy-efficient propulsion and sustainable aviation, the integrated design of guide vanes within a super-aggressive intermediate turbine duct (SAITD) is a key strategy for enhancing engine performance, effectively reducing axial length and improving the thrust-to-weight ratio of aeroengines. However, the highly diffusive flow within the SAITD makes vane surfaces prone to separation, and the associated flow complexity renders conventional low-pressure turbine aerodynamic design methods ineffective for integrated vane design. Therefore, this study proposes a systematic approach to improving the aerodynamic performance of SAITD-integrated counter-rotating low-pressure turbine vanes using intelligent optimization techniques. Initially, a database of 3,000 parameterized blade geometries was constructed using an in-house parametric modeling framework integrated with numerical simulations. Subsequently, a Transformer-based neural network surrogate model was trained to predict aerodynamic performance and incorporated into a Bayesian optimization framework to determine the optimal blade configuration. Finally, the optimized design was assessed through detailed analysis of the flow field topology. The optimization results obtained using the proposed design methodology demonstrate a 15.24 % reduction in the outlet total pressure loss coefficient compared to the initial integrated guide vane design. This improvement is primarily attributed to geometric modifications at the leading and trailing edges, which effectively suppressed the unstable leading-edge spanwise vortex and improved the sealing flow characteristics at the trailing-edge. The findings provide an effective strategy for reducing aerodynamic losses in SAITD and offer new insights into the aerodynamic design of integrated guide vanes.

Suggested Citation

  • Li, Minghao & Luo, Lei & Song, Youfu & Du, Wei & Yan, Han & Jia, Qiankun & Li, Yue, 2025. "Super-aggressive intermediate turbine duct integrated counter-rotating low-pressure turbine vane: an aerodynamic performance optimization study considering sealing flow," Energy, Elsevier, vol. 335(C).
  • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225037545
    DOI: 10.1016/j.energy.2025.138112
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225037545
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.138112?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. Wu, Siyuan & Cai, Chang & Zhang, Lei & Hu, Zhiqiang & Sun, Xiangyu & Zhong, Xiaohui & Peng, Chaoyi & Meng, Keqilao & Kou, Jianyu & Li, Qing’an, 2025. "Optimizing wind turbine blade performance: A multi-objective approach for power, load and stall characteristics," Energy, Elsevier, vol. 331(C).
    2. Jiang, Ruiqi & Luo, Lei & Wang, Zhangjun & Yan, Han & Jia, Qiankun & Luo, Qiao & Du, Wei, 2025. "The application of bowing blades in a low aspect ratio integrated inter-turbine duct using splitters," Energy, Elsevier, vol. 321(C).
    3. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    4. Guo, Jia & Zeng, Pan & Lei, Liping, 2019. "Performance of a straight-bladed vertical axis wind turbine with inclined pitch axes by wind tunnel experiments," Energy, Elsevier, vol. 174(C), pages 553-561.
    5. 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).
    6. Ferreira, D.N. & Gato, L.M.C. & Eça, L., 2023. "Efficiency of biradial impulse turbines concerning rotor blade angle, guide-vane deflection and blockage," Energy, Elsevier, vol. 266(C).
    7. 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. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
    2. Tang, Bo & Jiang, Hongsheng & Zhuge, Weilin & Qian, Yuping & Zhang, Yangjun, 2025. "Perceiving flow fields and aerodynamic characteristics of turbomachinery via sparse detection data: a graph data mining model," Energy, Elsevier, vol. 325(C).
    3. 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).
    4. Li, Lele & Zhang, Weihao & Li, Ya & Zhang, Ruifeng & Liu, Zongwang & Wang, Yufan & Mu, Yumo, 2024. "A non-parametric high-resolution prediction method for turbine blade profile loss based on deep learning," Energy, Elsevier, vol. 288(C).
    5. 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).
    6. Jiang, Ruiqi & Luo, Lei & Wang, Zhangjun & Yan, Han & Jia, Qiankun & Luo, Qiao & Du, Wei, 2025. "The application of bowing blades in a low aspect ratio integrated inter-turbine duct using splitters," Energy, Elsevier, vol. 321(C).
    7. Li, Zuobiao & Wen, Fengbo & Wan, Chenxin & Zhao, Zhiyuan & Luo, Yuxi & Wen, Dongsheng, 2024. "A pyramid-style neural network model with alterable input for reconstruction of physics field on turbine blade surface from various sparse measurements," Energy, Elsevier, vol. 308(C).
    8. Acarer, Sercan & Uyulan, Çağlar & Karadeniz, Ziya Haktan, 2020. "Optimization of radial inflow wind turbines for urban wind energy harvesting," Energy, Elsevier, vol. 202(C).
    9. 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).
    10. Rosario Nastasi & Giovanni Labrini & Simone Salvadori & Daniela Anna Misul, 2024. "Shape Optimization of a Diffusive High-Pressure Turbine Vane Using Machine Learning Tools," Energies, MDPI, vol. 17(22), pages 1-21, November.
    11. Li, Zuobiao & Wen, Fengbo & Liu, Zhongqi & Luo, Yuxi & Zhao, Zhiyuan & Wen, Dongsheng & Wang, Songtao, 2025. "A novel dual attention network for sparse reconstruction of turbine blade surface fields," Energy, Elsevier, vol. 317(C).
    12. Bingzheng Dou & Zhanpei Yang & Michele Guala & Timing Qu & Liping Lei & Pan Zeng, 2020. "Comparison of Different Driving Modes for the Wind Turbine Wake in Wind Tunnels," Energies, MDPI, vol. 13(8), pages 1-17, April.
    13. Yin, Jiabao & Meng, Xianghui & Cheng, Shuai, 2025. "Enhancing wind turbine energy efficiency: Tribo-dynamics modeling and shape modification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    14. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
    15. Gao, Xiaoxia & Hu, Yingjun & Zhao, Fei & Chen, Hanye & Zhu, Xiaoxun & Yin, Qianqian & Wang, Yu, 2025. "Quantification of 4D spatial-temporal inhomogeneous added turbulence intensity in wake region with validations from LiDAR-based observation," Energy, Elsevier, vol. 339(C).
    16. Gong, Wenbin & Lei, Zhao & Nie, Shunpeng & Liu, Gaowen & Lin, Aqiang & Feng, Qing & Wang, Zhiwu, 2023. "A novel combined model for energy consumption performance prediction in the secondary air system of gas turbine engines based on flow resistance network," Energy, Elsevier, vol. 280(C).
    17. Jiang, Yichen & Liu, Shijie & Zao, Peidong & Yu, Yanwei & Zou, Li & Liu, Liqin & Li, Jiawen, 2022. "Experimental evaluation of a tree-shaped quad-rotor wind turbine on power output controllability and survival shutdown capability," Applied Energy, Elsevier, vol. 309(C).
    18. Chenguang Song & Guoqing Wu & Weinan Zhu & Xudong Zhang & Jicong Zhao, 2019. "Numerical Investigation on the Effects of Airfoil Leading Edge Radius on the Aerodynamic Performance of H-Rotor Darrieus Vertical Axis Wind Turbine," Energies, MDPI, vol. 12(19), pages 1-14, October.
    19. Jintao Zhang & Chao Wang & Wenhao Liu & Jianyang Zhu & Yangyang Yan & Hui Zhao, 2023. "Optimization of the Energy Capture Performance of the Lift-Drag Hybrid Vertical-Axis Wind Turbine Based on the Taguchi Experimental Method and CFD Simulation," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    20. Pietrykowski, Konrad & Kasianantham, Nanthagopal & Ravi, Dineshkumar & Jan Gęca, Michał & Ramakrishnan, Prakash & Wendeker, Mirosław, 2023. "Sustainable energy development technique of vertical axis wind turbine with variable swept area – An experimental investigation," Applied Energy, Elsevier, vol. 329(C).

    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:eee:energy:v:335:y:2025:i:c:s0360544225037545. 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.