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Shroud leakage flow models and a multi-dimensional coupling CFD (computational fluid dynamics) method for shrouded turbines

Author

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  • Zou, Zhengping
  • Liu, Jingyuan
  • Zhang, Weihao
  • Wang, Peng

Abstract

Multi-dimensional coupling simulation is an effective approach for evaluating the flow and aero-thermal performance of shrouded turbines, which can balance the simulation accuracy and computing cost effectively. In this paper, 1D leakage models are proposed based on classical jet theories and dynamics equations, which can be used to evaluate most of the main features of shroud leakage flow, including the mass flow rate, radial and circumferential momentum, temperature and the jet width. Then, the 1D models are expanded to 2D distributions on the interface by using a multi-dimensional scaling method. Based on the models and multi-dimensional scaling, a multi-dimensional coupling simulation method for shrouded turbines is developed, in which, some boundary source and sink are set on the interface between the shroud and the main flow passage. To verify the precision, some simulations on the design point and off design points of a 1.5 stage turbine are conducted. It is indicated that the models and methods can give predictions with sufficient accuracy for most of the flow field features and will contribute to pursue deeper understanding and better design methods of shrouded axial turbines, which are the important devices in energy engineering.

Suggested Citation

  • Zou, Zhengping & Liu, Jingyuan & Zhang, Weihao & Wang, Peng, 2016. "Shroud leakage flow models and a multi-dimensional coupling CFD (computational fluid dynamics) method for shrouded turbines," Energy, Elsevier, vol. 103(C), pages 410-429.
    • Handle: RePEc:eee:energy:v:103:y:2016:i:c:p:410-429
    DOI: 10.1016/j.energy.2016.02.070
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    Citations

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    Cited by:

    1. Zou, Zhengping & Shao, Fei & Li, Yiran & Zhang, Weihao & Berglund, Albin, 2017. "Dominant flow structure in the squealer tip gap and its impact on turbine aerodynamic performance," Energy, Elsevier, vol. 138(C), pages 167-184.
    2. 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).
    3. Touil, Kaddour & Ghenaiet, Adel, 2019. "Simulation and analysis of vane-blade interaction in a two-stage high-pressure axial turbine," Energy, Elsevier, vol. 172(C), pages 1291-1311.
    4. Zhang, Mingjie & Yang, Jiangang & Zhang, Wanfu & Gu, Qianlei, 2024. "Turbomachines seal flow resistance enhancement and leakage reduction based on flow control method with bow-shaped auxiliary teeth," Energy, Elsevier, vol. 300(C).
    5. Woosung Choi & Kanmaniraja Radhakrishnan & Nam-Ho Kim & Jun Su Park, 2021. "Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades," Energies, MDPI, vol. 14(2), pages 1-15, January.
    6. 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).

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