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An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine

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  • Xuanlin Peng

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jianzhong Zhou

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chu Zhang

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Ruhai Li

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yanhe Xu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Diyi Chen

    (Institute of Water Resources and Hydropower Research, Northwest A&F University, Yangling 712100, China)

Abstract

In this paper, a new methodology is proposed to reduce the vortex-induced vibration (VIV) and improve the performance of the stay vane in a 200-MW Francis turbine. The process can be divided into two parts. Firstly, a diagnosis method for stay vane vibration based on field experiments and a finite element method (FEM) is presented. It is found that the resonance between the Kármán vortex and the stay vane is the main cause for the undesired vibration. Then, we focus on establishing an intelligent optimization model of the stay vane’s trailing edge profile. To this end, an approach combining factorial experiments, extreme learning machine (ELM) and particle swarm optimization (PSO) is implemented. Three kinds of improved profiles of the stay vane are proposed and compared. Finally, the profile with a Donaldson trailing edge is adopted as the best solution for the stay vane, and verifications such as computational fluid dynamics (CFD) simulations, structural analysis and fatigue analysis are performed to validate the optimized geometry.

Suggested Citation

  • Xuanlin Peng & Jianzhong Zhou & Chu Zhang & Ruhai Li & Yanhe Xu & Diyi Chen, 2017. "An Intelligent Optimization Method for Vortex-Induced Vibration Reducing and Performance Improving in a Large Francis Turbine," Energies, MDPI, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1901-:d:119513
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    References listed on IDEAS

    as
    1. Ram Chandra Adhikari & Jerson Vaz & David Wood, 2016. "Cavitation Inception in Crossflow Hydro Turbines," Energies, MDPI, vol. 9(4), pages 1-12, March.
    2. Teran, Leonel Alveyro & Larrahondo, Francisco Jose & Rodríguez, Sara Aida, 2016. "Performance improvement of a 500-kW Francis turbine based on CFD," Renewable Energy, Elsevier, vol. 96(PA), pages 977-992.
    3. Pugliese, Francesco & De Paola, Francesco & Fontana, Nicola & Giugni, Maurizio & Marini, Gustavo, 2016. "Experimental characterization of two Pumps As Turbines for hydropower generation," Renewable Energy, Elsevier, vol. 99(C), pages 180-187.
    4. KC, Anup & Lee, Young Ho & Thapa, Bhola, 2016. "CFD study on prediction of vortex shedding in draft tube of Francis turbine and vortex control techniques," Renewable Energy, Elsevier, vol. 86(C), pages 1406-1421.
    5. Trivedi, Chirag & Cervantes, Michel J., 2017. "Fluid-structure interactions in Francis turbines: A perspective review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 87-101.
    6. Emma Frosina & Dario Buono & Adolfo Senatore, 2017. "A Performance Prediction Method for Pumps as Turbines (PAT) Using a Computational Fluid Dynamics (CFD) Modeling Approach," Energies, MDPI, vol. 10(1), pages 1-19, January.
    7. Wenlong Tian & Zhaoyong Mao & Fuliang Zhao, 2017. "Design and Numerical Simulations of a Flow Induced Vibration Energy Converter for Underwater Mooring Platforms," Energies, MDPI, vol. 10(9), pages 1-20, September.
    8. Koirala, Ravi & Thapa, Bhola & Neopane, Hari Prasad & Zhu, Baoshan, 2017. "A review on flow and sediment erosion in guide vanes of Francis turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1054-1065.
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