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Kinematic optimization of a flapping foil power generator using a multi-fidelity evolutionary algorithm

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  • Liu, Zhengliang
  • Bhattacharjee, Kalyan Shankar
  • Tian, Fang-Bao
  • Young, John
  • Ray, Tapabrata
  • Lai, Joseph C.S.

Abstract

For hydrodynamic optimization using computational fluid dynamics methods, the high computational cost impedes the use of evolutionary algorithms since they require evaluation of numerous solutions. In this paper, a multi-fidelity evolutionary algorithm, implemented with a dynamic stall model and a modified discrete vortex method, is used to find values of kinematic parameters for high energy extraction performance from a flapping foil. A single objective problem with two variables is first used to illustrate the benefits of the multi fidelity optimization strategy. Then the efficiency and the power output characterized by five design variables are optimized by the multi-fidelity evolutionary algorithm. The nondominated solutions obtained from the exercise are evaluated by the lattice Boltzmann method for detailed analysis. The results show that despite the use of low fidelity models and limited budget of computational resources, the multi-fidelity strategy is capable of finding kinematic conditions suitable for high energy extraction performance from a flapping foil. In addition, detailed flow analysis reveals that high energy extraction performance is associated with the detachment of the leading edge vortex near stroke reversal, resulting in a horseshoe-shaped vorticity wake with a width approximating the swept distance of the foil behind the turbine plane.

Suggested Citation

  • Liu, Zhengliang & Bhattacharjee, Kalyan Shankar & Tian, Fang-Bao & Young, John & Ray, Tapabrata & Lai, Joseph C.S., 2019. "Kinematic optimization of a flapping foil power generator using a multi-fidelity evolutionary algorithm," Renewable Energy, Elsevier, vol. 132(C), pages 543-557.
    • Handle: RePEc:eee:renene:v:132:y:2019:i:c:p:543-557
    DOI: 10.1016/j.renene.2018.08.015
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    References listed on IDEAS

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    1. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2010. "Power optimization of wind turbines with data mining and evolutionary computation," Renewable Energy, Elsevier, vol. 35(3), pages 695-702.
    2. Shehata, Ahmed S. & Xiao, Qing & Selim, Mohamed M. & Elbatran, A.H. & Alexander, Day, 2017. "Enhancement of performance of wave turbine during stall using passive flow control: First and second law analysis," Renewable Energy, Elsevier, vol. 113(C), pages 369-392.
    3. Salehyar, Sara & Zhu, Qiang, 2015. "Aerodynamic dissipation effects on the rotating blades of floating wind turbines," Renewable Energy, Elsevier, vol. 78(C), pages 119-127.
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    Cited by:

    1. Liu, Zhen & Qu, Hengliang & Shi, Hongda, 2020. "Energy-harvesting performance of a coupled-pitching hydrofoil under the semi-passive mode," Applied Energy, Elsevier, vol. 267(C).
    2. Li, Yunzhu & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Deep learning based real-time energy extraction system modeling for flapping foil," Energy, Elsevier, vol. 246(C).
    3. Arun Raj Shanmugam & Ki Sun Park & Chang Hyun Sohn, 2023. "Comparison of the Power Extraction Performance of an Oscillating Hydrofoil Turbine with Different Deflector Designs," Energies, MDPI, vol. 16(8), pages 1-29, April.

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