Ocean Systems Engineering

  • 제9권2호
  • /
  • Pages.111-133
  • /
  • 2019
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  • 2093-6702(pISSN)
  • /
  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
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Optimization of a horizontal axis marine current turbine via surrogate models

  • Thandayutham, Karthikeyan (Wave Energy and Fluids Engineering Laboratory (WEFEL), Department of Ocean Engineering, Indian Institute of Technology Madras) ;
  • Avital, E.J. (School of Engineering and Material Science, Queen Mary University of London) ;
  • Venkatesan, Nithya (School of Electrical Engineering, VIT University) ;
  • Samad, Abdus (Wave Energy and Fluids Engineering Laboratory (WEFEL), Department of Ocean Engineering, Indian Institute of Technology Madras)
  • 투고 : 2018.12.12
  • 심사 : 2019.05.10
  • 발행 : 2019.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

Flow through a scaled horizontal axis marine current turbine was numerically simulated after validation and the turbine design was optimized. The computational fluid dynamics (CFD) code Ansys-CFX 16.1 for numerical modeling, an in-house blade element momentum (BEM) code for analytical modeling and an in-house surrogate-based optimization (SBO) code were used to find an optimal turbine design. The blade-pitch angle (${\theta}$) and the number of rotor blades (NR) were taken as design variables. A single objective optimization approach was utilized in the present work. The defined objective function was the turbine's power coefficient ($C_P$). A $3{\times}3$ full-factorial sampling technique was used to define the sample space. This sampling technique gave different turbine designs, which were further evaluated for the objective function by solving the Reynolds-Averaged Navier-Stokes equations (RANS). Finally, the SBO technique with search algorithm produced an optimal design. It is found that the optimal design has improved the objective function by 26.5%. This article presents the solution approach, analysis of the turbine flow field and the predictability of various surrogate based techniques.

키워드

과제정보

연구 과제 주관 기관 : UK India Education and Research Initiative (UKIERE)

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피인용 문헌

  1. Surrogate-Based Optimization of Horizontal Axis Hydrokinetic Turbine Rotor Blades vol.14, pp.13, 2019, https://doi.org/10.3390/en14134045
  2. Multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI vol.93, pp.6, 2019, https://doi.org/10.1108/aeat-02-2021-0055