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Multi-objective optimization of hydrofoil geometry used in horizontal axis tidal turbine blade designed for operation in tropical conditions of South East Asia

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  • Attukur Nandagopal, Rajaram
  • Narasimalu, Srikanth

Abstract

Improved hydrodynamic design of a Horizontal Axis Tidal Turbine (HATT) blade is key to increasing the efficiency and annual power production of the turbine. One of the crucial stages in hydrodynamic design is the selection of the 2D cross-section (hydrofoil) of the blade. Selecting the hydrofoils for a blade design that results in superior turbine characteristics for a given flow condition is tedious. In this study instead of choosing hydrofoils for a given flow condition, a base hydrofoil geometry is optimized to obtain a new hydrofoil that has superior characteristics for the given flow conditions. Hydrofoils were optimized for the flow conditions prevalent in South East Asia. Optimization was performed until the desired objectives were met while satisfying a set of constraints. Maximizing lift-to-drag ratio and lift coefficient of the hydrofoil was set as objectives (non-conflicting) while avoiding cavitation during turbine operation was one of the constraints. OpenMDAO and NSGAII were used to set up and solve the multi-objective optimization problem respectively to generate four optimized hydrofoils. Harp_opt was used to design a 1 m rotor HATT blade using the optimized hydrofoils which exhibited better performance than another 1 m rotor HATT blade designed with NREL hydrofoils as 2D sections.

Suggested Citation

  • Attukur Nandagopal, Rajaram & Narasimalu, Srikanth, 2020. "Multi-objective optimization of hydrofoil geometry used in horizontal axis tidal turbine blade designed for operation in tropical conditions of South East Asia," Renewable Energy, Elsevier, vol. 146(C), pages 166-180.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:166-180
    DOI: 10.1016/j.renene.2019.05.111
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    Cited by:

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    3. Nachtane, M. & Tarfaoui, M. & Goda, I. & Rouway, M., 2020. "A review on the technologies, design considerations and numerical models of tidal current turbines," Renewable Energy, Elsevier, vol. 157(C), pages 1274-1288.
    4. Wang, Longyan & Xu, Jian & Luo, Wei & Luo, Zhaohui & Xie, Junhang & Yuan, Jianping & Tan, Andy C.C., 2022. "A deep learning-based optimization framework of two-dimensional hydrofoils for tidal turbine rotor design," Energy, Elsevier, vol. 253(C).
    5. Xu, Jian & Wang, Longyan & Yuan, Jianping & Luo, Zhaohui & Wang, Zilu & Zhang, Bowen & Tan, Andy C.C., 2024. "DLFSI: A deep learning static fluid-structure interaction model for hydrodynamic-structural optimization of composite tidal turbine blade," Renewable Energy, Elsevier, vol. 224(C).
    6. Li, Changming & Liu, Bin & Wang, Shujie & Yuan, Peng & Lang, Xianpeng & Tan, Junzhe & Si, Xiancai, 2024. "Tidal turbine hydrofoil design and optimization based on deep learning," Renewable Energy, Elsevier, vol. 226(C).

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