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Numerical models for robust shape optimization of wind turbine blades

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  • Vučina, Damir
  • Marinić-Kragić, Ivo
  • Milas, Zoran

Abstract

A computational framework for the shape optimization of wind turbine blades is developed for variable operating conditions specified by local wind speed distributions. The numerical workflow consists of a genetic algorithm based optimizer, a computational fluid dynamics based simulator and a 3D geometric modeller. The developed numerical workflow also implements the coupling of the process flows as well as passing data amongst the individual applications including the corresponding data mining. Several approaches to modeling 3D shapes are developed and employed by the workflow. They include parametric curves defining 2D curves lofted into 3D shapes in combination with applying computational geometry operators and full 3D parametric surface models which enable generic 3D shapes to be represented. The proposed definitions of excellence include annual energy production for given wind speed distributions and net-present-value and internal-rate-of-return based indicators as potential constituents of the fitness functions. Several case studies are presented with promising results towards the aspired custom-shaped wind turbine blades for optimum performance for any given specific location. The developed computational workflow can therefore be seen as a numerical device for custom optimization of performance of renewable energy systems.

Suggested Citation

  • Vučina, Damir & Marinić-Kragić, Ivo & Milas, Zoran, 2016. "Numerical models for robust shape optimization of wind turbine blades," Renewable Energy, Elsevier, vol. 87(P2), pages 849-862.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:849-862
    DOI: 10.1016/j.renene.2015.10.040
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    References listed on IDEAS

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    1. Lanzafame, R. & Messina, M., 2007. "Fluid dynamics wind turbine design: Critical analysis, optimization and application of BEM theory," Renewable Energy, Elsevier, vol. 32(14), pages 2291-2305.
    2. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
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    Citations

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    Cited by:

    1. Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems & M. Mujahid Rafique, 2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review," Energies, MDPI, vol. 11(3), pages 1-34, February.
    2. Nour Khlaifat & Ali Altaee & John Zhou & Yuhan Huang & Ali Braytee, 2020. "Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia," Energies, MDPI, vol. 13(9), pages 1-26, May.
    3. Wang, Haipeng & Zhang, Bo & Qiu, Qinggang & Xu, Xiang, 2017. "Flow control on the NREL S809 wind turbine airfoil using vortex generators," Energy, Elsevier, vol. 118(C), pages 1210-1221.
    4. Rehman, Naveed ur & Uzair, Muhammad, 2022. "Concentrator shape optimization using particle swarm optimization for solar concentrating photovoltaic applications," Renewable Energy, Elsevier, vol. 184(C), pages 1043-1054.
    5. Baniassadi, Amir & Shirinbakhsh, Mehrdad & Torabi, Farschad, 2017. "Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building," Renewable Energy, Elsevier, vol. 101(C), pages 1021-1029.
    6. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.
    7. Zhang, Xiaoling & Zhang, Kejia & Yang, Xiao & Fazeres-Ferradosa, Tiago & Zhu, Shun-Peng, 2023. "Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling," Renewable Energy, Elsevier, vol. 206(C), pages 552-565.

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