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Design Optimization of a Multi-Megawatt Wind Turbine Blade with the NPU-MWA Airfoil Family

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  • Jianhua Xu

    (Institute of Aerodynamic and Multidisciplinary Design Optimization, National Key Laboratory of Science and Technology on Aerodynamic Design and Research, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China)

  • Zhonghua Han

    (Institute of Aerodynamic and Multidisciplinary Design Optimization, National Key Laboratory of Science and Technology on Aerodynamic Design and Research, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China)

  • Xiaochao Yan

    (Institute of Aerodynamic and Multidisciplinary Design Optimization, National Key Laboratory of Science and Technology on Aerodynamic Design and Research, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China)

  • Wenping Song

    (Institute of Aerodynamic and Multidisciplinary Design Optimization, National Key Laboratory of Science and Technology on Aerodynamic Design and Research, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

A new airfoil family, called NPU-MWA (Northwestern Polytechnical University Multi-megawatt Wind-turbine A-series) airfoils, was designed to improve both aerodynamic and structural performance, with the outboard airfoils being designed at high design lift coefficient and high Reynolds number, and the inboard airfoils being designed as flat-back airfoils. This article aims to design a multi-megawatt wind turbine blade in order to demonstrate the advantages of the NPU-MWA airfoils in improving wind energy capturing and structural weight reduction. The distributions of chord length and twist angle for a 5 MW wind turbine blade are optimized by a Kriging surrogate model-based optimizer, with aerodynamic performance being evaluated by blade element-momentum theory. The Reynolds-averaged Navier–Stokes equations solver was used to validate the improvement in aerodynamic performance. Results show that compared with an existing NREL (National Renewable Energy Laboratory) 5 MW blade, the maximum power coefficient of the optimized NPU 5 MW blade is larger, and the chord lengths at all span-wise sections are dramatically smaller, resulting in a significant structural weight reduction (9%). It is shown that the NPU-MWA airfoils feature excellent aerodynamic and structural performance for the design of multi-megawatt wind turbine blades.

Suggested Citation

  • Jianhua Xu & Zhonghua Han & Xiaochao Yan & Wenping Song, 2019. "Design Optimization of a Multi-Megawatt Wind Turbine Blade with the NPU-MWA Airfoil Family," Energies, MDPI, vol. 12(17), pages 1-24, August.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3330-:d:262015
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    References listed on IDEAS

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    1. Zhang, Xu & Li, Wei & Liu, Hailong, 2015. "Numerical simulation of the effect of relative thickness on aerodynamic performance improvement of asymmetrical blunt trailing-edge modification," Renewable Energy, Elsevier, vol. 80(C), pages 489-497.
    2. Lee, Sung Gun & Park, Sang Jun & Lee, Kyung Seo & Chung, Chinwha, 2012. "Performance prediction of NREL (National Renewable Energy Laboratory) Phase VI blade adopting blunt trailing edge airfoil," Energy, Elsevier, vol. 47(1), pages 47-61.
    3. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Yang, Xiang I.A., 2019. "A two-dimensional Jensen model with a Gaussian-shaped velocity deficit," Renewable Energy, Elsevier, vol. 141(C), pages 46-56.
    4. Xiao Chen & Wei Zhao & Xiao Lu Zhao & Jian Zhong Xu, 2014. "Failure Test and Finite Element Simulation of a Large Wind Turbine Composite Blade under Static Loading," Energies, MDPI, vol. 7(4), pages 1-24, April.
    5. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    6. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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    Cited by:

    1. Zheng, Ke-feng & Zhang, Shi-qiang & Song, Wen-ping & Nie, Han & Xu, Jian-hua & Han, Zhong-hua & Zhou, Kang-yuan, 2024. "Wind turbine airfoil family design method based on prescribed pressure gradient distributions," Renewable Energy, Elsevier, vol. 224(C).
    2. Ali M. H. A. Khajah & Simon P. Philbin, 2022. "Techno-Economic Analysis and Modelling of the Feasibility of Wind Energy in Kuwait," Clean Technol., MDPI, vol. 4(1), pages 1-21, January.

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