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A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades

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  • Mustafa Kaya

    (Faculty of Aeronautics and Astronautics, Ankara Yildirim Beyazit University, Cankiri Cad., Ulus, 06050 Ankara, Turkey)

Abstract

Computational fluid dynamics (CFD) is a powerful tool to estimate accurately the aerodynamic loads on wind turbine blades at the expense of high requirements like the duration of computation. Such requirements grow in the case of blade shape optimization in which several analyses are needed. A fast and reliable way to mimic the CFD solutions is to use surrogate models. In this study, a machine learning technique, the support vector regression (SVR) method based on a set of CFD solutions, is used as the surrogate model. CFD solutions are calculated by solving the Reynolds-averaged Navier–Stokes equation with the k-epsilon turbulence model using a commercial solver. The support vector regression model is then trained to give a functional relationship between the spanwise twist distribution and the generated torque. The smooth twist distribution is defined using a three-node cubic spline with four parameters in total. The optimum twist is determined for two baseline blade cases: the National Renewable Energy Laboratory (NREL) Phase II and Phase VI rotor blades. In the optimization process, extremum points that give the maximum torque are easily determined since the SVR gives an analytical model. Results show that it is possible to increase the torque generated by the NREL VI blade more than 10% just by redistributing the spanwise twist without carrying out a full geometry optimization of the blade shape with many shape-defining parameters. The increase in torque for the NREL II case is much higher.

Suggested Citation

  • Mustafa Kaya, 2019. "A CFD Based Application of Support Vector Regression to Determine the Optimum Smooth Twist for Wind Turbine Blades," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4502-:d:259199
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    1. Cheng, Biyi & Yao, Yingxue, 2023. "Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines," Energy, Elsevier, vol. 278(PA).
    2. Mohammad Omidi & Shu-Jie Liu & Soheil Mohtaram & Hui-Tian Lu & Hong-Chao Zhang, 2019. "Improving Centrifugal Compressor Performance by Optimizing the Design of Impellers Using Genetic Algorithm and Computational Fluid Dynamics Methods," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    3. Mohammed Debbache & Messaoud Hazmoune & Semcheddine Derfouf & Dana-Alexandra Ciupageanu & Gheorghe Lazaroiu, 2021. "Wind Blade Twist Correction for Enhanced Annual Energy Production of Wind Turbines," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    4. Ji, Baifeng & Zhong, Kuanwei & Xiong, Qian & Qiu, Penghui & Zhang, Xu & Wang, Liang, 2022. "CFD simulations of aerodynamic characteristics for the three-blade NREL Phase VI wind turbine model," Energy, Elsevier, vol. 249(C).

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