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NREL Ⅵ rotor blade: numerical investigation and winglet design and optimization using CFD

机译:NRELⅥ转子叶片:使用CFD进行数值研究和小翼设计与优化

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The main objectives of this study were to aerodynamically design and optimize a winglet for a wind turbine blade by using computational fluid dynamics (CFD) and to investigate its effect on the power production. For validation and as a baseline rotor, the National Renewable Energy Laboratory Phase Ⅵ wind turbine rotor blade is used. The Reynolds-averaged Navier-Stokes equations are solved, and k-g Launder-Sharma turbulence model was used. The numerical results have shown a considerable agreement with the experimental data. The genetic algorithm was used as the optimization technique with the help of artificial neural network to reduce the computational cost. In the winglet design, the variable parameters are the cant and twist angles of the winglet and the objective function the torque. Multipoint optimization is carried out for three different operating wind speeds, and a total of 24 CFD cases are run in the design. The final optimized winglet showed around 9% increase in the power production.
机译:这项研究的主要目标是通过使用计算流体动力学(CFD)对风力涡轮机叶片进行空气动力学设计和优化小翼,并研究其对发电的影响。为了进行验证,并使用美国国家可再生能源实验室Ⅵ期风力涡轮机转子叶片作为基准转子。求解雷诺平均的Navier-Stokes方程,并使用k-g Launder-Sharma湍流模型。数值结果表明与实验数据相当吻合。利用遗传算法作为优化技术,借助人工神经网络来降低计算成本。在小翼设计中,可变参数是小翼的倾斜角和扭转角,而目标函数是扭矩。针对三种不同的运行风速进行了多点优化,设计中总共运行了24个CFD案例。最终优化的小翼显示出发电量增加了约9%。

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