The Parametric Modeling and Two-Objective Optimal Design of a Downwind Blade

• Published in: Frontiers in energy research 2021-08, Vol.9
• Main Authors: Xu, Bofeng, Li, Zhen, Zhu, Zixuan, Cai, Xin, Wang, Tongguang, Zhao, Zhenzhou
• Format: Article
• Language: English
• Subjects: downwind load; genetic algorithm; parametric modeling; two-objective optimization; wind turbine blade
• ISSN: 2296-598X; 2296-598X
• DOI: 10.3389/fenrg.2021.708230

To cope with the future challenges to the blade that will be introduced by the development of extreme-scale wind turbines, this study focuses on the optimization design of the aerodynamic shape of a downwind blade via the inverse design method. Moreover, the genetic algorithm is used to optimize the chord, twist angle, and pre-bending parameters of the blade to maximize the energy production of the rotor and minimize the flapping bending moment of the blade root. By taking a 5-MW wind turbine as the optimization object, the two-objective optimization design of the downwind blade is carried out, and Pareto optimal solutions in line with the expectations are obtained. After analyzing four representatives of the Pareto optimal solutions, while a more ideal solution is found to sacrifice 9.41% of the energy production of the rotor, the flapping bending moment of the blade root is reduced by 42.92%, thereby achieving the lightweight optimization design of an extreme-scale wind turbine blade. Furthermore, based on the selected four sets of blades, the influence mechanisms of the chord, twist angle, and pre-bending on the optimization goal are analyzed, and it is found that the pre-bending parameter has the greatest influence on the two optimization goals.