Multi-objective optimization design for blunt trailing-edge airfoil based on multi-island genetic algorithm

被引：0
|
作者：
Qin K. [1 ]
Yin P. [1 ]
Liu Z. [1 ]
Huang D. [1 ]
机构：
[1] School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 09期
关键词：
Blunt trailing-edge airfoil; Multi-Island Genetic Algorithm; Optimization design; Wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2021-0253
中图分类号：
学科分类号：
摘要：
A multi-objective optimization method for blunt trailing-edge airfoil is proposed to solve the problem that large horizontal axis wind turbine has a high requirement on aerodynamic performance and structural strength of blades under complex operating conditions. A parameterization method combining Hicks-Henne type function and blunt trailing edge function is adopted to fit the blunt trailing-edge airfoil. A Matlab code is using to call XFOIL for the airfoil aerodynamic performance analysis and to calculate the objective function. Finally, based on Multi-Island Genetic Algorithm(MIGA), a multi-objective and multi-working condition optimization process of the airfoil is integrated into the Isight platform which achieves automatic optimization. Using the above method, NACA63921 airfoil is selected as the initial airfoil for multi-objective optimization. Fluent Transition model is adapted to verify the aerodynamic performance of the optimized blunt trailing- edge airfoil and then the simulation results are compared with several common airfoils of the same thickness such as FFA, DU series. Numerical verification shows that the lift- drag ratio of the optimized blunt trailing- edge airfoil is higher than that of several common wind turbine airfoils with the same thickness under most operating conditions. In stall conditions, the flow separation of optimized airfoil is delayed which leads to more stable flow on the airfoil surface. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：218 / 225
页数：7
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