Multi-objective optimization of horizontal axis tidal current turbine using particle swarm optimization

被引：0
|
作者：
Zhang D.-S. [1 ]
Liu A. [1 ]
Chen J. [1 ]
Zhao R.-J. [1 ]
Shi W.-D. [1 ,2 ]
机构：
[1] Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang
[2] College of Mechanical Engineering, Nantong University, Nantong
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2018年 / 52卷 / 12期
关键词：
Blade airfoil; Cavitation; Multi-objective optimization; Particle swarm optimization (PSO); Tidal current turbine;
D O I：
10.3785/j.issn.1008-973X.2018.12.013
中图分类号：
学科分类号：
摘要：
A multi-objective optimization method for airfoil performance based on particle swarm optimization (PSO) was proposed in order to improve the cavitation performance of blade airfoil of horizontal axis tidal current turbine. Optimization mainly aimed at the minimum surface pressure coefficient of airfoil at large attack angle. In order to ensure the hydrodynamic performance of airfoil at the same time, the multi-objective optimization function was established by parameters such as the lift coefficient of airfoil and the minimum of the pressure coefficient. To improve the optimization efficiency, the process analysis of the optimized airfoil pressure coefficient was taken by the program XFoil instead of the CFD analysis. The NACA63-815 airfoils were optimized by this method. The CFD method was used to study the comparison of the cavitation distributions between the optimized airfoils and the original airfoils at three cavitation numbers (1.0, 1.5 and 2.0) under two attack angles. Results show that the minimum pressure coefficient of the optimized airfoil increases by 17.0% and 45.8% and the maximum lift-to-drag ratio increased by 6.0% and 61.1%, at the angle of attack being 6.8° and 10.8°, respectively. The cavitation initial and full cavitation performance of the optimized airfoil is significantly improved and the hydrodynamic performance is also improved, which verifies the correctness of the optimization method. © 2018, Zhejiang University Press. All right reserved.
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页码：2349 / 2355
页数：6
相关论文
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