Optimization of Flatback Airfoils for Wind Turbine Blades Using a Multi-Objective Genetic Algorithm
Optimization of Flatback Airfoils for Wind Turbine Blades Using a Multi-Objective Genetic Algorithm
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DOI：
10.2514/6.2012-2722
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发表时间：
2010-01
期刊：
影响因子：
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通讯作者：
Xiaoming Chen;R. Agarwal
中科院分区：
文献类型：
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作者：
Xiaoming Chen;R. Agarwal
近年来，具有钝后缘的翼型（称为平背翼型）已被提议用于大型风力涡轮机叶片的内侧区域，因为它们提供了一些结构和气动性能优势。在之前的论文ASME ES 2010 -90373中，我们采用单目标遗传算法（GA）对平背翼型进行形状优化，以产生最大升阻比。遗传算法的计算效率显着提高与人工神经网络（ANN）。采用商用软件FLUENT，使用雷诺平均纳维尔-斯托克斯（RANS）方程结合湍流模型计算流场。在本文中，我们采用多目标遗传算法来优化的平背翼型，以实现两个目标，即最大的升力以及最大的升阻比的产生。结果表明，与单目标遗传算法相比，多目标遗传算法优化能够生成上级的平背翼型。Copyright © 2012 by ASME
In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In a previous paper, ASME ES2010-90373, we employed a single objective genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA was significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT was employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. In this paper, we employ a multi-objective GA to optimize the flatback airfoils to achieve two objectives, namely the generation of maximum lift as well as the maximum lift to drag ratio. It is shown that the multi-objective GA optimization can generate superior flatback airfoils compared to those obtained by using single objective GA algorithm.Copyright © 2012 by ASME