TY - GEN
T1 - Optimization of flatback airfoils for wind turbine blades using a multi-objective genetic algorithm
AU - Chen, Xiaomin
AU - Agarwal, Ramesh
PY - 2012
Y1 - 2012
N2 - 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, AIAA 2010-1423, 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 multiobjective 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.
AB - 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, AIAA 2010-1423, 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 multiobjective 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.
UR - http://www.scopus.com/inward/record.url?scp=84881243060&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84881243060
SN - 9781600869334
T3 - 42nd AIAA Fluid Dynamics Conference and Exhibit 2012
BT - 42nd AIAA Fluid Dynamics Conference and Exhibit 2012
T2 - 42nd AIAA Fluid Dynamics Conference and Exhibit 2012
Y2 - 25 June 2012 through 28 June 2012
ER -