Author's School

School of Engineering & Applied Science

Author's Department/Program

Mechanical Engineering and Materials Science


English (en)

Date of Award

Spring 1-20-2014

Degree Type


Degree Name

Doctor of Philosophy (PhD)

Chair and Committee

Ramesh Agarwal


Shape optimization is widely used in the design of wind turbine blades. In this dissertation, a numerical optimization method called Genetic Algorithm (GA) is applied to address the shape optimization of wind turbine airfoils and blades. 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. The FX, DU and NACA 64 series airfoils are thick airfoils widely used for wind turbine blade application. They have several advantages in meeting the intrinsic requirements for wind turbines in terms of design point, off-design capabilities and structural properties. This research employ both single- and multi-objective genetic algorithms (SOGA and MOGA) for shape optimization of Flatback, FX, DU and NACA 64 series airfoils to achieve maximum lift and/or maximum lift to drag ratio.

The commercially available software FLUENT is employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a two-equation Shear Stress Transport (SST) turbulence model and a three equation k-kl-ω turbulence model. The optimization methodology is validated by an optimization study of subsonic and transonic airfoils (NACA0012 and RAE 2822 airfoils). All the optimization results have demonstrated that the GA technique can be employed efficiently and accurately to produce globally optimal airfoils with excellent aerodynamic properties using a desired objective value (minimum Cd and/or maximum Cl /Cd). It is also shown that the multi-objective genetic algorithm based optimization can generate superior airfoils compared to those obtained by using the single objective genetic algorithm.

The applications of thick airfoils are extended to the assessment of wind turbine performance. It is well established that the power generated by a Horizontal-Axis Wind Turbine (HAWT) is a function of the number of blades B, the tip speed ratio λ (blade tip speed/wind free stream velocity) and the lift to drag ratio (Cl /Cd) of the airfoil sections of the blade. The airfoil sections used in HAWT are generally thick airfoils such as the S, DU, FX, Flat-back and NACA 6-series of airfoils. These airfoils vary in (Cl /Cd) for a given B and λ, and therefore the power generated by HAWT for different blade airfoil sections will vary. Another goal of this study is to evaluate the effect of different airfoil sections on HAWT performance using the Blade Element Momentum (BEM) theory.

In this dissertation, we employ DU 91-W2-250, FX 66-S196-V1, NACA 64421, and Flat-back series of airfoils (FB-3500-0050, FB-3500-0875, and FB-3500-1750) and compare their performance with S809 airfoil used in NREL Phase II and III wind turbines; the lift and drag coefficient data for these airfoils sections are available. The output power of the turbine is calculated using these airfoil section blades for a given B and λ and is compared with the original NREL Phase II and Phase III turbines using S809 airfoil section. It is shown that by a suitable choice of airfoil section of HAWT blade, the power generated by the turbine can be significantly increased. Parametric studies are also conducted by varying the turbine diameter. In addition, a simplified dynamic inflow model is integrated into the BEM theory. It is shown that the improved BEM theory has superior performance in capturing the instantaneous behavior of wind turbines due to the existence of wind turbine wake or temporal variations in wind velocity.

The dissertation also considers the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal -Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally position the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

Finally, some preliminary investigation of shape optimization of 3D wind turbine blades at low Reynolds numbers is conducted. The optimization employs a 3D straight untapered wind turbine blade with cross section of NACA 0012 airfoils as the geometry of baseline blade. The optimization objective is to achieve maximum Cl/Cd as well as maximum Cl. The multi-objective genetic algorithm is employed together with the commercially available software FLUENT for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a one-equation Sparlart-Allmaras turbulence model. The results show excellent performance of the optimized wind turbine blade and indicate the feasibility of optimization on real wind turbine blades with more complex shapes in the future.



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