**Aerofólio S809 - 1 - s2 0 - s0960148112004156 - main**

Aerofólio S809

(Parte **1** de 2)

Aerodynamic analysis of different wind-turbine-blade proﬁles using ﬁnite-volume method

Mohamed A. Sayed, Hamdy A. Kandil⇑, Ahmed Shaltot Mechatronics Engineering Department, Faculty of Engineering and Materials Science, German University in Cairo (GUC) Cairo, Egypt article i nfo

Article history: Available online 26 September 2012

Keywords: Wind energy Wind turbines Blade proﬁles Computational Fluid Dynamics Finite volume method abstra ct

In order to economically gain the maximum energy from the wind turbine, the performance of the blade proﬁle must be obtained. In this paper, the results of aerodynamic simulations of the steady low-speed ﬂow past two-dimensional S-series wind-turbine-blade proﬁles, developed by the National Renewable Energy Laboratory (NREL), are presented. The aerodynamic simulations were performed using a Computational Fluid Dynamics (CFD) method based on the ﬁnite-volume approach. The governing equations used in the simulations are the Reynolds-Averaged-Navier–Stokes (RANS) equations. The wind conditions during the simulations were developed from the wind speeds over different sites in Egypt. The lift and drag forces are the most important parameters in studying the wind-turbine performance. Therefore, an attempt to study the lift and drag forces on the wind turbine blades at various sections is presented. The maximum sliding ratio (lift/drag ratio) is desired in order to gain the maximum power from the wind turbine. The performance of different blade proﬁles at different wind speeds was investigated and the optimum blade proﬁle for each wind speed is determined based on the maximum sliding ratio. Moreover, the optimum Angle Of Attack (AOA) for each blade proﬁle is determined at the different wind speeds. The numerical results are benchmarked against wind tunnel measurements. The comparisons show that the CFD code used in this study can accurately predict the wind-turbine blades aerodynamic loads. 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Wind power has the greatest future prospects among all types of renewable and sustainable energy technologies. Moreover, wind energy is becoming more interesting throughout the world because it is found everywhere and it is useful renewable energy while it does not affect green house due to any radiation [1].B y the 11th century, people in the Middle East were using wind mills for food production [2]. The Dutch reﬁned the wind mills and utilized them for draining lakes and swamps. When settlers took this technology to the new world in the late 19th century they began using wind mills to pump water to farms. Later at the last decade of the 19th century, it was improved to generate electricity for homes and industry [2]. However industrialization sparked the development of larger wind mills to generate electricity which is commonly called wind turbines that appeared in Denmark as early as 1890 [3]. During 1940 the largest wind turbine operated on Vermont hill top known as Grandpa’s Knob. Afterwards, wind turbines have been improved gradually at the last decade of the twentieth century with a rapidly hugeness in their size [4,5]. At the end of 1989 a 300-kW wind turbine with a 30-meter-rotor diameter was considered very large, while by early 2004 a 4–5 MW wind turbines were commercially available [4].

The efﬁciency of wind energy constructions are getting important because wind energy applications have rapidly increased in the world. Moreover, wind energy is a low density source of power. To make wind power economically feasible, it is important to maximize the efﬁciency of converting wind energy into mechanical energy. Among the different aspects involved is rotor aerodynamics which is a key determinant for achieving this goal. There are three different approaches that may be used to analyze the ﬂow around and downstream of a wind turbine as stated by [6] which are: ﬁeld testing, which provides accurate results but is highly complex and expensive; analytical and semi-empirical models, which adopt simplifying assumptions and are thus not universally reliable; and CFD, which offers the best alternative to direct measurements.

Nowadays, industrial rotor design codes are still built based on

Blade Element Momentum theory (BEM) [6–9]. Nevertheless, in the last decade the opinion has been reached that aerodynamic modeling of Horizontal-Axis-Wind-Turbine (HAWT) rotors by means of the conventional engineering methods has reached a point where no further improvement can be expected without a

0196-8904/$ - see front matter 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enconman.2012.05.030

⇑ Corresponding author. Address: Faculty of Engineering, Alexandria University, Alexandria, Egypt. Tel.: +20 2 2759 5539x1139, mobile: +20 100 6559603, hotline: 16482; fax: +20 2 2758 1041.

E-mail addresses: mohammed.sayed@guc.edu.eg (M.A. Sayed), hamdy.kandil@- guc.edu.eg (H.A. Kandil), ahmed.shaltot@student.guc.edu.eg (A. Shaltot).

Energy Conversion and Management 64 (2012) 541–550 Contents lists available at SciVerse ScienceDirect

Energy Conversion and Management jo urn al homep ag e: w. elsev ier .co m/l oca te/e nco nma n full understanding of the ﬂow physics [9]. Thus, the recent years have seen the rise of numerical studies on all HAWT aerodynamics features, performed on many different levels, ranging from BEM methods integrated by CFD calculations to full 3D-Navier–Stokes models.

Two-dimensional computations employing the CFD-RANS equations have been carried out at low Reynolds numbers for the wind-turbine blade proﬁles S809 and S826 by Sayed et al. [10]. The simulations were carried out at a wind speed of 1 m/s which is the average wind speed at Gulf of El-Zayt in Egypt. Also, 2D CFD-RANS simulations have been carried out at high Reynolds number (Re > 106) for the wind turbine blade proﬁles NACA 0008 and NACA0012 by Hoogedoorn et al. [1]. The unsteady separated turbulent ﬂow around an oscillating NACA 0012 airfoil pitching in a sinusoidal pattern was investigated numerically employing the URANS approach with free stream velocity of V = 14 m/s by Wang et al. [12]. The literature shows that most researchers focus is on the range of the high wind speeds and there is no researchers focus on the low-speed range.

In order to increase the efﬁciency of the wind turbine, the maximum power developed by the wind turbine is desired. The power is determined in the present study by analyzing the ﬂow around the turbine blades. The wind turbine blade proﬁles are selected from the proﬁles developed by the NREL. When comparing different airfoils as presented in this study, lift and drag coefﬁcients for each proﬁle are determined at different angles of attack.

Lift and drag coefﬁcients are dimensionless numbers used to measure the aerodynamic lift and drag forces that vary with the angle of attack (a) and the shape of the airfoil. They can be deﬁned as follows:

Therefore, the sliding ratio can be deﬁned as, e ¼ CL

The purpose of this study is to present the aerodynamic loads of the wind-turbine blade proﬁles in order to ﬁnd the suitable proﬁle for the wind conditions in Egypt. In order to determine the aerodynamic loads on the selected blade proﬁles, ANSYS commercial software is used.

2. Method of analysis

Wind turbine blades cross sections use airfoils proﬁles in order to develop mechanical power. The dimensions of the blades are determined based on the desired aerodynamic performance; the maximum desired power, the airfoil properties and strength considerations. Modern HAWT blades have been consistently designed using well tested airfoil ‘families’. That is, the blade tip is designed using a thin airfoil, for high sliding ratio, and the root region is designed using a thick version of the same airfoil for structural support. Before the detailed design of wind turbine power production are explained, aerodynamic characteristics related to airfoils need to be determined [2].

The aerodynamic simulations of the steady low-speed ﬂow past 2D S-series wind-turbine blade-proﬁles, developed by NREL [13] are performed using a CFD technique based on the ﬁnite-volume approach. The governing equations used in the simulations are the RANS equations. The blades performances at different wind speeds are conducted and the suitable blade proﬁle for each wind speed will be determined based on the maximum power the blade can produce. The maximum achievable power coefﬁcient, Cp, for turbines with an optimum blade shape but with a ﬁnite number of blades and aerodynamic drag has been calculated by Wilson et al. (1976), [2]. Their results ﬁt the available data with accuracy within 0.5% for tip speed ratios from 4 to 20, sliding ratio from 25 to inﬁnity and from 1 to 3 blades (B), where:

Nomenclature

CL lift coefﬁcient CD drag coefﬁcient CP pressure coefﬁcient

Cp power coefﬁcient Re Reynolds number c airfoil chord (m) L lift force (N) D drag force (N) q air density (kg/m3)

V wind speed (m/s) A cross-sectional area of the blade (m2) B number of blades a angle of attack ( ) e sliding ratio k tip speed ratio 2D two-dimensional 3D three-dimensional

Fig. 1. Maximum achievable power coefﬁcients of a three-bladed optimum rotor a function of the sliding ratio C /C [2].

Table 1 Selected Reynolds numbers for the present study at different wind speeds.

Wind speed(m/s) Reynolds numbers (Re)

542 M.A. Sayed et al./Energy Conversion and Management 64 (2012) 541–550

eðk þ 1 2BÞ ð4Þ

The effect of the sliding ratio on maximum achievable power coefﬁcients for a three-bladed rotor is shown in Fig. 1. It is clear from the results on the ﬁgure that there is a signiﬁcant reduction in maximum achievable power as the airfoil drag increases.

In this study, for each airfoil, the lift and drag coefﬁcient are calculated at different mean wind speeds, as shown in Table 1.

The blade proﬁles used in the simulations are the S-series proﬁles, namely; S809, S814, S815, S817, S818, S819, S820, S821, S822, S823, S825, S826, S827, S828, S829, S830, S831, S832, S833, S834 and S835 that are depicted in Fig. 2. The ANSYS CFX [14] has been used to simulate the ﬂow ﬁeld on the selected proﬁles and deter-

Fig. 2. NREL selected proﬁles.

M.A. Sayed et al./Energy Conversion and Management 64 (2012) 541–550 543

mine the forces acting on the surfaces and their surrounding boundaries.

The computational domain size is optimized to get a domainsize independent solution and the ﬁnal domain size is shown in Fig. 3. Moreover, the discretized domain is optimized based on the number of cells and the cell shape and the ﬁnal mesh used in the simulations is shown in Fig. 4.

For the prediction of wind turbine aerodynamics, The Shear

Stress Transport model (SST) method has been chosen to capture the turbulence. The SST model was chosen for accurate boundary layer detection due to its ability to capture the inﬂuence of different factors that affect transition such as the free-stream turbulence and pressure gradients. For controlling and reducing the numerical solution errors, the upwind scheme method has been selected. In near wall regions, predicting the velocity gradients produced by boundary layer phenomena need elements with high aspect ratios. Inﬂation is applied on the airfoil surface in order to capture the effect of the boundary layer on the aerodynamic behavior. The meshing strategy selected in this study is an extruded 2D mesh in order to generate a 2D mesh of one cell thickness.

The boundary conditions applied to all regions in the domain are inlets, outlets, walls or symmetry planes. It should be noted that the ﬂow is always subsonic due to the low range of velocity used in the study. The boundary condition at the airfoil surface has been set to no-slip-solid wall boundary. Inlet turbulence was assumed with a medium intensity (intensity = 5%). Pressure boundary conditions are applied at the domain outlets, and the average static pressure method is used in order to allow the pressure to vary locally on the boundary. Whereas for all selected airfoils under consideration, the target residual for convergence was set at 10 5.

3. Results and discussion

The simulations were carried out at different airﬂow velocities, as listed in Table 1. The wind speed over different sites in Egypt lies within that range which is the reason for selecting that range of velocity. The simulations are conducted for the whole range of the AOA from 5 to 15 because it is the normal operating range of the wind turbine designs. The objective of the simulation also is to ﬁnd the optimum operating AOA that produces the maximum power from the wind turbine blades based on the maximum lift to drag forces. The simulations are performed for the NREL blade proﬁles shown in Fig. 2.

Fig. 5a–f shows the results of the sliding ratios for the selected

NREL proﬁles which are plotted against the AOA for the proposed velocities.

As shown, as the wind speed increases the sliding ratio increases for all proﬁles. At a constant wind speed, the results show that the sliding ratio ﬁrst increases with increasing the AOA to reach a maximum value then decreases to almost the same low value for all proﬁles. The AOA at which the maximum sliding value exists changes with changing the proﬁle.

The geometric parameters that affect the aerodynamic performance of an airfoil include: the leading edge radius, mean camber line, maximum thickness and thickness distribution of the proﬁle and the trailing edge angle, as shown in Fig. 6. At low AOA the lift coefﬁcient can be increased and drag can often be decreased by using a cambered airfoil [2]. So, the proﬁles S818, S825, S826, S830, S831 and S832 have the highest sliding ratio because all of them have the highest camber.

Moreover, the proﬁles S814, S815, S817, S820, S821, S822, S823,

S827, S828, S833, S834 and S835 have the intermediate sliding ratio and the proﬁles S809, S819 and S829 have the lowest sliding ratio because they are almost symmetric proﬁles which results in a slight difference in pressure between the upper surface and the lower surface of the aerofoil at zero angle of attack. In order to determine the best operating AOA range of the proﬁles, the

Fig. 3. Computational domain. Fig. 4. The ﬁnal mesh.

544 M.A. Sayed et al./Energy Conversion and Management 64 (2012) 541–550

(a) Wind speed of 5 m/sec

(b) Wind speed of 7 m/sec (c) Wind speed of 9 m/sec

(d) Wind speed of 1 m/sec.

(e) Wind speed of 13 m/sec

(f) Wind speed of 15 m/sec

Fig. 5. The sliding ratio of the NREL proﬁles at different wind speeds. (a) Wind speed of 5 m/s, (b) wind speed of 7 m/s, (c) wind speed of 9 m/s (d) wind speed of 1 m/s,(e) wind speed of 13 m/s and (f) wind speed of 15 m/s.

Fig. 6. Airfoil nomenclature [2].

(Parte **1** de 2)