influence analysis of blade chord length on the performance of a four bladed wollongong wind turbine

Influence analysis of blade chord length on the performance of a four-bladed Wollongong wind turbine Zhaoyong Mao, Wenlong Tian, and Shaokun Yan Citation: Journal of Renewable and Sustainable Energy 8, 023303 (2016); doi: 10.1063/1.4943093 View online: http://dx.doi.org/10.1063/1.4943093 View Table of Contents: http://aip.scitation.org/toc/rse/8/2 Published by the American Institute of Physics JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 8, 023303 (2016) Influence analysis of blade chord length on the performance of a four-bladed Wollongong wind turbine Zhaoyong Mao,a) Wenlong Tian, and Shaokun Yan School of Marine Science and Technology, Northwestern Polytechnical University, 710072 Xi’an, China (Received 26 September 2015; accepted 19 February 2016; published online March 2016) The Wollongong wind turbine is a new kind of vertical axis wind turbine (VAWT) with its blades rotated by only 180 for each full revolution of the main rotor A computational study on the effect of blade chord length on the turbine output performance of a four-bladed Wollongong turbine has been conducted using the commercial computational fluid dynamics (CFD) code ANSYS 13.0 A validation study was performed using a Savonius turbine and good agreement was obtained with experimental data Both rotating and steady CFD simulations were conducted to investigate the performance of the VAWT Rotating two-dimensional CFD simulations demonstrated that a turbine with a blade length of 550 mm has the highest power curve with a maximum averaged power coefficient of 0.3639, which is almost twice as high as that of a non-modified Savonius turbine Steady twodimensional CFD simulations indicated that the Wollongong turbine has a good self-starting capability with an averaged static torque coefficient of 1.09, which is C 2016 Author(s) All article about six times as high as that of a Savonius turbine V content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License [http://dx.doi.org/10.1063/1.4943093] I INTRODUCTION In recent years, people have shown increasing attention on renewable energy with increasing environmental pollution, rising energy demand, and depleting fossil fuel resources Wind energy utilization has become a research hotspot because it is economical and experimental Wind turbines can be classified into two groups: horizontal axis wind turbine (HAWT) and vertical axis wind turbine (VAWT), depending on the relative direction between the turbine rotational axis and the wind direction VAWTs rotate around an axis perpendicular to the wind direction VAWTs are less efficient than HAWTs, but they operate effectively in the presence of highly unstable, turbulent wind flow patterns1 and are more suitable for power generation in places with a complex terrain, such as remote rural areas where erratic wind flow patterns are quite common Moreover, VAWTs can operate regardless of the flow direction2 and are suitable for small scale, distributed power generation Previous research on VAWT mainly focused on two types of turbines,3 Darrieus turbine and Savonius turbine The Darrieus turbine is one of the best-known VAWTs, which has three or four straight airfoils to create lift and is the most efficient type of VAWT Considerable effort has been made to model the dynamic forces on the Darrieus turbine.4,5 The Darrieus turbine has also been studied extensively using CFD and experimental methods to optimize the performance.6,7 Daroczy et al studied the effects of turbulence models in the simulation of HDarrieus rotors.8 Variable pitch control mechanism,9 channelling device,10 twisted blades,11 and new airfoil shapes12 have been adopted to further improve starting torque and efficiency, and a) Author to whom correspondence should be addressed Electronic mail: maozhaoyong@nwpu.edu.cn 1941-7012/2016/8(2)/023303/12 8, 023303-1 C Author(s) 2016 V 023303-2 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) reduce shaking of the Darrieus turbine Recently, Chowdhury et al.13 studied the performance of the Darrieus wind turbine in upright and tilted configurations The Savonius turbine generates torque through the combined effects of drag and inside forces and typically has two or three bucket-shaped blades.14 The Savonius turbine has been studied experimentally and numerically to examine the effects of various design parameters such as the rotor aspect ratio, the overlap, the number of buckets, the rotor endplates, and the influence of bucket stacking.14–16 In addition, many researchers have worked to improve the efficiency and the starting torque characteristics of the Savonius rotor Some of these include adding guide vanes or deflector plates in front of the rotor preventing the negative torque opposite the rotor rotation.17,18 As for the novel blades of the Savonius turbine, researchers have studied numerically the performance of the turbine with arc-type blades19 and blades developed from Myring equations.20 A numerical study was also carried out to investigate the interaction between multi-turbines.21 Cooper and Kennedy developed a novel VAWT with actively pitched blades, called a Wollongong turbine.22 Fig 1(a) shows the 3-D schematic of a four-bladed Wollongong turbine FIG Schematic of a four-bladed Wollongong turbine: (a) 3-D view and (b) plan view 023303-3 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) The turbine has four blades with their axes held parallel to the main rotor axis The blades were simple flat plates, the pitch of which was controlled by a transmission chain of bevel gears so that the blades rotated about their mid-chord axis by only 180 for each full revolution of the main rotor A detailed description of mechanical transmission principle can be found in Ref 22 The Wollongong turbine has good self-starting performance and generates relatively high torque However, few accurate theoretical models or CFD analysis can be found in the existing literature This research aims to explore the output performance and self-starting capability of a fourbladed Wollongong turbine, and to investigate the effect of blade chord length on the performance of the turbine The analysis has been performed with a two-dimensional computational fluid dynamics (CFD) method A sliding mesh model, which uses a time averaged solution to determine the turbine performance, and has been proven to give relatively accurate results for a two-dimensional rotating rotor, was used in the rotating simulations to investigate the dynamic torque and power characteristics of the turbine Steady simulations have also been conducted to predict the self-starting capability of the turbine II NUMERICAL METHOD The simulations were performed using the commercial code Fluent 13.0,23 which is based on the finite volume method and has been widely used in wind turbine simulations The computational domain discretization was generated with the mesh tool in ANSYS Workbench A sliding mesh method was applied to perform the unsteady simulation A Simplified physical model Since the straight blades have the same cross section, the blade span effect can be ignored and two-dimensional simulations are chosen In two-dimensional simulations, the blade has a unit span of m Fig illustrates the situation where the turbine rotates with an constant angular velocity, x, in a flow with an inlet velocity, U The main parameters of the simplified physical model are shown in Table I B Governing equations The governing equations are given by the incompressible form of the Navier-Stokes equations, including the continuity equation and momentum equation, as shown below:23 @q ỵ rq~ v ẳ 0; @t (1) À * Á * @ * * ðq~ v ị ỵ rq~ v~ v ị ỵ q 2x ~ v ỵ x x ~ v ẳ rq ỵ rs ỵ F; @t (2) where q is the density* of wind, v is the relative velocity, x is the angular velocity, s refers to the stress tensor, and F stands for external body forces To predict the turbulence effects in the transient predictions, a standard two-equation k À e model was used,20 which is based on the transport equations for the turbulence kinetic energy, k, and its dissipation rate, e The standard k À e model has the advantages of robustness, economy, and reasonable accuracy, and is widely used and able to simulate many flow regimes:  # li @k lỵ ỵ Gk ỵ Gb qe YM ỵ Sk ; ak @xj (3)  # li @e e e2 lỵ ỵ C1e Gk ỵ C3e Gb ị C2e q ỵ Se ; k ae @xj k (4) @ qkị @ qkui ị @ ẳ þ @xj @xi @t @ ðqeÞ @ ðqeui Þ @ þ ¼ @xi @xj @t " " 023303-4 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG Simplified physical model where Gk is the generation of turbulent kinetic energy due to mean velocity gradients, Gb is the generation of turbulent kinetic energy due to buoyancy, YM represents the contribution of the fluctuating dilatation of the overall dissipation rate, ak and ae are the turbulent Prandtl numbers for k and e C Computational domains, boundary conditions, and grid generation The computational domain was a rectangle with a width of m and a length of 12 m, the turbine was placed in the symmetry axis of the top and bottom boundary and at a distance of m from the left boundary (see Fig 3) The overall domain is split into six subdomains, including an external stationary domain, an internal rotational domain, and four subdomains that contain the four blades The boundary conditions employed consist of a velocity inlet on the left side, a pressure outlet on right, and two sliding walls on top and bottom No-slip boundary conditions were imposed at the surface of the blades Siding interfaces exist between the external stationary domain and the internal rotational domain, allowing the transport of the flow properties Sliding interfaces also exist between the four subdomains and the internal rotational domain The above method of domain division is reasonable because the velocities of both the internal rotational domain and subdomains can be defined individually using the User Defined Function (UDF) The rotational motion of the internal rotational domain represents the motion of the turbine, and the motion of the four subdomains represents that of the four blades The domain is discretized with rectangular elements, with a total number of about 80 000 Grids closest to the profiles of the blades were refined with rectangular boundary elements to TABLE I Main parameters of the simplified physical model Parameter Number of the blade Blade chord length Inlet velocity Radius of the turbine Symbol c U ¼ m=s R ¼ 500 mm Angular velocity of the turbine x Azimuth angle of the turbine u 023303-5 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG Computing domains and boundary conditions describe with sufficient precision the boundary layer flow The height of the first elements above the wall surface was set such that the yỵ value was between 30 and 100, depending on the rotation velocity of the rotor and the position of the elements on the blade Grid node density was higher in the subdomains than in the external stationary domain and internal rotational domain Moreover, to precisely present the flow field inside the turbine, grid node density is higher near the blades The grids were created so that the control volumes are finer near the blades and coarser towards the boundaries (Fig 4) In this paper, all simulations were carried out with a constant wind velocity, m/s, at the inlet of the domain The outlet pressure is set at standard atmospheric pressure, P0 ¼  105 Pa The air was considered incompressible with a density of 1.2084 kg/m3 and a dynamic viscosity of 1:7979  10À5 Pa Blades of different chord lengths, from 300 mm to 600 mm, were studied to find the optimal chord length For each case, several simulations were studied, for flow coefficient k ranging between 0.2 and 0.6 The flow coefficient represents the ratio of blade rotating speed to free wind speed, and has the following expression: k¼ xR : U (5) The rotating simulations were conducted for three rotor revolutions with a time step of 2 / step and in each step, 100 convergences were determined by the order of magnitude of the residuals The drop of all scaled residuals below 10À5 was employed as convergence criterion D Numerical method validation In order to validate the accuracy of the sliding mesh method used in this paper, we calculated the averaged torque of a two-bladed Savonius turbine for a range of rotation velocities The numerical results are then compared with wind tunnel experimental data from Hayashi’s work.24 Fig illustrates the schematic of the Savonius turbine and detailed geometric parameters can be found in Ref 24 Fig shows the averaged torque of the Savonius turbine at different rotation velocities It can be seen from Fig that the numerical results agree well with the experimental data, especially when the rotation velocity exceeds 30 rad/s The curve of the numerical results is a little higher than that of the experimental data This phenomenon may be explained by the fact that experimental conditions are not perfectly ideal and two-dimensional simulations not consider the torque loss at the end of the blades 023303-6 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG Computational grid of: (a) external stationary domain and (b) internal rotational domain and subdomains III RESULTS AND DISCUSSION Performance of a wind turbine can be characterized by the manner in which the two main indicators—power coefficient (CP ) and torque coefficient (Cm )—vary with k For analysis, the following relations have been used: M ; 0:5qU RS (6) P ¼ kCm ; 0:5qU S (7) Cm ¼ CP ¼ where M is the dimensional torque, P is the dimensional power, q is the air density, U is the inflow speed, and R is the rotor radius The swept area of the rotor, S, is given by the relationship S ẳ 2R ỵ 0:5cÞH, where c is the blade chord length and H is the rotor height Since only 2D simulations were performed, the unit height H ¼ 1m was used 023303-7 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG Schematic of the Savonius turbine A Torque characteristic analysis Fig shows the variation of averaged torque coefficient with respect to k for seven configurations with chord length ranging from 300 mm to 600 mm It can be seen in Fig that chord length can significantly affect the averaged torque coefficient of the turbine A turbine with a larger chord length has a higher torque curve when the chord length is smaller than 550 mm However, further increasing the chord length will lower the torque curve The averaged torque coefficient decreases as k increases There exists an inflection point on each averaged torque curve, and as the chord length increases the inflection point moves forward After the inflection point, the torque coefficient shows a nearly linear decrease as the k increases, and this behaviour is quite similar to other drag type turbines The effect of the chord length on the averaged torque coefficient was further investigated by computing the dynamic torque on a single blade throughout one revolution Fig shows the dynamic torque coefficient of three blades with different length throughout one revolution at k ¼ 0:5 The peak positive torque coefficient occurs at approximately u ¼ 25 and the peak negative torque coefficient occurs near u ẳ 270 In the region u ẳ ẵ120 ; 270 Š, FIG Averaged torque versus rotation velocity 023303-8 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG Averaged torque coefficient of the turbine with respect to k the torque coefficient curves first rise and then fall with small fluctuation A large region of positive torque exists in u ẳ ẵ40 ; 110 (torques are the same in the regions u ẳ ẵ40 ; 0 and u ẳ ẵ320 ; 360 due to the periodical rotating of the turbine), and a relatively small region of negative torque exists in u ¼ ½230 ; 320 Š Blades with larger chord length generate larger positive torque, but they also generate larger negative torque Fig shows the pressure contours of the turbine with chord length c ¼ 550 mm at k ¼ 0:5 and u ¼ 0 , 20 , 40 , 60 Because two adjacent blades are 90 apart, Fig illustrates the pressure contours of 16 blade positions When u ¼ 20 (see the blade at the bottom left in Fig 9(b)), the blade has the most obvious pressure drop between the upwind surface and the downwind surface This position corresponds to the maximum torque point in Fig Fig 10 shows the velocity vectors of the turbine with chord length c ¼ 550 mm at k ¼ 0:5 and u ¼ 40 , 60 It can be seen from Fig 10 that the two downstream blades lie in the wake flow of the two FIG Dynamic torque coefficients of three blades with different length throughout one revolution at k ¼ 0:5 023303-9 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG Pressure contours at k ¼ 0:5 with chord length c ¼ 550 mm (a) u ¼ 0 , (b) u ¼ 20 , (c) u ¼ 40 , and (d) u ¼ 60 upstream blades Because the wake flow is irregular turbulent flow, torque generated on the two downstream blades will fluctuate, corresponding to that in the region u ẳ ẵ120 ; 270 Š in Fig The wind slows down as it passes the upstream blades Due to the wind velocity loss, the pressure drop on the both sides of the downstream blades, as can be seen in Figs 9(c) and 9(d), is not as obvious as that of the upstream blades This illustrates that the torque coefficient in the region u ẳ ẵ120 ; 270 is far smaller than in u ẳ ẵ40 ; 110 B Power characteristic analysis Fig 11 shows the effects of chord length on the averaged power coefficient with respect to k It can be seen from the figure that for a specific chord length, the averaged power coefficient FIG 10 Velocity vectors at k ¼ 0:5 with chord length c ¼ 550 mm (a) u ¼ 40 and (b) u ¼ 60 023303-10 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG 11 Averaged power coefficient of the turbine with respect to k increases with k until obtaining the maximum point; however, further increases in flow coefficient will lead to a decrease in the averaged power coefficient Chord length can significantly affect the power coefficient of the turbine As chord length increases, the power curve rises first and then lowers Table II shows the maximum power coefficient and the corresponding flow coefficient and coefficient of torque for different chord lengths It can be seen that turbines with different chord lengths all obtain their maximum power coefficient at k ¼ 0:5 The turbine with a chord length of 550 mm has the highest power curve with a maximum averaged power coefficient of 0.3639, which is almost twice as high as that of a non-modified Savonius turbine.11 C Self-starting performance analysis Fig 12 demonstrates that the static torque on a blade has two positive regions in a cycle The larger positive region is u ẳ ẵ60 ; 120 (static torques are the same in the regions u ẳ ẵ60 ; 0 and u ẳ ẵ300 ; 360 due to the periodical rotating of the turbine) and the smaller region is u ẳ ẵ170 ; 250 Two small negative regions exist between the positive regions The area of the positive region u ẳ ẵ60 ; 120 is considerably larger than the other regions The static torque coefficient of a single blade maintains around 0.9 in the region u ẳ ẵ30 ; 110 The curves of torque coefficient of the four blades show the same variation trend and have a phase difference of 90 between two adjacent blades The total static torque coefficient of the turbine, which is shown as a thick solid line in Fig 12, has a mean value of 1.09, which is far larger TABLE II Maximum coefficient of power and the corresponding k and coefficient of torque for different chord lengths The boldface values correspond to the turbine with the highest CPmax Chord length CPmax k corresponding to CPmax Cm corresponding to CPmax 300 0.3092 0.5 0.6185 350 0.3320 0.5 0.6639 400 450 0.3505 0.3606 0.5 0.5 0.7010 0.7212 500 0.3662 0.5 0.7323 550 600 0.3696 0.3671 0.5 0.5 0.7393 0.7341 023303-11 Mao, Tian, and Yan J Renewable Sustainable Energy 8, 023303 (2016) FIG 12 Predicted static torque coefficients with respect to azimuth angle than the mean static torque coefficient of the Savonius rotor (0.16 as presented in Ref 11) This means that the Wollongong turbine has a good self-starting capability and could start well in any original azimuth position IV CONCLUSIONS This paper describes numerical studies of a Wollongong turbine, which is a straight blade type VAWT adopting active blade control by a transmission chain of bevel gears Rotating two-dimensional CFD simulations demonstrated that a turbine with a larger chord length has a higher torque curve when the chord length is smaller than 550 mm There exists an inflection point on each averaged torque curve, and the torque coefficient shows a nearly linear decrease as k increases after the inflection point Rotating two-dimensional CFD simulations also demonstrated that as chord length increases, the power curve rises first and then 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7915–7929 (2015) 21 M Shaheen, M El-Sayed, and S Abdallah, “Numerical study of two-bucket Savonius wind turbine cluster,” J Wind Eng Ind Aerodyn 137, 78–89 (2015) 22 P Cooper and O C Kennedy, “Development and analysis of a novel vertical axis wind turbine,” in Proceedings of Solar 2004, Australia, 2004, pp 1–9 23 Fluent 13.0 User’s Guide, ANSYS, Inc., 2011 24 T Hayashi, Y Li, Y Hara et al., “Wind tunnel tests on a three-stage out-phase Savonius rotor,” JSME Int J 48, 9–16 (2005) ...JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 8, 023303 (2016) Influence analysis of blade chord length on the performance of a four- bladed Wollongong wind turbine Zhaoyong Mao ,a) Wenlong Tian, and... self-starting capability of a fourbladed Wollongong turbine, and to investigate the effect of blade chord length on the performance of the turbine The analysis has been performed with a two-dimensional computational... of blade chord length on the turbine output performance of a four- bladed Wollongong turbine has been conducted using the commercial computational fluid dynamics (CFD) code ANSYS 13.0 A validation