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The advancements in the wind turbine technology specially associated with Vertical Axis Wind Turbines (VAWT), has been improved for last couple of years. This is due to extensive use of computational techniques. This paper investigates dependency of torque on aerofoil geometry by performing numerical simulation on Darrieustype VAWT with fix pitch blades. Coordinate points for aerofoil was generated using Java Foil software. Reynolds-Averaged Navier Stokes (RANS) turbulence modelling was used for predicting the flow and efficiency of the three blades VAWT. The unsteady flow condition was considered to make simulation as realistic as possible. In order to visualize high strain flow and separation, we used two equation models i.e. k-ε with RNG. NACA 0012 aerofoil was used and camber variations were carried out for developing samples of aerofoil to check the enhancement in performance of VAWT. Results demonstrate the torque and power along with its coefficients. It has been investigated that the performance efficiency was significantly improved by changing the aerofoil camber, demonstrating highest torque with camber (C3) aerofoil and the least performance was observed using camber (C0).

In general, two wind turbines can be found i.e. VAWT and horizontal axis wind turbine (HAWT), which extracts and converts wind energy into useful electrical energy. Though, VAWT is very popular nowadays as it is utilizing wind energy from any direction for generation of electricity, it does not require yaw mechanism and it can be hold on a lighter weight tower. The merit of VAWT over HAWT is that the sections of this wind turbine are uniform and untwisted mostly, thereby giving ease in fabrication, reducing the maintenance and fabrication cost. The problem associated with it includes its inability to self-start and low efficiency [

Geometry parameters were considered from Biadgo, Simonović [

After designing model of wind turbine an enclosure was made around the aerofoil.

CFD analysis of VAWT was performed on ANSYS Fluent module. Triangular type mesh element with 108,384 number of nodes and 141,154 number of elements were used for meshing as it is considered more appropriate for 2D model [

In CFD we want to compute velocities and pressure at different points in model. For

this we consider a control volume and by using conservation of mass and momentum principle we develop N-S equations as sown by following equations [

where

If we solve N-S equations numerically, method is known as DNS which is difficult to compute. To overcome this difficulty we compute average solution of actual solution and this is done by using RANS model in which value of any variable in turbulent flow can be found by sum of mean value plus fluctuating value. N-S equations solved after averaging operation is known as RANS equations as shown in Equation (5).

where

where,

Air is used as compressible fluid flowing around the wind turbine for power generation. Velocity of fluid at inlet was set to 5 m/s and symmetry wall was set to no slip condition. Internal rotor and outer domain was set stationary. Ring containing aerofoil was considered movable with 0.5 and 4 tip speed ratio. Monitors for moment, coefficients of drag (C_{D}), lift (C_{L}) and moments (C_{M}) for individual as well as combined blades was established.

Data for TSR 0.5 was compared with Biadgo, Simonović [_{D}, C_{L}, C_{M} and torque was collected for one complete revolution after periodic behavior of residuals was achieved.

Values of lift, drag and co-efficient of moment at different azimuth angles during one revolution collected from fluent was exported to excel. Performance of the wind turbine can be predicted using power, torque and there co-efficient which depends upon area of the wind turbine, velocity and density of the fluid. Torque was calculated using co-effi- cient of torque or moment formula shown in Equation (8) [

where C_{P} is the co-efficient of power, C_{M} is the co-efficient of moment, A is area of the wind turbine, _{T} and coefficient of moment C_{M} is same and can be calculated from same Equation (8) by replacing M with T.

Results were collected right after seven revolutions were carried out. By doing this residuals were periodic and convergence criteria was met. Value of C_{M} was obtained using the Fluent directly for all VAWT models for one revolution out of 7 revolutions. Comparison of aerofoils at different cambers were made and it can be observed from the _{M} and azimuth angle that C_{M} for camber 3 approached the highest value in the graph and C_{M} for camber value 1 is in the lowest point in case of TSR 4.

Torque was calculated by utilizing C_{M} values collected from fluent multiplying with available wind power as in Equation (4). It can be seen from Equation (4) that torque and C_{M} is dependent upon each other so there graph trend will be same but values will

be different. Therefore, it can be visualized in

Before attempting these simulations, initially the data of the torque of Camber 0 aerofoil with TSR 0.5 was compared with Biadgo, Simonović [

Fluctuation of torque and C_{M} with azimuth angle provide good approximations as viewed from _{L} and is maximum in case of C3 aerofoil.

Power values cannot be directly collected from software so it was calculated using torque value from _{P} graph provides a good observation of performance of aerofoil which will provides ease in selecting an aerofoil for VAWT case.

For each camber, C_{M} data was collected directly from the Fluent and a mean value was used for comparison. Camber effect on C_{M} and torque can be seen from _{M} value shows increment however as the camber was increased beyond 3, the value of Cm decreased. Graphs indicate the similar trend for both Cm and torque that is due to dependency on each other both have identical reason for increase and decrease.

The bar chart in _{M} by changing the camber percentage from zero to seven. It can be observed that highest C_{M} obtained is 0.089 for the case of 3% camber, whereas, the minimum C_{M} value is 0.031 for 0% camber. Furthermore, chart illustrates that first trend of C_{M} rise fast till 3% and then begin to fall. Chart in

Blade | Mean Torque (This Paper) | Torque [ | % error |
---|---|---|---|

Blade Right | 3.25 | 3.05 | 6.10 |

Blade Left Up | 3.25 | 3.02 | 7.00 |

Blade Left Down | 2.46 | 2.24 | 9.80 |

Combines blades | 8.50 | 8.00 | 5.80 |

can be seen that value of torque was 4 Nm in case of symmetric aerofoil whereas by varying camber it reaches 10.5 Nm for 3% camber, which is considered optimum. The slight decrease in the trend was encountered by increasing camber percentage further. The torque average values from camber 0 to 7 for TSR 4.0 demonstrate to be within the range as predicted by Beri and Yao [

The chart in

in the power generated which illustrates that by increasing camber further above 7%, the power generation rate will decrease below 65 W. Coefficient of power is a constant value that depicts conversion efficiency of wind energy to electricity. _{P} for different camber values, it can be determined that there is sudden jump in C_{P} from camber C0 to C1 and it kept on increasing till C3 and after that a decline in performance value was observed. This provides result that at camber value 3% can provide maximum electricity generation as efficiency of conversion is at its peak.

It can be seen from

become more aerodynamic for moving in circular region but further increase in camber decrease performance as geometry become less aerodynamic. This probability occurs due to more drag and early flow separation from aerofoil. Thus, making flow more turbulent and causing interfering towards other aerofoil of VAWT model.

Bar graph in

The camber increment has shown a relative increase in the performance of the VAWT, however, as the camber changes from 3% to 5% a downward trend is observed. This sudden drop in the performance shows that there is a certain limit for elevating the efficacy of the VAWT using camber. The initial increment of the performance from camber 0% to 3% can be due to the changing pressure differential profile around the aerofoil geometry. Furthermore, the drop after 3% camber is due to increased turbulence and flow separation. Future work may involve 3-D analysis of complex model of VAWT.

Loya, A., Khan, M.Z.U., Bhutta, R.A. and Saeed, M. (2016) Dependency of Torque on Aerofoilcamber Variation in Vertical Axis Wind Turbine. World Journal of Mechanics, 6, 472-486. http://dx.doi.org/10.4236/wjm.2016.611033

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