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This paper presents effects of design factors on mechanical performance of Vertical Axis Wind Turbines (VAWTs), and an experimental investigation of optimal VAWT performance under low wind speed conditions in Thailand. Design factors include types of wind turbines, number of blades, types of materials, height-to-radius ratios, and design modifications. Potential VAWT models with different design factors are numerically analyzed within a virtual wind tunnel at various wind speeds by utilizing Xflow
^{TM}
Computational Fluid Dynamics (CFD) software. The performance curves of each VAWT are obtained as plots of power coefficients against tip speed ratios. It is found that the type of wind turbine, number of blades, and height-to-radius ratio have significant effects on mechanical performance whereas types of materials result in shifts of operating speeds of VAWTs. Accordingly, an optimal VAWT prototype is developed to operate under actual low speed wind conditions. The performance curve from experimental results agrees with the CFD results. The proposed methodology can be used in the computer design of VAWTs to improve mechanical performance before physical fabrication.

Wind has been an important energy source due to its renewability and sustainability. There are two types of wind turbines: Vertical Axis Wind Turbines (VAWTs) and Horizontal Axis Wind Turbines (HAWTs), which are commonly used to convert the kinetic energy of wind into mechanical energy of wind turbines. In this work, VAWTs have efficiently omnidirectional capability to harvest wind energy at low wind speed conditions.

For Thailand, wind speeds are statistically recorded up to 6 m/s at a height of 40 m [

Bhutta et al. reviewed previous works, which are related to various configurations and design techniques for VAWTs [

Those studies yield design knowledge for optimizing VAWTs with high efficiency. However, there is a deficiency of quantitative relationships between design factors and mechanical performance of VAWTs. Design factors of VAWTs are types (Savonius and Darrieus), number of turbine blades, types of materials, height to radius ratios, and design modifications.

In this study, VAWTs with various design factors are numerically simulated within virtual wind tunnels at different wind speeds by utilizing Xflow^{TM} Computational Fluid Dynamics (CFD) software. Correspondingly, the power coefficients are obtained with respect to tip speed ratios. An optimal design of VAWTs is chosen according to those analytical experiences. For experimental investigation, the VAWT is fabricated accordingly, and it is operated under actual low wind speed conditions.

In this study, each VAWT model is developed for a given geometry by using computer-aided design software, such as AutoCAD^{TM} and SolidWorks^{TM}. Except for types of materials, design factors, such as types of wind turbines, number of turbine blades, height to radius ratios, and design modification, can be varied in this step. The types of materials are adjusted by setting the values of density in CFD simulation. Under a virtual wind tunnel, the VAWTs are simulated to investigate effects of the design factors on mechanical performance under low wind speed conditions from 1 m/s to 6 m/s.

For CFD-based case studies, ^{2}. The arrow heads indicate the flow direction of air within a virtual wind tunnel at a given wind speed. For the boundary conditions, a uniform flow of air arrives at the entrance through the virtual wind tunnel with a constant speed. A VAWT, which is installed within the virtual wind tunnel, has no rotation initially. Discretization parameters, such as number, grid resolution, and airflow conditions, are set up in the CFD software and listed in ^{3}, 2440 kg/m^{3}, and 3500 kg/m^{3} in this work.

Parameters | Values | Units |
---|---|---|

Virtual Wind Tunnel Dimension (x, y, z) | (2, 0.7, 2) | m |

Air Properties | ||

Molecular Weight | 28.996 | g/mol |

Density | 1.255 | kg/m^{3 } |

Temperature | 288.15 | K |

Viscosity | 1.7894 × 10^{−}^{5 } | m^{2}/s |

Flow Model | Single phase | |

Analysis Type | External flow | |

Thermal Model | Isothermal | |

Turbulence Model | Wall-adapting local-Eddy | |

Turbulence Intensity | 3.5% | |

Refine Algorithm | Adaptive refinement | |

Number of grids | 1,200,000 | |

Grid model | Octree-like grid | |

Resolved Scale (grid size of wind tunnel) | 0.01 m | |

Target Resolved Scale (grid size near wind turbine) | 0.01 m |

In CFD-based analysis, the VAWT are simulated at various wind speeds with constant external torsional loads. The angular speeds of the VAWT are recorded under steady state conditions where the external torsional load is equal to the torque of the VAWT shaft. The mechanical power can be determined from multiplication of angular speed and torque. The performance curve is defined between the power coefficient and the tip speed ratio. The tip speed ratio can be determined by:

λ = ω R V (1)

where λ is the tip speed ratio, ω is the angular speed of wind turbine (rad/s), R is the radius of wind turbine (m) and V is the wind speed (m/s).

The power coefficient is defined as the ratio of mechanical power of wind turbine to wind power as expressed in Equation (2).

C p = P t P w (2)

where C p is the power coefficient, P t is the mechanical power of turbine (W), and P w is the wind power (W).

In this section, an optimal VAWT design is chosen, according to analytical studies. The schematic diagram of the VAWT model is a 90˚ twisted two-layer Savonius-type VAWT with covers, as shown in

power of a VAWT is determined from the rate of change in kinetic energy of the VAWT shaft. The performance curve from CFD plots of the power coefficient against tip speed ratio is compared with the experimental results in Section 3.

In this section, CFD-based studies of mechanical performances are presented with respect to design factors: material types, number of turbine blades, height to radius ratios, design modification, and turbine patterns.

A Savonius-type VAWT is set up within a virtual wind tunnel, as shown in

The values of density are 1000 kg/m^{3} (plastic), 2440 kg/m^{3} (glass fiber), and 3500 kg/m^{3} (metal) for the three case studies.

Savonius-type VAWTs models, with two, three, and four blades, are used in CFD simulations under various wind conditions. It is seen from

The radius of each model is adjusted with respect to a unity height such a way that the swept area remains constant. As shown in

There are three interesting design modifications: helix pattern, cover-added pattern, and double layer pattern, as shown in

than the wind speed. The value of the power coefficient reaches a maximum. However, if the torsional loading on the VAWT is less (or more), the power coefficient declines rapidly. A design for power generation must be considered for a suitable load torque in this case. Furthermore, the power coefficient of Darrieus VAWTs is higher than Savonius VAWTs, while they also run at a higher tip speed ratio.

The design factors have been studied in previous sections. It is found that the Savonius VAWT with two blades has a geometric advantage in suitably harvesting wind energy in most rotational positions of the VAWT. However, there is a singularity in the VAWT-blade position, parallel to wind flow. Therefore, another VAWT is installed by a position of the VAWT blade, perpendicular to the other. This 90˚ twisted two-layer Savonius VAWT yields the highest power coefficients among other studied VAWT. The Darriues VAWT is incapable of

starting to rotate by itself, especially under low wind speed conditions. ^{TM}. The power coefficient is varied according to tip speed ratios. It is observed that the power coefficient increases as the tip speed ratio increases. Once it reaches a maximum value of 3.51% at around a tip speed ratio of 0.55, it decreases to zero, where the VAWT is run under no-load conditions. It should be noted that the power coefficient is lower than the commonly-known values of 0.15 to 0.2 since dimensions and geometry of wind turbines in this work are different from other turbines in literature. For example, influence of the end plates on both sides of the rotor causes different turbulent effects.

For an experiment, the VAWT is installed under actual wind conditions. Angular speeds of the VAWT are measured by an electric encoder, while the wind speed is measured by a hot-wire anemometer. In

speeds and wind speeds are illustrated with respect to time. The mean wind speed and standard deviation are 5.32 m/s and 1.12 m/s, respectively. They are used to determine the tip speed ratios in Equation (1). Accordingly, the power of a VAWT is determined by the rate of change in kinetic energy. The power coefficient is calculated by using Equation (2). In Figure12, the performance curve of CFD results are fitted well to the power coefficients against tip speed ratios, which are obtained from the experiment. The averaged relative difference between experimental data and CFD results are 3.30%. The deviations may be caused by uncertainties in measurement, airflow, and/or VAWT structure. However, it is confirmed that an optimal VAWT design can be obtained from the CFD analysis before physical fabrication. The power coefficient against tip speed ratio is used as a performance curve in power generation under various wind speed conditions.

Effects of design factors: material types, number of turbine blades, height to radius ratios, design modification, and turbine patterns, are analyzed by using CFD-based simulations within a virtual wind tunnel. The mechanical performances of VAWTs under low speed wind conditions are determined from plots of power coefficient against tip speed ratio. The Savonius-type VAWT can be selected for high torque at low angular speeds while the Darrieus-type VAWT can be chosen for power generation at high angular speeds. A number of blades and height to radius ratio have significant effects on operating conditions of rotation due to inertia. In this study, the Savonius-type VAWT, with a cover- added pattern and perpendicular double layers, yields great mechanical performance. The CFD results of the performance curve have good agreement with the experimental results of the optimal VAWT, which is operated under actual wind

conditions. The proposed methodology can be used to find the optimal VAWT from CFD analysis before physical fabrication.

Unsakul, S., Sranpat, C., Chaisiriroj, P. and Leephakpreeda, T. (2017) CFD-Based Performance Analysis and Experimental Investigation of Design Factors of Vertical Axis Wind Turbines under Low Wind Speed Conditions in Thailand. Journal of Flow Control, Measurement & Visualization, 5, 86-98. https://doi.org/10.4236/jfcmv.2017.54007