# Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Saudi Arabia’s Wind Power Research and Development Update

## 3. Wind Turbine Basics and Types

- (1)
- Based on the orientation of the rotor, these are classified as, Goldstein [70]
- (2)
- Based on the wind speed or the Reynolds number (Re) at which they operate, they are classified as [72,73]
- Low-speed wind turbine (Re < 10
^{3}) - Medium-speed wind turbine (10
^{3}< Re < 10^{5}) - High-speed wind turbine (Re > 10
^{5})

- (3)
- Based on the positioning of turbine to flow direction, they are classified as, Kress et al. [74]
- Upwind positioned wind turbine
- Downwind positioned wind turbine

- (4)
- Based on type of aerodynamics [72]
- Drag type wind turbine
- Lift type wind turbine

- (5)
- Based on the number of blades on the rotor, Morcos [75]
- Single-bladed wind turbine
- Multi-bladed wind turbine

- (6)
- Based on the location of the wind turbine [69]
- Offshore wind turbine
- Onshore wind turbine

- Flat-plate airfoil
- Symmetric airfoil
- Circular-arc airfoil

## 4. Description of Wind Flow around an Airfoil and its Effect on its Performance

## 5. Approaches used to Design Wind Turbine Blades

#### 5.1. Experimental Approach to Blade Design and Analysis

_{0}is the non-dimensional plunging amplitude. Chang et al. [81] validated analytical blade design method through a series of experimental tests and compared with the standard airfoils. They found out that the designed airfoils have maximum power coefficient when designed for maximum power.

_{L}and L/D ratios for low Reynolds number (<10

^{2}).

#### 5.2. Numerical Investigations of Blade Design and Analysis

#### 5.3. Theoretical and Analytical Approaches for Blade Design and Analysis

_{P}) at the operating conditions. The model was developed using an actuator disc and was validated using both a free-wake lifting line method and three-dimensional Navier-Stokes Solver. The study reported C

_{P}= 0.51 and pointed out that it increases and decreases towards the root and tip, respectively.

## 6. Approaches Used to Study the Performance of Wind Turbine

#### 6.1. Experimental Approaches for Wind Turbine Performance Analysis

^{2}which corresponds to a rated power of about 50 kW. Until the beginning of the twentieth century, all wind turbines were small, at least in terms of power output, and were used for water pumping and milling rather than producing electricity, Wood [112]. This book demonstrates that, a century later, small wind turbines can be designed and built to avoid many of the problems that faced Grim-wade.

_{w}and U

_{∞}are the treadmill speed and free stream velocity, F

_{L}and F

_{D}are the lift and drag forces, ϕ and ϖ are the relative wind angle and spinning speed of cylinder, ${\mathsf{\Omega}}_{r}$ and r

_{c}are the rotor angular speed and radial coordinates. This concept is applied to increase the lift to drag ratio in a wind turbine. Sedaghat et al. [113] used the Magnus effect to increase the lift to drag ratio and obtained a high lift to drag ratio for a wind turbine blade. The Magnus effect is produced by rotating any symmetrical shapes (symmetrical airfoil NACA 0015 in his case) and computationally tested it as wind turbine blade. A very high lift to drag ratio of 278 was obtained exploiting this effect, compared with 200 which is a maximum possible reported in the literature. Wind turbines are not always operated at steady loads hence the output power fluctuates considerably. In this context, Kress et al. [74] assessed a downwind turbine experimentally by tilting and coning the rotor to minimize the load fluctuations and found out that tilting and coning have an opposite effect on downwind turbine when compared to a upwind turbine. The tilting decreases the fluctuations while coning increases the fluctuations on the downwind turbine. They concluded that large tilt angles and moderate cone angles should be preferred for downwind turbines.

#### 6.2. Numerical Investigations for Wind Turbine Performance Analysis

_{p}is the wind energy utilization efficiency. Winglets (shown in Figure 11) are vertical projections on an airfoil blade which help reduce the drag and increase L/D ratio. Elfarra et al. [116] designed a winglet on National Renewable Energy Laboratory (NREL) rotor blade and optimized for the computational cost using the artificial neural network at different wind speeds. The study reported around 9% increase in the power for the horizontal axis wind turbine. To study this effect, a free stream turbulence was considered with a large integral scale on an airfoil-blade (S-809) by Maldonado et al. [117]. Their results concluded that very large scale eddies significantly improved the aerodynamic performance, i.e., L/D ratio increased for all the attack angles except 0°.

#### 6.3. Analytical Approaches for Wind Turbine Performance Analysis

_{D}/C

_{L}ratio showed an increase in the power coefficient [79].

#### 6.3.1. Blade Element Momentum Theory

#### 6.3.2. Other Proposed Theories

## 7. Wind Turbine’s Performance Optimization Techniques

## 8. Dynamic Load Mitigation on Wind Turbines

## 9. Flow Separation Techniques for Wind Turbine’s Efficiency Enhancement

#### 9.1. Active Flow Control Techniques

#### 9.2. Passive Flow Control Techniques

_{P}). The torque was shown to increase up to 26% and C

_{P}to increase up to 43%. The operational range was also found to increase by 67%. Morphing also helps wind turbine adapt to varying load conditions in addition to act a passive flow control technique as mentioned above. Binci et al. [171] studied the flow field past a dimpled laminar airfoil using Computational Fluid-Dynamics (CFD) to analyze the flow field induced by dimples on the NACA 64-014A laminar airfoil at Re = 1.75 × 10

^{5}at α = 0°. Large-Eddy Simulation results provided good agreement with experimental data, while Reynolds Averaged Navier-Stokes equations approach overestimated the laminar separation bubble (LSB) extension of dimpled and un-dimpled configurations.

## 10. Stall Control

_{L}and reduced C

_{D}and a net increase in C

_{P}. To further illustrate the effect of rotation, an experimental study was conducted by Lee and Wu [175] where they demonstrated that reversed flow is larger for a static airfoil. The experimental analysis was carried out using Tomographic particle image velocimetry (Tomo-PIV) which measured volumetric velocity fields in all three directions. A larger angle of attack resulted due to larger Coriolis forces which delayed the stall on the turbine blades. Foussekis et al. [176] presented the effect of steady and unsteady separated flow around an airfoil experimentally and numerically to quantify the phenomenon of stall, in which they found a double separation on the studied profile. Although, most utility wind turbines operate at constant speed, but there has been a considerable interest in variable-speed turbines. The following advantages are expected for a wind turbine operated under variable-speed configuration.

- (1)
- Increase the energy capture
- (2)
- Drivetrain loads are reduced
- (3)
- Run smoothly
- (4)
- The quality of power is enhanced
- (5)
- Most importantly, doesn’t require a flow control technique which burdens the turbine mechanically and economically.

## 11. Cut-in-Speed Reduction Techniques

## 12. Starting Behavior of the Wind Turbines

## 13. Wind Turbine Blade Materials

## 14. Concluding Remarks

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Nomenclature

AEY | Annual Energy Yield |

AIF | Axial Induction Factor |

BEM | Blade Element Momentum theory |

BF | Blade Fatigue |

BM | Blade Mass |

BK | Buckling |

COE | Cost of Energy |

CFD | Computational Fluid Dynamics |

CKS | Control Kind of Stall |

DRT | Drive Train |

DT | Displacement |

DSF | Damage and Static Failure |

FEM | Finite Element Modeling |

GC | Ground Clearance |

GCI | Grid Convergence Index |

GRE | General Richardson Extrapolation |

GW | Giga Watt |

HAWT | Horizontal Axis Wind Turbine |

HH | Hub Height |

HWA | Hot Wire Anemometry |

kW | Kilo Watt |

kWh | Kilo Watt hour |

LI | Linear Inequality |

LSV | Laser Sheet Visualization |

MC | Maximum Chord |

MW | Mega Watt |

MWh | Mega Watt hour |

NF | Natural Frequency |

NL | Noise Level |

PIV | Particle Image Velocimetry |

RP | Rated Power |

SN | Strain |

SS | Stress |

SL | Solidity |

SAT | Shell and Airfoil Thickness |

SP | Separation Point |

SFV | Smoke Flow Visualization |

ST | Shaft Torque |

TV | Tower Vibrations |

TR | Thrust |

VAWT | Vertical axis wind turbine |

WFA | Wind Farm Area |

WTP | Wind Turbine Power |

C_{D} | Drag coefficient |

C_{L} | Lift coefficient |

C_{P} | Power coefficient |

## References

- Soler-Bientz, R. Preliminary results from a network of stations for wind resource assessment at North of Yucatan Peninsula. Energy
**2011**, 36, 538–548. [Google Scholar] [CrossRef] - Global Wind Report (GWEC). Available online: http://www.gwec.net/wp-content/uploads/vip/GWEC_PRstats2016_EN_WEB.pdf (accessed on 11 January 2018).
- Wind Turbine Schematic. Available online: http://www.delahyde.com/lubang/imagesl_2013/Wind_Turbine_Schematic_M.jpg (accessed on 11 January 2018).
- Tsai, K.; Pan, C.; Cooperman, A.M.; Johnson, S.J.; Dam, C.P. Van An Innovative Design of a Microtab Deployment Mechanism for Active Aerodynamic Load Control. Energies
**2015**, 8, 5885–5897. [Google Scholar] [CrossRef] - Fernandez-gamiz, U.; Zulueta, E.; Boyano, A.; Ramos-hernanz, J.A.; Lopez-guede, J.M. Microtab Design and Implementation on a 5 MW Wind Turbine. Appl. Sci.
**2017**, 7, 536. [Google Scholar] [CrossRef] - KACARE White Paper. Available online: https://www.kacare.gov.sa/en/FutureEnergy/RenewableEnergy/Pages/default.aspx (accessed on 11 January 2018).
- Elsevier BV. Scopus Journal Analyzer. 2014. Available online: http://www.scopus.com/source/eval.url (accessed on 11 January 2018).
- Zheng, Q.; Rehman, S.; Alam, M.; Alhems, L.M.; Lashin, A. Decomposition of wind speed fluctuations at different time scales. J. Earth Syst. Sci.
**2017**, 126. [Google Scholar] [CrossRef] - Alam, M.M.; Rehman, S.; Al-Hadhrami, L.M.; Meyer, J.P. Extraction of the inherent nature of wind speed using wavelets and FFT. Energy Sustain. Dev.
**2014**, 22, 34–47. [Google Scholar] [CrossRef] - Siddiqi, A.H.; Khan, S.; Rehman, S. Wind Speed Simulation Using Wavelets. Am. J. Appl. Sci.
**2005**, 2, 557–564. [Google Scholar] [CrossRef] - Rehman, S.; Ali, S.; Khan, S. Wind Farm Layout Design Using Cuckoo Search Algorithms. Appl. Artif. Intell.
**2016**, 30, 899–9224. [Google Scholar] [CrossRef] - Rehman, S.; Khan, S. Fuzzy Logic Based Multi-Criteria Wind Turbine Selection Strategy—A Case Study of Qassim, Saudi Arabia. Energies
**2016**, 9, 872. [Google Scholar] [CrossRef] - Shoaib, M.; Siddiqui, I.; Rehman, S.; Rehman, S.; Khan, S.; Lashin, A. Comparison of Wind Energy Generation Using the Maximum Entropy Principle and the Weibull Distribution Function. Energies
**2016**, 9, 842. [Google Scholar] [CrossRef] - Mohandes, M.; Rehman, S.; Abido, M.; Badran, S. Convertible wind energy based on predicted wind speed at hub-height. Energy Sources Part A Recover. Util. Environ. Eff.
**2016**, 38, 140–148. [Google Scholar] [CrossRef] - Mohandes, M.; Rehman, S.; Rahman, S.M. Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl. Energy
**2011**, 88, 4024–4032. [Google Scholar] [CrossRef] - Mohandes, M.A.; Rehman, S.; Rahman, S.M. Spatial estimation of wind speed. Int. J. Energy Res.
**2012**, 36, 545–552. [Google Scholar] [CrossRef] - Mohandes, M.A.; Rehman, S. Short term wind speed estimation in Saudi Arabia. J. Wind Eng. Ind. Aerodyn.
**2014**, 128, 37–53. [Google Scholar] [CrossRef] - Mohandes, M.A.; Halawani, T.O.; Rehman, S.; Hussain, A.A. Support vector machines for wind speed prediction. Renew. Energy
**2004**, 29, 939–947. [Google Scholar] [CrossRef] - Mohandes, M.A.; Rehman, S.; Halawani, T.O. A neural networks approach for wind speed prediction. Renew. Energy
**1998**, 13, 645–654. [Google Scholar] [CrossRef] - Bagiorgas, H.S.; Mihalakakou, G.; Rehman, S.; Al-Hadhrami, L.M. Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function. Int. J. Green Energy
**2016**, 13, 703–714. [Google Scholar] [CrossRef] - Bagiorgas, H.S.; Mihalakakou, G.; Rehman, S.; Al-Hadhrami, L.M. Offshore wind speed and wind power characteristics for ten locations in Aegean and Ionian Seas. J. Earth Syst. Sci.
**2012**, 121, 975–987. [Google Scholar] [CrossRef] - Bagiorgas, H.S.; Mihalakakou, G.; Rehman, S.; Al-Hadhrami, L.M. Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function. J. Renew. Sustain. Energy
**2012**, 4. [Google Scholar] [CrossRef] - Bagiorgas, H.S.; Giouli, M.; Rehman, S.; Al-Hadhrami, L.M. Weibull parameters estimation using four different methods and most energy-carrying wind speed analysis. Int. J. Green Energy
**2011**, 8, 529–554. [Google Scholar] [CrossRef] - Rehman, S.; Al-hadhrami, L.M.; Bagiorgas, H.S. Offshore Wind Characteristics in Ionian Sea. Trans. Control Mech. Syst.
**2012**, 1, 229–234. [Google Scholar] - Himri, Y.; Rehman, S.; Himri, S.; Mohammadi, K.; Sahin, B.; Malik, A.S. Investigation of wind resources in Timimoun region, Algeria. Wind Eng.
**2016**, 40, 250–260. [Google Scholar] [CrossRef] - Himri, Y.; Rehman, S.; Agus Setiawan, A.; Himri, S. Wind energy for rural areas of Algeria. Renew. Sustain. Energy Rev.
**2012**, 16, 2381–2385. [Google Scholar] [CrossRef] - Himri, Y.; Rehman, S.; Draoui, B.; Himri, S. Wind power potential assessment for three locations in Algeria. Renew. Sustain. Energy Rev.
**2008**, 12, 2488–2497. [Google Scholar] [CrossRef] - Khan, S.A.; Rehman, S. Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey. Renew. Sustain. Energy Rev.
**2013**, 19, 370–384. [Google Scholar] [CrossRef] - Bassyouni, M.; Gutub, S.; Javaid, U.; Awais, M.; Rehman, S.; Abdel-Hamid, S.; Abdel-Aziz, M.; Abouel-Kasem, A.; Shafeek, H. Assessment and analysis of wind power resource using weibull parameters. Energy Explor. Exploit.
**2015**, 33, 105–122. [Google Scholar] [CrossRef] - Baseer, M.A.; Meyer, J.P.; Alam, M.M.; Rehman, S. Wind speed and power characteristics for Jubail industrial city, Saudi Arabia. Renew. Sustain. Energy Rev.
**2015**, 52, 1193–1204. [Google Scholar] [CrossRef] - Rehman, S.; El-Amin, I.M.; Ahmad, F.; Shaahid, S.M.; Al-Shehri, A.M.; Bakhashwain, J.M. Wind power resource assessment for Rafha, Saudi Arabia. Renew. Sustain. Energy Rev.
**2007**, 11, 937–950. [Google Scholar] [CrossRef] - Rehman, S.; Alam, M.M.; Meyer, J.P.; Al-Hadhrami, L.M. Wind speed characteristics and resource assessment using Weibull parameters. Int. J. Green Energy
**2012**, 9, 800–814. [Google Scholar] [CrossRef] - Rehman, S.; Halawani, T.O.; Mohandes, M. Wind power cost assessment at twenty locations in the Kingdom of Saudi Arabia. Renew. Energy
**2003**, 28, 573–583. [Google Scholar] [CrossRef] - Rehman, S.; Ahmad, A. Assessment of wind energy potential for coastal locations of the Kingdom of Saudi Arabia. Energy
**2004**, 29, 1105–1115. [Google Scholar] [CrossRef] - Proietti, S.; Sdringola, P.; Castellani, F.; Astolfi, D.; Vuillermoz, E. On the contribution of renewable energies for feeding a high altitude Smart Mini Grid. Appl. Energy
**2017**, 185, 1694–1701. [Google Scholar] [CrossRef] - Rehman, S. Wind energy resources assessment for Yanbo, Saudi Arabia. Energy Convers. Manag.
**2004**, 45, 2019–2032. [Google Scholar] [CrossRef] - Rehman, S. Offshore wind power assessment on the east coast of Saudi Arabia. Wind Eng.
**2005**, 29, 409–419. [Google Scholar] [CrossRef] - Rehman, S.; Ahmad, A.; El-Amin, I.; Al-Hadhrami, L.M. Assessment of wind power, wind exponent, local air density and air turbulence intensity for an isolated site. Int. J. Sustain. Energy
**2009**, 28, 217–230. [Google Scholar] [CrossRef] - Rehman, S.; Sahin, A.Z. A wind-solar PV hybrid power system with battery backup for water pumping in remote localities. Int. J. Green Energy
**2016**, 13, 1075–1083. [Google Scholar] [CrossRef] - Ur Rehman, S.; Rehman, S.; Qazi, M.U.; Shoaib, M.; Lashin, A. Feasibility study of hybrid energy system for off-grid rural electrification in southern Pakistan. Energy Explor. Exploit.
**2016**, 34, 468–482. [Google Scholar] [CrossRef] - Rehman, S.; El-Amin, I. Study of a solar pv/wind/diesel hybrid power system for a remotely located population near Arar, Saudi Arabia. Energy Explor. Exploit.
**2015**, 33, 591–620. [Google Scholar] [CrossRef] - Rehman, S.; Alam, M.M.; Meyer, J.P.; Al-Hadhrami, L.M. Feasibility study of a wind-pv-diesel hybrid power system for a village. Renew. Energy
**2012**, 38, 258–268. [Google Scholar] [CrossRef] - Rehman, S.; Al-Hadhrami, L.M. Study of a solar PV-diesel-battery hybrid power system for a remotely located population near Rafha, Saudi Arabia. Energy
**2010**, 35, 4986–4995. [Google Scholar] [CrossRef] - Rehman, S.; El-Amin, I.M.; Ahmad, F.; Shaahid, S.M.; Al-Shehri, A.M.; Bakhashwain, J.M.; Shash, A. Feasibility study of hybrid retrofits to an isolated off-grid diesel power plant. Renew. Sustain. Energy Rev.
**2007**, 11, 635–653. [Google Scholar] [CrossRef] [Green Version] - Rehman, S. Prospects of wind farm development in Saudi Arabia. Renew. Energy
**2005**, 30. [Google Scholar] [CrossRef] - Rehman, S.; Baseer, M.A.; Meyer, J.P.; Alam, M.M.; Alhems, L.M.; Lashin, A.; Al Arifi, N. Suitability of utilizing small horizontal axis wind turbines for off grid loads in eastern region of Saudi Arabia. Energy Explor. Exploit.
**2016**, 34, 449–467. [Google Scholar] [CrossRef] - Rehman, S. Performance Evaluation of Vertical Axis Wind Turbine for Small off grid loads in North-Eastern Region of Saudi Arabia Performance Evaluation of Vertical Axis Wind Turbine for Small off grid loads in North-Eastern Region of Saudi Arabia. Wulfenia J.
**2015**, 22, 146–165. [Google Scholar] - Rehman, S.; Sahin, A. Comparing the use of diesel and wind power in pumping water in Saudi Arabia. Energy Environ.
**2014**, 25, 369–388. [Google Scholar] [CrossRef] - Rehman, S.; Sahin, A.Z. Wind power utilization for water pumping using small wind turbines in Saudi Arabia: A techno-economical review. Renew. Sustain. Energy Rev.
**2012**, 16, 4470–4478. [Google Scholar] [CrossRef] - Baseer, M.A.; Meyer, J.P.; Rehman, S.; Alam, M.M.; Al-Hadhrami, L.M.; Lashin, A. Performance evaluation of cup-anemometers and wind speed characteristics analysis. Renew. Energy
**2016**, 86, 733–744. [Google Scholar] [CrossRef] - Rehman, S. Tower distortion and scatter factors of co-located wind speed sensors and turbulence intensity behavior. Renew. Sustain. Energy Rev.
**2014**, 34, 20–29. [Google Scholar] [CrossRef] - Rehman, S.; Al-Hadhrami, L.M.; Alam, M.M.; Meyer, J.P. Empirical correlation between hub height and local wind shear exponent for different sizes of wind turbines. Sustain. Energy Technol. Assess.
**2013**, 4, 45–51. [Google Scholar] [CrossRef] - Alam, M.M.; Rehman, S.; Meyer, J.P.; Al-Hadhrami, L.M. Review of 600–2500 kW sized wind turbines and optimization of hub height for maximum wind energy yield realization. Renew. Sustain. Energy Rev.
**2011**, 15, 3839–3849. [Google Scholar] [CrossRef] - Rehman, S. Long-term wind speed analysis and detection of its trends using Mann-Kendall test and linear regression method. Arab. J. Sci. Eng.
**2013**, 38, 421–437. [Google Scholar] [CrossRef] - Rahman, F.; Rehman, S.; Abdul-Majeed, M.A. Overview of energy storage systems for storing electricity from renewable energy sources in Saudi Arabia. Renew. Sustain. Energy Rev.
**2012**, 16, 274–283. [Google Scholar] [CrossRef] - Rehman, S.; Ahmad, A.; Al-Hadhrami, L.M. Development and economic assessment of a grid connected 20 MW installed capacity wind farm. Renew. Sustain. Energy Rev.
**2011**, 15, 833–838. [Google Scholar] [CrossRef] - Rehman, S.; Ahmad, A.; Al-Hadhrami, L.M. Detailed analysis of a 550-MW installed capacity wind farm in Saudi Arabia. Int. J. Green Energy
**2010**, 7, 410–421. [Google Scholar] [CrossRef] - Rehman, S.; Al-Abbadi, N.M. Wind power characteristics on the North West coast of Saudi Arabia. Energy Environ.
**2009**, 20–21, 1257. [Google Scholar] [CrossRef] - Al-Abbadi, N.M.; Rehman, S. Wind speed and wind power characteristics for Gassim, Saudi Arabia. Int. J. Green Energy
**2009**, 6, 201–217. [Google Scholar] [CrossRef] - Rehman, S.; Al-Abbadi, N.M. Wind shear coefficient, turbulence intensity and wind power potential assessment for Dhulom, Saudi Arabia. Renew. Energy
**2008**, 33, 2653–2660. [Google Scholar] [CrossRef] - Rehman, S.; Al-Abbadi, N.M. Wind shear coefficients and energy yield for Dhahran, Saudi Arabia. Renew. Energy
**2007**, 32, 738–749. [Google Scholar] [CrossRef] - Rehman, S.; Al-Abbadi, N.M. Wind shear coefficients and their effect on energy production. Energy Convers. Manag.
**2005**, 46, 2578–2591. [Google Scholar] [CrossRef] - Rehman, S.; Halawani, T.O. Statistical characteristics of wind in Saudi Arabia. Renew. Energy
**1994**, 4, 949–956. [Google Scholar] [CrossRef] - Rehman, S.; Halawani, T.O.; Husain, T. Weibull parameters for wind speed distribution in Saudi Arabia. Sol. Energy
**1994**, 53, 473–479. [Google Scholar] [CrossRef] - Alam, M.; Rehman, S.; Al-hadhrami, L.M.; Russel, M.; Meyer, J.P. Quantifying the contributions of different time-scales to wind speed using wavelets. In Proceedings of the International Conference on Mechanical, Industrial and Energy Engineering, Khulna, Bangladesh, 25–26 December 2014. [Google Scholar]
- Manwell, J.F.; McGowan, J.G.; Rogers, A.L. Wind Energy Explained: Theory, Design and Application; John Wiley & Sons: Hoboken, NJ, USA, 2010. [Google Scholar]
- Wind Power. 2014. Available online: http://energystorage.org/energy-storage/applications-energy-storage-technology (accessed on 11 January 2018).
- Fthenakis, V.; Kim, H.C. Land use and electricity generation: A life-cycle analysis. Renew. Sustain. Energy Rev.
**2009**, 13, 1465–1474. [Google Scholar] [CrossRef] - Wind Energy Technologies and Applications. Available online: https://energy.gov/sites/prod/files/2016/08/f33/2015-Wind-Technologies-Market-Report-08162016.pdf (accessed on 11 January 2018).
- Goldstein, L. A proposal and a theoretical analysis of a novel concept of a tilted-axis wind turbine. Energy
**2015**, 84, 247–254. [Google Scholar] [CrossRef] - Ohya, Y.; Karasudani, T. A Shrouded Wind Turbine Generating High Output Power with Wind-lens Technology. Energies
**2010**, 3, 634–649. [Google Scholar] [CrossRef] - Savonius Wind Power Report. Available online: http://www.uvm.edu/extension/cropsoil/wp-content/uploads/savonius_windpower_report.pdf (accessed on 11 January 2018).
- Chen, J.; Yang, H.; Yang, M.; Xu, H.; Hu, Z. A comprehensive review of the theoretical approaches for the airfoil design of lift-type vertical axis wind turbine. Renew. Sustain. Energy Rev.
**2015**, 51, 1709–1720. [Google Scholar] [CrossRef] - Kress, C.; Chokani, N.; Abhari, R.S. Passive minimization of load fluctuations on downwind turbines. Renew. Energy
**2016**, 89, 543–551. [Google Scholar] [CrossRef] - Morcos, V.H. Aerodynamic performance analysis of horizontal axis wind turbines. Renew. Energy
**1994**, 4, 505–518. [Google Scholar] [CrossRef] - Malhotra, P.; Hyers, R.W.; Manwell, J.F.; McGowan, J.G. A review and design study of blade testing systems for utility-scale wind turbines. Renew. Sustain. Energy Rev.
**2012**, 16, 284–292. [Google Scholar] [CrossRef] - Ma, P.C.; Zhang, Y. Perspectives of carbon nanotubes/polymer nanocomposites for wind blade materials. Renew. Sustain. Energy Rev.
**2014**, 30, 651–660. [Google Scholar] [CrossRef] - White, F. Fluid Mechanics, 8th ed.; McGraw-Hill: New York, NY, USA, 2015. [Google Scholar]
- Moshfeghi, M.; Song, Y.J.; Xie, Y.H. Effects of near-wall grid spacing on SST-K-ω model using NREL Phase VI horizontal axis wind turbine. J. Wind Eng. Ind. Aerodyn.
**2012**, 107–108, 94–105. [Google Scholar] [CrossRef] - Lu, K.; Xie, Y.; Zhang, D.; Xie, G. Systematic investigation of the flow evolution and energy extraction performance of a flapping-airfoil power generator. Energy
**2015**, 89, 138–147. [Google Scholar] [CrossRef] - Chang, J.; Zhu, W.; Fischer, A.; Garcla, N.R.; Madsen, J.; Chen, J.; Shen, W.Z. Design and validation of the high performance and low noise CQU-DTU-LN1 airfoils. Wind Energy
**2014**, 17, 1817–1833. [Google Scholar] [CrossRef] - Yavuz, T.; Koc, E.; Kilkis, B.; Erol, O.; Balas, C.; Aydemir, T. Performance analysis of the airfoil-slat arrangements for hydro and wind turbine applications. Renew. Energy
**2015**, 74, 414–421. [Google Scholar] [CrossRef] - Singh, R.K.; Ahmed, M.R.; Zullah, M.A.; Lee, Y.-H. Design of a low Reynolds number airfoil for small horizontal axis wind turbines. Renew. Energy
**2012**, 42, 66–76. [Google Scholar] [CrossRef] - Devinant, P.; Laverne, T.; Hureau, J. Experimental study of wind-turbine airfoil aerodynamics in high turbulence. J. Wind Eng. Ind. Aerodyn.
**2002**, 90, 689–707. [Google Scholar] [CrossRef] - McTavish, S.; Feszty, D.; Nitzsche, F. Evaluating Reynolds number effects in small-scale wind turbine experiments. J. Wind Eng. Ind. Aerodyn.
**2013**, 120, 81–90. [Google Scholar] [CrossRef] - Bons, J.P.; Sondergaard, R.; Rivir, R.B. The Fluid Dynamics of LPT Blade Separation Control Using Pulsed Jets. J. Turbomach.
**2002**, 124, 77. [Google Scholar] [CrossRef] - Gul, M.; Uzol, O.; Akmandor, I.S. An Experimental Study on Active Flow Control Using Synthetic Jet Actuators over S809 Airfoil. J. Phys. Conf. Ser.
**2014**, 524, 12101. [Google Scholar] [CrossRef] - Aramendia, I.; Fernandez-Gamiz, U.; Ramos-Hernanz, J.; Sancho, J.; Lopez-Guede, J.; Zulueta, E. Flow Control Devices for Wind Turbines; Springer International Publishing: Munchem, Germany, 2017; pp. 629–655. [Google Scholar]
- Aramendia, I.; Fernández-Gámiz, U.; Sancho, J.; Zulueta, E. State of the Art of Active and Passive Flow Control. Fluid Mech.
**2016**, 1–9. [Google Scholar] [CrossRef] - Abdulrahim, A.; Anik, E.; Ostovan, Y.; Uzol, O. Effects of tip injection on the performance and near wake characteristics of a model wind turbine rotor. Renew. Energy
**2016**, 88, 73–82. [Google Scholar] [CrossRef] - Gharali, K.; Johnson, D.A. Numerical modeling of an S809 airfoil under dynamic stall, erosion and high reduced frequencies. Appl. Energy
**2012**, 93, 45–52. [Google Scholar] [CrossRef] - Sutherland, H.; Beattie, A.; Hansche, B.; Musial, W.; Allread, J.; Johnson, J.; Summers, M. The Application of Non-Destructive Techniques to the Testing of a Wind Turbine Blade; Sandia National Laboratories: Springfield, VA, USA, 1994; p. 22161.
- Zarouchas, D.S.; Makris, A.A.; Sayer, F.; Van Hemelrijck, D.; Van Wingerde, A.M. Investigations on the mechanical behavior of a wind rotor blade subcomponent. Compos. Part B
**2012**, 43, 647–654. [Google Scholar] [CrossRef] - Asl, M.; Niezrecki, C.; Sherwood, J.; Peter, A. Scaled Composite I-Beams for Subcomponent Testing of Wind Turbine Blades: An Experimental Study. In Mechanics of Composite and Multi-Functional Materials; Springer International Publishing: Munchem, Germany, 2018; pp. 71–78. [Google Scholar]
- Asl, M.E.; Niezrecki, C.; Sherwood, J.; Avitabile, P. Similitude analysis of thin-walled composite I-beams for subcomponent testing of wind turbine blades. Wind Eng.
**2017**, 41, 297–312. [Google Scholar] [CrossRef] - Lee, S.G.; Park, S.J.; Lee, K.S.; Chung, C. Performance prediction of NREL (National Renewable Energy Laboratory) Phase VI blade adopting blunt trailing edge airfoil. Energy
**2012**, 47, 47–61. [Google Scholar] [CrossRef] - Wood, D.H. Some effects of compressibility on small horizontal-axis wind turbines. Renew. Energy
**1997**, 10, 11–17. [Google Scholar] [CrossRef] - Almohammadi, K.M.; Ingham, D.B.; Ma, L.; Pourkashan, M. Computational fluid dynamics (CFD) mesh independency techniques for a straight blade vertical axis wind turbine. Energy
**2013**, 58, 483–493. [Google Scholar] [CrossRef] - Whale, J.; Fisichella, C.J.; Selig, M.S. Correcting Inflow Measurements from Wind Turbines Using a Lifting-Surface Code. J. Sol. Energy Eng.
**2000**, 122, 196–202. [Google Scholar] [CrossRef] - Giguère, P.; Selig, M.S.; Tangler, J.L. Blade Design Trade-Offs Using Low-Lift Airfoils for Stall-Regulated HAWTs. J. Sol. Energy Eng.
**1999**, 121, 217–223. [Google Scholar] [CrossRef] - Selig, M.S.; Tangler, J.L. Development and Application of a Multipoint Inverse Design Method for Horizontal Axis Wind Turbines. Wind Eng.
**1995**, 19, 91–105. [Google Scholar] - Selig, M.S. Multipoint Inverse Design of an Infinite Cascade of Airfoils. AIAA J.
**1994**, 32, 774–782. [Google Scholar] [CrossRef] - Selig, M.S.; Maughmert, M.D. Generalized Multipoint Inverse Airfoil Design. AIAA J.
**1992**, 30, 2618–2625. [Google Scholar] [CrossRef] - Selig, M.S.; Maughmert, M.D. Multipoint Inverse Airfoil Design Method Based on Conformal Mapping. AIAA J.
**1992**, 30, 1162–1170. [Google Scholar] [CrossRef] - Bermúdez, L.; Velázquez, A.; Matesanz, A. Viscous-inviscid method for the simulation of turbulent unsteady wind turbine airfoil flow. J. Wind Eng. Ind. Aerodyn.
**2002**, 90, 643–661. [Google Scholar] [CrossRef] - Filippone, A. Airfoil inverse design and optimization by means of viscous-inviscid techniques. J. Wind Eng. Ind. Aerodyn.
**1995**, 56, 123–136. [Google Scholar] [CrossRef] - Cyr, S.; Newman, B.G. Flow past two-dimensional membrane aerofoils with rear separation. J. Wind Eng. Ind. Aerodyn.
**1996**, 63, 1–16. [Google Scholar] [CrossRef] - Johansen, J.; Madsen, H.A.; Gaunaa, M.; Bak, C.; Srensen, N.N. Design of a wind turbine rotor for maximum aerodynamic efficiency. Wind Energy
**2009**, 12, 261–273. [Google Scholar] [CrossRef] - Rocha, P.A.C.; Rocha, H.H.B.; Carneiro, F.O.M.; da Silva, M.E.V.; de Andrade, C.F. A case study on the calibration of the k-ω SST (shear stress transport) turbulence model for small scale wind turbines designed with cambered and symmetrical airfoils. Energy
**2016**, 97, 144–150. [Google Scholar] [CrossRef] - Grant, I.; Mo, M.; Pan, X.; Parkin, P.; Powell, J.; Reinecke, H.; Shuang, K.; Coton, F.; Lee, D. An experimental and numerical study of the vortex filaments in the wake of an operational, horizontal-axis, wind turbine. J. Wind Eng. Ind. Aerodyn.
**2000**, 85, 177–189. [Google Scholar] [CrossRef] - Hirahara, H.; Hossain, M.Z.; Kawahashi, M.; Nonomura, Y. Testing basic performance of a very small wind turbine designed for multi-purposes. Renew. Energy
**2005**, 30, 1279–1297. [Google Scholar] [CrossRef] - Wood, D. Green Energy and Technology, 1st ed.; Springer: London, UK; Dordrecht, The Netherlands; Heidelberg, Germany; New York, NY, USA; Calgary, AB, Canada, 2011; ISBN 978-1-84996-174-5. [Google Scholar]
- Sedaghat, A.; Samani, I.; Ahmadi-baloutaki, M.; El Assad, M.; Gaith, M. Computational Study on Novel circulating Aerofoils for use in Magnus wind turbine blades. Energy
**2015**, 91, 393–403. [Google Scholar] [CrossRef] - Bottasso, C.L.; Croce, A.; Gualdoni, F.; Montinari, P. Load mitigation for wind turbines by a passive aeroelastic device. J. Wind Eng. Ind. Aerodyn.
**2016**, 148, 57–69. [Google Scholar] [CrossRef] [Green Version] - Han, W.; Yan, P.; Han, W.; He, Y. Design of wind turbines with shroud and lobed ejectors for efficient utilization of low-grade wind energy. Energy
**2015**, 89, 687–701. [Google Scholar] [CrossRef] - Elfarra, M.A.; Sezer-Uzol, N.; Akmandor, I.S. NREL VI rotor blade: Numerical investigation and winglet design and optimization using CFD. Wind Energy
**2014**, 17, 657–669. [Google Scholar] [CrossRef] - Maldonado, V.; Castillo, L.; Thormann, A.; Meneveau, C. The role of free stream turbulence with large integral scale on the aerodynamic performance of an experimental low Reynolds number S809 wind turbine blade. J. Wind Eng. Ind. Aerodyn.
**2015**, 142, 246–257. [Google Scholar] [CrossRef] - Lanzafame, R.; Mauro, S.; Messina, M. Wind turbine CFD modeling using a correlation-based transitional model. Renew. Energy
**2013**, 52, 31–39. [Google Scholar] [CrossRef] - Yu, G.; Shen, X.; Zhu, X.; Du, Z. An insight into the separate flow and stall delay for HAWT. Renew. Energy
**2011**, 36, 69–76. [Google Scholar] [CrossRef] - Wang, F.; Bai, L.; Fletcher, J.; Whiteford, J.; Cullen, D. The methodology for aerodynamic study on a small domestic wind turbine with scoop. J. Wind Eng. Ind. Aerodyn.
**2008**, 96, 1–24. [Google Scholar] [CrossRef] [Green Version] - Wang, F.; Bai, L.; Fletcher, J.; Whiteford, J.; Cullen, D. Development of small domestic wind turbine with scoop and prediction of its annual power output. Renew. Energy
**2008**, 33, 1637–1651. [Google Scholar] [CrossRef] - Fagbenro, K.A.; Mohamed, M.A.; Wood, D.H. Computational modeling of the aerodynamics of windmill blades at high solidity. Energy Sustain. Dev.
**2014**, 22, 13–20. [Google Scholar] [CrossRef] - Li, Y.; Castro, A.M.; Sinokrot, T.; Prescott, W.; Carrica, P.M. Coupled multi-body dynamics and CFD for wind turbine simulation including explicit wind turbulence. Renew. Energy
**2015**, 76, 338–361. [Google Scholar] [CrossRef] - Li, C.; Lin, Q.; Ding, X.; Ye, X. Performance, aeroacoustics and feature extraction of an axial flow fan with abnormal blade angle. Energy
**2016**, 103, 322–339. [Google Scholar] [CrossRef] - Bukala, J.; Damaziak, K.; Kroszczynski, K.; Krzeszowiec, M.; Malachowski, J. Investigation of parameters influencing the efficiency of small wind turbines. J. Wind Eng. Ind. Aerodyn.
**2015**, 146, 29–38. [Google Scholar] [CrossRef] - Purazarm, P.; Modarres-Sadegh, Y.; Lackner, M. A parametric study of coupled-mode flutter for MW-size wind turbine blades. Wind Energy
**2016**, 19, 497–514. [Google Scholar] [CrossRef] - Capuzzi, M.; Pirrera, A.; Weaver, P.M. A novel adaptive blade concept for large-scale wind turbines. Part II: Structural design and power performance. Energy
**2014**, 73, 25–32. [Google Scholar] [CrossRef] - Capuzzi, M.; Pirrera, A.; Weaver, P.M. A novel adaptive blade concept for large-scale wind turbines. Part I: Aeroelastic behaviour. Energy
**2014**, 73, 15–24. [Google Scholar] [CrossRef] - Wang, L.; Liu, X.; Kolios, A. State of the art in the aeroelasticity of wind turbine blades: Aeroelastic modelling. Renew. Sustain. Energy Rev.
**2016**, 64, 195–210. [Google Scholar] [CrossRef] - Buck, J.A.; Garvey, S.D. Redefining the design objectives of large offshore wind turbine rotors. Wind Energy
**2015**, 18, 835–850. [Google Scholar] [CrossRef] - Yang, H.; Shen, W.; Xu, H.; Hong, Z.; Liu, C. Prediction of the wind turbine performance by using BEM with airfoil data extracted from CFD. Renew. Energy
**2014**, 70, 107–115. [Google Scholar] [CrossRef] - Sun, Z.; Chen, J.; Shen, W.Z.; Zhu, W.J. Improved blade element momentum theory for wind turbine aerodynamic computations. Renew. Energy
**2016**, 96, 824–831. [Google Scholar] [CrossRef] - Sant, T. Improving BEM—Based Aerodynamic Models in Wind Turbine Design Codes Improving BEM—Based Aerodynamic Models Tonio Sant Tonio Sant Improving BEM-Based Aerodynamic Models in Wind Turbine Design Codes; Print Right Ltd., Marsa: Msida, Malta, 2007; ISBN 978-99932-0-483-1. [Google Scholar]
- Prado, R.A. Reformulation of the momentum theory applied to wind turbines. J. Wind Eng. Ind. Aerodyn.
**1995**, 58, 277–292. [Google Scholar] [CrossRef] - Jamieson, P. Extraction in a Linear Constant Velocity Flow Field. Wind Energy
**2008**, 11, 445–457. [Google Scholar] [CrossRef] - Liu, Y.; Yoshida, S. An extension of the Generalized Actuator Disc Theory for aerodynamic analysis of the diffuser-augmented wind turbines. Energy
**2015**, 93, 1852–1859. [Google Scholar] [CrossRef] - Ohya, Y.; Karasudani, T.; Sakurai, A. Development of a shrouded wind turbine with a flanged diffuser. J. Wind Eng. Ind. Aerodyn.
**2008**, 96, 524–539. [Google Scholar] [CrossRef] - Abe, K.; Nishida, M.; Sakurai, A.; Ohya, Y.; Kihara, H.; Wada, E.; Sato, K. Experimental and numerical investigations of flow fields behind a small wind turbine with a flanged diffuser. J. Wind Eng. Ind. Aerodyn.
**2005**, 93, 951–970. [Google Scholar] [CrossRef] - Wang, Q.; Wang, Z.X.; Song, J.J.; Xu, Y.; Xu, J.Z. Study on a new aerodynamic model of HAWT based on panel method and Reduced Order Model using Proper Orthogonal Decomposition. Renew. Energy
**2012**, 48, 436–447. [Google Scholar] [CrossRef] - Ahmed, N.; Archer, R. Testing of highly loaded horizontal axis wind turbines designed for optimum performance. Renew. Energy
**2002**, 25, 613–618. [Google Scholar] [CrossRef] - Sanderson, R.J.; Archer, R.D. Optimum propeller wind turbines. J. Energy
**1983**, 7, 695–701. [Google Scholar] [CrossRef] - Narayana, M.; Putrus, G.A.; Jovanovic, M.; Leung, P.S.; McDonald, S. Generic maximum power point tracking controller for small-scale wind turbines. Renew. Energy
**2012**, 44, 72–79. [Google Scholar] [CrossRef] - Thumthae, C.; Chitsomboon, T. Optimal angle of attack for untwisted blade wind turbine. Renew. Energy
**2009**, 34, 1279–1284. [Google Scholar] [CrossRef] - Perkin, S.; Garrett, D.; Jensson, P. Optimal wind turbine selection methodology: A case-study for Búrfell, Iceland. Renew. Energy
**2015**, 75, 165–172. [Google Scholar] [CrossRef] - Pandey, M.M.; Pandey, K.P.; Ojha, T.P. An analytical approach to optimum design and peak performance prediction for horizontal axis wind turbines. J. Wind Eng. Ind. Aerodyn.
**1989**, 32, 247–262. [Google Scholar] [CrossRef] - Vu, D.; Marini, I.; Milas, Z. Numerical models for robust shape optimization of wind turbine blades. Renew. Energy
**2016**, 87, 849–862. [Google Scholar] [CrossRef] - Zhu, W.J.; Shen, W.Z.; Sørensen, J.N. Integrated airfoil and blade design method for large wind turbines. Renew. Energy
**2014**, 70, 172–183. [Google Scholar] [CrossRef] - Sessarego, M.; Dixon, K.R.; Rival, D.E.; Wood, D.H. A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization. Eng. Optim.
**2015**, 47, 1043–1062. [Google Scholar] [CrossRef] - Wang, L.; Wang, T.; Wu, J.; Chen, G. Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design. Energy
**2017**, 120, 346–361. [Google Scholar] [CrossRef] - Wang, L.; Wang, T.; Luo, Y. Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades. Appl. Math. Mech.
**2011**, 32, 739–748. [Google Scholar] [CrossRef] - Shen, X.; Chen, J.-G.; Zhu, X.-C.; Liu, P.-Y.; Du, Z.-H. Multi-objective optimization of wind turbine blades using lifting surface method. Energy
**2015**, 90 Pt 1, 1–11. [Google Scholar] [CrossRef] - Zhang, M.; Tan, B.; Xu, J. Smart fatigue load control on the large-scale wind turbine blades using different sensing signals. Renew. Energy
**2016**, 87, 111–119. [Google Scholar] [CrossRef] - Smit, J.; Bernhammer, L.O.; Navalkar, S.T.; Leonardo, B.; Gaunaa, M. Sizing and control of trailing edge flaps on a smart rotor for maximum power generation in low fatigue wind regimes. Wind Energy
**2015**, 19, 607–624. [Google Scholar] [CrossRef] - Van Wingerden, J.W.; Hulskamp, A.W.; Barlas, T.; Marrant, B.; van Kuik, G.A.M.; Molenaar, D.P.; Varheagen, M. On the Proof of Concept of a “Smart” Wind Turbine Rotor Blade for Load Alleviation. Wind Energy
**2008**, 11, 265–280. [Google Scholar] [CrossRef] - Hulskamp, A.W.; van Wingerden, J.W.; Barlas, T.; Champliaud, H.; Kuik, G.A.M.; Bersee, H.E.N.; Verhaegen, M. Design of a scaled wind turbine with a smart rotor for dynamic load control experiments. Wind Energy
**2010**, 14, 339–354. [Google Scholar] [CrossRef] - Schlichting, H.; Gersten, K. Boundary-Layer Theory; Springer Science & Business Media: Munchem, Germany, 2003. [Google Scholar]
- Yousefi, K.; Saleh, R. Three-dimensional suction flow control and suction jet length optimization of NACA 0012 wing. Meccanica
**2015**, 50, 1481–1494. [Google Scholar] [CrossRef] - MacPhee, D.W.; Beyene, A. Experimental and Fluid Structure Interaction analysis of a morphing wind turbine rotor. Energy
**2015**, 90, 1055–1065. [Google Scholar] [CrossRef] - McNally, J.; Fernandez, E.; Robertson, G.; Kumar, R.; Taira, K.; Alvi, F.; Yamaguchi, Y.; Murayama, K. Drag reduction on a flat-back ground vehicle with active flow control. J. Wind Eng. Ind. Aerodyn.
**2015**, 145, 292–303. [Google Scholar] [CrossRef] - Vernet, J.A.; Örlü, R.; Alfredsson, P.H. Separation control by means of plasma actuation on a half cylinder approached by a turbulent boundary layer. J. Wind Eng. Ind. Aerodyn.
**2015**, 145, 318–326. [Google Scholar] [CrossRef] - Walker, S.; Segawa, T. Mitigation of flow separation using DBD plasma actuators on airfoils: A tool for more efficient wind turbine operation. Renew. Energy
**2012**, 42, 105–110. [Google Scholar] [CrossRef] - Oye, S. The effect of vortex generators on the performance of the Elkraft 1000 kW turbine. In Proceedings of the 9th Symposium on Aerodynamics of Wind Turbines, Stockholm, Sweden, 11–12 December 1995; pp. 9–14. [Google Scholar]
- Gao, L.; Zhang, H.; Liu, Y.; Han, S. Effects of vortex generators on a blunt trailing-edge airfoil for wind turbines. Renew. Energy
**2015**, 76, 303–311. [Google Scholar] [CrossRef] - Khan, Z.U.; Johnston, J.P. On vortex generating jets. Int. J. Heat Fluid Flow
**2000**, 21, 506–511. [Google Scholar] [CrossRef] - Hwangbo, H.; Ding, Y.; Eisele, O.; Weinzierl, G.; Lang, U.; Pechlivanoglou, G. Quantifying the effect of vortex generator installation on wind power production: An academia-industry case study. Renew. Energy
**2017**, 113, 1589–1597. [Google Scholar] [CrossRef] - Fernandez-gamiz, U.; Zulueta, E.; Boyano, A.; Ansoategui, I.; Uriarte, I. Five Megawatt Wind Turbine Power Output Improvements by Passive Flow Control Devices. Energies
**2017**, 10, 742. [Google Scholar] [CrossRef] - Urkiola, A.; Fernandez-gamiz, U.; Errasti, I.; Zulueta, E. Computational characterization of the vortex generated by a Vortex Generator on a flat plate for different vane angles. Aerosp. Sci. Technol.
**2017**, 65, 18–25. [Google Scholar] [CrossRef] - Martı´nez-Filgueira, P.; Fernandez-Gamiz, U.; Zulueta, E.; Errasti, I.; Fernandez-Gauna, B. Parametric study of low-profile vortex generators. Int. J. Hydrogen Energy
**2017**, 42, 17700–17712. [Google Scholar] [CrossRef] - Chamorro, L.P.; Arndt, R.E.A.; Sotiropoulos, F. Drag reduction of large wind turbine blades through riblets: Evaluation of Riblet geometry and application strategies. Renew. Energy
**2013**, 50, 1095–1105. [Google Scholar] [CrossRef] - Belamadi, R.; Djemili, A.; Ilinca, A.; Mdouki, R. Aerodynamic performance analysis of slotted airfoils for application to wind turbine blades. J. Wind Eng. Ind. Aerodyn.
**2016**, 151, 79–99. [Google Scholar] [CrossRef] - Binci, L.; Clementi, G.; Alessandro, V.D.; Montelpare, S.; Ricci, R. Study of the flow field past dimpled aerodynamic surfaces: Numerical simulation and experimental verification Study of the flow field past dimpled aerodynamic surfaces: Numerical simulation and experimental verification. In Proceedings of the 35th UIT Heat Transfer Conference (UIT 2017), Ancona, Italy, 26–28 June 2017; IOP Publishing: Bristol, UK, 2017; p. 12030. [Google Scholar]
- Du, Z.; Selig, M.S. The effect of rotation on the boundary layer of a wind turbine blade. Renew. Energy
**2000**, 20, 167–181. [Google Scholar] [CrossRef] - Hu, D.; Hua, O.; Du, Z. A study on stall-delay for horizontal axis wind turbine. Renew. Energy
**2006**, 31, 821–836. [Google Scholar] [CrossRef] - Wood, D.H. A three-dimensional analysis of stall-delay on a horizontal-axis wind turbine. J. Wind Eng. Ind. Aerodyn.
**1991**, 37, 1–14. [Google Scholar] [CrossRef] - Lee, H.M.; Wu, Y. An experimental study of stall delay on the blade of a horizontal-axis wind turbine using tomographic particle image velocimetry. J. Wind Eng. Ind. Aerodyn.
**2013**, 123, 56–68. [Google Scholar] [CrossRef] - Foussekis, D.; Frauni, P.; Bdguier, C. Steady and unsteady separated flows around a profile. Application on the wind turbines. J. Wind Eng. Ind. Aerodyn.
**1992**, 39, 41–49. [Google Scholar] [CrossRef] - Muljadi, E.; Pierce, K.; Migliore, P. Soft-stall control for variable-speed stall-regulated wind turbines. J. Wind Eng. Ind. Aerodyn.
**2000**, 85, 277–291. [Google Scholar] [CrossRef] - Singh, R.K.; Ahmed, M.R. Blade design and performance testing of a small wind turbine rotor for low wind speed applications. Renew. Energy
**2013**, 50, 812–819. [Google Scholar] [CrossRef] - Ebert, P.R.; Wood, D.H. Observations of the Starting Behaviour of a Small Horizontal-Axis Wind Turbine. Renew. Energy
**1997**, 12, 1–13. [Google Scholar] [CrossRef] - Pourrajabian, A.; Nazmi Afshar, P.A.; Ahmadizadeh, M.; Wood, D. Aero-structural design and optimization of a small wind turbine blade. Renew. Energy
**2016**, 87, 837–848. [Google Scholar] [CrossRef] - Scappatici, L.; Bartolini, N.; Castellani, F.; Astol, D.; Garinei, A.; Pennicchi, M. Optimizing the design of horizontal-axis small wind turbines: From the laboratory to market. Int. J. Wind Eng. Ind. Aerodyn.
**2016**, 154, 58–68. [Google Scholar] [CrossRef] - Larsen, K. Recycling wind turbine blades of larger and larger turbines. Renew. Energy Focus
**2009**, 9, 70–73. [Google Scholar] [CrossRef] - Jackson, K.J.; Zuteck, M.D.; Van Dam, C.P.; Standish, K.J.; Berry, D. Innovative design approaches for large wind turbine blades. Wind Energy
**2005**, 8, 141–171. [Google Scholar] [CrossRef] - Brøndsted, P.; Lilholt, H.; Lystrup, A. Composite Materials for WInd Power. Annu. Rev. Mater. Res.
**2005**, 35, 505–538. [Google Scholar] [CrossRef] - De Goeij, W.C.; Van Tooren, M.J.L.; Beukers, A. Implementation of bending-torsion coupling in the design of a wind-turbine rotor-blade. Appl. Energy
**1999**, 63, 191–207. [Google Scholar] [CrossRef] - Puterbaugh, M.; Beyene, A. Parametric dependence of a morphing wind turbine blade on material elasticity. Energy
**2011**, 36, 466–474. [Google Scholar] [CrossRef] - Cherrington, R.; Goodship, V.; Meredith, J.; Wood, B.M.; Coles, S.R.; Vuillaume, A.; Feito-boirac, A.; Spee, F.; Kirwan, K. Producer responsibility : Defining the incentive for recycling composite wind turbine blades in Europe. Energy Policy
**2012**, 47, 13–21. [Google Scholar] [CrossRef] - Asl, M.; Niezrecki, C.; Sherwood, J.; Avitabile, P. Static performance assessment of recyclable bio-based resin for wind turbine blades using sub-component testing. In Proceedings of the American Society for Composites—Thirty-Second Technical Conference, West Lafayette, IN, USA, 23–25 October 2017; DEStech Publications, Inc.: West Lafayette, IN, USA, 2017. [Google Scholar]

**Figure 1.**Cumulative global wind power installed capacity [2].

**Figure 2.**Annual global added wind power capacity [2].

**Figure 5.**Variation of efficiency (η) vs reduced frequency (k) for a flapping airfoil blade with H

_{0}[80].

**Figure 7.**Detachable airfoil with synthetic jet actuator used in active flow controlled (AFC) wind turbine design [87].

**Figure 8.**Very small wind turbine designed by Hirahara et al. [111].

**Figure 9.**Forces and wind velocity over the spinning element due to Magnus Effect [113].

**Figure 10.**Comparison of wind turbine with shroud and lobed ejector with other different other turbines [115].

**Figure 11.**Airfoil with winglet [116].

**Figure 12.**A 3D wind turbine model by Lanzafame et al. [118].

**Figure 13.**Comparison of Power Output of a scooped blade with a non-scooped blade [120].

**Figure 14.**Windmill circular blade used in wind turbine, Fagbenro et al. [122].

**Figure 15.**Configuration of rib-let, Chamorro et al. [169].

**Figure 16.**Rib-let partially covering the wind turbine blade, Chamorro et al. [169].

**Figure 17.**Airfoil with slot—Passive flow control [170].

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Rehman, S.; Alam, M.M.; Alhems, L.M.; Rafique, M.M.
Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review. *Energies* **2018**, *11*, 506.
https://doi.org/10.3390/en11030506

**AMA Style**

Rehman S, Alam MM, Alhems LM, Rafique MM.
Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review. *Energies*. 2018; 11(3):506.
https://doi.org/10.3390/en11030506

**Chicago/Turabian Style**

Rehman, Shafiqur, Md. Mahbub Alam, Luai M. Alhems, and M. Mujahid Rafique.
2018. "Horizontal Axis Wind Turbine Blade Design Methodologies for Efficiency Enhancement—A Review" *Energies* 11, no. 3: 506.
https://doi.org/10.3390/en11030506