Investigation of a small Horizontal–Axis wind turbine performance with and without winglet. (15th November 2019)

• Record Type: Journal Article
• Authors: Khaled, Mohamed; Ibrahim, Mostafa M.; Abdel Hamed, Hesham E.; AbdelGwad, Ahmed F.
• Is Part Of: Energy. Volume 187(2019)
• Publication Date: 2019-11-15
• Subjects: Wind turbine rotor -- Winglets -- Power coefficient -- Thrust coefficient -- Artificial neural network; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.115921
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The objective of this study is to demonstrate computationally the effect of winglet length and cant angle on the performance of a small horizontal-axis wind turbine. The computational study was done using the ANSYS Fluent 15 software for a steady-state flow. Different designs of winglet with different lengths and cant angles were numerically studied and optimized using Artificial Neural Network (ANN). The winglet length was changed from 1% to 7% of the wind turbine rotor radius with cant angles from 15 0 to 90 0 . The parameters of the wind turbine performance, which are power coefficient and thrust force coefficient were investigated for different winglet configurations. This was carried out from cut-in wind speed (3.12 m/s) to wind speed (12 m/s). It demonstrated that, there were noticeable enhancements in power and thrust coefficients in the presence of winglet. The best improvement in the performance was achieved when winglet length was 6.32% and cant angle 48.3 0 . At this case, the percentage increase in power coefficient equals to the percentage increase in thrust coefficient which was 8.787%. Highlights: Computational study for a small HAWT with and without winglet. Effect of winglet length on the pressure coefficient across the wind turbine blade. Effect of winglet lengths from 1% to 7% on power and thrust coefficients. Effect of cant angles from 15 0 to 90 0 on power and thrust coefficients for design TSR. Using Neural Network to obtain the optimum dimensions of winglet.