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Experiments in Fluids

, 57:150 | Cite as

An experimental study on the aeromechanics and wake characteristics of a novel twin-rotor wind turbine in a turbulent boundary layer flow

  • Zhenyu Wang
  • Wei Tian
  • Ahmet Ozbay
  • Anupam Sharma
  • Hui HuEmail author
Research Article

Abstract

The aeromechanic performance and wake characteristics of a novel twin-rotor wind turbine (TRWT) design, which has an extra set of smaller, auxiliary rotor blades appended in front of the main rotor, was evaluated experimentally, in comparison with those of a conventional single-rotor wind turbine (SRWT) design. The comparative study was performed in a large-scale wind tunnel with scaled TRWT and SRWT models mounted in the same incoming turbulent boundary layer flow. In addition to quantifying power outputs and the dynamic wind loadings acting on the model turbines, the wake characteristics behind the model turbines were also measured by using a particle image velocimetry system and a Cobra anemometry probe. The measurement results reveal that, while the TRWT design is capable of harnessing more wind energy from the same incoming airflow by reducing the roots losses incurred in the region near the roots of the main rotor blades, it also cause much greater dynamic wind loadings acting on the TRWT model and higher velocity deficits in the near wake behind the TRWT model, in comparison with those of the SRWT case. Due to the existence of the auxiliary rotor, more complex vortex structures were found to be generated in the wake behind the TRWT model, which greatly enhanced the turbulent mixing in the turbine wake, and caused a much faster recovery of the velocity deficits in the turbine far wake. As a result, the TRWT design was also found to enable the same downstream turbine to generate more power when sited in the wake behind the TRWT model than that in the SRWT wake, i.e., by mitigating wake losses in typical wind farm settings.

Keywords

Wind Turbine Wind Farm Rotor Blade Particle Image Velocimetry Measurement Velocity Deficit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors want to thank Mr. Bill Rickard of Iowa State University for helpful discussions in conducting for the present study. The funding support from the Iowa Energy Center with Grant No. 14-008-OG and National Science Foundation (NSF) with Grant Numbers of CBET-1133751 and CBET-1438099 is gratefully acknowledged.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Zhenyu Wang
    • 1
  • Wei Tian
    • 1
  • Ahmet Ozbay
    • 1
  • Anupam Sharma
    • 1
  • Hui Hu
    • 1
    Email author
  1. 1.Department of Aerospace EngineeringIowa State UniversityAmesUSA

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