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Design and performance analysis of an airborne wind turbine for high-altitude energy harvesting

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  • Ali, Qazi Shahzad
  • Kim, Man-Hoe

Abstract

The airborne wind turbine (AWT) offers the opportunity to be installed in challenging heights. For this purpose, the present study focuses on the aerodynamic design of an airborne rotor lifted to high altitude from the viewpoint of airborne technology. The rotor blade is modeled by imposing the elevated wind dataset while the outer shape is comprised of multi-profiles of thick airfoils. Numerical approaches of the BEM and RANS-CFD are employed for insight into performance analysis. The extracted results of the CFD data agreed well with the empirical computations. The induced effect of the diffuser on the rotor is demonstrated through an auxiliary shell structure. A range of 3D simulations varying the airloads are conducted to investigate the on-design and off-design behavior of the rotor. The power coefficient (Cp), effective forces and mass amplification are computed and discussed in terms of performance indicators. The design analysis reveals the significance of the chosen design specification to an operational height of 400 m. The noticeable finding confirms that the optimal Cp of the resulting rotor always occurs at rated conditions and equally valid for both configurations. Additionally, a power gain of 21.3% is obtained from the rotor equipped with the diffuser geometry.

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  • Ali, Qazi Shahzad & Kim, Man-Hoe, 2021. "Design and performance analysis of an airborne wind turbine for high-altitude energy harvesting," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s036054422101077x
    DOI: 10.1016/j.energy.2021.120829
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    References listed on IDEAS

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    Cited by:

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    2. Hongye pan, & Jia, Changyuan & Li, Haobo & Zhou, Xianzheng & Fang, Zheng & Wu, Xiaoping & Zhang, Zutao, 2022. "A renewable energy harvesting wind barrier based on coaxial contrarotation for self-powered applications on railways," Energy, Elsevier, vol. 258(C).
    3. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
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    5. Shahzad Ali, Qazi & Kim, Man-Hoe, 2022. "Quantifying impacts of shell augmentation on power output of airborne wind energy system at elevated heights," Energy, Elsevier, vol. 239(PA).

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