Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions

Bingzheng Dou, Michele Guala, Liping Lei, Pan Zeng

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Predicting the spatial evolution of horizontal-axis turbine wakes is a key factor to enhance the performance of wind and hydrokinetic power plants. The yaw angle misalignment is not only important to account for the uncertainty of the wind direction, but also a control strategy to improve the energy production averaged over the turbine array. In this paper, a new wake model, based on the skew-normal distribution and conservation of mass and momentum, is proposed to predict the turbine wake velocity distribution for given yaw angles. The new model is experimentally validated through wake measurements of yawed miniature wind and hydrokinetic turbines, in wind tunnel and open channel flows, respectively. Predictive capabilities extend from the spatial distribution of the maximum velocity deficit at hub height, to the spanwise asymmetry of the velocity distribution about the wake center.

Original languageEnglish (US)
Pages (from-to)1383-1395
Number of pages13
JournalApplied Energy
Volume242
DOIs
StatePublished - May 15 2019

Bibliographical note

Funding Information:
The work was supported by National Natural Science Foundation of China (No. 51575296 and No. 51875305 ). The support provided by China Scholarship Council ( 201706210200 ) during a visit of Bingzheng Dou to University of Minnesota is acknowledged. Besides, Jing Lu, Qiuyan Zhang from Northeast Normal University and Lina Ji from Beijing Normal University also gave some valuable suggestions about the velocity distribution during their visits to University of Minnesota . The authors are grateful to Giulia Ravanelli from Universita' di Trento and Mirko Musa at University of Minnesota for providing the hydrokinetic turbine dataset.

Keywords

  • Hydrokinetic
  • Renewable energy
  • Rivers
  • Wake model
  • Wind turbine
  • Yaw

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