4.7 Article

A two-dimensional Jensen model with a Gaussian-shaped velocity deficit

期刊

RENEWABLE ENERGY
卷 141, 期 -, 页码 46-56

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2019.03.127

关键词

Wind-turbine wake; Mass conservation; Jensen model; Gaussian shape of velocity deficit

资金

  1. National Key R&D Program of China [2017YFE0109000]
  2. National Natural Science Foundation of China [11772128]
  3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources [LAPS17007]
  4. Fundamental Research Funds for the Central Universities [2017MS022]
  5. Beijing science and Technology Commission matching subject for National Key Research and Development Program [Z161100002616039]
  6. China Scholarship Council

向作者/读者索取更多资源

The one-dimensional (1D) Jensen model is probably the most often used model for engineering analysis of wind turbine wakes. Identifying a more realistic shape function for the near and far wakes behind a wind turbine and incorporating the identified shape function into a wake model can significantly improve the accuracy of wake modelling. The conventional approach is to first solve the 1D Jensen model and subsequently redistribute the wake using a specified shape function. The above procedure conserves mass globally and is useful in wake modelling. However, it needs to solve a top-hat wake using Jensen model first, which inevitably violates the local mass conservation. In this work, we propose a two-dimensional (2D) wake model that conserves mass locally and globally. The model is a direct extension of Jensen model, and the wake decay rate is the only model parameter. In addition, by accounting for the pressure recovery region, which is often neglected in wake models, the present model can provide accurate prediction of the velocity deficit behind a wind turbine. The present model is compared with the high-fidelity simulations, wind-tunnel measurements, and field observations. A reasonably good agreement is found between the model and the validation data. (C) 2019 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据