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Citation:
　Maokun Ye,Min Li,Mingqiu Liu,et al.Overview of Data-Driven Models for Wind Turbine Wake Flows[J].Journal of Marine Science and Application,2025,(1):1-20.[doi:10.1007/s11804-025-00683-8]
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Overview of Data-Driven Models for Wind Turbine Wake Flows

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Title:
Overview of Data-Driven Models for Wind Turbine Wake Flows
Author(s):
Maokun Ye;  Min Li;  Mingqiu Liu;  Chengjiang Xiao;  Decheng Wan
Affilations:
Author(s):
Maokun Ye;  Min Li;  Mingqiu Liu;  Chengjiang Xiao;  Decheng Wan
Computational Marine Hydrodynamics Lab (CMHL), School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Keywords:
Data-driven; Machine learning; Artificial neural networks; Wind turbine wake; Wake models
分类号:
-
DOI:
10.1007/s11804-025-00683-8
Abstract:
With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications, an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes. These models leverage the ability to capture complex, high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models. As a result, data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output. This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches. It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature. Subsequently, the related studies are categorized into four key areas: wind turbine power prediction, data-driven analytic wake models, wake field reconstruction, and the incorporation of explicit physical constraints. The accuracy of data-driven models is influenced by two primary factors: the quality of the training data and the performance of the model itself. Accordingly, both data accuracy and model structure are discussed in detail within the review.

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Memo

Memo:
Received date:2024-10-29;Accepted date:2024-12-25。
Foundation item:Supported by the National Natural Science Foundation of China under Grant No. 52131102.
Corresponding author:Decheng Wan,E-mail:dcwan@sjtu.edu.cn
Last Update: 2025-02-26