Data-driven fluid mechanics of wind farms: A review

Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review


With the growing number of wind farms over the last few decades and the availability of large datasets, research in wind-farm flow modeling-one of the key components in optimizing the design and operation of wind farms-is shifting toward data-driven techniques. However, given that most current data-driven algorithms have been developed for canonical problems, the enormous complexity of fluid flows in real wind farms poses unique challenges for data-driven flow modeling. These include the high-dimensional multiscale nature of turbulence at high Reynolds numbers, geophysical and atmospheric effects, wake-flow development, and incorporating wind-Turbine characteristics and wind-farm layouts, among others. In addition, data-driven wind-farm flow models should ideally be interpretable and have some degree of generalizability. The former is important to avoid a lack of trust in the models with end-users, while the most popular strategy for the latter is to incorporate known physics into the models. This article reviews a collection of recent studies on wind-farm flow modeling, covering both purely data-driven and physics-guided approaches. We provide a thorough analysis of their modeling approach, objective, and methodology and specifically focus on the data utilized in the reviewed works.

TidsskriftJournal of Renewable and Sustainable Energy
Antal sider14
StatusE-pub ahead of print - 26 apr. 2022


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