Data-driven fluid mechanics of wind farms: A review

Navid Zehtabiyan-Rezaie, Alexandros Iosifidis, Mahdi Abkar*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-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.

Original languageEnglish
Article number032703
JournalJournal of Renewable and Sustainable Energy
Number of pages14
Publication statusE-pub ahead of print - 26 Apr 2022


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