Data-driven fluid mechanics of wind farms: A 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.

Automated summary

With the increasing number of wind farms, research in wind-farm flow modeling is shifting towards data-driven techniques. However, the complexity of fluid flows in real wind farms poses unique challenges for data-driven modeling, requiring models to be interpretable and have some degree of generalizability.