TY - JOUR

T1 - Data-driven fluid mechanics of wind farms: A review

AU - Zehtabiyan-Rezaie, Navid

AU - Iosifidis, Alexandros

AU - Abkar, Mahdi

PY - 2022/4/26

Y1 - 2022/4/26

N2 - 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.

AB - 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.

U2 - 10.1063/5.0091980

DO - 10.1063/5.0091980

M3 - Journal article

VL - 14

JO - Journal of Renewable and Sustainable Energy

JF - Journal of Renewable and Sustainable Energy

SN - 1941-7012

IS - 3

M1 - 032703

ER -