Data-driven fluid mechanics of wind farms: A review

TL;DR

This review discusses recent advances in data-driven and physics-guided modeling of wind-farm flows, highlighting challenges, methodologies, and the importance of interpretability and generalizability in complex fluid dynamics.

Contribution

It provides a comprehensive analysis of current data-driven wind-farm flow models, emphasizing their approaches, data use, and integration of physics for improved accuracy and trust.

Findings

Data-driven models face challenges due to flow complexity and turbulence.

Physics-guided models enhance interpretability and generalizability.

Recent studies show promising integration of physics with data-driven techniques.

Abstract

With the growing number of wind farms over the last 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 towards 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.