Wake effects within a wind farm are identified as one of the main factors impacting wind farm
power production and fatigue loading on wind turbines' structural elements. An efficient …

Wind turbine parameterization models, which are often employed to avoid the computational
cost of resolving the blade aerodynamics, are critical for the capability of large-eddy …

This survey investigates wall modeling in large-eddy simulations (LES) using data-driven
machine-learning (ML) techniques. To this end, we implement three ML wall models in an …

Computational fluid dynamics using the Reynolds-averaged Navier–Stokes (RANS) remains
the most cost-effective approach to study wake flows and power losses in wind farms. The …

Wind-farm flow control stands at the forefront of grand challenges in wind-energy science.
The central issue is that current algorithms are based on simplified models and, thus, fall …

Computational fluid dynamics (CFD) represents a valuable tool in the design process of built
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …

In this study, we present an analytical modeling framework for wind turbine wakes under an
arbitrary pressure gradient imposed by the base flow. The model is based on the …

This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML)
modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high …

The power performance and the wind velocity field of an onshore wind farm are predicted
with machine learning models and the pseudo‐2D RANS model, then assessed against …

The use of data-driven methods in fluid mechanics has surged dramatically in recent years
due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as …