What is it about?
We present a simplified analytical model for predicting wind turbine wakes in atmospheric boundary layer (ABL) flows, explicitly accounting for wind veer, thermal stratification, and turbine yaw. Under neutral conditions, yawing a turbine deflects its wake, reducing wake interactions and enhancing the performance of downstream turbines. In contrast, under stable atmospheric conditions, stronger wind veer leads to a sheared wake structure that can offset the wake curling effect induced by yaw, thereby affecting downstream turbine performance. The model captures these distinct wake features—curling under neutral conditions and shearing under stable stratification—across a range of ABL regimes. Validation against Large-Eddy Simulation (LES) data shows that the model predicts velocity deficits with less than 2% error in the far wake regions, achieving 90–95% accuracy in estimating wake-induced power losses across different yaw angles and atmospheric conditions.
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Why is it important?
Accurate prediction of wake-induced power losses under varying wind veer and atmospheric stability conditions is essential for optimizing wind farm performance. Many existing models are primarily developed for neutral ABL conditions, which can result in inaccurate power output estimates under realistic atmospheric scenarios. Our simplified analytical wake model accounts for these atmospheric effects, capturing wake structures across different ABL conditions and achieving 90–95% accuracy in predicting wake-induced power losses. This provides a reliable and computationally efficient alternative to high-fidelity simulations.
Perspectives
I believe wind farm modelers will find our wake model valuable for making first-order estimates of annual energy production at potential wind farm sites, while realistically accounting for atmospheric boundary layer (ABL) physics. There is significant potential to extend this framework to incorporate additional ABL processes such as diurnal transitions, wave effects in marine environments, wake behavior over complex terrains, and forest canopies. Advancing simplified models like this not only aids in practical wind farm design but also contributes to reducing the carbon footprint associated with running large-scale, computationally intensive simulations.
Ghanesh Narasimhan
University of Minnesota Twin Cities
Read the Original
This page is a summary of: An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer, Journal of Renewable and Sustainable Energy, May 2025, American Institute of Physics,
DOI: 10.1063/5.0251305.
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