Self-consistent model for active control of wind turbine wakes

TL;DR

This paper introduces a computationally efficient, physics-based wake model that accurately predicts wind turbine wake recovery under active control strategies, significantly reducing simulation time while maintaining high accuracy.

Contribution

The study develops a novel wake model combining resolvent and eddy viscosity approaches, validated against LES, enabling fast and accurate prediction of wake recovery for active control.

Findings

Model predicts wake recovery with less than 8% error compared to LES.

Reduces computational time from thousands of CPU hours to minutes.

Effectively captures frequency-dependent wake dynamics.

Abstract

Active wake control (AWC) has emerged as a promising strategy for enhancing wind turbine wake recovery, but accurately modelling its underlying fluid mechanisms remains challenging. This study presents a computationally efficient wake model that provides end-to-end prediction capability from rotor actuation to wake recovery enhancement by capturing the coupled dynamics of wake meandering and meanflow modification, requiring only two inputs: a reference wake without control and a user-defined AWC strategy. The model combines physics-based resolvent modelling for large-scale coherent structures and an eddy viscosity modelling for small-scale turbulence. A Reynolds stress model is introduced to account for the influence of both coherent and incoherent wake fluctuations, so that the time-averaged wake recovery enhanced by the AWC can be quantitatively predicted. Validation against large-eddy simulations (LES) across various AWC approaches and actuating frequencies demonstrates the model's predictive capability, accurately capturing AWC-specific and frequency-dependent mean wake recovery with less than 8% error from LES while reducing computational time from thousands of CPU hours to minutes. The efficiency and accuracy of the model makes it a promising tool for practical AWC design and optimization of large-scale wind farms.