Modeling the effects of active wake mixing on wake behavior through large-scale coherent structures

Abstract. The use of active wake mixing (AWM) to mitigate downstream turbine wakes has created new opportunities for reducing power losses in wind farms. However, many current analytical or semi-empirical wake models do not capture the flow instabilities that are excited through the blade pitch actuation. In this work, we develop a framework, which accounts for the impacts of the large-scale coherent structures and turbulence on the mean flow, for modeling AWM. The framework uses a triple-decomposition approach for the unsteady flow field and models the mean flow and fine-scale turbulence with a parabolized Reynolds-averaged Navier–Stokes (RANS) system. The wave components are modeled using a simplified spatial linear stability formulation that captures the growth and evolution of the coherent structures. Comparisons with high-fidelity large eddy simulations (LESs) of the turbine wakes showed that this framework was able to capture the additional wake mixing and faster wake recovery in the far-wake regions for both the pulse and helix AWM strategies with minimal computational expense. In the near-wake region, some differences are observed in both the RANS velocity profiles and initial growth of the large-scale structures, which may be due to some simplifying assumptions used in the model.

Authors

Institutions

Publication Details

Journal
Wind energy science
Published
2025-07-18
DOI
https://doi.org/10.5194/wes-10-1403-2025
Citations
1
Primary Topic
Fluid Dynamics and Turbulent Flows
Type
article
Field-Weighted Citation Impact
1.33

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling the effects of active wake mixing on wake behavior through large-scale coherent structures

Michael Sprague, Alan Hsieh, Marc Day, Marc Henry de Frahan et al.
1 citations
Wind energy science
Fluid Dynamics and Turbulent Flows
1.33
article

Modeling the effects of active wake mixing on wake behavior through large-scale coherent structures

Michael Sprague, Alan Hsieh, Marc Day, Marc Henry de Frahan, Daniel Houck, Nathaniel deVelder, Kenneth Brown, Lawrence C.C. Cheung, Gopal Yalla, Prakash Mohan
article en
1 citations

Abstract

Abstract. The use of active wake mixing (AWM) to mitigate downstream turbine wakes has created new opportunities for reducing power losses in wind farms. However, many current analytical or semi-empirical wake models do not capture the flow instabilities that are excited through the blade pitch actuation. In this work, we develop a framework, which accounts for the impacts of the large-scale coherent structures and turbulence on the mean flow, for modeling AWM. The framework uses a triple-decomposition approach for the unsteady flow field and models the mean flow and fine-scale turbulence with a parabolized Reynolds-averaged Navier–Stokes (RANS) system. The wave components are modeled using a simplified spatial linear stability formulation that captures the growth and evolution of the coherent structures. Comparisons with high-fidelity large eddy simulations (LESs) of the turbine wakes showed that this framework was able to capture the additional wake mixing and faster wake recovery in the far-wake regions for both the pulse and helix AWM strategies with minimal computational expense. In the near-wake region, some differences are observed in both the RANS velocity profiles and initial growth of the large-scale structures, which may be due to some simplifying assumptions used in the model.

Wind energy scienceVol. 10(7)
National Laboratory of the Rockies (US), Sandia National Laboratories California (US), Sandia National Laboratories (US)
U.S. Department of Energy
Affordable and clean energy
Openalex Percentile: Top 20%
Fluid Dynamics and Turbulent Flows
1.33
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.