The AWAKEN wind farm benchmark – Part 2: Modeling results

Abstract. Accurately modeling wind farm performance in complex atmospheric flows remains a challenge. This paper presents the modeling results of the American WAKE experimeNt (AWAKEN) wind farm benchmark, a collaborative effort involving 16 research groups from academia and industry within the International Energy Agency Wind Technology Collaboration Programme Task 57. The study evaluates a diverse suite of simulation tools, ranging from fast-running engineering wake models to high-fidelity large-eddy simulations, against a diurnal case study observed during the AWAKEN campaign. The benchmark utilized a three-phase structure to progressively assess model performance as observational data availability increased. Initial blind predictions showed that higher-fidelity models did not uniformly outperform simpler simulation tools. A distinct spatial bias was observed where models struggled to resolve the interplay between a low-level jet, wakes, and terrain-induced flow acceleration. In subsequent phases, leveraging additional measurements for model improvement led to a reduction in mean absolute error across the model ensemble; however, this effect was most pronounced in engineering wake models, where targeted calibration reduced error by up to 40 %. Overall, the study demonstrates that inflow characterization remains a primary prerequisite for accuracy, particularly for models relying on coarse forcing datasets. While the limited ability to resolve local terrain-flow interactions under single-day conditions represent a recognized constraint, the overall findings on wake modeling and real-world validation still provide valuable guidance for model application and for mitigating this limitation.

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Publication Details

Journal
Wind energy science
Published
2026-08-27
DOI
https://doi.org/10.5194/wes-11-3107-2026
Primary Topic
Wind Energy Research and Development
Type
article
Field-Weighted Citation Impact
0.00

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article

The AWAKEN wind farm benchmark – Part 2: Modeling results

Paula Doubrawa, Cristina L. Archer, Colleen Kaul, Bruno Carmo et al.
Wind energy science
Wind Energy Research and Development
article

The AWAKEN wind farm benchmark – Part 2: Modeling results

Paula Doubrawa, Cristina L. Archer, Colleen Kaul, Bruno Carmo, Regis Thedin, P. van der Laan, Carlo L. Bottasso, William Corrêa Radünz, Patrick Moriarty, Christopher Kelley, Kisorthman Vimalakanthan, Rogier Floors, Adam Wise, Julian Quick, Oscar García-Santiago, Nicola Bodini, Lawrence C.C. Cheung, Mike Optis, Alfredo Peña, Julie K. Lundquist, Anh Kiet Nguyen, Camille Dubreuil, Knut S. Seim, Ru LI, Luan R. C. Parada, David Ricarte, Ali Kanjari, Myra Blaylock, Thomas Herges, Desirae Major, Daniel Houck, Jonas Schulte, Raj K. Rai
article en

Abstract

Abstract. Accurately modeling wind farm performance in complex atmospheric flows remains a challenge. This paper presents the modeling results of the American WAKE experimeNt (AWAKEN) wind farm benchmark, a collaborative effort involving 16 research groups from academia and industry within the International Energy Agency Wind Technology Collaboration Programme Task 57. The study evaluates a diverse suite of simulation tools, ranging from fast-running engineering wake models to high-fidelity large-eddy simulations, against a diurnal case study observed during the AWAKEN campaign. The benchmark utilized a three-phase structure to progressively assess model performance as observational data availability increased. Initial blind predictions showed that higher-fidelity models did not uniformly outperform simpler simulation tools. A distinct spatial bias was observed where models struggled to resolve the interplay between a low-level jet, wakes, and terrain-induced flow acceleration. In subsequent phases, leveraging additional measurements for model improvement led to a reduction in mean absolute error across the model ensemble; however, this effect was most pronounced in engineering wake models, where targeted calibration reduced error by up to 40 %. Overall, the study demonstrates that inflow characterization remains a primary prerequisite for accuracy, particularly for models relying on coarse forcing datasets. While the limited ability to resolve local terrain-flow interactions under single-day conditions represent a recognized constraint, the overall findings on wake modeling and real-world validation still provide valuable guidance for model application and for mitigating this limitation.

Wind energy scienceVol. 11(8)
Lawrence Livermore National Laboratory (US), National Laboratory of the Rockies (US), Pacific Northwest National Laboratory (US), Johns Hopkins University (US), Netherlands Organisation for Applied Scientific Research (NL), Universidade de São Paulo (BR), University of Colorado Boulder (US), Sandia National Laboratories California (US), Sandia National Laboratories (US), Météo-France (FR), Fraunhofer Institute for Wind Energy Systems (DE), Equinor (Norway) (NO), North Island College (CA), Technical University of Munich (DE), University of Delaware (US), University of California, Berkeley (US), Technical University of Denmark (DK)
U.S. Department of Energy, Danmarks Frie Forskningsfond, International Energy Agency, Office of Energy Efficiency and Renewable Energy, Horizon 2020 Framework Programme, Wind Energy Technologies Office, Office of Energy Efficiency, Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory
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