the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jun 2026
Experimental wind tunnel dataset of wake and turbine measurements for wind farm control
Abstract. Open-access experimental datasets play a central role in validating and benchmarking numerical models used in wind energy and wind-farm control research. However, publicly available datasets providing time-resolved turbine loads, actuator commands, and inflow characterisation under controlled operation remain scarce.
This paper presents a new open-access experimental dataset from wind-tunnel experiments featuring actuated, instrumented, scaled wind turbine models. The database includes time-resolved measurements of tower-base and rotating-shaft moments, rotor speed, generated torque and power, blade pitch angles, nacelle yaw angle, and controller commands. The inflow conditions are described in terms of wind speed, wind direction, air density, and wake-flow measurements, enabling detailed analyses of turbine response and controller behaviour under consistent, repeatable conditions.
The experiments cover a wide range of wake-control strategies, including yaw-based wake steering, curtailment and derating, Helix control, dynamic yaw actuation, Pulse wake mixing, individual pitch control, and several combinations of these strategies. The simultaneous availability of actuator commands, measured turbine response, and time-resolved structural loads enables detailed investigation of the controller performance, load variability, and dynamic turbine behaviour induced by active wake-control strategies.
In addition to the experimental measurements, the dataset is complemented by numerical models of the experiments, providing a reproducible experimental-numerical benchmarking framework that enables dataset extension, sensitivity analyses, and systematic validation of control-oriented aeroelastic and wake-interaction models.
The dataset is intended to support the validation and benchmarking of numerical tools for wind-farm control, the assessment of fatigue-relevant loading under wake-control operations, and to strengthen community efforts to improve transparency, reproducibility, and model fidelity in wind-farm design and control research.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- CC1: 'Comment on wes-2026-86', J. Gordon Leishman, 21 Jun 2026
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CC2: 'Comment on wes-2026-86', J. Gordon Leishman, 21 Jun 2026
Because the Editor-in-Chief is listed as a co-author and corresponding author, the public record would benefit from explicit clarification that he was fully recused from editorial handling, reviewer selection, discussion moderation, and decision-making. The identification of a handling editor is helpful, but it does not by itself clarify whether the Editor-in-Chief had no role in the editorial process for a manuscript on which he is a co-author.
Disclaimer: this community comment is written by an individual and does not necessarily reflect the opinion of their employer.Citation: https://doi.org/10.5194/wes-2026-86-CC2 -
RC1: 'Comment on wes-2026-86', Anonymous Referee #1, 01 Jul 2026
The authors present an extensive experimental dataset from a collection of wind-tunnel campaigns aimed at wind farm control studies. The experiments concern actuated, instrumented, scaled wind turbine models under controlled ocnditions. The data set includes a vast number of parameters, from inflow velocity, to turbine power and loads, to wake velocities, to actuator commands. The data set is not new per se, as it has been presented in a number of publications in the past, but it is made available in its entirety and presented with the required detail for the first time. Measurement uncertainties and data corrections are presented in detail.
The data set is unique and has the potential to be a reference case for wind farm control in the future.
The experimental data are also accompanied by a series of numerical models, from CAD to ALM-LES model of the turbines. This is also nice to have, as potential users of the dataset can also make use of one of the available numerical models, should they see this fit. At the same time, I expect this will create a bias, with most future users employing the same inflow/ALM etc model. Since no model is perfect, this is not ideal, but this is no reason not include the numerical models.
One aspect the authors could consider is the continuous update/modification of the data description. A data description article review can obviously not check whether the description is entirely complete, or if something can be improved, in the same way an actual user of the data set could. The authors could consider making this explicit and ask for feedback in the paper.
Some minor comments are also included inthe attached file. -
RC2: 'Comment on wes-2026-86', Anonymous Referee #2, 01 Jul 2026
Review of ‘Experimental wind tunnel dataset of wake and turbine measurements for wind farm control’
This article presents an extensive open access wind tunnel dataset of wake, aerodynamic and load measurements under a range of wake control strategies. The dataset can prove useful for the wind energy community for numerical model validation. I would recommend the article for publication given the following comments can be addressed.
Major Comments
- Some details of the measurement equipment are probably too much for the article. For instance, from my perspective, adding the model number for each component in section 2.2 is not necessary. Similarly, sections 2.2.3, 2.2.4 and some details in the beginning of section 2.2.6 can be omitted from the main manuscript. These details could be provided as supplementary material. The intended users of the dataset are numerical modelers – burdening them with too much technical details of the experimental setup is probably not necessary.
- On a similar note, why is there a need for section 3.1 in the article? Could this be better suited in supplementary notes?
- Given the length that the authors go to in order to explain the experimental setup, it seems rather superficial that they simply assume that the yaw and tilt biases are stationary in section 3.4.1. There should be some verification/validation for this assumption.
- In comparison to the details provided on the experimental setup and execution, the description of the datasets seems rather brief. For a potential user of the data, this section might be of the most interest. I would have expected the authors to provide details of the experimental conditions for each dataset. For instance, lines 595-597 mention that turbines are operated under a range of control conditions. However, no further details on control strategies are presented. Same can be said of other subsections in this section. I understand that the user can consult the metadata on it, however, given the detail given to the earlier sections, I would have expected a nice and clean overview of experimental conditions for each campaign.
- Section 5 does not have a proper motivation. If the intention of the article is to provide an open access wind tunnel dataset for independent development/validation of numerical codes, providing numerical implementation in some existing codes adds a bias factor, and may unintentionally redirect the potential user towards using these codes rather than independent development/testing/validation.
- Finally, it needs to be pointed out that as detailed in section 4, the dataset presented in this work is not ‘new’ but is a collection of many experiments (which are very much appreciated) going back as far as 2016. Still, the conclusion starts with ‘This paper presents a new open-access experimental dataset …’, which seems a bit contradictory and incorrect to me. I think the article should be explicit about which experiments are new or unique to it, and which are old. This also brings in the question that does making a collection of existing datasets open access constitute the basis for a data description article, or should it be a new dataset that has not been published/accessible before? Perhaps the handling editor can be a better judge on that than me, but it is a question that needs to be addressed.
Minor Comments
- G1 rotor is first mentioned in section 2.1.1 without any prior/proper introduction. For a reader unfamiliar with this rotor, it is difficult to relate/contextualize the boundary layer characteristics with the rotor. For instance, on first read, I do not know what the hub/top/bottom height of G1 is, so the turbulence intensity number with respect to it is not very intuitive.
- Section 2.1.2, line 124: what is the distance between the pitot tube and the wind farm cluster? Is the tube sufficiently upstream such that it is not influenced by the blockage of the rotor?
- I think some of the variables are missing from the nomenclature section. For example, I could not find P_R in the nomenclature.
- In fig 7 it may be beneficial to add a 1:1 line as well.
- I would have appreciated a few figures visualizing some key data from the dataset as well.
Citation: https://doi.org/10.5194/wes-2026-86-RC2
Data sets
Experimental inflow mapping Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18749773
Single wake measurement Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18732794
Double wakes measurement Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18743973
Multiple wakes measurement Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18731994
Wind turbine data under steady wind direction Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18723855
Wind turbine data under dynamically varying wind direction Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18743431
3D CAD models Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.19047499
Synthetic inflows 1 Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18860646
Synthetic inflows 2 Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.19004026
2-DOF Simulink model Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18755240
OpenFast model Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18772302
AMR-Wind model Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso https://doi.org/10.5281/zenodo.18874300
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The manuscript presents an open-access wind-tunnel dataset for wind-farm-control studies using scaled G1 turbine models. While the dataset itself may have some practical value, the manuscript is not sufficiently strong as a journal article. It reads mainly like a laboratory data manual, with lengthy descriptions of hardware, sensors, actuator specifications, calibration procedures, data channels, acquisition rates, post-processing steps, and numerical model files. Much of this information may be useful for repository documentation, but it does not constitute a scientific contribution.
The central weakness is that the manuscript does not critically establish the value, fidelity, or limits of the dataset. Scaled wind-tunnel wake-control experiments are subject to Reynolds-number mismatch, artificial inflow generation, limited atmospheric realism, actuator-bandwidth scaling, structural-scaling compromises, blockage effects, and simplified farm geometry. These limitations directly affect whether the data can meaningfully validate wind-farm-control models for full-scale turbines. The manuscript describes the apparatus in detail but gives too little critical assessment of what the dataset can and cannot validate.
The manuscript also blurs the boundaries among measurement, correction, and modeling. Several quantities depend on post-processing, sensor corrections, load transformations, imbalance removal, derived quantities, and numerical models. A credible benchmark dataset should make these distinctions central, namely, what is directly measured, what is inferred, what is corrected, and what is model-generated. Instead, the paper presents procedural details without a sufficiently rigorous assessment of uncertainty and sensitivity.
Overall, I do not recommend the publication of this article. The dataset may be appropriate for an institutional repository, a technical report, or a supplementary data release, but the manuscript does not provide sufficient scientific analysis, critical interpretation, or validation to justify publication as a journal article. I recommend that the paper be declined in its present form.