Articles | Volume 8, issue 4
https://doi.org/10.5194/wes-8-515-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-515-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Apr 2023
Research article |
| 12 Apr 2023
| 12 Apr 2023
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Tamino Wetz
CORRESPONDING AUTHOR
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt e.V., Oberpfaffenhofen, Germany
Norman Wildmann
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt e.V., Oberpfaffenhofen, Germany
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Norman Wildmann and Tamino Wetz
Atmos. Meas. Tech., 15, 5465–5477, https://doi.org/10.5194/amt-15-5465-2022, https://doi.org/10.5194/amt-15-5465-2022, 2022
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Multicopter uncrewed aerial systems (UAS, also known as drones) are very easy to use systems for collecting data in the lowest part of the atmosphere. Wind and turbulence are parameters that are particularly important for understanding the dynamics in the atmosphere. Only with three-dimensional measurements of the wind can a full understanding can be achieved. In this study, we show how even the vertical wind through the UAS can be measured with good accuracy.
Tamino Wetz, Norman Wildmann, and Frank Beyrich
Atmos. Meas. Tech., 14, 3795–3814, https://doi.org/10.5194/amt-14-3795-2021, https://doi.org/10.5194/amt-14-3795-2021, 2021
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A fleet of quadrotors is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to calculate the horizontal wind is based on the principle of aerodynamic drag and the related quadrotor dynamics. The validation reveals that an average accuracy of < 0.3 m s−1 for the wind speed and < 8° for the wind direction was achieved.
Jeffrey D. Thayer, Gerard Kilroy, and Norman Wildmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-38, https://doi.org/10.5194/wes-2025-38, 2025
Revised manuscript accepted for WES
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With increasing wind energy in the German energy grid, it is crucial to better understand how different types of weather (including thunderstorms) can impact wind turbines and the surrounding atmosphere. We find rapid wind changes associated with the leading edge of thunderstorm outflows within the height range of wind turbines that would quickly increase wind power output, with longer-lasting changes in the near-surface atmosphere that would affect subsequent wind turbine operations.
Norman Wildmann and Laszlo Györy
EGUsphere, https://doi.org/10.5194/egusphere-2025-241, https://doi.org/10.5194/egusphere-2025-241, 2025
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Fast temperature sensors are deployed on drones to accurately measure temperature changes and fluctuations in the atmosphere. Compared to standard sensors, these new sensors showed better accuracy, especially in rapidly changing temperatures. Over 100 drone flights confirmed the sensors' ability to measure temperature fast enough to compare to standard meteorological instruments. This new method provides valuable data for understanding atmospheric energy balance.
Johannes Kistner, Lars Neuhaus, and Norman Wildmann
Atmos. Meas. Tech., 17, 4941–4955, https://doi.org/10.5194/amt-17-4941-2024, https://doi.org/10.5194/amt-17-4941-2024, 2024
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We use a fleet of multicopter drones to measure wind. To improve the accuracy of this wind measurement and to evaluate this improvement, we conducted experiments with the drones in a wind tunnel under various conditions. This wind tunnel can generate different kinds and intensities of wind. Here we measured with the drones and with other sensors as a reference and compared the results. We were able to improve our wind measurement and show how accurately it works in different situations.
Linus Wrba, Antonia Englberger, Andreas Dörnbrack, Gerard Kilroy, and Norman Wildmann
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It is crucial to understand the loads and power production of wind turbines under different atmospheric situations (e.g. night and day changes). Computational simulations are a widely used tool to get more knowledge of the performance and the wake of wind turbines. In this study realistic velocity profiles of the atmosphere are used as input for simulations so that these simulations become more realistic. The generated realistic flow is used as inflow for wind-turbine simulations.
Andreas Forstmaier, Jia Chen, Florian Dietrich, Juan Bettinelli, Hossein Maazallahi, Carsten Schneider, Dominik Winkler, Xinxu Zhao, Taylor Jones, Carina van der Veen, Norman Wildmann, Moritz Makowski, Aydin Uzun, Friedrich Klappenbach, Hugo Denier van der Gon, Stefan Schwietzke, and Thomas Röckmann
Atmos. Chem. Phys., 23, 6897–6922, https://doi.org/10.5194/acp-23-6897-2023, https://doi.org/10.5194/acp-23-6897-2023, 2023
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Large cities emit greenhouse gases which contribute to global warming. In this study, we measured the release of one important green house gas, methane, in Hamburg. Multiple sources that contribute to methane emissions were located and quantified. Methane sources were found to be mainly caused by human activity (e.g., by release from oil and gas refineries). Moreover, potential natural sources have been located, such as the Elbe River and lakes.
Norman Wildmann and Tamino Wetz
Atmos. Meas. Tech., 15, 5465–5477, https://doi.org/10.5194/amt-15-5465-2022, https://doi.org/10.5194/amt-15-5465-2022, 2022
Short summary
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Multicopter uncrewed aerial systems (UAS, also known as drones) are very easy to use systems for collecting data in the lowest part of the atmosphere. Wind and turbulence are parameters that are particularly important for understanding the dynamics in the atmosphere. Only with three-dimensional measurements of the wind can a full understanding can be achieved. In this study, we show how even the vertical wind through the UAS can be measured with good accuracy.
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209, https://doi.org/10.5194/gmd-15-5195-2022, https://doi.org/10.5194/gmd-15-5195-2022, 2022
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The ultimate objective of this model evaluation is to improve boundary layer flow representation over complex terrain. The numerical model is tested against observations retrieved during the Perdigão 2017 field campaign (moderate complex terrain). We observed that the inclusion of a forest parameterization in the numerical model significantly improves the representation of the wind field in the atmospheric boundary layer.
Andreas Luther, Julian Kostinek, Ralph Kleinschek, Sara Defratyka, Mila Stanisavljević, Andreas Forstmaier, Alexandru Dandocsi, Leon Scheidweiler, Darko Dubravica, Norman Wildmann, Frank Hase, Matthias M. Frey, Jia Chen, Florian Dietrich, Jarosław Nȩcki, Justyna Swolkień, Christoph Knote, Sanam N. Vardag, Anke Roiger, and André Butz
Atmos. Chem. Phys., 22, 5859–5876, https://doi.org/10.5194/acp-22-5859-2022, https://doi.org/10.5194/acp-22-5859-2022, 2022
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Coal mining is an extensive source of anthropogenic methane emissions. In order to reduce and mitigate methane emissions, it is important to know how much and where the methane is emitted. We estimated coal mining methane emissions in Poland based on atmospheric methane measurements and particle dispersion modeling. In general, our emission estimates suggest higher emissions than expected by previous annual emission reports.
Sven Krautwurst, Konstantin Gerilowski, Jakob Borchardt, Norman Wildmann, Michał Gałkowski, Justyna Swolkień, Julia Marshall, Alina Fiehn, Anke Roiger, Thomas Ruhtz, Christoph Gerbig, Jaroslaw Necki, John P. Burrows, Andreas Fix, and Heinrich Bovensmann
Atmos. Chem. Phys., 21, 17345–17371, https://doi.org/10.5194/acp-21-17345-2021, https://doi.org/10.5194/acp-21-17345-2021, 2021
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Quantification of anthropogenic CH4 emissions remains challenging, but it is essential for near-term climate mitigation strategies. We use airborne remote sensing observations to assess bottom-up estimates of coal mining emissions from one of Europe's largest CH4 emission hot spots located in Poland. The analysis reveals that emissions from small groups of shafts can be disentangled, but caution is advised when comparing observations to commonly reported annual emissions.
Etienne Cheynet, Martin Flügge, Joachim Reuder, Jasna B. Jakobsen, Yngve Heggelund, Benny Svardal, Pablo Saavedra Garfias, Charlotte Obhrai, Nicolò Daniotti, Jarle Berge, Christiane Duscha, Norman Wildmann, Ingrid H. Onarheim, and Marte Godvik
Atmos. Meas. Tech., 14, 6137–6157, https://doi.org/10.5194/amt-14-6137-2021, https://doi.org/10.5194/amt-14-6137-2021, 2021
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The COTUR campaign explored the structure of wind turbulence above the ocean to improve the design of future multi-megawatt offshore wind turbines. Deploying scientific instruments offshore is both a financial and technological challenge. Therefore, lidar technology was used to remotely measure the wind above the ocean from instruments located on the seaside. The experimental setup is tailored to the study of the spatial correlation of wind gusts, which governs the wind loading on structures.
Julian Kostinek, Anke Roiger, Maximilian Eckl, Alina Fiehn, Andreas Luther, Norman Wildmann, Theresa Klausner, Andreas Fix, Christoph Knote, Andreas Stohl, and André Butz
Atmos. Chem. Phys., 21, 8791–8807, https://doi.org/10.5194/acp-21-8791-2021, https://doi.org/10.5194/acp-21-8791-2021, 2021
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Abundant mining and industrial activities in the Upper Silesian Coal Basin lead to large emissions of the potent greenhouse gas methane. This study quantifies these emissions with continuous, high-precision airborne measurements and dispersion modeling. Our emission estimates are in line with values reported in the European Pollutant Release and Transfer Register (E-PRTR 2017) but significantly lower than values reported in the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2).
Tamino Wetz, Norman Wildmann, and Frank Beyrich
Atmos. Meas. Tech., 14, 3795–3814, https://doi.org/10.5194/amt-14-3795-2021, https://doi.org/10.5194/amt-14-3795-2021, 2021
Short summary
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A fleet of quadrotors is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to calculate the horizontal wind is based on the principle of aerodynamic drag and the related quadrotor dynamics. The validation reveals that an average accuracy of < 0.3 m s−1 for the wind speed and < 8° for the wind direction was achieved.
Alina Fiehn, Julian Kostinek, Maximilian Eckl, Theresa Klausner, Michał Gałkowski, Jinxuan Chen, Christoph Gerbig, Thomas Röckmann, Hossein Maazallahi, Martina Schmidt, Piotr Korbeń, Jarosław Neçki, Pawel Jagoda, Norman Wildmann, Christian Mallaun, Rostyslav Bun, Anna-Leah Nickl, Patrick Jöckel, Andreas Fix, and Anke Roiger
Atmos. Chem. Phys., 20, 12675–12695, https://doi.org/10.5194/acp-20-12675-2020, https://doi.org/10.5194/acp-20-12675-2020, 2020
Short summary
Short summary
A severe reduction of greenhouse gas emissions is necessary to fulfill the Paris Agreement. We use aircraft- and ground-based in situ observations of trace gases and wind speed from two flights over the Upper Silesian Coal Basin, Poland, for independent emission estimation. The derived methane emission estimates are within the range of emission inventories, carbon dioxide estimates are in the lower range and carbon monoxide emission estimates are slightly higher than emission inventory values.
Cited articles
Abkar, M. and Porté-Agel, F.: Influence of atmospheric stability on
wind-turbine wakes: A large-eddy simulation study, Phys. Fluids, 27, 035104, https://doi.org/10.1063/1.4913695, 2015. a
Abraham, A., Dasari, T., and Hong, J.: Effect of turbine nacelle and tower on
the near wake of a utility-scale wind turbine, J. Wind Eng. Indust. Aerodynam., 193, 103981, https://doi.org/10.1016/j.jweia.2019.103981, 2019. a
Abraham, A., Martínez-Tossas, L. A., and Hong, J.: Mechanisms of dynamic
near-wake modulation of a utility-scale wind turbine, J. Fluid Mech., 926,
A29, https://doi.org/10.1017/jfm.2021.737, 2021. a, b
Aitken, M. L. and Lundquist, J. K.: Utility-Scale Wind Turbine Wake
Characterization Using Nacelle-Based Long-Range Scanning Lidar, J. Atmos. Ocean. Tech., 31, 1529–1539, https://doi.org/10.1175/jtech-d-13-00218.1, 2014. a
Alaoui-Sosse, S., Durand, P., and Médina, P.: In Situ Observations of
Wind Turbines Wakes with Unmanned Aerial Vehicle BOREAL within the MOMEMTA Project, Atmosphere, 13, 775, https://doi.org/10.3390/atmos13050775, 2022. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energy, 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of
wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541,
https://doi.org/10.1017/jfm.2016.595, 2016. a, b, c
Bastankhah, M. and Porté-Agel, F.: Wind tunnel study of the wind turbine
interaction with a boundary-layer flow: Upwind region, turbine performance,
and wake region, Phys. Fluids, 29, 065105, https://doi.org/10.1063/1.4984078, 2017. a, b, c, d
Brugger, P., Debnath, M., Scholbrock, A., Fleming, P., Moriarty, P., Simley,
E., Jager, D., Roadman, J., Murphy, M., Zong, H., and Porté-Agel, F.:
Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models, Wind Energ. Sci., 5, 1253–1272,
https://doi.org/10.5194/wes-5-1253-2020, 2020. a
Crespo, A., Hernández, J., and Frandsen, S.: Survey of modelling methods for wind turbine wakes and wind farms, Wind Energy, 2, 1–24,
https://doi.org/10.1002/(sici)1099-1824(199901/03)2:1<1::aid-we16>3.0.co;2-7, 1999. a
Dasari, T., Wu, Y., Liu, Y., and Hong, J.: Near-wake behaviour of a
utility-scale wind turbine, J. Fluid Mech., 859, 204–246,
https://doi.org/10.1017/jfm.2018.779, 2018. a
Doubrawa, P., Quon, E. W., Martinez-Tossas, L. A., Shaler, K., Debnath, M.,
Hamilton, N., Herges, T. G., Maniaci, D., Kelley, C. L., Hsieh, A. S.,
Blaylock, M. L., Laan, P., Andersen, S. J., Krueger, S., Cathelain, M.,
Schlez, W., Jonkman, J., Branlard, E., Steinfeld, G., Schmidt, S., Blondel,
F., Lukassen, L. J., and Moriarty, P.: Multimodel validation of single wakes
in neutral and stratified atmospheric conditions, Wind Energy, 23, 2027–2055, https://doi.org/10.1002/we.2543, 2020. a
Englberger, A., Dörnbrack, A., and Lundquist, J. K.: Does the rotational
direction of a wind turbine impact the wake in a stably stratified
atmospheric boundary layer?, Wind Energ. Sci., 5, 1359–1374,
https://doi.org/10.5194/wes-5-1359-2020, 2020. a
Fuertes, F. C., Markfort, C., and Porté-Agel, F.: Wind Turbine Wake
Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model
Validation, Remote Sens., 10, 668, https://doi.org/10.3390/rs10050668, 2018. a, b, c
Hand, M., Simms, D., Fingersh, L., Jager, D., Cotrell, J., Schreck, S., and Larwood, S.: Unsteady aerodynamics experiment phase vi: Wind tunnel test configurations and available data campaigns, Technical report NREL/TP-500-29955, NREL, https://doi.org/10.2172/15000240, 2001. a
Herges, T. G. and Keyantuo, P.: Robust Lidar Data Processing and Quality
Control Methods Developed for the SWiFT Wake Steering Experiment, J. Phys.:
Conf. Ser., 1256, 012005, https://doi.org/10.1088/1742-6596/1256/1/012005, 2019. a
IEA – International Energy Agency: Wind Electricity, https://www.iea.org/reports/wind-electricity (last access: 15 January 2023), 2022a. a
IEA – International Energy Agency: World Energy Outlook 2022,
https://iea.blob.core.windows.net/assets/830fe099-5530-48f2-a7c1-11f35d510983/WorldEnergyOutlook2022.pdf (last access: 15 January 2023), 2022b. a
Jensen, N.: A note on wind generator interaction, no. 2411 in Risø-M,
Risø National Laboratory, ISBN 87-550-0971-9, 1983. a
Jiménez, Á., Crespo, A., and Migoya, E.: Application of a LES
technique to characterize the wake deflection of a wind turbine in yaw, Wind
Energy, 13, 559–572, https://doi.org/10.1002/we.380, 2009. a, b
Keane, A.: Advancement of an analytical double-Gaussian full wind turbine wake model, Renew. Energy, 171, 687–708, https://doi.org/10.1016/j.renene.2021.02.078,
2021. a
Keane, A., Aguirre, P. E. O., Ferchland, H., Clive, P., and Gallacher, D.: An
analytical model for a full wind turbine wake, J. Phys.: Conf. Ser., 753,
032039, https://doi.org/10.1088/1742-6596/753/3/032039, 2016. a
Kocer, G., Mansour, M., Chokani, N., Abhari, R., and Müller, M.: Full-Scale Wind Turbine Near-Wake Measurements Using an Instrumented Uninhabited Aerial Vehicle, J. Sol. Energ. Eng., 133, 041011, https://doi.org/10.1115/1.4004707, 2011. a
Krogstad, P.-Å. and Adaramola, M. S.: Performance and near wake
measurements of a model horizontal axis wind turbine, Wind Energy, 15,
743–756, https://doi.org/10.1002/we.502, 2011. a, b, c
Li, Z., Pu, O., Pan, Y., Huang, B., Zhao, Z., and Wu, H.: A study on measuring wind turbine wake based on UAV anemometry system, Sustain. Energ. Technol. Assess., 53, 102537, https://doi.org/10.1016/j.seta.2022.102537, 2022. a
Lignarolo, L., Ragni, D., Krishnaswami, C., Chen, Q., Ferreira, C. S., and van Bussel, G.: Experimental analysis of the wake of a horizontal-axis
wind-turbine model, Renew. Energy, 70, 31–46, https://doi.org/10.1016/j.renene.2014.01.020, 2014. a
Lu, H. and Porté-Agel, F.: Large-eddy simulation of a very large wind
farm in a stable atmospheric boundary layer, Phys. Fluids, 23, 065101,
https://doi.org/10.1063/1.3589857, 2011. a
Machefaux, E., Larsen, G. C., Koblitz, T., Troldborg, N., Kelly, M. C.,
Chougule, A., Hansen, K. S., and Rodrigo, J. S.: An experimental and numerical study of the atmospheric stability impact on wind turbine wakes,
Wind Energy, 19, 1785–1805, https://doi.org/10.1002/we.1950, 2015. a, b, c
Maeda, T., Kamada, Y., Murata, J., Yonekura, S., Ito, T., Okawa, A., and
Kogaki, T.: Wind tunnel study on wind and turbulence intensity profiles in
wind turbine wake, J. Therm. Sci., 20, 127–132, https://doi.org/10.1007/s11630-011-0446-9, 2011. a
Magnusson, M.: Near-wake behaviour of wind turbines, J. Wind Eng. Indust. Aerodynam., 80, 147–167, https://doi.org/10.1016/s0167-6105(98)00125-1, 1999. a
Manwell, J. F.: Wind energy explained, Wiley, ISBN 0470015004, 2009. a
Mauz, M., Rautenberg, A., Platis, A., Cormier, M., and Bange, J.: First
identification and quantification of detached-tip vortices behind a wind
energy converter using fixed-wing unmanned aircraft system, Wind Energ. Sci., 4, 451–463, https://doi.org/10.5194/wes-4-451-2019, 2019. a
Mehta, D., van Zuijlen, A., Koren, B., Holierhoek, J., and Bijl, H.: Large Eddy Simulation of wind farm aerodynamics: A review, J. Wind. Eng. Indust. Aerodynam., 133, 1–17, https://doi.org/10.1016/j.jweia.2014.07.002, 2014. a
Menke, R., Vasiljević, N., Hansen, K. S., Hahmann, A. N., and Mann, J.:
Does the wind turbine wake follow the topography? A multi-lidar study in
complex terrain, Wind Energ. Sci., 3, 681–691, https://doi.org/10.5194/wes-3-681-2018, 2018. a, b
Mikkelsen, T., Angelou, N., Hansen, K., Sjöholm, M., Harris, M., Slinger, C., Hadley, P., Scullion, R., Ellis, G., and Vives, G.: A spinner-integrated wind lidar for enhanced wind turbine control, Wind Energy, 16, 625–643,
https://doi.org/10.1002/we.1564, 2012. a
Mohan, M.: Analysis of various schemes for the estimation of atmospheric
stability classification, Atmos. Environ., 32, 3775–3781, https://doi.org/10.1016/s1352-2310(98)00109-5, 1998. a
NEWA Consortium: NEWA, https://map.neweuropeanwindatlas.eu, last access: 5 January 2023. a
NREL: FLORIS, Version 2.4, Zenodo [code], https://doi.org/10.5281/zenodo.6687458, 2021. a
Odemark, Y. and Fransson, J. H. M.: The stability and development of tip and
root vortices behind a model wind turbine, Exp. Fluids, 54, 1591,
https://doi.org/10.1007/s00348-013-1591-6, 2013. a, b
Platis, A., Hundhausen, M., Lampert, A., Emeis, S., and Bange, J.: The Role of Atmospheric Stability and Turbulence in Offshore Wind-Farm Wakes in the
German Bight, Bound.-Lay. Meteorol., 182, 441–469, https://doi.org/10.1007/s10546-021-00668-4, 2021. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and
Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59,
https://doi.org/10.1007/s10546-019-00473-0, 2019. a, b, c, d
Reuder, J., Båserud, L., Kral, S., Kumer, V., Wagenaar, J. W., and Knauer, A.: Proof of Concept for Wind Turbine Wake Investigations with the RPAS SUMO, Energ. Proced., 94, 452–461, https://doi.org/10.1016/j.egypro.2016.09.215,
2016. a
Sanderse, B., Pijl, S., and Koren, B.: Review of computational fluid dynamics
for wind turbine wake aerodynamics, Wind Energy, 14, 799–819,
https://doi.org/10.1002/we.458, 2011. a
Sherry, M., Nemes, A., Jacono, D. L., Blackburn, H. M., and Sheridan, J.: The
interaction of helical tip and root vortices in a wind turbine wake, Phys.
Fluids, 25, 117102, https://doi.org/10.1063/1.4824734, 2013. a, b, c
Simley, E., Angelou, N., Mikkelsen, T., Sjöholm, M., Mann, J., and Pao, L. Y.: Characterization of wind velocities in the upstream induction zone of a wind turbine using scanning continuous-wave lidars, J. Renew. Sustain.
Energ., 8, 013301, https://doi.org/10.1063/1.4940025, 2016. a
Thielicke, W., Hübert, W., Müller, U., Eggert, M., and Wilhelm, P.: Towards accurate and practical drone-based wind measurements with an ultrasonic anemometer, Atmos. Meas. Tech., 14, 1303–1318,
https://doi.org/10.5194/amt-14-1303-2021, 2021. a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki,
V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J.,
Robertson, A., Sanz Rodrigo, J., Sempreviva, A. M., Smith, J. C., Tuohy, A.,
and Wiser, R.: Grand challenges in the science of wind energy, Science, 366,
eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a
Veers, P., Dykes, K., Basu, S., Bianchini, A., Clifton, A., Green, P.,
Holttinen, H., Kitzing, L., Kosovic, B., Lundquist, J. K., Meyers, J., O'Malley, M., Shaw, W. J., and Straw, B.: Grand Challenges: wind energy research needs for a global energy transition, Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, 2022. a
Vermeer, L., Sørensen, J., and Crespo, A.: Wind turbine wake aerodynamics,
Prog. Aerosp. Sci., 39, 467–510, https://doi.org/10.1016/s0376-0421(03)00078-2, 2003. a, b
Wetz, T. and Wildmann, N.: Spatially distributed and simultaneous wind
measurements with a fleet of small quadrotor UAS, J. Phys.: Conf. Ser., 2265, 022086, https://doi.org/10.1088/1742-6596/2265/2/022086, 2022. a, b, c
Wetz, T., Wildmann, N., and Beyrich, F.: Distributed wind measurements with
multiple quadrotor unmanned aerial vehicles in the atmospheric boundary
layer, Atmos. Meas. Tech., 14, 3795–3814, https://doi.org/10.5194/amt-14-3795-2021,
2021. a
Wetz, T., Zink, J., Bange, J., and Wildmann, N.: Analyses of Spatial
Correlation and Coherence in ABL flow with a Fleet of UAS, Research Square, https://doi.org/10.21203/rs.3.rs-2033943/v1, 2022. a
Wildmann, N. and Wetz, T.: Towards vertical wind and turbulent flux estimation with multicopter uncrewed aircraft systems, Atmos. Meas. Tech., 15, 5465–5477, https://doi.org/10.5194/amt-15-5465-2022, 2022. a
Wildmann, N., Hofsäß, M., Weimer, F., Joos, A., and Bange, J.: MASC
– a small Remotely Piloted Aircraft (RPA) for wind energy research, Adv. Sci. Res., 11, 55–61, https://doi.org/10.5194/asr-11-55-2014, 2014. a
Wildmann, N., Bernard, S., and Bange, J.: Measuring the local wind field at an escarpment using small remotely-piloted aircraft, Renew. Energy, 103, 613–619, https://doi.org/10.1016/j.renene.2016.10.073, 2017. a
Wildmann, N., Kigle, S., and Gerz, T.: Coplanar lidar measurement of a single
wind energy converter wake in distinct atmospheric stability regimes at the
Perdigão 2017 experiment, J. Phys.: Conf. Ser., 1037, 052006,
https://doi.org/10.1088/1742-6596/1037/5/052006, 2018a. a, b
Wildmann, N., Vasiljevic, N., and Gerz, T.: Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup, Atmos. Meas. Tech., 11, 3801–3814, https://doi.org/10.5194/amt-11-3801-2018,
2018b. a
Wildmann, N., Gerz, T., and Lundquist, J. K.: Long-range Doppler lidar
measurements of wind turbine wakes and their interaction with turbulent
atmospheric boundary-layer flow at Perdigao 2017, J. Phys.: Conf. Ser., 1618,
032034, https://doi.org/10.1088/1742-6596/1618/3/032034, 2020. a, b
Wildmann, N., Hagen, M., and Gerz, T.: Enhanced resource assessment and
atmospheric monitoring of the research wind farm WiValdi, J. Phys.: Conf. Ser., 2265, 022029, https://doi.org/10.1088/1742-6596/2265/2/022029, 2022. a
Wu, Y.-T. and Porté-Agel, F.: Atmospheric Turbulence Effects on Wind-Turbine Wakes: An LES Study, Energies, 5, 5340–5362,
https://doi.org/10.3390/en5125340, 2012.
a, b, c, d
Yang, X., Hong, J., Barone, M., and Sotiropoulos, F.: Coherent dynamics in the rotor tip shear layer of utility-scale wind turbines, J. Fluid Mech., 804, 90–115, https://doi.org/10.1017/jfm.2016.503, 2016. a
Zhang, W., Markfort, C. D., and Porté-Agel, F.: Near-wake flow structure
downwind of a wind turbine in a turbulent boundary layer, Exp. Fluids, 52,
1219–1235, https://doi.org/10.1007/s00348-011-1250-8, 2011. a, b, c
Short summary
In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field measurements on an operating 2 MW wind turbine (WT) in complex terrain. The fleet of multirotors has the potential to fill the meteorological gap of observations in the near wake of WTs with high-temporal and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. The flow up- and downstream of the WT is measured simultaneously at multiple spatial positions.
In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field...
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