Articles | Volume 10, issue 12
https://doi.org/10.5194/wes-10-2821-2025
© Author(s) 2025. 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-10-2821-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying
Xiaowei Zhu
Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97201, USA
Shuolin Xiao
Ralph O’Connor Sustainable Energy Institute, Johns Hopkins University, Baltimore, MD 21218, USA
Ghanesh Narasimhan
Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
St. Anthony Falls Lab, University of Minnesota, Minneapolis, MN 55455, USA
Luis A. Martinez-Tossas
National Renewable Energy Laboratory, Golden, CO 80401, USA
Michael Schnaubelt
Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA
Gerard Lemson
Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA
Hanxun Yao
Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Alexander S. Szalay
Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA
Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
Dennice F. Gayme
Ralph O’Connor Sustainable Energy Institute, Johns Hopkins University, Baltimore, MD 21218, USA
Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Ralph O’Connor Sustainable Energy Institute, Johns Hopkins University, Baltimore, MD 21218, USA
Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD 21218, USA
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Related authors
No articles found.
Etienne Muller, Simone Gremmo, Felix Houtin-Mongrolle, Laurent Beaudet, Juliette Coussy, Luis A. Martínez-Tossas, and Pierre Bénard
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-37, https://doi.org/10.5194/wes-2026-37, 2026
Preprint under review for WES
Short summary
Short summary
The energy yield of wind turbines within wind farms can be increased with control strategies. One way consists in misaligning the turbines with respect to the wind direction, to redirect their wake away from downstream turbines. Using models and advanced simulations, this work confirms a true potential but stresses how measurement biases on the wind direction and the flow complexity may critically affect both the performance in the field, and the reliability of the field validation campaigns.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Paul Hulsman, Luis A. Martínez-Tossas, Nicholas Hamilton, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-112, https://doi.org/10.5194/wes-2023-112, 2023
Manuscript not accepted for further review
Short summary
Short summary
This paper presents an approach to analytically estimate the wake deficit within the near-wake region by modifying the curled wake model. This is done by incorporating a new initial condition at the rotor using an azimuth-dependent Gaussian profile, an adjusted turbulence model in the near-wake region and the far-wake region and an iterative process to determine the velocity field, while considering the relation of the pressure gradient and accounting the conservation of mass.
Ryan Scott, Luis Martínez-Tossas, Juliaan Bossuyt, Nicholas Hamilton, and Raúl B. Cal
Wind Energ. Sci., 8, 449–463, https://doi.org/10.5194/wes-8-449-2023, https://doi.org/10.5194/wes-8-449-2023, 2023
Short summary
Short summary
In this work we examine the relationship between wind speed and turbulent stresses within a wind turbine wake. This relationship changes further from the turbine as the driving physical phenomena vary throughout the wake. We propose a model for this process and test the effectiveness of our model against existing formulations. Our approach increases the accuracy of wind speed predictions, which will lead to better estimates of wind plant performance and promote more efficient wind plant design.
Kelsey Shaler, Benjamin Anderson, Luis A. Martínez-Tossas, Emmanuel Branlard, and Nick Johnson
Wind Energ. Sci., 8, 383–399, https://doi.org/10.5194/wes-8-383-2023, https://doi.org/10.5194/wes-8-383-2023, 2023
Short summary
Short summary
Free-vortex wake (OLAF) and low-fidelity blade-element momentum (BEM) structural results are compared to high-fidelity simulation results for a flexible downwind turbine for varying inflow conditions. Overall, OLAF results were more consistent than BEM results when compared to SOWFA results under challenging inflow conditions. Differences between OLAF and BEM results were dominated by yaw misalignment angle, with varying shear exponent and turbulence intensity causing more subtle differences.
Michael J. LoCascio, Christopher J. Bay, Majid Bastankhah, Garrett E. Barter, Paul A. Fleming, and Luis A. Martínez-Tossas
Wind Energ. Sci., 7, 1137–1151, https://doi.org/10.5194/wes-7-1137-2022, https://doi.org/10.5194/wes-7-1137-2022, 2022
Short summary
Short summary
This work introduces the FLOW Estimation and Rose Superposition (FLOWERS) wind turbine wake model. This model analytically integrates the wake over wind directions to provide a time-averaged flow field. This new formulation is used to perform layout optimization. The FLOWERS model provides a smooth flow field over an entire wind plant at fraction of the computational cost of the standard numerical integration approach.
Jennifer King, Paul Fleming, Ryan King, Luis A. Martínez-Tossas, Christopher J. Bay, Rafael Mudafort, and Eric Simley
Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, https://doi.org/10.5194/wes-6-701-2021, 2021
Short summary
Short summary
This paper highlights the secondary effects of wake steering, including yaw-added wake recovery and secondary steering. These effects enhance the value of wake steering especially when applied to a large wind farm. This paper models these secondary effects using an analytical model proposed in the paper. The results of this model are compared with large-eddy simulations for several cases including 2-turbine, 3-turbine, 5-turbine, and 38-turbine cases.
Luis A. Martínez-Tossas, Jennifer King, Eliot Quon, Christopher J. Bay, Rafael Mudafort, Nicholas Hamilton, Michael F. Howland, and Paul A. Fleming
Wind Energ. Sci., 6, 555–570, https://doi.org/10.5194/wes-6-555-2021, https://doi.org/10.5194/wes-6-555-2021, 2021
Short summary
Short summary
In this paper a three-dimensional steady-state solver for flow through a wind farm is developed and validated. The computational cost of the solver is on the order of seconds for large wind farms. The model is validated using high-fidelity simulations and SCADA.
Cited articles
Abkar, M. and Porté-Agel, F.: The effect of free-atmosphere stratification on boundary-layer flow and power output from very large wind farms, Energies, 6, 2338–2361, https://doi.org/10.3390/en6052338, 2013. a
Abkar, M. and Porté-Agel, F.: Mean and turbulent kinetic energy budgets inside and above very large wind farms under conventionally-neutral condition, Renewable Energy, 70, 142–152, https://doi.org/10.1016/j.renene.2014.03.050, 2014. a
Aitken, M. L., Kosović, B., Mirocha, J. D., and Lundquist, J. K.: Large eddy simulation of wind turbine wake dynamics in the stable boundary layer using the Weather Research and Forecasting Model, J. Renewable Sustainable Energy, 6, https://doi.org/10.1063/1.4885111, 2014. a
Aiyer, A. K., Deike, L., and Mueller, M. E.: A dynamic wall modeling approach for large eddy simulation of offshore wind farms in realistic oceanic conditions, J. Renewable Sustainable Energy, 16, https://doi.org/10.1063/5.0159019, 2024. a
Albertson, J. D.: Large eddy simulation of land-atmosphere interaction, Ph.D. thesis, University of California, Davis, 1996. a
Albertson, J. D. and Parlange, M. B.: Surface length scales and shear stress: Implications for land-atmosphere interaction over complex terrain, Water Resour. Res., 35, 2121–2132, https://doi.org/10.1029/1999WR900094, 1999. a
Alexakis, A., Marino, R., Mininni, P. D., van Kan, A., Foldes, R., and Feraco, F.: Large-scale self-organization in dry turbulent atmospheres, Science, 383, 1005–1009, https://doi.org/10.1126/science.adg8269, 2024. a
Allaerts, D. and Meyers, J.: Large eddy simulation of a large wind-turbine array in a conventionally neutral atmospheric boundary layer, Phys. Fluids, 27, https://doi.org/10.1063/1.4922339, 2015. a
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in conventionally neutral wind farms, J. Fluid Mech., 814, 95–130, https://doi.org/10.1017/jfm.2017.11, 2017. a
Ayala, M., Sadek, Z., Ferčák, O., Cal, R. B., Gayme, D. F., and Meneveau, C.: A moving surface drag model for LES of wind over waves, Boundary Layer Meteorol., 190, 39, https://doi.org/10.1007/s10546-024-00884-8, 2024. a
Bodini, N., Optis, M., Redfern, S., Rosencrans, D., Rybchuk, A., Lundquist, J. K., Pronk, V., Castagneri, S., Purkayastha, A., Draxl, C., Krishnamurthy, R., Young, E., Roberts, B., Rosenlieb, E., and Musial, W.: The 2023 National Offshore Wind data set (NOW-23), Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, 2024. a
Bou-Zeid, E., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness, Water Resour. Res., 40, https://doi.org/10.1029/2003WR002475, 2004. a
Bou-Zeid, E., Meneveau, C., and Parlange, M.: A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows, Phys. Fluids, 17, https://doi.org/10.1063/1.1839152, 2005. a, b
Chatelain, P., Backaert, S., Winckelmans, G., and Kern, S.: Large eddy simulation of wind turbine wakes, Flow Turbul. Combust., 91, 587–605, https://doi.org/10.1007/s10494-013-9474-8, 2013. a
Chung, W. T., Jung, K. S., Chen, J. H., and Ihme, M.: BLASTNet: A call for community-involved big data in combustion machine learning, Appl. Energy Combust. Sci., 12, 100 087, https://doi.org/10.1016/j.jaecs.2022.100087, 2022. a
Churchfield, M., Lee, S., Moriarty, P., Martinez, L., Leonardi, S., Vijayakumar, G., and Brasseur, J.: A large-eddy simulation of wind-plant aerodynamics, in: 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 537, https://doi.org/10.2514/6.2012-537, 2012. a
Duraisamy, K., Iaccarino, G., and Xiao, H.: Turbulence modeling in the age of data, Annu. Rev. Fluid Mech., 51, 357–377, https://doi.org/10.1146/annurev-fluid-010518-040547, 2019. a
Durran, D. R. and Klemp, J. B.: A compressible model for the simulation of moist mountain waves, Mon. Weather Rev., 111, 2341–2361, https://doi.org/10.1175/1520-0493(1983)111<2341:ACMFTS>2.0.CO;2, 1983. a
Gadde, S. N. and Stevens, R. J.: Interaction between low-level jets and wind farms in a stable atmospheric boundary layer, Phys. Rev. Fluids, 6, 014603, https://doi.org/10.1103/PhysRevFluids.6.014603, 2021. a
Gebraad, P. M., Teeuwisse, F. W., Van Wingerden, J., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through yaw control using a parametric model for wake effects – a CFD simulation study, Wind Energy, 19, 95–114, https://doi.org/10.1002/we.1822, 2016. a
Gharaati, M., Xiao, S., Wei, N. J., Martínez-Tossas, L. A., Dabiri, J. O., and Yang, D.: Large-eddy simulation of helical-and straight-bladed vertical-axis wind turbines in boundary layer turbulence, J. Renewable Sustainable Energy, 14, https://doi.org/10.1063/5.0100169, 2022. a, b, c
Gharaati, M., Xiao, S., Martínez-Tossas, L. A., Araya, D. B., and Yang, D.: Large-eddy simulations of turbulent wake flows behind helical-and straight-bladed vertical axis wind turbines rotating at low tip speed ratios, Phys. Rev. Fluids, 9, 074603, https://doi.org/10.1103/PhysRevFluids.9.074603, 2024. a, b, c
Graham, J., Kanov, K., Yang, X., Lee, M., Malaya, N., Lalescu, C., Burns, R., Eyink, G., Szalay, A., Moser, R., and Meneveau, C.: A web services accessible database of turbulent channel flow and its use for testing a new integral wall model for LES, J. Turbul., 17, 181–215, https://doi.org/10.1080/14685248.2015.1088656, 2016. a, b
Howland, M. F., Bossuyt, J., Martínez-Tossas, L. A., Meyers, J., and Meneveau, C.: Wake structure in actuator disk models of wind turbines in yaw under uniform inflow conditions, J. Renewable Sustainable Energy, 8, https://doi.org/10.1063/1.4955091, 2016. a
Jha, P. K., Churchfield, M. J., Moriarty, P. J., and Schmitz, S.: Guidelines for volume force distributions within actuator line modeling of wind turbines on large-eddy simulation-type grids, J. Sol. Energy Eng., 136, 031003, https://doi.org/10.1115/1.4026252, 2014. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW reference wind turbine for offshore system development, Technical Report NREL/TP-500-38060, National Renewable Energy Laboratory (NREL), Golden, CO, USA, 2009. a
Kosović, B. and Curry, J. A.: A large eddy simulation study of a quasi-steady, stably stratified atmospheric boundary layer, J. Atmos. Sci., 57, 1052–1068, https://doi.org/10.1175/1520-0469(2000)057<1052:ALESSO>2.0.CO;2, 2000. a
Kumar, V., Kleissl, J., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of a diurnal cycle of the atmospheric boundary layer: Atmospheric stability and scaling issues, Water Resour. Res., 42, https://doi.org/10.1029/2005WR004651, 2006. a
Kusiak, A.: Renewables: Share data on wind energy, Nature, 529, 19–21, 2016. a
Li, C., Liu, L., Lu, X., and Stevens, R. J.: Analytical model of fully developed wind farms in conventionally neutral atmospheric boundary layers, J. Fluid Mech., 948, A43, https://doi.org/10.1017/jfm.2022.732, 2022. a
Li, Y., Perlman, E., Wan, M., Yang, Y., Meneveau, C., Burns, R., Chen, S., Szalay, A., and Eyink, G.: A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence, J. Turbul., 9, 31, https://doi.org/10.1080/14685240802376389, 2008. a, b, c, d, e, f
Lilly, D.: The representation of small-scale turbulence in numerical simulation experiments, Technical report, National Center for Atmospheric Research (NCAR), 1966. a
Liu, L. and Stevens, R. J.: Vertical structure of conventionally neutral atmospheric boundary layers, P. Natl. Acad. Sci. USA, 119, e2119369119, https://doi.org/10.1073/pnas.2119369119, 2022. a
Liu, L., Lu, X., and Stevens, R. J.: Geostrophic drag law in conventionally neutral atmospheric boundary layer: simplified parametrization and numerical validation, Boundary Layer Meteorol., 190, 37, https://doi.org/10.1007/s10546-024-00878-6, 2024. a
Martínez-Tossas, L., Churchfield, M., and Meneveau, C.: Optimal smoothing length scale for actuator line models of wind turbine blades based on Gaussian body force distribution, Wind Energy, 20, 1083–1096, https://doi.org/10.1002/we.2081, 2017. a
Martínez-Tossas, L. A. and Meneveau, C.: Filtered lifting line theory and application to the actuator line model, J. Fluid Mech., 863, 269–292, https://doi.org/10.1017/jfm.2018.994, 2019. a
Martínez-Tossas, L. A., Churchfield, M. J., and Meneveau, C.: Large eddy simulation of wind turbine wakes: detailed comparisons of two codes focusing on effects of numerics and subgrid modeling, in: J. Phys.: Conference Series, IOP Publishing, 625, 012024, https://doi.org/10.1088/1742-6596/625/1/012024, 2015. a, b, c
Martínez-Tossas, L. A., Sakievich, P., Churchfield, M. J., and Meneveau, C.: Generalized filtered lifting line theory for arbitrary chord lengths and application to wind turbine blades, Wind Energy, 27, 101–106, https://doi.org/10.1002/we.2872, 2024. a
McWilliams, J. C., Weiss, J. B., and Yavneh, I.: Anisotropy and coherent vortex structures in planetary turbulence, Science, 264, 410–413, https://doi.org/10.1126/science.264.5157.410, 1994. a
Meyers, J. and Meneveau, C.: Optimal turbine spacing in fully developed wind farm boundary layers, Wind Energy, 15, 305–317, https://doi.org/10.1002/we.469, 2012. a
Miles, A., jakirkham, Bussonnier, M., Moore, J., Papadopoulos Orfanos, D., Bourbeau, J., Fulton, A., Lee, G., Patel, Z., Bennett, D., Rocklin, M., AWA BRANDON AWA, Chopra, S., Abernathey, R., Kristensen, M. R. B., Sales de Andrade, E., Durant, M., Schut, V., Dussin, R., Verma, S., Chaudhary, S., Barnes, C., Hamman, J., Nunez-Iglesias, J., Williams, B., Mohar, B., Noyes, C., and Bolarinwa, E.: zarr-developers/zarr-python: v2.15.0, Zenodo [code], https://doi.org/10.5281/zenodo.8039103, 2023. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Contrib. Geophys. Inst. Acad. Sci. USSR, 151, e187, 1954. a
Munters, W., Meneveau, C., and Meyers, J.: Shifted periodic boundary conditions for simulations of wall-bounded turbulent flows, Phys. Fluids, 28, https://doi.org/10.1063/1.4941912, 2016. a, b
Narasimhan, G., Gayme, D. F., and Meneveau, C.: Analytical wake modeling in atmospheric boundary layers: accounting for wind veer and thermal stratification, J. Phys.: Conference Series, IOP Publishing, 2767, 092018, https://doi.org/10.1088/1742-6596/2767/9/092018, 2024a. a, b, c
Narasimhan, G., Gayme, D. F., and Meneveau, C.: Analytical model coupling Ekman and surface layer structure in atmospheric boundary layer flows, Boundary Layer Meteorol., 190, 16, https://doi.org/10.1007/s10546-024-00859-9, 2024b. a
Narasimhan, G., Gayme, D. F., and Meneveau, C.: An extended analytical wake model and applications to yawed wind turbines in atmospheric boundary layers with different levels of stratification and veer, J. Renewable Sustainable Energy, 17, https://doi.org/10.1063/5.0251305, 2025. a
NCAR: HPE SGI ICE XA – Cheyenne, NCAR, https://doi.org/10.5065/D6RX99HX, 2025. a
Perlman, E., Burns, R., Li, Y., and Meneveau, C.: Data exploration of turbulence simulations using a database cluster, in: Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, 1–11, https://doi.org/10.1145/1362622.1362654, 2007. a, b
Porté-Agel, F., Meneveau, C., and Parlange, M. B.: A scale-dependent dynamic model for large-eddy simulation: application to a neutral atmospheric boundary layer, J. Fluid Mech., 415, 261–284, https://doi.org/10.1017/S0022112000008776, 2000. a
Sescu, A. and Meneveau, C.: A control algorithm for statistically stationary large-eddy simulations of thermally stratified boundary layers, Q. J. R. Meteorolog. Soc., 140, 2017–2022, https://doi.org/10.1002/qj.2266, 2014. a, b
Shapiro, C. R., Gayme, D. F., and Meneveau, C.: Modelling yawed wind turbine wakes: a lifting line approach, J. Fluid Mech., 841, R1, https://doi.org/10.1017/jfm.2018.75, 2018. a
Shapiro, C. R., Gayme, D. F., and Meneveau, C.: Generation and decay of counter-rotating vortices downstream of yawed wind turbines in the atmospheric boundary layer, J. Fluid Mech., 903, R2, https://doi.org/10.1017/jfm.2020.717, 2020. a
Smagorinsky, J.: General circulation experiments with the primitive equations: I. The basic experiment, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a
Sørensen, J. N. and Shen, W. Z.: Numerical modeling of wind turbine wakes, J. Fluids Eng., 124, 393–399, https://doi.org/10.1115/1.1471361, 2002. a
Stevens, R. J. and Meneveau, C.: Flow structure and turbulence in wind farms, Annu. Rev. Fluid Mech., 49, 311–339, https://doi.org/10.1146/annurev-fluid-010816-060206, 2017. a, b, c
Stevens, R. J., Graham, J., and Meneveau, C.: A concurrent precursor inflow method for large eddy simulations and applications to finite length wind farms, Renewable Energy, 68, 46–50, https://doi.org/10.1016/j.renene.2014.01.024, 2014. a, b
Stevens, R. J., Martínez-Tossas, L. A., and Meneveau, C.: Comparison of wind farm large eddy simulations using actuator disk and actuator line models with wind tunnel experiments, Renewable Energy, 116, 470–478, https://doi.org/10.1016/j.renene.2017.08.072, 2018. a, b, c
Xiao, S., Zhu, X., Narasimhan, G., Gayme, D. F., and Meneveau, C.: Wind farm dynamics over a diurnal cycle: analysis of a comprehensive large eddy simulation, web-services accessible dataset, arXiv [preprint], https://doi.org/10.48550/arXiv.2510.05005, 2025. a, b, c, d
Yang, D., Meneveau, C., and Shen, L.: Large-eddy simulation of offshore wind farm, Phys. Fluids, 26, https://doi.org/10.1063/1.4863096, 2014. a
Yang, X., Milliren, C., Kistner, M., Hogg, C., Marr, J., Shen, L., and Sotiropoulos, F.: High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm, Appl. Energy, 281, 116115, https://doi.org/10.1016/j.apenergy.2020.116115, 2021. a
Yu, H., Kanov, K., Perlman, E., Graham, J., Frederix, E., Burns, R., Szalay, A., Eyink, G., and Meneveau, C.: Studying Lagrangian dynamics of turbulence using on-demand fluid particle tracking in a public turbulence database, J. Turbul., 13, 12, https://doi.org/10.1080/14685248.2012.674643, 2012. a, b
Zhang, C., Duan, L., and Choudhari, M. M.: Direct numerical simulation database for supersonic and hypersonic turbulent boundary layers, AIAA Journal, 56, 4297–4311, https://doi.org/10.2514/1.J057296, 2018. a
Zhang, F., Yang, X., and He, G.: Multiscale analysis of a very long wind turbine wake in an atmospheric boundary layer, Phys. Rev. Fluids, 8, 104605, https://doi.org/10.1103/PhysRevFluids.8.104605, 2023. a
Zhu, X., Xiao, S., Narasimhan, G., Martinez-Tossas, L. A., Schnaubelt, M., Lemson, G., Szalay, A., Gayme, D. F., and Meneveau, C.: Large wind farm under 1-hour conventionally neutral atmospheric conditions Johns Hopkins Turbulence Databases – Wind [data set], https://doi.org/10.26144/D8ES-FC15, 2025. a, b
Zilitinkevich, S., Baklanov, A., Rost, J., Smedman, A.-s., Lykosov, V., and Calanca, P.: Diagnostic and prognostic equations for the depth of the stably stratified Ekman boundary layer, Q. J. R. Meteorolog. Soc., 128, 25–46, https://doi.org/10.1256/00359000260498770, 2002. a
Short summary
The paper describes a new approach to democratize access to results from expensive high-performance computer simulations of atmospheric boundary layer flow interacting with wind turbines, in large wind farms. Users interact with the data using a virtual sensor array methodology and essentially stream the data on demand to their analysis or visualization programs rather than having to download files and worrying about data formats, etc.
The paper describes a new approach to democratize access to results from expensive...
Altmetrics
Final-revised paper
Preprint