Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Paula Doubrawa"'
Autor:
Stefano Letizia, Peter Brugger, Nicola Bodini, Raghavendra Krishnamurthy, Andrew Scholbrock, Eric Simley, Fernando Porté-Agel, Nicholas Hamilton, Paula Doubrawa, Patrick Moriarty
Publikováno v:
Frontiers in Mechanical Engineering, Vol 9 (2023)
This article provides a comprehensive review of the most recent advances in the planning, execution, and analysis of inflow and wake measurements from nacelle-mounted wind Doppler lidars. Lidars installed on top of wind turbines provide a holistic vi
Externí odkaz:
https://doaj.org/article/d597dad12b4848e894057ae10fb518a2
Autor:
Jeanie A. Aird, Eliot W. Quon, Rebecca J. Barthelmie, Mithu Debnath, Paula Doubrawa, Sara C. Pryor
Publikováno v:
Remote Sensing, Vol 13, Iss 21, p 4438 (2021)
We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully
Externí odkaz:
https://doaj.org/article/36772c8fdc2b464cb6bde6b1ac612052
Autor:
Paula Doubrawa, Domingo Muñoz-Esparza
Publikováno v:
Atmosphere, Vol 11, Iss 4, p 345 (2020)
Recent computational and modeling advances have led a diverse modeling community to experiment with atmospheric boundary layer (ABL) simulations at subkilometer horizontal scales. Accurately parameterizing turbulence at these scales is a complex prob
Externí odkaz:
https://doaj.org/article/f855661c5c44445b8163dcd65bb99e0f
Publikováno v:
Energies, Vol 12, Iss 14, p 2780 (2019)
Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in wh
Externí odkaz:
https://doaj.org/article/a22104b4bfeb4b00917724657a0a5a30
Publikováno v:
Remote Sensing, Vol 8, Iss 11, p 939 (2016)
Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) da
Externí odkaz:
https://doaj.org/article/1eb7b93182b84fb588ec7694c6fc8623
According to the international standard for wind turbine design, the effects of wind turbine wakes on structural loads can be considered in two ways: (1) by augmenting the ambient turbulence levels with the effective turbulence model (EFF) and then c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::210eb36245a42372b759191834eabe81
https://wes.copernicus.org/preprints/wes-2023-26/
https://wes.copernicus.org/preprints/wes-2023-26/
Publikováno v:
Wind Energy Science, Vol 6, Pp 935-948 (2021)
Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore w
Autor:
Kelsey Shaler, Nicholas Hamilton, Eliot Quon, Christopher Lee Kelley, Dave Maniaci, Emmanuel Branlard, Gerald Steinfeld, Paula Doubrawa, Thomas Herges, Alan S. Hsieh, W. Schlez, Søren Juhl Andersen, Marie Cathelain, Patrick Moriarty, Sonja Krueger, Paul van der Laan, Frédéric Blondel, Sascha Schmidt, Myra Blaylock, Luis A. Martínez-Tossas, Laura J. Lukassen, Mithu Debnath, Jason Jonkman
Publikováno v:
Wind Energy
Wind Energy, Wiley, 2020, 23 (11), pp.2027-2055. ⟨10.1002/we.2543⟩
Wind Energy, Wiley, 2020, 23 (11), pp.2027-2055. ⟨10.1002/we.2543⟩
International audience; Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade‐off between accuracy and computational cost can be well informed and appropr
Autor:
Sara C. Pryor, Eliot Quon, Jeanie A. Aird, Mithu Debnath, Paula Doubrawa, Rebecca Jane Barthelmie
Publikováno v:
Remote Sensing; Volume 13; Issue 21; Pages: 4438
Remote Sensing, Vol 13, Iss 4438, p 4438 (2021)
Remote Sensing, Vol 13, Iss 4438, p 4438 (2021)
We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully