- About
- Editorial board
- Articles
- Special issues
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
Journal cover
Journal topic
Wind Energy Science
The interactive open-access journal of the European Academy of Wind Energy
Journal topic
- About
- Editorial board
- Articles
- Special issues
- Manuscript tracking
- Subscribe to alerts
- Peer review
- For authors
- For reviewers
Abstracted/indexed
Abstracted/indexed
WES | Articles | Volume 3, issue 2
Wind Energ. Sci., 3, 589–613, 2018
https://doi.org/10.5194/wes-3-589-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-589-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Wind Energ. Sci., 3, 589–613, 2018
https://doi.org/10.5194/wes-3-589-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-589-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article 04 Sep 2018
Research article | 04 Sep 2018
Large-eddy simulation sensitivities to variations of configuration and forcing parameters in canonical boundary-layer flows for wind energy applications
Jeffrey D. Mirocha et al.
Related authors
No articles found.
Tobias Ahsbahs, Galen Maclaurin, Caroline Draxl, Christopher R. Jackson, Frank Monaldo, and Merete Badger
Wind Energ. Sci., 5, 1191–1210, https://doi.org/10.5194/wes-5-1191-2020, https://doi.org/10.5194/wes-5-1191-2020, 2020
Short summary
Short summary
Before constructing wind farms we need to know how much energy they will produce. This requires knowledge of long-term wind conditions from either measurements or models. At the US East Coast there are few wind measurements and little experience with offshore wind farms. Therefore, we created a satellite-based high-resolution wind resource map to quantify spatial variations in the wind conditions over potential sites for wind farms and found larger variation than modelling suggested.
Martine G. de Vos, Wilco Hazeleger, Driss Bari, Jörg Behrens, Sofiane Bendoukha, Irene Garcia-Marti, Ronald van Haren, Sue Ellen Haupt, Rolf Hut, Fredrik Jansson, Andreas Mueller, Peter Neilley, Gijs van den Oord, Inti Pelupessy, Paolo Ruti, Martin G. Schultz, and Jeremy Walton
Geosci. Commun., 3, 191–201, https://doi.org/10.5194/gc-3-191-2020, https://doi.org/10.5194/gc-3-191-2020, 2020
Short summary
Short summary
At the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Open science offers manifold opportunities but goes beyond sharing code and data. Besides domain-specific technical challenges, we observed that the main challenges are non-technical and impact the system of science as a whole.
Paul Fleming, Jennifer King, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, David Jager, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 5, 945–958, https://doi.org/10.5194/wes-5-945-2020, https://doi.org/10.5194/wes-5-945-2020, 2020
Short summary
Short summary
This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study for which the first phase was reported in a companion paper (https://wes.copernicus.org/articles/4/273/2019/). The authors implemented wake steering on two turbine pairs and compared results with the latest FLORIS model of wake steering, showing good agreement in overall energy increase.
Pedro Santos, Jakob Mann, Nikola Vasiljević, Elena Cantero, Javier Sanz Rodrigo, Fernando Borbón, Daniel Martínez-Villagrasa, Belén Martí, and Joan Cuxart
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-89, https://doi.org/10.5194/wes-2020-89, 2020
Preprint under review for WES
Short summary
Short summary
This study presents results from the Alaiz Experiment (ALEX17), featuring the characterization of two cases with flow features ranging from 0.1 to 10 km in complex terrain. We show that multiple scanning lidars can capture in detail a type of atmospheric wave that can happen up to 10 % of the time at this site. The results are in agreement with multiple ground observations and demonstrate the role of atmospheric stability in flow phenomena that need to be better captured by numerical models.
David Painemal, Fu-Lung Chang, Richard Ferrare, Sharon Burton, Zhujun Li, William L. Smith Jr., Patrick Minnis, Yan Feng, and Marian Clayton
Atmos. Chem. Phys., 20, 7167–7177, https://doi.org/10.5194/acp-20-7167-2020, https://doi.org/10.5194/acp-20-7167-2020, 2020
Short summary
Short summary
Aerosol–cloud interactions (ACIs) are the most uncertain aspect of anthropogenic forcing. Although satellites provide the observational dataset for the global ACI quantification, retrievals are limited to vertically integrated quantities (e.g., aerosol optical depth – AOD), which are typically used as an aerosol proxy. This study demonstrates that matching vertically resolved aerosol from CALIOP at the cloud-layer height with satellite cloud retrievals reduces uncertainties in ACI estimates.
Caroline Draxl, Rochelle P. Worsnop, Geng Xia, Yelena Pichugina, Duli Chand, Julie K. Lundquist, Justin Sharp, Garrett Wedam, James M. Wilczak, and Larry K. Berg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-77, https://doi.org/10.5194/wes-2020-77, 2020
Revised manuscript under review for WES
Short summary
Short summary
Mountain waves can create oscillations in low-level wind speeds and subsequently in the power output of wind plants. We document such oscillations by analyzing sodar and lidar observations, nacelle wind speeds, power observations, and WRF model simulations. This research describes how maintain waves form in the Columbia Basin in the United States, how they affect wind energy production, and their impacts on operational forecasting, wind plant layout, and integration of power into the grid.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-73, https://doi.org/10.5194/wes-2020-73, 2020
Revised manuscript accepted for WES
Short summary
Short summary
A test implementation of a method to mitigate adverse interactions between wind turbines in a wind farm is investigated in terms of behavior and success. Measurements of the atmospheric flow around a pair of wind turbines and power readings of the wind turbines itself are analyzed for this purpose. The behavior of the wake agrees with the predictions of newer analytical models and a bias of the wind direction perceived by the wind turbine under certain conditions is reducing the power gains.
Mingxuan Wu, Xiaohong Liu, Hongbin Yu, Hailong Wang, Yang Shi, Kang Yang, Anton Darmenov, Chenglai Wu, Zhien Wang, Tao Luo, Yan Feng, and Ziming Ke
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-160, https://doi.org/10.5194/acp-2020-160, 2020
Preprint under review for ACP
Short summary
Short summary
The spatiotemporal distributions of dust aerosol simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate dust extinction profiles, optical depth, and surface concentrations simulated in four GCMs, including one reanalysis model, against multiple satellite retrievals and surface observations to gain process-level understanding. Our results highlight the importance of correctly representing dust emission, dry/wet deposition, and size distribution in GCMs.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-23, https://doi.org/10.5194/gmd-2020-23, 2020
Revised manuscript accepted for GMD
Short summary
Short summary
This is the second of two papers that documents the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-57, https://doi.org/10.5194/wes-2020-57, 2020
Preprint under review for WES
Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-12, https://doi.org/10.5194/gmd-2020-12, 2020
Revised manuscript under review for GMD
Short summary
Short summary
Atmospheric models have often limitations in simulating geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulations of meteorology and dynamics by through better representation of the topography. The study have implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joseph B. Olson, Jaymes S. Kenyon, Aditya Choukulkar, Larry K. Berg, Harindra J. S. Fernando, Eric P. Grimit, Raghavendra Krishnamurthy, Julie K. Lundquist, Paytsar Muradyan, Mikhail Pekour, Yelena Pichugina, Mark T. Stoelinga, and David D. Turner
Geosci. Model Dev., 12, 4803–4821, https://doi.org/10.5194/gmd-12-4803-2019, https://doi.org/10.5194/gmd-12-4803-2019, 2019
Short summary
Short summary
During the second Wind Forecast Improvement Project, improvements to the parameterizations were applied to the High Resolution Rapid Refresh model and its nested version. The impacts of the new parameterizations on the forecast of 80 m wind speeds and power are assessed, using sodars and profiling lidars observations for comparison. Improvements are evaluated as a function of the model’s initialization time, forecast horizon, time of the day, season, site elevation, and meteorological phenomena.
Yohei Shinozuka, Pablo E. Saide, Gonzalo A. Ferrada, Sharon P. Burton, Richard Ferrare, Sarah J. Doherty, Hamish Gordon, Karla Longo, Marc Mallet, Yan Feng, Qiaoqiao Wang, Yafang Cheng, Amie Dobracki, Steffen Freitag, Steven G. Howell, Samuel LeBlanc, Connor Flynn, Michal Segal-Rosenhaimer, Kristina Pistone, James R. Podolske, Eric J. Stith, Joseph Ryan Bennett, Gregory R. Carmichael, Arlindo da Silva, Ravi Govindaraju, Ruby Leung, Yang Zhang, Leonhard Pfister, Ju-Mee Ryoo, Jens Redemann, Robert Wood, and Paquita Zuidema
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-678, https://doi.org/10.5194/acp-2019-678, 2019
Revised manuscript accepted for ACP
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Douglas S. Hamilton, Rachel A. Scanza, Yan Feng, Joseph Guinness, Jasper F. Kok, Longlei Li, Xiaohong Liu, Sagar D. Rathod, Jessica S. Wan, Mingxuan Wu, and Natalie M. Mahowald
Geosci. Model Dev., 12, 3835–3862, https://doi.org/10.5194/gmd-12-3835-2019, https://doi.org/10.5194/gmd-12-3835-2019, 2019
Short summary
Short summary
MIMI v1.0 was designed for use within Earth system models to simulate the 3-D emission, atmospheric processing, and deposition of iron and its soluble fraction. Understanding the iron cycle is important due to its role as an essential micronutrient for ocean phytoplankton; its supply limits primary productivity in many of the world's oceans. Human activity has perturbed the iron cycle, and MIMI is capable of diagnosing many of these impacts; hence, it is important for future climate studies.
Jiali Wang, Cheng Wang, Vishwas Rao, Andrew Orr, Eugene Yan, and Rao Kotamarthi
Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, https://doi.org/10.5194/gmd-12-3523-2019, 2019
Short summary
Short summary
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (parameter estimation tool – PEST) to high-performance computing clusters and adapted it to work with WRF-Hydro. The results show significant speedup for model calibration.
Tyler C. McCandless and Sue Ellen Haupt
Wind Energ. Sci., 4, 343–353, https://doi.org/10.5194/wes-4-343-2019, https://doi.org/10.5194/wes-4-343-2019, 2019
Short summary
Short summary
Often in wind power forecasting the mean wind speed is forecasted at a plant, converted to power, and multiplied by the number of turbines to predict the plant's generating capacity. This methodology ignores the variability among turbines caused by localized weather, terrain, and array orientation. We show that the wind farm mean wind speed approach for power conversion is impacted by Jensen's inequality, quantify the differences, and show machine learning can overcome these differences.
Sara Porchetta, Orkun Temel, Domingo Muñoz-Esparza, Joachim Reuder, Jaak Monbaliu, Jeroen van Beeck, and Nicole van Lipzig
Atmos. Chem. Phys., 19, 6681–6700, https://doi.org/10.5194/acp-19-6681-2019, https://doi.org/10.5194/acp-19-6681-2019, 2019
Short summary
Short summary
Two-way feedback occurs between offshore wind and waves. Using an extensive data set of offshore measurements, we show that the wave roughness affecting the wind is dependent on the alignment between the wind and wave directions. Moreover, we propose a new roughness parameterization that takes into account the dependence on alignment. Using this in numerical models will facilitate a better representation of offshore wind, which is relevant to wind energy and and climate modeling.
Paul Fleming, Jennifer King, Katherine Dykes, Eric Simley, Jason Roadman, Andrew Scholbrock, Patrick Murphy, Julie K. Lundquist, Patrick Moriarty, Katherine Fleming, Jeroen van Dam, Christopher Bay, Rafael Mudafort, Hector Lopez, Jason Skopek, Michael Scott, Brady Ryan, Charles Guernsey, and Dan Brake
Wind Energ. Sci., 4, 273–285, https://doi.org/10.5194/wes-4-273-2019, https://doi.org/10.5194/wes-4-273-2019, 2019
Short summary
Short summary
Wake steering is a form of wind farm control in which turbines use yaw offsets to affect wakes in order to yield an increase in total energy production. In this first phase of a study of wake steering at a commercial wind farm, two turbines implement a schedule of offsets. For two closely spaced turbines, an approximate 14 % increase in energy was measured on the downstream turbine over a 10° sector, with a 4 % increase in energy production of the combined turbine pair.
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019, https://doi.org/10.5194/acp-19-4367-2019, 2019
Short summary
Short summary
To improve the parameterization of the turbulence dissipation rate (ε) in numerical weather prediction models, we have assessed its temporal and spatial variability at various scales in the Columbia River Gorge during the WFIP2 field experiment. The turbulence dissipation rate shows large spatial variability, even at the microscale, with larger values in sites located downwind of complex orographic structures or in wind farm wakes. Distinct diurnal and seasonal cycles in ε have also been found.
Luis A. Martínez-Tossas, Jennifer Annoni, Paul A. Fleming, and Matthew J. Churchfield
Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, https://doi.org/10.5194/wes-4-127-2019, 2019
Short summary
Short summary
A new control-oriented model is developed to compute the wake of a wind turbine under yaw. The model uses a simplified version of the Navier–Stokes equation with assumptions. Good agreement is found between the model-proposed and large eddy simulations of a wind turbine in yaw.
Hunter Brown, Xiaohong Liu, Yan Feng, Yiquan Jiang, Mingxuan Wu, Zheng Lu, Chenglai Wu, Shane Murphy, and Rudra Pokhrel
Atmos. Chem. Phys., 18, 17745–17768, https://doi.org/10.5194/acp-18-17745-2018, https://doi.org/10.5194/acp-18-17745-2018, 2018
Short summary
Short summary
In climate models, organic carbon (OC) in wildfire smoke has been treated as an atmospheric cooling component by reflecting sunlight back to space. This study incorporates the observationally identified absorbing brown carbon component of OC into the Community Earth System Model, improving the agreement between the model and observations and effectively increasing absorption of solar radiation. This change contributes to altered atmospheric dynamics and changes in cloud cover in the model.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
Short summary
Short summary
Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
Short summary
Short summary
This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Jessie M. Creamean, Maximilian Maahn, Gijs de Boer, Allison McComiskey, Arthur J. Sedlacek, and Yan Feng
Atmos. Chem. Phys., 18, 555–570, https://doi.org/10.5194/acp-18-555-2018, https://doi.org/10.5194/acp-18-555-2018, 2018
Short summary
Short summary
We report on airborne observations from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program's Fifth Airborne Carbon Measurements (ACME-V) campaign along the North Slope of Alaska during the summer of 2015. We show how local oil extraction activities, 2015's central Alaskan wildfires, and, to a lesser extent, long-range transport introduce aerosols and trace gases higher in concentration than previously reported in Arctic haze measurements to the North Slope.
Tirtha Banerjee, Frederik De Roo, and Rodman Linn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-595, https://doi.org/10.5194/hess-2017-595, 2017
Revised manuscript not accepted
Short summary
Short summary
The conceptual model of turbulent flow through vegetation canopies is a phenomenological one that is developed from experimental observations. However, standard numerical simulations of canopy turbulence usually don't resolve the canopy as solid obstructions. We seek to reconcile such numerical simulations with the observations using large eddy simulations and information theory. We find out that the traditional drag based representation contains signatures of the phenomenological model.
Louis Marelle, Jean-Christophe Raut, Kathy S. Law, Larry K. Berg, Jerome D. Fast, Richard C. Easter, Manish Shrivastava, and Jennie L. Thomas
Geosci. Model Dev., 10, 3661–3677, https://doi.org/10.5194/gmd-10-3661-2017, https://doi.org/10.5194/gmd-10-3661-2017, 2017
Short summary
Short summary
We develop the WRF-Chem 3.5.1 model to improve simulations of aerosols and ozone in the Arctic. Both species are important air pollutants and climate forcers, but models often struggle to reproduce observations in the Arctic. Our developments concern pollutant emissions, mixing, chemistry, and removal, including processes related to snow and sea ice. The effect of these changes are quantitatively validated against observations, showing significant improvements compared to the original model.
Jean-Christophe Raut, Louis Marelle, Jerome D. Fast, Jennie L. Thomas, Bernadett Weinzierl, Katharine S. Law, Larry K. Berg, Anke Roiger, Richard C. Easter, Katharina Heimerl, Tatsuo Onishi, Julien Delanoë, and Hans Schlager
Atmos. Chem. Phys., 17, 10969–10995, https://doi.org/10.5194/acp-17-10969-2017, https://doi.org/10.5194/acp-17-10969-2017, 2017
Short summary
Short summary
We study the cross-polar transport of plumes from Siberian fires to the Arctic in summer, both in terms of transport pathways and efficiency of deposition processes. Those plumes containing soot may originate from anthropogenic and biomass burning sources in mid-latitude regions and may impact the Arctic climate by depositing on snow and ice surfaces. We evaluate the role of the respective source contributions, investigate the transport of plumes and treat pathway-dependent removal of particles.
Paul Fleming, Jennifer Annoni, Jigar J. Shah, Linpeng Wang, Shreyas Ananthan, Zhijun Zhang, Kyle Hutchings, Peng Wang, Weiguo Chen, and Lin Chen
Wind Energ. Sci., 2, 229–239, https://doi.org/10.5194/wes-2-229-2017, https://doi.org/10.5194/wes-2-229-2017, 2017
Short summary
Short summary
In this paper, a field test of wake-steering control is presented. In the campaign, an array of turbines within an operating commercial offshore wind farm have the normal yaw controller modified to implement wake steering according to a yaw control strategy. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture.
Javier Sanz Rodrigo, Matthew Churchfield, and Branko Kosovic
Wind Energ. Sci., 2, 35–54, https://doi.org/10.5194/wes-2-35-2017, https://doi.org/10.5194/wes-2-35-2017, 2017
Short summary
Short summary
The series of GABLS model intercomparison benchmarks is revisited in the context of wind energy atmospheric boundary layer (ABL) models. GABLS 1 and 2 are used for verification purposes. Then GABLS 3 is used to develop a methodology for using realistic mesoscale forcing for microscale ABL models. The method also uses profile nudging to dynamically reduce the bias. Different data assimilation strategies are discussed based on typical instrumentation setups of wind energy campaigns.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
Short summary
Short summary
Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
Short summary
Short summary
Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Ivan Ortega, Sean Coburn, Larry K. Berg, Kathy Lantz, Joseph Michalsky, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, and Rainer Volkamer
Atmos. Meas. Tech., 9, 3893–3910, https://doi.org/10.5194/amt-9-3893-2016, https://doi.org/10.5194/amt-9-3893-2016, 2016
Short summary
Short summary
We present an inherently calibrated retrieval to measure aerosol optical depth (AOD) and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity by the University of Colorado two-dimensional (2-D) MAX-DOAS. The retrievals are maximally sensitive at low AOD and do not require absolute radiance calibration. We compare results with data from independent sensors.
K. K. Shukla, K. Niranjan Kumar, D. V. Phanikumar, R. K. Newsom, V. R. Kotamarthi, T. B. M. J. Ouarda, and M. V. Ratnam
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-162, https://doi.org/10.5194/amt-2016-162, 2016
Revised manuscript not accepted
Short summary
Short summary
Estimation of Cloud base height was carried out by using various ground based instruments (Doppler Lidar and Ceilometer) and satellite datasets (MODIS) over central Himalayan region for the first time. The present study demonstrates the potential of Doppler Lidar in precise estimation of cloud base height and updraft velocities. More such deployments will be invaluable inputs for regional weather prediction models over complex Himalayan terrains.
Chun Zhao, Maoyi Huang, Jerome D. Fast, Larry K. Berg, Yun Qian, Alex Guenther, Dasa Gu, Manish Shrivastava, Ying Liu, Stacy Walters, Gabriele Pfister, Jiming Jin, John E. Shilling, and Carsten Warneke
Geosci. Model Dev., 9, 1959–1976, https://doi.org/10.5194/gmd-9-1959-2016, https://doi.org/10.5194/gmd-9-1959-2016, 2016
Short summary
Short summary
In this study, the latest version of MEGAN is coupled within CLM4 in WRF-Chem. In this implementation, MEGAN shares a consistent vegetation map with CLM4. This improved modeling framework is used to investigate the impact of two land surface schemes on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models.
Y. Feng, V. R. Kotamarthi, R. Coulter, C. Zhao, and M. Cadeddu
Atmos. Chem. Phys., 16, 247–264, https://doi.org/10.5194/acp-16-247-2016, https://doi.org/10.5194/acp-16-247-2016, 2016
Short summary
Short summary
Aerosol radiative effects are of great importance for climate studies over South Asia, such as the weakening of the South Asian monsoon in the 20th century. This study reveals the altitude dependence of commonly underestimated aerosol radiative properties over this region. It further demonstrates the importance of constraining aerosol vertical distributions and partitioning of scattering vs absorbing aerosols in simulating the subsequent regional dynamical and hydrological responses to aerosols.
B. A. Drewniak, U. Mishra, J. Song, J. Prell, and V. R. Kotamarthi
Biogeosciences, 12, 2119–2129, https://doi.org/10.5194/bg-12-2119-2015, https://doi.org/10.5194/bg-12-2119-2015, 2015
L. K. Berg, M. Shrivastava, R. C. Easter, J. D. Fast, E. G. Chapman, Y. Liu, and R. A. Ferrare
Geosci. Model Dev., 8, 409–429, https://doi.org/10.5194/gmd-8-409-2015, https://doi.org/10.5194/gmd-8-409-2015, 2015
Short summary
Short summary
This work presents a new methodology for representing regional-scale impacts of cloud processing on both aerosol and trace gases in sub-grid-scale convective clouds. Using the new methodology, we can better simulate the aerosol lifecycle over large areas. The results presented in this work highlight the potential change in column-integrated amounts of black carbon, organic aerosol, and sulfate aerosol, which were found to range from -50% for black carbon to +40% for sulfate.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
Short summary
Short summary
Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
D. Müller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid
Atmos. Meas. Tech., 7, 3487–3496, https://doi.org/10.5194/amt-7-3487-2014, https://doi.org/10.5194/amt-7-3487-2014, 2014
E. Kassianov, J. Barnard, M. Pekour, L. K. Berg, J. Shilling, C. Flynn, F. Mei, and A. Jefferson
Atmos. Meas. Tech., 7, 3247–3261, https://doi.org/10.5194/amt-7-3247-2014, https://doi.org/10.5194/amt-7-3247-2014, 2014
A. J. Scarino, M. D. Obland, J. D. Fast, S. P. Burton, R. A. Ferrare, C. A. Hostetler, L. K. Berg, B. Lefer, C. Haman, J. W. Hair, R. R. Rogers, C. Butler, A. L. Cook, and D. B. Harper
Atmos. Chem. Phys., 14, 5547–5560, https://doi.org/10.5194/acp-14-5547-2014, https://doi.org/10.5194/acp-14-5547-2014, 2014
V. S. Manoharan, R. Kotamarthi, Y. Feng, and M. P. Cadeddu
Atmos. Chem. Phys., 14, 1159–1165, https://doi.org/10.5194/acp-14-1159-2014, https://doi.org/10.5194/acp-14-1159-2014, 2014
Y. Feng, V. Ramanathan, and V. R. Kotamarthi
Atmos. Chem. Phys., 13, 8607–8621, https://doi.org/10.5194/acp-13-8607-2013, https://doi.org/10.5194/acp-13-8607-2013, 2013
B. Drewniak, J. Song, J. Prell, V. R. Kotamarthi, and R. Jacob
Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, https://doi.org/10.5194/gmd-6-495-2013, 2013
Related subject area
Wind and turbulence
How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine
Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements
Multi-lidar wind resource mapping in complex terrain
Clustering wind profile shapes to estimate airborne wind energy production
Validation of Sentinel-1 offshore winds and average wind power estimation around Ireland
Validation of uncertainty reduction by using multiple transfer locations for WRF–CFD coupling in numerical wind energy assessments
Decreasing wind speed extrapolation error via domain-specific feature extraction and selection
Dynamic wake meandering model calibration using nacelle-mounted lidar systems
First characterization of a new perturbation system for gust generation: the chopper
Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar
Lidar measurements of yawed wind turbine wakes: characterisation and validation of analytical models
The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds
Exploring the complexities associated with full-scale wind plant wake mitigation control experiments
Mitigating impact of spatial variance of turbulence in wind energy applications
Comparing abnormalities in onshore and offshore vertical wind profiles
Europe's offshore winds assessed with synthetic aperture radar, ASCAT and WRF
Rossby number similarity of an atmospheric RANS model using limited-length-scale turbulence closures extended to unstable stratification
Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations
The digital terrain model in the computational modelling of the flow over the Perdigão site: the appropriate grid size
Theory and Verification of a new 3D RANS Wake Model
How to improve the state of the art in metocean measurement datasets
The Power Curve Working Group's assessment of wind turbine power performance prediction methods
Multipoint Reconstruction of Wind Speeds
Evaluation of the Lattice Boltzmann Method for wind modelling in complex terrain
Aeroelastic response of a multi-megawatt upwind horizontal axis wind turbine (HAWT) based on fluid–structure interaction simulation
An alternative form of the super-Gaussian wind turbine wake model
Effect of tip spacing, thrust coefficient and turbine spacing in multi-rotor wind turbines and farms
Cluster wakes impact on a far-distant offshore wind farm's power
A double-multiple streamtube model for vertical axis wind turbines of arbitrary rotor loading
On the self-similarity of wind turbine wakes in a complex terrain using large eddy simulation
Adjoint-based calibration of inlet boundary condition for atmospheric computational fluid dynamics solvers
Wind turbine load dynamics in the context of turbulence intermittency
Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems
Significant multidecadal variability in German wind energy generation
First identification and quantification of detached-tip vortices behind a wind energy converter using fixed-wing unmanned aircraft system
Lidar estimation of rotor-effective wind speed – an experimental comparison
Does the rotational direction of a wind turbine impact the wake in a stably stratified atmospheric boundary layer?
Detection of wakes in the inflow of turbines using nacelle lidars
Detection and characterization of extreme wind speed ramps
Extreme wind fluctuations: joint statistics, extreme turbulence, and impact on wind turbine loads
More accurate aeroelastic wind-turbine load simulations using detailed inflow information
Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm
Low-level jets over the North Sea based on ERA5 and observations: together they do better
Brief communication: Wind inflow observation from load harmonics – wind tunnel validation of the rotationally symmetric formulation
Micro-scale model comparison (benchmark) at the moderately complex forested site Ryningsnäs
Experimental validation of a ducted wind turbine design strategy
Near-wake analysis of actuator line method immersed in turbulent flow using large-eddy simulations
Assessing variability of wind speed: comparison and validation of 27 methodologies
Do wind turbines pose roll hazards to light aircraft?
Analysis of control-oriented wake modeling tools using lidar field results
Patrick Murphy, Julie K. Lundquist, and Paul Fleming
Wind Energ. Sci., 5, 1169–1190, https://doi.org/10.5194/wes-5-1169-2020, https://doi.org/10.5194/wes-5-1169-2020, 2020
Short summary
Short summary
We present and evaluate an improved method for predicting wind turbine power production based on measurements of the wind speed and direction profile across the rotor disk for a wind turbine in complex terrain. By comparing predictions to actual power production from a utility-scale wind turbine, we show this method is more accurate than methods based on hub-height wind speed or surface-based atmospheric characterization.
Davide Conti, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 5, 1129–1154, https://doi.org/10.5194/wes-5-1129-2020, https://doi.org/10.5194/wes-5-1129-2020, 2020
Short summary
Short summary
We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. The uncertainty of aeroelastic load predictions is quantified against wind turbine on-board sensor data. This work demonstrates the applicability of nacelle-mounted lidar measurements to extend load and power validations under wake conditions and highlights the main challenges.
Robert Menke, Nikola Vasiljević, Johannes Wagner, Steven P. Oncley, and Jakob Mann
Wind Energ. Sci., 5, 1059–1073, https://doi.org/10.5194/wes-5-1059-2020, https://doi.org/10.5194/wes-5-1059-2020, 2020
Short summary
Short summary
The estimation of wind resources in complex terrain is challenging as the wind conditions change significantly over short distances, different to flat terrain, where spatial changes are small. We demonstrate in this work that wind lidars can remotely map wind resources over large areas. This will have implications for the planning of wind energy projects and ultimately reduce uncertainties in wind resource estimations in complex terrain, making such areas more interesting for future development.
Mark Schelbergen, Peter C. Kalverla, Roland Schmehl, and Simon J. Watson
Wind Energ. Sci., 5, 1097–1120, https://doi.org/10.5194/wes-5-1097-2020, https://doi.org/10.5194/wes-5-1097-2020, 2020
Short summary
Short summary
We have presented a methodology for including multiple wind profile shapes in a wind resource description that are identified using a data-driven approach. These shapes go beyond the height range for which conventional wind profile relationships are developed. Moreover, they include non-monotonic shapes such as low-level jets. We demonstrated this methodology for an on- and offshore reference location using DOWA data and efficiently estimated the annual energy production of a pumping AWE system.
Louis de Montera, Tiny Remmers, Ross O'Connell, and Cian Desmond
Wind Energ. Sci., 5, 1023–1036, https://doi.org/10.5194/wes-5-1023-2020, https://doi.org/10.5194/wes-5-1023-2020, 2020
Short summary
Short summary
This paper provides a validation of satellite-acquired wind speed measurements (Sentinel-1 synthetic aperture radar Level 2 OCN) against four weather buoys and three coastal stations located around Ireland. It is shown that such satellite measurements provide a accurate assessment of long-term wind resource characteristics for both coastal and far from shore locations. This work was conducted in order to allow the use of satellite data in the planning of offshore wind farms in Ireland.
Rolf-Erik Keck and Niklas Sondell
Wind Energ. Sci., 5, 997–1005, https://doi.org/10.5194/wes-5-997-2020, https://doi.org/10.5194/wes-5-997-2020, 2020
Short summary
Short summary
A method for performing numerical wind resource assessments in the absence of on-site measurements is presented and validated against field measurements. Numerical wind resource assessment is at least 2 orders of magnitude faster and less expensive than using conventional site measurements. This enables analysis of a larger number of projects and thereby increases the chances of discovering the best available sites. The uncertainty in mean wind speed predictions is found to be about 4 %.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 5, 959–975, https://doi.org/10.5194/wes-5-959-2020, https://doi.org/10.5194/wes-5-959-2020, 2020
Short summary
Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.
Inga Reinwardt, Levin Schilling, Peter Dalhoff, Dirk Steudel, and Michael Breuer
Wind Energ. Sci., 5, 775–792, https://doi.org/10.5194/wes-5-775-2020, https://doi.org/10.5194/wes-5-775-2020, 2020
Short summary
Short summary
This study presents a measurement campaign, which consists of two nacelle-mounted lidar systems in a densely packed onshore wind farm. The aim of the campaign is to validate and improve wake models for load and power estimations in wind farms. Based on the findings from the measurements, the formulation of the wake degradation in the dynamic wake meandering model has been adjusted, so that the recalibrated model coincides very well with the measurements and thereby reduces the uncertainties.
Ingrid Neunaber and Caroline Braud
Wind Energ. Sci., 5, 759–773, https://doi.org/10.5194/wes-5-759-2020, https://doi.org/10.5194/wes-5-759-2020, 2020
Short summary
Short summary
Motivated by the need for wind turbine rotor blade tests in flows with atmospheric-like properties like gusts, we present a new setup to generate strong, rapid, turbulent gusts in a wind tunnel. The setup consists of a rotating bar that cuts through the inlet of the wind tunnel which generates the gust, and it is called
the chopper. In this work, the flow generated by the chopper is characterized, and we show how the gust and its turbulence evolve downstream.
Felix Kelberlau and Jakob Mann
Wind Energ. Sci., 5, 519–541, https://doi.org/10.5194/wes-5-519-2020, https://doi.org/10.5194/wes-5-519-2020, 2020
Short summary
Short summary
Wind speeds can be measured remotely from the ground with lidars. Their estimates are accurate for mean speeds, but turbulence leads to measurement errors. We predict these errors using computer-generated data and compare lidar measurements with data from a meteorological mast. The comparison shows that deviations depend on wind direction, measurement height, and wind conditions. Our method to reduce the measurement error is successful when the wind is aligned with one of the lidar beams.
Peter Brugger, Mithu Debnath, Andrew Scholbrock, Paul Fleming, Patrick Moriarty, Eric Simley, David Jager, Mark Murphy, Haohua Zong, and Fernando Porté-Agel
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-73, https://doi.org/10.5194/wes-2020-73, 2020
Revised manuscript accepted for WES
Short summary
Short summary
A test implementation of a method to mitigate adverse interactions between wind turbines in a wind farm is investigated in terms of behavior and success. Measurements of the atmospheric flow around a pair of wind turbines and power readings of the wind turbines itself are analyzed for this purpose. The behavior of the wake agrees with the predictions of newer analytical models and a bias of the wind direction perceived by the wind turbine under certain conditions is reducing the power gains.
Nicola Bodini and Mike Optis
Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, https://doi.org/10.5194/wes-5-489-2020, 2020
Short summary
Short summary
An accurate assessment of the wind resource at hub height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine-learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.
James B. Duncan Jr., Brian D. Hirth, and John L. Schroeder
Wind Energ. Sci., 5, 469–488, https://doi.org/10.5194/wes-5-469-2020, https://doi.org/10.5194/wes-5-469-2020, 2020
Short summary
Short summary
Results highlight some of the complexities associated with executing and analyzing wind plant control at full scale using brief experimental control periods. Some difficulties include (1) the ability to accurately implement the desired control changes on smaller timescales, (2) identifying reliable data sources and methods to quantify these control changes, and (3) attributing variations in wake structure to turbine control changes rather than a response to the underlying atmospheric conditions.
Jonas Kazda and Jakob Mann
Wind Energ. Sci., 5, 439–450, https://doi.org/10.5194/wes-5-439-2020, https://doi.org/10.5194/wes-5-439-2020, 2020
Short summary
Short summary
This work presents the first analytical solution for the quantification of the spatial variance of the second-order moment of correlated wind speeds. The spatial variance is defined as random differences in the sample variance of wind speed between different points in space. The approach is successfully verified using simulation and field data. The impact of the spatial variance on wind farm control, the verification of wind turbine performance and sensor verification are then investigated.
Mathias Møller, Piotr Domagalski, and Lars Roar Sætran
Wind Energ. Sci., 5, 391–411, https://doi.org/10.5194/wes-5-391-2020, https://doi.org/10.5194/wes-5-391-2020, 2020
Short summary
Short summary
This work has analyzed historical data of 10 min averaged wind speed measurements to investigate the accuracy of the commonly used equations for describing the wind velocity as a function of the height above ground. The results of analyzing data from a wide range of sites show that the common equations do not sufficiently describe certain physical phenomena, especially at offshore and coastal locations. The results imply that there is a need for more advanced models.
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci., 5, 375–390, https://doi.org/10.5194/wes-5-375-2020, https://doi.org/10.5194/wes-5-375-2020, 2020
Short summary
Short summary
Europe's offshore wind resource mapping is part of the New European Wind Atlas (NEWA) international consortium effort. This study presents the results of analysis of synthetic aperture radar (SAR) ocean wind maps based on Envisat and Sentinel-1 with a brief description of the wind retrieval process and Advanced Scatterometer (ASCAT) ocean wind maps. Furthermore, the Weather Research and Forecasting (WRF) offshore wind atlas of NEWA is presented.
Maarten Paul van der Laan, Mark Kelly, Rogier Floors, and Alfredo Peña
Wind Energ. Sci., 5, 355–374, https://doi.org/10.5194/wes-5-355-2020, https://doi.org/10.5194/wes-5-355-2020, 2020
Short summary
Short summary
The design of wind turbines and wind farms can be improved by increasing the accuracy of the inflow models representing the atmospheric boundary layer (ABL). In this work we employ numerical simulations of the idealized ABL, which can represent the mean effects of Coriolis and buoyancy forces and surface roughness. We find a new model-based similarity that provides a better understanding of the idealized ABL. In addition, we extend the model to include effects of convective buoyancy forces.
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, https://doi.org/10.5194/wes-5-309-2020, 2020
Short summary
Short summary
We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
José M. L. M. Palma, Carlos A. M. Silva, Vitor M. C. Gomes, Alexandre Silva Lopes, Teresa Simões, Paula Costa, and Vasco T. P. Batista
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-96, https://doi.org/10.5194/wes-2019-96, 2020
Revised manuscript accepted for WES
Short summary
Short summary
The digital terrain model is the first input in the computational modelling of atmospheric flows. The ability of thee meshes (high, medium and low resolution) to replicate the Perdigão experiment site was appraised in two ways: by their ability to replicate the terrain attributes, elevation and slope, and by their effect on the wind flow computational results. At least 40 m horizontal resolution, is required in computational modelling of the flow over Perdigão.
Philip Bradstock and Wolfgang Schlez
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-33, https://doi.org/10.5194/wes-2020-33, 2020
Revised manuscript accepted for WES
Short summary
Short summary
The ProPlanEn team developed WakeBlaster, a new very fast numerical model for simulating the power output of wind farms. Accurate modelling of the waked flow enables the reduction of wind farm losses. By modelling the whole wind farm, WakeBlaster replaces simpler models which superimpose symmetrical solutions of the flow behind individual wind turbines. The paper describes the fundamental equations, discusses the scalability of the solution, and demonstrates the 3D flow in an offshore wind farm.
Erik Quaeghebeur and Michiel B. Zaaijer
Wind Energ. Sci., 5, 285–308, https://doi.org/10.5194/wes-5-285-2020, https://doi.org/10.5194/wes-5-285-2020, 2020
Short summary
Short summary
Meteorological and oceanic datasets are fundamental to the modeling of offshore wind farms. Data quality issues in one such dataset led us to conduct a study to establish whether such issues are more generally present in these datasets. The answer is yes and users should be aware of this. We therefore also investigated how such issues can be avoided. The result is a set of techniques and recommendations for dataset producers, leading to substantial quality improvements with limited extra effort.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Christian Behnken, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2020-5, https://doi.org/10.5194/wes-2020-5, 2020
Revised manuscript accepted for WES
Short summary
Short summary
We extend the common characterisation and modelling of wind time series with respect to higher order statistics. We present an approach which enables to get hold of the general multipoint statistics of wind times series measured. This work is an important step in a more comprehensive description of wind including also extreme events. Important is that we show how stochastic equations can be derived form measured wind data which can be used to model long time series.
Alain Schubiger, Sarah Barber, and Henrik Nordborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-106, https://doi.org/10.5194/wes-2019-106, 2020
Revised manuscript accepted for WES
Short summary
Short summary
In this study, a LES simulation using the LBM framework Palabos was implemented to calculate the wind field over the complex terrain of the Bolund Hill.
The results were compared to RANS and DES simulations using ANSYS Fluent and field measurements. The computational costs of these three models were compared and it has been shown that LBM, even in this not-yet fully optimised set-up of the simulation, can perform 5 times faster than DES and lead to reasonably accurate results.
Yasir Shkara, Martin Cardaun, Ralf Schelenz, and Georg Jacobs
Wind Energ. Sci., 5, 141–154, https://doi.org/10.5194/wes-5-141-2020, https://doi.org/10.5194/wes-5-141-2020, 2020
Short summary
Short summary
A computational fluid dynamics (CFD) solver is coupled with a structure solver to predict the dynamic response of a horizontal axis wind turbine structure. CFD provides much more accurate and more realistic aerodynamic loads that cannot be achieved by traditional methods such as blade element momentum theory. As a result, the aeroelastic response of the wind turbine structure, taking into account blade–tower interactions, is described in more detail.
Frédéric Blondel and Marie Cathelain
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-99, https://doi.org/10.5194/wes-2019-99, 2020
Revised manuscript accepted for WES
Short summary
Short summary
Analytical wind turbine wake models are of high interest for wind farm designers: they provide an estimation of wake losses for a given layout at a low computational cost. Consequently they are heavily used for wind farm design and power production evaluation. While most analytical models focus on far wake characteristics, we propose an approach that is able to represent both near and far wake velocity deficit, enabling the simulation of closely packed wind farms.
Niranjan S. Ghaisas, Aditya S. Ghate, and Sanjiva K. Lele
Wind Energ. Sci., 5, 51–72, https://doi.org/10.5194/wes-5-51-2020, https://doi.org/10.5194/wes-5-51-2020, 2020
Short summary
Short summary
Wakes of a multi-rotor wind turbine configuration are evaluated using numerical simulations. Compared to equivalent conventional single-rotor turbine wakes, multi-rotor turbine wakes are found to recover faster and generate less turbulence; thus, multi-rotor turbine wind farms are more efficient, with smaller wake losses. The benefits of multi-rotor wind farms over conventional wind farms are sensitive to tip spacing, thrust coefficient and turbine spacing.
Jörge Schneemann, Andreas Rott, Martin Dörenkämper, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, https://doi.org/10.5194/wes-5-29-2020, 2020
Short summary
Short summary
Offshore wind farm clusters cause reduced wind speeds in downstream regions which can extend over more than 50 km.
We analysed the impact of these so-called cluster wakes on a distant wind farm using remote-sensing wind measurements and power production data.
Cluster wakes caused power losses up to 55 km downstream in certain atmospheric states.
A better understanding of cluster wake effects reduces uncertainties in offshore wind resource assessment and improves offshore areal planning.
Anis A. Ayati, Konstantinos Steiros, Mark A. Miller, Subrahmanyam Duvvuri, and Marcus Hultmark
Wind Energ. Sci., 4, 653–662, https://doi.org/10.5194/wes-4-653-2019, https://doi.org/10.5194/wes-4-653-2019, 2019
Short summary
Short summary
This work presents an improvement to a popular algorithm used in the prediction of the power of vertical axis wind turbines. While, until now, the algorithm was considered valid only for low rotor speeds and a small number of blades, our improvement makes it valid for any configuration. The predictions of the improved algorithm were found to be of considerable accuracy, when validated with measurements of a small model turbine acquired in a compressed air wind tunnel.
Arslan Salim Dar, Jacob Berg, Niels Troldborg, and Edward G. Patton
Wind Energ. Sci., 4, 633–644, https://doi.org/10.5194/wes-4-633-2019, https://doi.org/10.5194/wes-4-633-2019, 2019
Short summary
Short summary
We have performed computer simulations of turbulent air over hills with wind turbines located on the hill tops. Behind the wind turbines the wind speed is reduced and the air is even more turbulent. Together we call these features for the wind turbine wake. We find that the wake has a self-similar shape. This means that its shape is only a function of the reduced wind speed found in the wake and the width of the wake and hence not of the complexity and shape of the hills.
Siamak Akbarzadeh, Hassan Kassem, Renko Buhr, Gerald Steinfeld, and Bernhard Stoevesandt
Wind Energ. Sci., 4, 619–632, https://doi.org/10.5194/wes-4-619-2019, https://doi.org/10.5194/wes-4-619-2019, 2019
Short summary
Short summary
The numerical flow simulation solvers are extensively used for site assessment in the wind energy industry. However, due to the complexity of flow regimes, it is essential to calibrate the important parameters of such algorithms with measurement data. In this paper, we present a computationally cheap (adjoint) solver that can be coupled with any standard gradient-based optimizer to calibrate the inflow boundary of a CFD solver using the wind speed measurements from the interior of a domain.
Carl Michael Schwarz, Sebastian Ehrich, and Joachim Peinke
Wind Energ. Sci., 4, 581–594, https://doi.org/10.5194/wes-4-581-2019, https://doi.org/10.5194/wes-4-581-2019, 2019
Short summary
Short summary
This work is dedicated to the non-Gaussian statistics of wind velocity jumps and their effect on wind turbines, more specifically on fatigue loads. Wind velocity jumps are commonly simplified to behave in a Gaussian manner. This study discusses how to compare non-Gaussian to Gaussian wind fields properly and shows that the non-Gaussianity in fact can make a difference with respect to fatigue loads. Additionally, we highlight the dependence of our findings on the wind field's coherence.
Markus Sommerfeld, Martin Dörenkämper, Gerald Steinfeld, and Curran Crawford
Wind Energ. Sci., 4, 563–580, https://doi.org/10.5194/wes-4-563-2019, https://doi.org/10.5194/wes-4-563-2019, 2019
Short summary
Short summary
Airborne wind energy systems aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind measurements and computational simulations both have advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the model generates a more accurate wind data set up to 1100 m. These wind data sets are used to estimate optimal AWES operating altitudes and power.
Jan Wohland, Nour Eddine Omrani, Noel Keenlyside, and Dirk Witthaut
Wind Energ. Sci., 4, 515–526, https://doi.org/10.5194/wes-4-515-2019, https://doi.org/10.5194/wes-4-515-2019, 2019
Short summary
Short summary
Wind park planning and power system design require robust wind resource information. While most assessments are restricted to the last four decades, we use centennial reanalyses to study wind energy generation variability in Germany. We find that statistically significant multi-decadal variability exists. These long-term effects must be considered when planning future highly renewable power systems. Otherwise, there is a risk of inefficient system design and ill-informed investments.
Moritz Mauz, Alexander Rautenberg, Andreas Platis, Marion Cormier, and Jens Bange
Wind Energ. Sci., 4, 451–463, https://doi.org/10.5194/wes-4-451-2019, https://doi.org/10.5194/wes-4-451-2019, 2019
Short summary
Short summary
UAS systems provide in situ measurements of turbulence and wind conditions. In the presented paper, the tip vortex generated by wind energy converters (WECs) is measured by a fixed-wing UAS and compared to an analytical model as well as a literature value. The results show good agreement. The presented method is a basis for future measurement campaigns to compare UAS measurements with numerical simulations of WEC wakes.
Dominique P. Held and Jakob Mann
Wind Energ. Sci., 4, 421–438, https://doi.org/10.5194/wes-4-421-2019, https://doi.org/10.5194/wes-4-421-2019, 2019
Short summary
Short summary
In this study a model of the coherence between turbine- and lidar-estimated rotor-effective wind speed (REWS) is presented. The model is compared against experimental data from two field tests using two- and four-beam nacelle-mounted lidar systems on a test turbine. The proposed model agrees better with the field data than previously used models. Also, it was shown that the advection speed can be estimated by the REWS measured by the lidar.
Antonia Englberger, Andreas Dörnbrack, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-45, https://doi.org/10.5194/wes-2019-45, 2019
Revised manuscript accepted for WES
Short summary
Short summary
At night, the wind direction often changes with height, and this veer affects structures near the surface like wind turbines. Wind turbines usually rotate clockwise, but this rotational direction interacts with veer to impact the flow field behind a wind turbine. If another turbine is located downwind, the direction of the upwind turbine's rotation will affect the downwind turbine's power production.
Dominique P. Held and Jakob Mann
Wind Energ. Sci., 4, 407–420, https://doi.org/10.5194/wes-4-407-2019, https://doi.org/10.5194/wes-4-407-2019, 2019
Short summary
Short summary
In this study the capabilities of detecting wakes in the inflow of turbines by nacelle-mounted lidars are investigated. It is shown that higher turbulence levels can be measured within a wake by estimating the Doppler spectrum width. In an experimental setup all half- and full-wake situations have been identified. A correction method for the influence of the wake on the lidar system has also been proposed..
Ásta Hannesdóttir and Mark Kelly
Wind Energ. Sci., 4, 385–396, https://doi.org/10.5194/wes-4-385-2019, https://doi.org/10.5194/wes-4-385-2019, 2019
Short summary
Short summary
The wind turbine safety standard includes a coherent gust model with a wind speed increase and direction change of 10 s. With the increasing rotor size of modern wind turbines this model is criticized for being uniform across these large rotors. In this study we investigate measurements of coherent gusts with a ramp-like increase in wind speed. We define a new method for ramp detection and characterization and compare it with the coherent gust model from the wind turbine safety standard.
Ásta Hannesdóttir, Mark Kelly, and Nikolay Dimitrov
Wind Energ. Sci., 4, 325–342, https://doi.org/10.5194/wes-4-325-2019, https://doi.org/10.5194/wes-4-325-2019, 2019
Short summary
Short summary
We investigate large wind speed fluctuations from a 10-year period at the Danish coastal site Høvsøre. The most extreme fluctuations are not turbulent but due to larger-scale weather phenomena. We find how these fluctuations impact wind turbines using simulations. The results are then compared to an extreme turbulence model described in the wind turbine safety standards, and it is found that the loads on the different turbine components are not the same as what the standard describes.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 303–323, https://doi.org/10.5194/wes-4-303-2019, https://doi.org/10.5194/wes-4-303-2019, 2019
Short summary
Short summary
In this paper, detailed inflow information extracted from measurements is used to improve the accuracy of simulated wind turbine fatigue loads. Inflow information from nearby met masts is utilised as well as information from a blade-mounted flow sensor in combination with a method to compensate for the disturbance to the flow caused by the presence of the wind turbine.
Thomas Duc, Olivier Coupiac, Nicolas Girard, Gregor Giebel, and Tuhfe Göçmen
Wind Energ. Sci., 4, 287–302, https://doi.org/10.5194/wes-4-287-2019, https://doi.org/10.5194/wes-4-287-2019, 2019
Short summary
Short summary
Wind turbine wake recovery is very sensitive to ambient atmospheric conditions. This paper presents a way of including a local turbulence intensity estimation from SCADA into the Jensen wake model to improve its accuracy. This new model procedure is used to optimize power production of an operating wind farm and shows that some gains can be expected even if uncertainties remain high. These optimized settings are to be implemented in a field test campaign in the scope of the SMARTEOLE project.
Peter C. Kalverla, James B. Duncan Jr., Gert-Jan Steeneveld, and Albert A. M. Holtslag
Wind Energ. Sci., 4, 193–209, https://doi.org/10.5194/wes-4-193-2019, https://doi.org/10.5194/wes-4-193-2019, 2019
Short summary
Short summary
A common assumption in the design of wind turbines and wind farms is that the wind field is quite uniform. This assumption is violated during so-called low-level jet events, when there is a distinct peak in the wind speed. Low-level jets modify loads on the turbines and also affect power production. To understand their impact and facilitate better planning and design, we present a detailed climatology of these events over the North Sea, based on offshore measurements and meteorological models.
Marta Bertelè, Carlo L. Bottasso, and Stefano Cacciola
Wind Energ. Sci., 4, 89–97, https://doi.org/10.5194/wes-4-89-2019, https://doi.org/10.5194/wes-4-89-2019, 2019
Short summary
Short summary
This paper describes a new formulation for estimating the wind
inflow at the rotor disk, based on measurements of the blade loads.
The new method improves on previous formulations by exploiting the
rotational symmetry of the problem. Experimental results obtained
with an aeroelastically scaled model in a boundary layer wind
tunnel are used for validating the proposed approach.
Stefan Ivanell, Johan Arnqvist, Matias Avila, Dalibor Cavar, Roberto Aurelio Chavez-Arroyo, Hugo Olivares-Espinosa, Carlos Peralta, Jamal Adib, and Björn Witha
Wind Energ. Sci., 3, 929–946, https://doi.org/10.5194/wes-3-929-2018, https://doi.org/10.5194/wes-3-929-2018, 2018
Short summary
Short summary
This article describes a study in which modellers were challenged to compute the wind at a forested site with moderately complex topography. The target was to match the measured wind profile at one exact location for three directions. The input to the models consisted of detailed information on forest densities and ground height. Overall, the article gives an overview of how well different types of models are able to capture the flow physics at a moderately complex forested site.
Benjamin Kanya and Kenneth D. Visser
Wind Energ. Sci., 3, 919–928, https://doi.org/10.5194/wes-3-919-2018, https://doi.org/10.5194/wes-3-919-2018, 2018
Short summary
Short summary
The energy capture of a wind turbine can be improved by completely surrounding it with an airfoil-shaped duct. This paper describes a new modeling strategy used to design an experimental 2.5 m ducted turbine, tested at the University of Waterloo wind turbine test facility. The wind tunnel data validated the predicted performance, indicating that the ducted turbine produced more than twice the power output of a conventional turbine design of the same size. Design implications are also discussed.
Jörn Nathan, Christian Masson, and Louis Dufresne
Wind Energ. Sci., 3, 905–917, https://doi.org/10.5194/wes-3-905-2018, https://doi.org/10.5194/wes-3-905-2018, 2018
Short summary
Short summary
The interaction between wind turbines through their wakes is an important aspect of the conception and operation of a wind farm. Wakes are characterized by an elevated turbulence level and a noticeable velocity deficit which causes a decrease
in energy output and fatigue on downstream turbines. In order to gain a better understanding of this phenomenon, this work uses large-eddy simulations together with an actuator line model and different ambient turbulences.
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, https://doi.org/10.5194/wes-3-845-2018, 2018
Short summary
Short summary
To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
Short summary
Short summary
Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Jennifer Annoni, Paul Fleming, Andrew Scholbrock, Jason Roadman, Scott Dana, Christiane Adcock, Fernando Porte-Agel, Steffen Raach, Florian Haizmann, and David Schlipf
Wind Energ. Sci., 3, 819–831, https://doi.org/10.5194/wes-3-819-2018, https://doi.org/10.5194/wes-3-819-2018, 2018
Short summary
Short summary
This paper addresses the modeling aspect of wind farm control. To implement successful wind farm controls, a suitable model has to be used that captures the relevant physics. This paper addresses three different wake models that can be used for controls and compares these models with lidar field data from a utility-scale turbine.
Cited articles
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. Renew. Sustain. Ener.,
6, 033137, https://doi.org/10.1063/1.4885111, 2014.
Arya, S. P.: Introduction to Micrometeorology, 2nd Edn., Academic Press, San
Diego, 2001.
Bailey, B. H.: The financial impact of wind plant uncertainty, NAWEA
Symposium, Boulder, CO, 26 August 2013.
Barthelmie, R. J., Folkerts, L., Ormel, T., Sanderhoff, P., Eecen, P. J.,
Stobbe, O., and Nielsen, N. M.: Offshore wind turbine wakes measured by
sodar, J. Atmos. Ocean. Tech., 20, 466–477,
https://doi.org/10.1175/1520-0426(2003)20<466:OWTWMB>2.0.CO;2, 2003.
Barthelmie, R. J., Pryor, S. C., Frandsen, S. T., Hansen, K. S., Shepers, J.
G., Rados, K., Schlez, W., Neubert, A., Jensen, L. E., and Neckelmann, S:
Quantifying the impact of wind turbine wakes on power output at offshore wind
farms, J. Atmos. Ocean. Tech., 27, 1302–1317,
https://doi.org/10.1175/2010JTECHA1398.1, 2010.
Bhaganagar, K. and Debnath, M.: Implications of stably stratified
atmospheric boundary layer turbulence on the near-wake structure of wind
turbines, Energies, 7, 5740–5763, https://doi.org/10.3390/en7095740, 2014.
Brasseur, J. G. and Wei, T.: Designing large-eddy simulation of the
turbulent boundary layer to capture law-of-the-wall scaling, Phys. Fluids, 22,
021303, https://doi.org/10.1063/1.3319073, 2010.
Churchfield, M. J., Lee, S., Michalakes, J., and Moriarty, P. J.: A numerical
study of the effects of atmospheric and wake turbulence on wind turbine
dynamics, J. Turbul., 13, N14, https://doi.org/10.1080/14685248.2012.668191, 2012.
Deardorff, J. W.: A numerical study of three-dimensional turbulent channel
flow at large Reynolds numbers, J. Fluid Mech., 41, 453–480,
https://doi.org/10.1017/S0022112070000691, 1970.
Deardorff, J. W.: Stratocumulus-capped mixed layers derived from a
three-dimensional model, Bound.-Lay. Meteorol., 18, 495–527,
https://doi.org/10.1007/BF00119502, 1980.
Ercolani, G., Gorlé, C., Corbari, C., and Mancini, M.: RAMS sensitivity to
grid spacing and grid aspect ratio in large-eddy simulations of the dry
neutral atmospheric boundary layer, Comput. Fluids, 146, 59–73,
https://doi.org/10.1016/j.compfluid.2017.01.010, 2017.
Hirth, B. M., Schroeder, J. L., Gunter, S., and J. Guynes: Measuring a
Utility Scale Turbine Wake Using the TTUKa Mobile Research Radars, J. Atmos. Ocean. Tech.,
29, 765–771, https://doi.org/10.1175/JTECH-D-12-00039.1, 2012.
Högström, U., Asimakopoulos, D. N., Kambezidis, H., Helmis, C. G., and
Smedman, A.: A Field Study of the Wake Behind a 2 MW Wind Turbine, Atmos. Environ.,
22, 803–820, https://doi.org/10.1016/0004-6981(88)90020-0, 1988.
Holton, J. R.: An Introduction to Dynamic Meteorology, 3rd Edn.,
Academic Press, USA, 1992.
Iungo, G., Wu, Y-T., and Porté-Agel, F.: Field measurements of wind turbine
wakes with Lidars, J. Atmos. Oceanic Technol., 30, 274–287, https://doi.org/10.1175/JTECH-D-12-00051.1, 2013.
Jonkman, J. M. and Buhl Jr., M. L.: FAST Manual User's Guide, NREL report
No. NREL/EL-500-38230, 2005.
Kelley, C. L. and Ennis, B. L.: SWiFT Site Atmospheric Characterization,
Tech. Rep. SAND2016-0216, Sandia National Laboratories, Albuquerque, NM,
2016.
Kosović, B.: Subgrid-scale modeling for the large-eddy simulation of
high-Reynolds-number boundary layers, J. Fluid Mech., 336, 151–182, https://doi.org/10.1017/S0022112096004697, 1997.
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.
Kosović B., Pullin, D. I., and Samtaney, R.: Subgrid-scale modeling for
large-eddy simulations of compressible turbulence, Phys. Fluids, 14,
1511–1522, https://doi.org/10.1063/1.1458006, 2002.
Lilly, D. K.: The representation of small-scale turbulence in numerical
experiment, Proc. IBM Scientific Computing Symp. on Environmental Sciences,
White Plains, NY, IBM, 195–210, 1967.
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.
Lund, T., Wu, X., and Squires, K.: Generation of turbulent inflow data for
spatially developing boundary-layer simulations, J. Comput. Phys.,
140, 233–258, https://doi.org/10.1006/jcph.1998.5882, 1998.
Mayor, S., Spalart, P., and Tripoli, G: Application of a perturbation
recycling method in the large-eddy simulation of a mesoscale convective
internal boundary layer, J. Atmos. Sci., 59, 2385–2395,
https://doi.org/10.1175/1520-0469(2002)059<2385:AOAPRM>2.0.CO;2, 2002.
Magnusson, M. and Smedman, A.: Influence of Atmospheric Stability on Wind
Turbine Wakes, Wind Energy, 18, 139–152, 1994.
Mehta, D., van Zuijlen, A. H., Koren, B., Holierhoek, J. G., and Bijl, H.:
Large Eddy Simulation of wind farm aerodynamics: A review, J. Wind Eng. Ind. Aerod., 133,
1–17, https://doi.org/10.1016/j.jweia.2014.07.002, 2014.
Mirocha, J. D., Lundquist, J. K., and Kosović, B.: Implementation of a
nonlinear subfilter turbulence stress model for large-eddy simulation in the
Advanced Research WRF Model, Mon. Weather Rev., 138,
4212–4228, https://doi.org/10.1175/2010MWR3286.1, 2010.
Mirocha, J. D., Kosović, B., and Kirkil, G.: Resolved turbulence
characteristics in large-eddy simulations nested within mesoscale
simulations using the Weather Research and Forecasting model, Mon. Weather Rev.,
142, 806–831, https://doi.org/10.1175/MWR-D-13-00064.1, 2014a.
Mirocha, J. D., Kosović, B., Aitken, M. L., and Lundquist, J. K.:
Implementation of a generalized actuator disk wind turbine model into the
weather research and forecasting model for large-eddy simulation
applications, J. Renew. Sustain. Ener., 6, 013104, https://doi.org/10.1063/1.4861061, 2014b.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.anl.wrfles.convective.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455026, 2018a.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.anl.wrfles.neutral.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455026, 2018b.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.lanl.higrad.convective.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455034, 2018c.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.lanl.higrad.neutral.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455035, 2018d.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.lanl.wrfles.convective.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455027, 2018e.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.lanl.wrfles.neutral.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455031, 2018f.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.llnl.wrfles.neutral.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455029, 2018g.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.nrel.sowfa.neutral.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455032, 2018h.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.pnnl.wrfles.convective.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455025, 2018i.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/mesoscale.nrel.hrrr.wfip2.d01, Atmosphere to Electrons (A2e),
https://doi.org/10.21947/1435011, 2018j.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/mesoscale.nrel.wrf.ttutower.d03, Atmosphere to Electrons (A2e),
https://doi.org/10.21947/1435012, 2012k.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/mesoscale.pnnl.wrf.ttutower.d03, Atmosphere to Electrons (A2e),
https://doi.org/10.21947/1435013, 2012l.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/tower.z01.a0, Atmosphere to Electrons (A2e), https://doi.org/10.15483/1461206,
2012m.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/tower.z01.00, Atmosphere to Electrons (A2e), https://doi.org/10.21947/1329252,
2018n.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/microscale.snl.sonic.convective.ttu, Atmosphere to Electrons (A2e),
https://doi.org/10.15483/1455028, 2012o.
Mirocha, J. D., Churchfield, M. J., Munoz-Esparza, D., Rai, R. K., Feng, Y.,
Kosovic, B., Haupt, S. E., Brown, B., Ennis, B. L., Draxl, C., Sanz Rodrigo,
J., Shaw, W. J., Berg, L. K., Moriarty, P. J., Linn, R. R., Kotamarthi, R.
V., Balakrishnan, R., Cline, J. W., Robinson, M. C., and Ananthan, S.:
mmc/radar.z01.00, Atmosphere to Electrons (A2e), https://doi.org/10.21947/1329730,
2018p.
Moeng, C.-H.: A Large-Eddy-Simulation Model for the Study of Planetary
Boundary-Layer Turbulence, J. Atmos. Sci., 41, 2052–2062,
https://doi.org/10.1175/1520-0469(1984)041<2052:ALESMF>2.0.CO;2, 1984.
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the
surface layer of the atmosphere, Tr.-Akad. Nauk SSSR Geofiz. Inst., 24, 163–187, 1954.
Muñoz-Esparza, D., Kosović, B., Mirocha, J. D., and van Beek, J.:
Bridging the transition from mesoscales to microscale turbulence in
atmospheric models, Bound.-Lay. Meteorol., 153, 409–440,
https://doi.org/10.1007/s10546-014-9956-9, 2014.
Muñoz-Esparza, D., Kosović, B., van Beek, D., and Mirocha, J. D.: A
stochastic perturbation method to generate inflow turbulence in large-eddy
simulation models: application to neutrally stratified atmospheric boundary
layers, Phys. Fluids, 27, 035102, https://doi.org/10.1063/1.4913572, 2015.
Muñoz-Esparza, D., Lundquist, J. K., Sauer, J. A., Kosović, B., and
Linn, R. R.: Coupled mesoscale-LES modeling of a diurnal cycle during the
CWEX-13 field campaign: From weather to boundary-layer eddies, J.
Adv. Model. Earth Sy., 9, 1572–1594, https://doi.org/10.1002/2017MS000960, 2017.
Nygaard, N. G.: Lidar wake measurements in an onshore wind farm,
VindKraftNet: Remote Sensing Workshop, Roskilde, Denmark, 2011.
OpenFOAM: The Open Source CFD Toolbox, created by OpenCFD Ltd (ESI Group),
distributed online at: http://www.openfoam.com, last access:
15 August 2018.
Rai, R. K., Berg, L. K., Kosović, B., Mirocha, J. D., Pekour, M. S., and
Shaw, W. J.: Comparison of Measured and Numerically Simulated Turbulence
Statistics in a Convective Boundary Layer Over Complex
Terrain, Bound.-Lay. Meteorol., 163, 69–89,
https://doi.org/10.1007/s10546-016-0217-y, 2017a.
Rai, R. K., Berg, L. K., Pekour, M., Shaw, W. J., Kosović, B., Mirocha,
J. D., and Ennis, B. L.: Spatiotemporal Variability of Turbulence Kinetic
Energy Budgets in the Convective Boundary Layer over Both Simple and Complex
Terrain, J. Appl. Meteorol. Clim., 56, 3285–3302,
https://doi.org/10.1175/JAMC-D-17-0124.1, 2017b.
Rhodes, M. E. and Lundquist, J. K.: The effect of wind-turbine wakes on
summertime US Mid-west atmospheric wind profiles as observed with
ground-based Doppler lidar, Bound.-Lay. Meteorol., 149,
85–103, https://doi.org/10.1007/s10546-013-9834-x, 2013.
Sanderse, B., van der Pijl, S. P., 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.
Sanz Rodrigo, J., Churchfield, M., and Kosovic, B.: A methodology for the
design and testing of atmospheric boundary layer models for wind energy
applications, Wind Energ. Sci., 2, 35–54,
https://doi.org/10.5194/wes-2-35-2017, 2017.
Sauer, J. A., Muñoz-Esparza, D., Canfield, J. M., Costigan, K. R., Linn,
R. R., and Kim, Y. J.: A Large-Eddy Simulation Study of Atmospheric Boundary
Layer Influence on Stratified Flows over Terrain, J. Atmos. Sci.,
73, 2615–2632, https://doi.org/10.1175/JAS-D-15-0282.1, 2016.
Shaw, W. J., Lundquist, J. K., and Schreck, S. J.: Workshop on Research Needs
for Wind Resource Characterization, B. Am. Meteorol. Soc.,
90, 535538, https://doi.org/10.1175/2008BAMS2729.1, 2009.
Sim, C., Basu, S., and Manuel, L.: The influence of stable boundary layer
flows on wind turbine fatigue loads, in: Proceedings of the AIAA aerospace
sciences meeting, Orlando, FL, 2009.
Skamarock, W. C.: Evaluating mesoscale NWP models using kinetic energy
spectra, Mon. Weather Rev., 132, 3019–3032, https://doi.org/10.1175/MWR2830.1, 2004.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
advanced research WRF version 3, Report No. NCAR/TN-4751STR, National Center
for Atmospheric Research, Boulder, CO, 2008.
Smagorinsky, J.: General circulation experiments with the primitive
equations, Mon. Weather Rev., 91, 99–152,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963.
Smalikho, I. N., Banakh, V. A., Pichugina, Y. L., Brewer, W. A., Banta, R.
M., Lundquist, J. K., and Kelley, N. D.: Lidar investigation of atmosphere
effect on a wind turbine wake, J. Atmos. Ocean. Tech.,
30, 2554–2570, https://doi.org/10.1175/JTECH-D-12-00108.1, 2013.
Sullivan, P. P., McWilliams, J. C., and Moeng, C.-H.: A subgrid-scale model
for large-eddy simulation of planetary boundary-layer flows, Bound.-Lay. Meteorol., 71,
247–276, https://doi.org/10.1007/BF00713741, 1994.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Kluwer Academic
Publisher, Netherlands, 1988.
Troldborg, N., Larsen, G. C., Madsen, H. A., Hansen, K. S., Sørensen, J.
N., and Mikkelsen, R.: Numerical simulations of wake interaction between two
wind turbines at various inflow conditions, Wind Energy,
14, 859–876, https://doi.org/10.1002/we.433, 2011.
Veers, P. S.: Three-Dimensional Wind Simulation, SAND88-0152, Sandia
National Laboratories, Albuquerque, NM, 1988.
Xie, Z. T. and Castro, I. P.: Efficient generation of inflow conditions for
large eddy simulations of street-scale flows, Flow Turbul. Combust., 81, 449–470,
https://doi.org/10.1007/s10494-008-9151-5, 2008.
Search articles
Download
Short summary
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
This paper validates the use of idealized large-eddy simulations with periodic lateral boundary...
Wind Energy Science
The interactive open-access journal of the European Academy of Wind Energy
All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.