Vertical profiles of the 3-D wind velocity retrieved from multiple wind lidars performing triple range-height-indicator scans

Debnath, Mithu; Iungo, Giacomo Valerio; Ashton, Ryan; Brewer, W. Alan; Choukulkar, Aditya; Delgado, Rubén; Lundquist, Julie K.; Shaw, W. J.; Wilczak, James M.; Wolfe, D. E.

doi:10.5194/amt-10-431-2017

Cited by 19 publications

(17 citation statements)

References 40 publications

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“…Based on a simple accuracy model (see ) the intersecting angle of 30 • results in an accuracy of about 0.25 m s −1 for the retrieved horizontal wind speed. In order to accurately retrieve the vertical wind speed, the elevation angles should be as steep as possible, preferably 90 • as indicated in Debnath et al (2017). Hence, based on this study's results, which uses a norm approach described in Simley et al (2016) to assess the suitability of the multi-Doppler setup, the elevation angles larger than 45 • provide means to accurately acquire the vertical component.…”

Section: Step 4: Experiments Layout Designmentioning

confidence: 99%

Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments

et al. 2017

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Abstract. The long-range and short-range WindScanner systems (LRWS and SRWS), multi-Doppler lidar instruments, when combined together can map the turbulent flow around a wind turbine and at the same time measure mean flow conditions over an entire region such as a wind farm. As the WindScanner technology is novel, performing field campaigns with the WindScanner systems requires a methodology that will maximize the benefits of conducting WindScannerbased experiments. Such a methodology, made up of 10 steps, is presented and discussed through its application in a pilot experiment that took place in a complex and forested site in Portugal, where for the first time the two WindScanner systems operated simultaneously. Overall, this resulted in a detailed site selection criteria, a well-thought-out experiment layout, novel flow mapping methods and high-quality flow observations, all of which are presented in this paper.

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Section: Step 4: Experiments Layout Designmentioning

confidence: 99%

Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments

et al. 2017

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“…The collected lidar data were further post-processed only when the carrier-to-noise ratio of the lidar signal was larger than −25 dB (Carbajo-Fuertes et al, 2014). The post-processing from the three radial velocities (U r ) to Cartesian wind velocity components (U , V , W ) was carried out following the standard triple-Doppler retrieval (Mikkelsen et al, 2008;Mann et al, 2009;CarbajoFuertes et al, 2014;Debnath et al, 2017;Choukulkar et al, 2017;Simley et al, 2016) by means of the following equations:…”

Section: Experimental Setup and Measurement Proceduresmentioning

confidence: 99%

“…The XPIA experiment was carried out at the National Oceanic and Atmospheric Administration (NOAA), Boulder Atmospheric Observatory (BAO), near Erie, Colorado, for the period 2 March-31 May 2015. An overview of the field campaign is provided in Lundquist et al (2016), while a detailed analysis of several multiple-Doppler scanning strategies performed with scanning lidars was provided in Choukulkar et al (2017), and vertical profiles of the three wind velocity components performed with triple RHI scans were presented in Debnath et al (2017).…”

Section: Introductionmentioning

confidence: 99%

“…To overcome limitations connected with multiplecomponent velocity measurements performed with a single instrument, multiple-Doppler scanning strategies have been explored, which require the simultaneous availability of multiple instruments (Newsom et al, 2005;Mikkelsen et al, 2008;Mann et al, 2009;Carbajo-Fuertes et al, 2014;Debnath et al, 2017;Choukulkar et al, 2017). Multiple-Doppler scans consist of probing the wind velocity field at a specific location with various non-parallel line-of-sight velocities in order to characterize the 3-D nature of the atmospheric boundary layer wind field, such as in the presence of wind shear, veer or wakes produced by upwind obstacles (e.g., wind turbines, buildings, topography), or stratified wind turbulence (Segalini and Arnqvist, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment

et al. 2017

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Abstract. During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were carried out with scanning wind lidars and Kaband radars. Specifically, step-stare measurements were collected simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars carried out simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual-tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind profiler. This survey shows that -despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements -a very good accuracy is achieved for both remote-sensing techniques for probing horizontal wind speed and wind direction with the virtual-tower scanning technique.

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“…Therefore, probing wakes generated by utility-scale wind turbines requires measurement techniques capable to cover such large volumes, while providing adequate spatio-temporal resolution to characterize wake turbulent flows. A very promising remote sensing technique for probing wakes generated by utility-scale wind turbines is light detection and ranging (LiDAR), allowing measurements of ABL flows [14][15][16] and wind turbine wakes [17][18][19] through a variety of scanning strategies ranging from 1D fast scans to characterize wind turbulence [20,21] to volumetric scans for characterizations of 3D ABL flows and wakes [12].…”

Section: Introductionmentioning

confidence: 99%

Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations

Iungo

Letizia

Zhan

2018

J. Phys.: Conf. Ser.

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Abstract. The axial induction exerted by utility-scale wind turbines for different operative and atmospheric conditions is estimated by coupling ground-based LiDAR measurements and RANS simulations. The LiDAR data are thoroughly post-processed in order to average the wake velocity fields by using as common reference frame their respective wake directions and the turbine hub location. The various LiDAR scans are clustered according to their incoming wind speed at hub height and atmospheric stability regime, namely Bulk Richardson number. Time-averaged velocity fields are then calculated as ensemble averages of the scans belonging to the same cluster. The LiDAR measurements are coupled with RANS simulations in order to estimate the rotor axial induction for each cluster of the LiDAR data. First, a control volume analysis of the streamwise momentum is applied to the time-averaged LiDAR velocity fields to obtain an initial estimate of the axial induction over the rotor disk. The calculated thrust force is imposed as forcing of an axisymmetric RANS simulation in order to estimate pressure, radial velocity and momentum fluxes. The latter are combined with the LiDAR streamwise velocity field in order to refine the estimate of the rotor axial induction through the control volume approach. This process is repeated iteratively until achieving convergence on the rotor axial induction while minimizing difference between LiDAR and RANS streamwise velocity fields. This procedure allows to single out the reduction in thrust load while the blade pitch angle is increased transitioning from region 2 to 3 of the power curve. Furthermore, an enhanced thrust force is observed for a fixed incoming wind speed and transitioning from stable to convective stability regimes. The presented technique is proposed as a data-driven alternative to the blade element momentum theory typically used with current actuator disk models in order to quantify rotor aerodynamic thrust for different operative and atmospheric conditions.

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Vertical profiles of the 3-D wind velocity retrieved from multiple wind lidars performing triple range-height-indicator scans

Cited by 19 publications

References 40 publications

Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments

Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments

Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment

Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations

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