Spatial averaging-effects on turbulence measured by a continuous-wave coherent lidar

Sjöholm, Mikael; Mikkelsen, Torben; Mann, Jakob; Enevoldsen, Karen; Courtney, Michael

doi:10.1127/0941-2948/2009/0379

Cited by 49 publications

(35 citation statements)

References 12 publications

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“…The deviation between the spectra occurs at approximately 0.03-0.07 Hz. Similar results were observed by Sjöholm et al, with a deviation in the spectra at 0.02-0.05 Hz [7]. As mentioned in Section 1, Cañadillas et al concluded that the spatial averaging had a negligible effect up to 0.21 Hz [8].…”

Section: Validation Of Lidar Measurementssupporting

confidence: 77%

“…As explained in Section 1, the spatial averaging along the Lidar beam removes some of the small-scale features of the wind, and hence the small fluctuations are not described as accurately as with the sonic anemometer. Similar results were observed by Sjöholm et al [7]. Figure 9 shows a correlation plot of one-dimensional ten-minute averaged velocities from the Lidar and the sonic anemometer.…”

Section: Validation Of Lidar Measurementssupporting

confidence: 75%

“…They investigated the performance of a StreamLine and WindCube Lidar compared to a sonic anemometer, and applied the methods of Lenschow to correct for white noise and limitations of spatiotemporal averaging of the measurements. Sjöholm et al [7] investigated the spatial averaging effect for a ZephIR continuous-wave Lidar by comparing one-dimensional velocities with sonic measurements projected onto the line-of-sight (LOS) of the Lidar. Two periods with different atmospheric conditions were investigated-one with low clouds and high backscattering, and one with clear conditions.…”

Section: Introductionmentioning

confidence: 99%

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Wind in Complex Terrain—Lidar Measurements for Evaluation of CFD Simulations

et al. 2018

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Abstract:Computational Fluid Dynamics (CFD) is widely used to predict wind conditions for wind energy production purposes. However, as wind power development expands into areas of even more complex terrain and challenging flow conditions, more research is needed to investigate the ability of such models to describe turbulent flow features. In this study, the performance of a hybrid Reynolds-Averaged Navier-Stokes (RANS)/Large Eddy Simulation (LES) model in highly complex terrain has been investigated. The model was compared with measurements from a long range pulsed Lidar, which first were validated with sonic anemometer data. The accuracy of the Lidar was considered to be sufficient for validation of flow model turbulence estimates. By reducing the range gate length of the Lidar a slight additional improvement in accuracy was obtained, but the availability of measurements was reduced due to the increased noise floor in the returned signal. The DES model was able to capture the variations of velocity and turbulence along the line-of-sight of the Lidar beam but overestimated the turbulence level in regions of complex flow.

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Section: Validation Of Lidar Measurementssupporting

confidence: 77%

Section: Validation Of Lidar Measurementssupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

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Wind in Complex Terrain—Lidar Measurements for Evaluation of CFD Simulations

et al. 2018

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“…The drop in the slope of the spectrum does not exactly coincide with the 22 Hz frequency mark because the intrinsic Lorentzian spatial weighting function of a continuous-wave lidar extends beyond the defined bounds of the probe length, therefore also acting as a filter on lower frequencies. The effect of spatial weighting is explained in detail by Sjöholm et al (2009). Also combining measurements from two lidars that each have a different probe volume causes an even larger effect in averaging out small turbulence scales over a more complex x-shaped volume (see Fig.…”

Section: Comparison Between Lidar and Hot-wire Probe Measurementsmentioning

confidence: 99%

Demonstration and uncertainty analysis of synchronised scanning lidar measurements of 2-D velocity fields in a boundary-layer wind tunnel

et al. 2017

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Abstract. This paper combines the research methodologies of scaled wind turbine model experiments in wind tunnels with short-range WindScanner lidar measurement technology. The wind tunnel at the Politecnico di Milano was equipped with three wind turbine models and two short-range WindScanner lidars to demonstrate the benefits of synchronised scanning lidars in such experimental surroundings for the first time. The duallidar system can provide fully synchronised trajectory scans with sampling timescales ranging from seconds to minutes. First, staring mode measurements were compared to hot-wire probe measurements commonly used in wind tunnels. This yielded goodness of fit coefficients of 0.969 and 0.902 for the 1 Hz averaged u and v components of the wind speed, respectively, validating the 2-D measurement capability of the lidar scanners. Subsequently, the measurement of wake profiles on a line as well as wake area scans were executed to illustrate the applicability of lidar scanning to the measurement of small-scale wind flow effects. An extensive uncertainty analysis was executed to assess the accuracy of the method. The downsides of lidar with respect to the hotwire probes are the larger measurement probe volume, which compromises the ability to measure turbulence, and the possible loss of a small part of the measurements due to hard target beam reflection. In contrast, the benefits are the high flexibility in conducting both point measurements and area scanning and the fact that remote sensing techniques do not disturb the flow during measuring. The research campaign revealed a high potential for using short-range synchronised scanning lidars to measure the flow around wind turbines in a wind tunnel and increased the knowledge about the corresponding uncertainties.

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“…This method involves convolving the true radial velocity field with a spatial weighting function that is controlled by the lidar beam pattern (e.g., Sjöholm et al, 2009;Sathe et al, 2011). Spatial weighting functions for both pulsed and continuous wave lidars are relatively straightforward (e.g., Sonnenschein and Horrigan, 1971).…”

Section: Fitting a Turbulence Modelmentioning

confidence: 99%

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Newman

Clifton

2017

Wind Energ. Sci.

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Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry.In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices.L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physicsbased corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

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Spatial averaging-effects on turbulence measured by a continuous-wave coherent lidar

Abstract: Sjöholm, Mikael et al."Spatial averaging-effects on turbulence measured by a continuous-wave coherent lidar".

Cited by 49 publications

References 12 publications

Wind in Complex Terrain—Lidar Measurements for Evaluation of CFD Simulations

Wind in Complex Terrain—Lidar Measurements for Evaluation of CFD Simulations

Demonstration and uncertainty analysis of synchronised scanning lidar measurements of 2-D velocity fields in a boundary-layer wind tunnel

An error reduction algorithm to improve lidar turbulence estimates for wind energy

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