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As a contribution to closing observational gaps in the atmospheric boundary layer (ABL), the Simultaneous Wind measurement with Uncrewed Flight Systems in 3D (SWUF-3D) fleet of uncrewed aerial systems (UASs) is utilized for in situ measurements of turbulence. To date, the coefficients for the transformation terms used in our algorithm for deriving wind speeds from avionic data have only been determined via calibration flights in the free field. Therefore, we present in this work calibration and verification under laboratory conditions. The UAS measurements are performed in a wind tunnel equipped with an active grid and constant temperature anemometers (CTAs) as a reference. Calibration is performed in x - and y -coordinate directions of the UAS body frame at wind speeds of 2 ... 18 m s -1. For systematic verification of the measurement capabilities and identification of limitations, different measurement scenarios like gusts, velocity steps, and turbulence are generated with the active grid. Furthermore, the measurement accuracy under different angles of sideslip (AoSs) and wind speeds is investigated, and we examined whether the calibration coefficients can be ported to other UASs in the fleet. Our analyses show that the uncertainty in measuring the wind speed depends on the wind speed magnitude and increases with extreme velocity changes and with higher wind speeds, resulting in a root-mean-square error (RMSE) of less than 0.2 m s -1 for steady wind speeds. Applying the calibration coefficients from one UAS to others within the fleet results in comparable accuracies. Flights in gusts of different strengths yield an RMSE of up to 0.6 m s -1. The maximal RMSE occurs in the most extreme velocity steps (i.e., a lower speed of 5 m s -1 and an amplitude of 10 m s -1) and exceeds 1.3 m s -1. For variances below approx. 0.5 and 0.3 m 2 s -2 , the maximal resolvable frequencies of the turbulence are about 2 and 1 Hz, respectively. The results indicate successful calibration but with susceptibility to high AoSs in high wind speeds, no necessity for wind tunnel calibration for individual UASs, and the need for further research regarding turbulence analysis. [ABSTRACT FROM AUTHOR]

As a contribution to closing observational gaps in the atmospheric boundary layer (ABL), the SWUF-3D fleet of unmanned aerial systems (UAS) is utilized for in situ measurements of turbulence. To date, the algorithm for wind measurement has only been calibrated in the free field. We therefore present in this work the calibration and verification under laboratory conditions. The UAS measurements are performed in a wind tunnel with active grid and constant temperature anemometers (CTAs) as a reference. Calibration is performed in x - and y -coordinate directions of the UAS body frame in wind speeds of 2...18 m s-1. For systematic verification of the measurement capabilities and identification of limitations, different measurement scenarios like gusts, velocity steps and turbulence are generated with the active grid. Furthermore, the measurement accuracy under different angles of sideslip (AoS) and wind speeds is investigated and it is examined whether the calibration coefficients can be ported to other UAS of the fleet. Our analyses show that the uncertainty depends on the wind speed magnitude and increases with higher wind speeds, resulting in an overall root-mean-square error (RMSE) of less than 0.2 m s-1. Applying the calibration coefficients from one UAS to others within the fleet results in comparable accuracies. Flights in different gusts yield an RMSE of up to 0.6 m s-1. The maximal RMSE occurs in the most extreme velocity steps (i.e. a lower speed of 5 m s-1 and an amplitude of 10 m s-1) and exceeds 1.3 m s-1. For variances below approx. 0.5 m2 s-2 and 0.3 m2 s-2, the maximal resolvable frequencies of the turbulence are about 2 Hz and 1 Hz respectively. We report successful calibration but with susceptibility to high AoS in high wind speeds, no necessity of wind tunnel calibration for individual UAS, and the need for further research regarding turbulence analysis. [ABSTRACT FROM AUTHOR]

With their constant increase in size, wind turbines are reaching unprecedented heights. Therefore, at these heights, they are influenced by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions becomes more and more likely. In this paper, the presence of the turbulent–non-turbulent interface (TNTI) in the atmosphere is investigated. Three different on- and offshore locations are investigated. Our fractal scaling analysis leads to typical values known from ideal laboratory and numerical studies. The height distribution of the probability of the TNTI is determined and shows a frequent occurrence at the height of the rotor of future multi-megawatt turbines. The indicated universality of the fractality of the TNTI allows the use of simplified models in laboratory and numerical investigations. [ABSTRACT FROM AUTHOR]

This paper describes the development of a model for the turbulence spectrum measured by a short-range, continuous-wave lidar. The lidar performance was assessed by measurements conducted with two WindScanners in an open jet wind tunnel equipped with an active grid, for a range of different turbulent wind conditions. A one-dimensional hot wire anemometer was used as a reference for characterising the lidar turbulence measurement. In addition to addressing the statistics, the correlation between the time series and the mean error on the wind speed, the lidar measurement turbulence spectrum is compared with a theoretical spectrum using Taylor's frozen turbulence hypothesis. A theoretical model for the probe length averaging effect is applied in the frequency domain, using a Lorentzian filter in combination with generated white noise, and evaluated by qualitatively matching the lidar measurement spectrum. High goodness of fit coefficients and low mean absolute errors between hot wire and WindScanner were observed for the measured time series. The correlation showed an inverse relationship with the prevalent turbulence intensity in the flow for cases with a comparable power spectrum shape. Larger flow structures can be captured more accurately by the lidar, whereas small-scale turbulent flow structures are partly filtered out as a result of the lidars' probe volume averaging. It is demonstrated that an accurate way to define the frequency at which the lidar power spectrum starts to deviate from the hot wire reference spectrum is the point at which the coherence drops below 0.5. This coherence-based cut-off frequency increases linearly with the mean wind speed and is generally an order of magnitude lower than the probe length cut-off frequency, estimated according to a simple model based on Taylor's frozen turbulence hypothesis. A convincing match between the modelled and the actual WindScanner power spectrum was found for various different cases, which confirmed that the deviation of the lidar measurement power spectrum in the higher frequency range can be analytically explained and modelled as a combination of a Lorentzian probe length averaging effect and white noise in the lidar measurement. [ABSTRACT FROM AUTHOR]