On turbulence models and lidar measurements for wind turbine control

To provide comprehensive information that will assist in making decisions regarding the adoption of lidar-assisted control (LAC) in wind turbine design, this paper investigates the impact of different turbulence models on the coherence between the rotor-effective wind speed and lidar measurement. First, the differences between the Kaimal and Mann models are discussed, including the power spectrum and spatial coherence. Next, two types of lidar systems are examined to analyze the lidar measurement coherence based on commercially available lidar scan patterns. Finally, numerical simulations have been performed to compare the lidar measurement coherence for different rotor sizes. This work confirms the association between the measurement coherence and the turbulence model. The results indicate that the lidar measurement coherence with the Mann turbulence model is lower than that with the Kaimal turbulence model. In other words, the potential value creation of LAC based on simulations during the wind turbine design phase, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors. As a result, this paper suggests that the impacts of different turbulence models should be considered uncertainties while evaluating the benefits of LAC.

Dual lidar wind measurements along an upstream horizontal line perpendicular to a suspension bridge

Remote wind sensing can complement traditional anemometry at a bridge site and contribute to an improved wind-resistant design of long-span bridges. This study examines wind lidar measurement data recorded along a 168-meter-long horizontal line perpendicular to the main span of a suspension bridge in complex terrain. The velocity data records are obtained by a pair of continuous-wave Doppler lidars (short-range WindScanners) installed on the bridge deck. The measurement data are explored in terms of the mean wind speed and mean wind direction upstream of the bridge. The spectral characteristics of turbulence along the line are also investigated in relation to the increasing sampling volumes of a continuous-wave lidar system at increasing distances from the monitored area. Wind characteristics observed by the lidars are compared to those derived from sonic anemometer data recorded above the bridge deck at midspan. The results provide new insight into the wind flow characteristics in a fjord and demonstrate the potential of lidar measurements in charting the wind flow around a bridge. A slight monotonic increase of the wind speed, as well as a decrease in the yaw angle, is observed as the distance to the bridge reduces from 160 m to 20 m, while lower wind velocities are accompanied by a more stable wind direction. Within 15 m from the bridge deck, the adopted lidar setup gives unreliable information due to the large angle between the lidar beams.

Modelling the Spectral Shape of Continuous-Wave Lidar Measurements in a Turbulent Wind Tunnel

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.

Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar

Real-time measurement of atmospheric wind field parameters plays an important role in weather analysis and forecasting, including improving the efficiency of wind energy, particle tracking, boundary layer measurements, and airport security. In this study, a wind profile coherent wind Light Detection and Ranging (Lidar) measurement with a wavelength of 1.55 µm was developed and demonstrated based on the principle of eight-beam velocimetry. The wind speed information was retrieved, and vertical and horizontal profiles were calculated via power spectrum estimation of sampled echo signals through the measurement of the atmospheric wind field in Hefei for several consecutive days. The experimental results show that the wind profiles produced using different techniques are quite consistent and the standard error is less than 0.42 m/s compared with three-beam and five-beam wind measurements.

Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.

Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System

Floating LIDAR systems (FLS) are a cost-effective way of surveying the wind energy potential of an offshore area. However, as turbulence intensity estimates are strongly affected by wave-induced buoy motion, it is essential to correct them. In this study, we quantify the turbulence intensity measurement error of a WindCube v2® mounted on a 12-ton anchored buoy as a function of met-ocean conditions, and we construct a subsequently applied correction method suitable for 10-min wind LIDAR data storage. To this end, we build a model to simulate the effect of buoyancy movements on the LIDAR’s wind measurements. We first apply the model to understand the mechanisms responsible for the wind LIDAR measurement error. The effect of the buoy’s rotational and translational motions on the radial wind speed measurements of the individual beams is first studied. Second, the temporality induced by the LIDAR operation is taken into account; the effect of motion subsampling and the interaction between the different measurement beam positions. From this model, a correction method is developed and successfully applied to a 13-week experimental campaign conducted off the shores of Fécamp (Normandie, France) involving the buoy-mounted WindCube v2® compared with cup anemometers from a met mast and a fixed WindCube v2® on a platform. The correction improves the linear regression against the fixed LIDAR turbulence intensity measurements, shifting the offset from ~0.03 to ~0.005 without post-processing the remaining peaks.

Small Lidar for Profiling Water Vapor, Aerosols and Winds from Planetary Landers

<p>The planetary boundary layer (PBL) is the lowest layer of the atmosphere that interacts directly with the surface. For Mars and Titan, processes within the PBL are very important scientifically because they control the transfer of heat, momentum, dust, water, and other constituents between surface and atmospheric reservoirs. For Mars understanding these processes is critical for understanding the modern climate, including the stability and development of the polar caps how the regolith exchanges with the atmosphere how wind shapes the landscape how dust is lifted and transported and for being able to validate and improve general circulation models (GCMs). The PBL is also critical for operations since it is the environment in which landed missions must operate.</p> <p>On Mars the PBL depth varies between roughly 1 and 10 km, depending on time of day, with the deepest layer occurring during the day when convective turbulence is greatest. The PBL is difficult to observe from orbit, and so detailed observations of it have been mostly limited to those just at the surface from landers. The lack of PBL observations has led to significant gaps of understanding in several key areas. These include diurnal variations of aerosols, water vapor and direct measurements of wind velocity, the combination of which provides information on the horizontal and vertical transport of water, dust, and other trace species and their exchange with the surface. The Mars atmosphere has complex interactions between its dust, water and CO<sub>2</sub> cycles. Because these quantities are interrelated and they partially drive the wind fields, it is important to measure the water vapor, aerosols, and winds simultaneously, ideally using a single instrument.</p> <p>We are developing and plan to demonstrate a breadboard of small, highly capable atmospheric lidar to address these needs for a future lander on Mars or Titan. The lidar is designed to measure vertically-resolved profiles of water vapor by using a single frequency laser. The laser will be tuned onto and off strong isolated water vapor lines near 1911 nm. The vertical distribution of water vapor will be determined from the on- and off-line backscatter profiles via the differential absorption lidar (DIAL) technique. The same laser is used for measuring aerosol and wind profiles via the Doppler shift in the backscatter. The laser beam is linearly polarized and a cross polarized receiver allows separating the backscatter of water ice from dust.  It emits two beams that are offset 30 deg from zenith and perpendicular to one another in azimuth, allowing directional wind profiles to be resolved. Both lidar measurement channels are otherwise identical and use common lens-type receiver telescopes.</p> <p>These lidar measurements address important science needs that are traceable to Mars Exploration Program Analysis Group (MEPAG) science goals relating to climate, surface-atmosphere interactions, and preparing for human exploration.  Our lidar will measure vertical profiles of water vapor, and dust and water ice aerosols and winds with km-scale vertical resolution from the surface to > 15 km altitude.  These measurements will directly profile the full planetary boundary layer, which is key for understanding how water, dust, CO<sub>2</sub> and trace species exchange between surface and atmosphere.  The lidar will provide observations of all quantities simultaneously. </p> <p>Only one atmospheric lidar has been previously flown on a planetary lander. The lidar on the Phoenix Mars lander mission (Komguem et al., 2013) successfully measured aerosol backscatter profiles at 1064 nm and 532 nm as a function of altitude and time (Whiteway, et al., 2008). The lidar also measured cloud and ice scattering profiles and measured falling ice over the Phoenix Lander site (Whiteway, 2009).</p> <p>Our lidar approach is designed to provide several important new capabilities. It will measure, for the first time, water vapor profiles from 100 m to 15 km, along with wind and aerosol profiles at 1911 nm. Our approach utilizes a highly sensitive HgCdTe avalanche photodiode detector as a key component of the lidar receiver. During the next 2 years of this project, our plan is to develop the remaining lidar components from TRL 2 to 4, and to use the breadboard lidar to demonstrate profile measurements of aerosols, water vapor and wind from the Mauna Kea Hawaii astronomy site</p> <p><em>Acknowledgement:</em> This work is supported by an award from the 2019 NASA PICASSO program.</p>

Single-shot ranging and velocimetry far beyond the coherence length of CW lasers

The spectral linewidth of the continuous-wave (CW) lasers is one of the key limitations on the coherent lidar systems, which defines the maximum detection range. Furthermore, precise phase or frequency sweeping requirements is a deterrent in many applications. Here, we present the Phase-Based Multi-Tone Continuous Wave (PB-MTCW) lidar measurement technique that eliminates the necessity of using high coherence laser sources as well as any form of phase or frequency sweeping while employing coherent detection. In particular, we modulate a CW laser source with multiple radio-frequency (RF) tones to generate optical sidebands. Then we utilize the relative phase variations between the sidebands that are free from laser phase noises to calculate the target distance via post-processing and triangulation algorithms. We prove that the PB-MTCW technique is capable of performing single-shot ranging and velocimetry measurements at more than 500× the coherence length of a CW laser in a benchtop experimental demonstration.

Curved-ribbon-based track modelling for minimum lap-time optimisation

Three-dimensional road models for vehicular minimum-lap-time manoeuvring are typically based on curvilinear coordinates and generalizations of the Frenet–Serret formulae. These models describe the road as a parametrized ‘ribbon’, which can be described in terms of three curvature variables. In this abstraction the road is assumed laterally flat. While this class of road models is appropriate in many situations, this is not always the case. In this research we extend the laterally-flat ribbon-type road model to include lateral curvature. This accommodates the case in which the road camber can change laterally across the track. Lateral-position-dependent camber is introduced as a generalisation that is required for some race tracks. A race track model with lateral curvature is constructed using high-resolution LiDAR measurement data. These ideas are demonstrated on a NASCAR raceway, which is characterized by large changes in lateral camber angle ($$\approx 10^\circ$$ ≈ 10 ∘ ) on some parts of the track. A free-trajectory optimization is employed to solve a minimum-lap-time optimal control problem. The calculations highlight the practically observed importance of lateral camber variations.