Work has begun towards model validation of wake dynamics for the large-eddy simulation (LES) code Nalu-Wind in the context of research-scale wind turbines in a neutral atmospheric boundary layer (ABL). Interest is particularly directed at the structures and spectra which are influential for wake recovery and downstream turbine loading. This initial work is to determine the feasibility of using nacelle-mounted, continuous-wave lidars to measure and validate wake physics via comparisons of full actuator line simulation results with those obtained from a virtual lidar embedded within the computational domain. Analyses are conducted on the dominant large-scale flow structures via proper orthogonal decomposition (POD) and on the various scales of wake-added turbulence through spectral comparisons. The virtual lidar adequately reproduces spatial structures and energies compared to the full simulation results. Correction of the higher-frequency turbulence spectra for volume-averaging attenuation was most successful at locations where mean gradients were not severe. The results of this work will aid the design of experiments for validation of high-fidelity wake models.
An uncertainty quantification technique for nacelle-mounted lidar is developed that extends conventional error analyses to precisely account for residual uncertainty due to observed non-ideal features in processed Doppler lidar spectra. The technique is applied after quality assurance/quality control (QAQC) processing to quantify residual error, both bias and random, from solid-body interference, shot noise, and any additional uncertainty introduced to the data from the QAQC process itself. The approach follows from the one-time construction of a high-dimensional parametric database of synthetic lidar spectra and subsequent processing with an existing QAQC technique. A model of the correspondence between the spectral shape and the associated residual errors due to non-ideal features is then developed for quantities of interest (QOIs) including the geometric median and spectral standard deviation of line-of-sight velocity. The model is preliminarily implemented within a neural network framework that is then applied in post-processing to sample returns from a DTU SpinnerLidar. The initial analysis uncovers the effects of specific sources of uncertainty in the context of both individual spectra and full-field maps of the measurement domain. The technique is described in terms of application to continuous wave (CW) lidar, though it is also relevant to pulsed lidar.
Nalu-Wind simulations of the neutral inflow Scaled Wind Farm Technology (SWiFT) benchmark were used to analyze which quantities of interest within the wind turbine wake and surrounding control volume are important in performing a momentum deficit analysis of the wind turbine thrust force. The necessary quantities of interest to conduct a full Reynolds-Averaged Navier-Stokes (RANS) formulation analysis were extracted along the control volume surfaces within the Nalu simulation domain over a 10 minute period. The thrust force calculated within the wake from two to eight diameters downstream using the control volume surfaces and the full RANS approach matched the thrust force that the wind turbine applied to the flowfield. A simplified one-dimension momentum analysis was included to determine if the inflow and wake velocities typically acquired during field campaigns would be sufficient to perform a momentum deficit analysis within a wind turbine wake. The one-dimensional analysis resulted in a 70% difference relative to the coefficient of thrust (Ct ) determined by the full RANS method at 2D downstream and a 40% difference from 5D to 8D, where D is the diameter of the turbine. This suggests that the quantities typically captured during field campaigns are insufficient to perform an accurate momentum deficit analysis unless streamwise pressure distribution is acquired, which reduced the relative difference to less than 10% for this particular atmospheric inflow.