More accurate aeroelastic wind-turbine load simulations using detailed inflow information

Abstract

Abstract. In this paper, inflow information is extracted from a measurement database and used for aeroelastic simulations to investigate if using more accurate inflow descriptions improves the accuracy of the simulated wind-turbine fatigue loads. The inflow information is extracted from nearby meteorological masts (met masts) and a blade-mounted five-hole pitot tube. The met masts provide measurements of the inflow at fixed positions some distance away from the turbine, whereas the pitot tube measures the inflow while rotating with the rotor. The met mast measures the free-inflow velocity; however the measured turbulence may evolve on its way to the turbine, pass beside the turbine or the mast may be in the wake of the turbine. The inflow measured by the pitot tube, in comparison, is very representative of the wind that acts on the turbine, as it is measured close to the blades and also includes variations within the rotor plane. Nevertheless, this inflow is affected by the presence of the turbine; therefore, an aerodynamic model is used to estimate the free-inflow velocities that would have occurred at the same time and position without the presence of the turbine. The inflow information used for the simulations includes the mean wind speed field and trend, the turbulence intensity, the wind-speed shear profile, atmospheric stability-dependent turbulence parameters, and the azimuthal variations within the rotor plane. In addition, instantaneously measured wind speeds are used to constrain the turbulence. It is concluded that the period-specific turbulence intensity must be used in the aeroelastic simulations to make the range of the simulated fatigue loads representative for the range of the measured fatigue loads. Furthermore, it is found that the one-to-one correspondence between the measured and simulated fatigue loads is improved considerably by using inflow characteristics extracted from the pitot tube instead of using the met-mast-based sensors as input for the simulations. Finally, the use of pitot-tube-recorded wind speeds to constrain the inflow turbulence is found to significantly decrease the variation of the simulated loads due to different turbulence realizations (seeds), whereby the need for multiple simulations is reduced.

Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals

“…We use the lidar simulator developed within the ViCon-DAR open-source numerical framework to virtually replicate lidar measurements (https://github.com/SWE-UniStuttgart/ ViConDAR, last access: 22 April 2021) (Pettas et al, 2020).…”

“…Other sources of uncertainty in the radial velocity estimation inherent to lidars, e.g., from the optics and internal signal processing, are accounted for by adding Gaussian white noise. Here we add noise at a level that results in a signal-to-noise ratio of −20 dB as in Pettas et al (2020). We do not investigate the sensitivity of the noise level in the present work.…”

“…The spatial resolution dx on the x axis depends on the simulated ambient wind speed at hub height: dx = (Ū amb T sim )/8192, where T sim is the simulation time in seconds. These dimensions ensure an adequate turbulence field for a 10 min wind field simulation over a large rotor and a space-time resolution such that the probe volume effects can be captured by the virtual lidar Pettas et al, 2020).…”

“…To evaluate currently available nacelle lidars' ability to perform wake characterization, we select a few standard scanning configurations and use them to perform load validation within wakes. These are a four-beam lidar (4P) (Held and Mann, 2019a, b); an extended configuration with seven beams, six arranged at the corner of a hexagon and a central beam (7P) (Pettas et al, 2020); the conical scanning lidar (Cone) (Medley et al, 2014;Borraccino et al, 2017;; the SpinnerLidar (SL) (Peña et al, 2019;Doubrawa et al, 2019); and a general grid pattern (Grid) covering the full turbulence box (see Fig. 4).…”

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

“…However, since the synthetic turbulence realization, as well as the wake meandering, are stochastic processes, the instantaneous velocities of the simulated wake wind field and the resulting load prediction time-series are not synchronized with the true observations. This can lead to simulation errors (Zwick and Muskulus, 2015) and introduces high statistical uncertainty on load predictions Pedersen et al, 2019). Further, statistics from a large set of simulations are required to compensate for the large load variations.…”