Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4 %–11 %). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.

We analyze SpinnerLidar measurements of a single wind turbine wake collected at the SWiFT facility and investigate the wake behaviour under different atmospheric turbulence conditions. The derived wake characteristics include the wake deficit, wake-added turbulence and wake meandering in both lateral and vertical directions. The atmospheric stability at the site is characterized using observations from a sonic anemometer. A wake-tracking technique, based on a bi-variate Gaussian wake shape, is implemented to monitor the wake center dis-placements in time to derive quasi-steady wake deficit and turbulence profiles in a meandering frame of reference. The analysis demonstrates the influence of atmospheric stability on the wake behaviour; a faster wake deficit recovery and a higher level of turbulence mixing are observed under unstable compared to stable atmospheric conditions. We also show that the wake me-andering is driven by large-scale turbulence structures, which are characterized by increasing energy content as the atmosphere becomes more unstable. These results suggest the suitability of the dataset for wake-model calibration and provide statistics of the wake deficit, turbulence levels, and meandering, which are key aspects for load validation studies.

Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists of incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time series, which are derived by fitting a bivariate Gaussian shape function to lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the dynamic wake meandering (DWM) model, i.e., the target fields. The lidar-reconstructed wake fields are then input into aeroelastic simulations of the DTU 10 MW wind turbine for carrying out the load validation analysis. The power and load time series, predicted with lidar-reconstructed fields, exhibit a high correlation with the corresponding target simulations, thus reducing the statistical uncertainty (realization-to-realization) inherent to engineering wake models such as the DWM model. We quantify a reduction in power and loads' statistical uncertainty by a factor of between 1.2 and 5, depending on the wind turbine component, when using lidar-reconstructed fields compared to the DWM model results. Finally, we show that the number of lidar-scanned points in the inflow and the size of the lidar probe volume are critical aspects for the accuracy of the reconstructed wake fields, power, and load predictions.

Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed and a continuous wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial- and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters and wake disturbances are present for two opposite wind direction sectors. We account for wake-induced effects by means of wind field parameters commonly used as inputs for load simulations, which are reconstructed using lidar measurements. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error and turbulence-spectra parameters. The uncertainty and bias of aero-elastic load predictions are quantified against wind turbine on-board sensor data. We consider mast-based load assessments in free wind as a reference case and assess the uncertainty in lidar-based power and load predictions when the turbine is operating in partial- and full-wake. Compared to the reference case, the simulations in wake conditions lead to an increase of the relative error as low as 4 %. It is demonstrated that the mean wind speed, turbulence intensity and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a rotor equivalent wind speed model are the most challenging aspects of load validation in wake conditions.

The aim of this work is to investigate the growth of the vasculature in the rat humeral head cartilage after the initial development of the secondary ossification centre until the adult organization. Rats aging from 5 weeks to 12 months were used. Histological observations on humeral heads were implemented with morphometrical analysis. Subsequently, vascular corrosion cast, that permits a three-dimensional observation of the vasculature, were prepared and observed by scanning electron microscopy. In young animals the epiphysis contains thin bone trabeculae and most of the epiphysis is occupied by bone marrow spaces. With age, the bone trabeculae progressively enlarge up to double their thickness. The percentage of bone tissue increases from 33.6 to 58.6% of the entire epiphysis, while the bone marrow spaces tend to increase very little in their mean dimension. Vascular corrosion casts show that the epiphyseal microcirculation is well distinguished from that of the diaphysis, and arises from the vessels present in the capsule and the periosteal networks. In young animals the only capillaries are bone marrow sinusoids and few subchondral capillaries. In adult animals small vessels run between the clusters of sinusoids forming the trabecular circulation. Capillary sprouts from sinusoids are always observed both in the young and adult animals. Thus, in adult rats different proper microcirculatory districts can be distinguished in the epiphysis: (a) the sinusoidal network, that supplies the hematopoiesis of the bone marrow and the adjacent osteogenic tissue; (b) the bone tissue microcirculation, limited to small vessels that supply the metabolism and the remodelling of the bone tissue. The reported microvascular organization and its adaptation to the epiphyseal growth represent the morphological basis for understanding the reciprocal interaction among the different tissues in developing and adult rat epiphysis.