Wind turbines are subjected to unsteady aerodynamic loads during operation, which are caused by factors such as wind shear, turbulence, and tower interference. In contrast to the unsteady loading caused by inflow turbulence, which spans a wide frequency spectrum, periodic loading induced by wind shear or the tower occurs at multiples of the rotor's rotational frequency . Among the periodic sources, tower-induced loading is governed by the aerodynamic interaction between the rotor and the tower's disturbed flow field. The tower alters the surrounding flow field by decelerating the incoming flow on the upstream side and accelerating it around the sides while generating a wake region with reduced wind speed and vortex shedding on the downstream side . As a rotor blade passes through this tower-affected region, an aerodynamic interaction between blade and tower occurs, referred to as rotor–tower interaction. This effect arises periodically with each blade passing and induces periodic load fluctuations on the tower at the blade-passing frequency (3P (three times per revolution) for three-bladed rotors).

The mechanisms of rotor–tower interaction vary depending on whether the turbine operates in an upwind or downwind configuration as the rotor blades pass through different regions of the disturbed flow field around the tower. In a downwind configuration, the rotor blades pass through the wake of the tower where a velocity deficit persists. This leads to a temporary change in the effective angle of attack, affecting both the aerodynamic blade loads and the generated rotor torque . This periodic blade–wake interaction has been associated with low-frequency acoustic emissions , which contributed to the near-complete disappearance of downwind rotor concepts in large-scale commercial applications . In addition, significantly increased fatigue loading has been reported. showed that, for a downwind turbine, the blade–wake interaction leads to fluctuations in the normal and tangential blade forces of up to 40 % relative to the free-stream level. A direct comparison of a 1.5MW wind turbine operating in upwind and downwind configuration, recently made by , shows a similar result. Increases in damage equivalent loads of up to around 25 % were observed. For an upwind turbine, the rotor blades pass through the decelerated incoming flow in front of the tower rather than through the wake. This has a smaller aerodynamic impact on the rotor blade compared to the downwind configuration. Nevertheless, as rotor diameters and hub heights in modern wind turbines continue to increase, maintaining sufficient blade–tower clearance becomes an increasingly critical design constraint . Therefore, reliable prediction of aerodynamic loading must account for the effects of rotor–tower interaction, even for the predominantly used upwind configuration. Consequently, rotor–tower interaction in upwind wind turbines has been investigated in numerous mostly numerical studies.

The turbines investigated in the literature range from the two-bladed NREL Phase VI turbine with a 10m rotor diameter to the NREL 15 MW reference turbine with a 240m rotor diameter, indicating that effects of rotor–tower interaction occur regardless of turbine size. However, the applied simulation approaches differ with respect to modeling assumptions and boundary conditions. The NREL Phase VI turbine represents one of the few wind turbines for which comprehensive and well-documented experimental reference data are available. The objective was to obtain accurate, quantitative aerodynamic and structural measurements of a wind turbine that was geometrically and dynamically representative of full-scale machines at that time. This was achieved by operating it in a wind tunnel under controlled inflow conditions, without turbulence or wind shear . The wind tunnel measurements obtained for this turbine were used to validate numerical models, which were then used to study rotor–tower interaction under varying inflow conditions. For multi-megawatt turbines, however, the available studies are based almost exclusively on numerical simulations. These simulations have been either validated against scaled model experiments or conducted on purely conceptual reference wind turbines. As these reference turbines do not physically exist, it is not possible to validate the corresponding simulation models experimentally. Accordingly, the reported effects of rotor–tower interaction show a strong dependence on the chosen simulation setup, particularly with respect to the inflow modeling and the consideration of structural elasticity. The most important effects are summarized below.

Several studies show that the rotor–tower interaction leads to periodic fluctuations of the aerodynamic forces acting on the blades as they pass the tower. Simulations by , , , and demonstrate that individual blades experience local reductions in aerodynamic forces and moments of 3%…9% when passing through the flow field in front of the tower. These fluctuating aerodynamic loads lead to variations in rotor torque and, consequently, rotor power. , , and reported rotor torque fluctuations of approximately 2%…4%. Simulations by showed rotor power fluctuations of approximately 2 %, while reported fluctuations of up to 7.5 % in a study of a wind turbine scaled to three different rotor diameters (20, 40, and 80 m), and the magnitude of these fluctuations was independent of turbine size. It was also found that the relative magnitude of these fluctuations diminishes with increasing wind speed .

A direct comparison of the studies by and shows that the use of elastic rotor blades reduces the influence of rotor–tower interaction on structural loading. Similar observations for flexible rotor blades were reported by . showed that both increasing yaw misalignment and stronger wind shear lead to a further reduction in the relative influence of the rotor–tower interaction. In both cases, pronounced 1P load fluctuations occur over the entire rotor revolution, meaning the overall loading is increasingly dominated by yawed inflow and wind shear, while the relative contribution of the rotor–tower interaction decreases. identified an additional influence of the turbine tilt when using flexible rotor blades. The drop in rotor torque induced by rotor–tower interaction decreased from 2.7 % in simulations without tilt to 1.42 % when a tower tilt of 5° was considered. performed simulations using a rigid model of a Siemens 3.6MW wind turbine without tilt. It was found that the tower shadow led to a reduction in thrust and torque of approximately 1%…2% during tower passage.

, , , and analyzed the aerodynamic interactions between rotor blades and the tower by means of computational fluid dynamics (CFD) simulations. and explained the interaction between blade and tower as a result of the dynamic change in bound circulation on the airfoil as the blade passes through the altered flow field in front of the tower. According to Kelvin's circulation theorem, a change in bound circulation requires the shedding of vorticity into the flow (analogous to the shed starting vortex by an airfoil accelerated from rest). The effect of the rotor–tower interaction becomes more pronounced with decreasing blade–tower clearance and increasing tower diameter. As a result of the rotor–tower interaction, three phenomena occur on the tower each time the rotor blade passes it :

Stagnation point displacement. The stagnation point on the tower surface is deflected in the direction of blade motion as the blade passes the tower. After the blade has passed, the transient flow dies out and the stagnation point returns to its original position. This periodic displacement of the stagnation point leads to a dynamic change in the pressure field around the tower, which causes pulsating lateral loads on the tower.

To characterize the rotor–tower interaction at a modern multi-megawatt wind turbine, the unsteady pressure fluctuations at the tower are measured. The measurements were conducted at the WiValdi wind energy research farm in Krummendeich, Germany. The wind farm consists of two Enercon E115-EP3-E4 wind turbines, each with a rotor diameter of 115.7m, a hub height of 92m, a tilt angle of 7°, and a rated power of 4.26MW at a rated speed of 13.2min-1.

Both wind turbines are equipped with extensive instrumentation (recorded at up to 200Hz), with all measurement data stored in a centralized data management system. Turbine operational parameters, including rotor position, blade root bending moments, and pitch angles, are recorded at a sampling rate of 100Hz. In the main wind direction, a meteorological mast is located 234 m upstream of the first turbine (OPUS 1), recording the atmospheric conditions at 10 Hz. At the time of the measurements, one cup anemometer at 33 m height and three cup anemometers at 91 m height were in operation. All measurement systems are time-synchronized .

To measure the flow around the tower, a pressure belt was designed at the Institute of Turbomachinery and Fluid Dynamics of Leibniz University Hannover and installed on the tower of turbine OPUS 1 in March 2023 (Fig. ). The belt was installed during turbine assembly while the tower segment was stored horizontally on the ground. After assembly, the belt is located at a height of 59 m above the ground, at mid-rotor level (Fig. ). The belt is 50 mm thick, has a total length of 13.2 m, and encircles the tower. A total of 72 pressure ports with a diameter of 4 mm are integrated into the belt, of which every second port was equipped with a sensor for this measurement campaign. The 36 differential pressure sensors measure the pressure difference between the outer tower surface and the interior of the tower. The distribution of the sensors around the tower in the earth-fixed coordinate system is shown in Fig. . To minimize distortion and response time of the pressure signal through the sensing tubes, the sensors are located less than 10 cm from their corresponding pressure ports in the belt. The differential pressure sensors used (Amsys AMS 4712-0050-D-B) have a pressure range of ±5000 Pa. All sensors share a common reference pressure line routed into the inside of the tower.

The measurement data for this study were collected in the period from 18 September 2024 to 19 November 2024. Throughout the entire measurement campaign, two sensors were identified as outliers. These sensors were excluded from the analysis entirely and are highlighted in red in Fig. .

According to the manufacturer, the sensors have a specified absolute measurement uncertainty of ±50 Pa. Before installation, the sensor calibration was tested over their whole range in 1000 Pa steps, indicating a linear response. At the start of the measurements on the wind turbine, pressure fluctuations below 50 Pa were observed, which were successfully resolved by the sensors. Since the belt had already been installed on the turbine, it was no longer possible to access the sensors directly. Laboratory tests using two identical sensors confirmed that the sensors also show a linear response in the low-pressure range below 50 Pa. However, individual sensors showed different offsets at a differential pressure of 0 Pa. An example of a sensor's response curve is shown in Fig. , where the offset is clearly visible. The scatter of the data points in this figure is caused by the pressure gauge used in the laboratory tests, which has a maximum resolution of 10 Pa. A linear fit of the data shows a coefficient of determination of R2=0.9996, indicating a linear sensor behavior. It was therefore concluded that these sensors can resolve the relevant pressure levels by performing an additional zeroing and calibration of the sensors.

To estimate the offset of the sensors already installed on the tower, time intervals with no wind were identified. Since the internal tower reference pressure was not measured but all sensors reference the same internal pressure, one sensor was selected as a reference. The offset of all other sensors was then determined relative to this sensor. For each sensor, at least 400 000 samples were analyzed across 4 different days between May and November 2024. The resulting offsets are shown in Fig. . Based on the standard deviation of these measurements, the individual uncertainty of each sensor was re-evaluated. Sensor no. 26 showed the highest standard deviation of 3.72 Pa. From the standard deviation, the 95 % confidence uncertainty was calculated using a t factor of 1.965. A measurement uncertainty of 7.3 Pa with an individual offset was then used for every sensor.

Two types of analyses were performed: one based on high-resolution data divided into 10 min windows and another based on long-term data averaged in 1 min bins. In both cases, the data were filtered to exclude periods with non-operational turbine states (e.g., idling or throttled power) or periods with missing sensor data (e.g., due to outages and maintenance work).

For the high-resolution analysis, 10 min averages of the filtered data over the entire measurement period were grouped into wind speed bins with a width of 1ms-1. Within each bin, the 10 min intervals were ranked based on the standard deviation of the wind speed. This allowed the identification of one representative 10 min interval per bin with the most stable wind conditions. The high-resolution data from these selected intervals were then used for further analysis. The extracted 10 min intervals for this study are shown in Table .

Since no wind speed measurements were available at the height of the pressure belt, wind speed values were interpolated to this level using vertical wind profiles. For each 10 min interval, a power-law wind profile was fitted based on the anemometer measurements at 33 and 90 m on the meteorological mast. The wind speed at the belt height, U∞,b, was then interpolated from this power-law profile.

The orientation of the sensors with respect to the wind direction is shown in Fig. . Sensor data were aligned based on the mean wind direction, which is defined as 0°, indicating the direction from which the wind is coming. Sensors are then represented with their sensor angle β within a range of -180 to 180°. The negative side corresponds to the side facing the approaching rotor blade and is referred to as the blade-facing side. The positive side, which faces in the direction of rotor rotation, is referred to as the blade-opposing side.

The raw differential pressure data were converted into non-dimensional pressure coefficients Cp to enable comparison across varying wind conditions and sensor positions. The pressure coefficients were calculated using the interpolated wind speed at belt height U∞,b: 1Cp=p-p∞12ρU∞,b2. The pressure coefficient was calculated based on the sensor output, defined as p-p∞=pout-pin. Air density ρ was determined using the air pressure and temperature measured at the meteorological mast. Since the reference pressure inside the tower was not recorded, a correction was applied to the pressure distribution. Based on the wind direction, the sensor closest to the inflow direction (0°), i.e., closest to the stagnation point, was identified and its Cp value was set to 1. The pressure coefficients of all other sensors were then corrected by the corresponding offset.

In Sect.  to , the results of the high-resolution pressure measurements obtained with the pressure belt are presented. This is followed by an analysis of the pressure belt long-term measurement data (Sect. ). Finally, Sect.  examines the influence of the rotor–tower interaction on the rotor blade.

Although rotor–tower interaction is generally less pronounced in upwind wind turbines compared to downwind configurations, it is still measurable under real operating conditions. A pressure belt proved to be a suitable instrumentation for experimentally investigating the effects caused by rotor–tower interaction on a multi-megawatt wind turbine.

The strongest dynamic loading on the tower due to rotor–tower interaction was observed at rated power, where the aerodynamic forces on the rotor blades are highest. As wind speed increases beyond this point, the tower is subjected to increasing static loads from the free-stream wind, while the relative contribution of dynamic pressure fluctuations decreases. Overall, rotor–tower interaction appears to be relatively weak in this turbine type as the maximum measured pressure amplitudes at the tower were around ±100Pa. Deviations of the bending moment at the blade root of approximately 1 % caused by rotor–tower interaction were also smaller than those reported by , , and . Nevertheless, the aerodynamic phenomena, such as velocity excess and stagnation point displacement, which were predicted by , could be confirmed in this study. This shows that the interaction between the rotor and the tower influences the flow field around the tower and that the underlying theoretical considerations accurately predict these effects.

Because the pressure fluctuations at low wind speeds were close to the sensor's specified uncertainty of ±50Pa, the pressure sensors had to be recalibrated. It was found that the Amsys AMS 4712-0050-D-B sensors all had individual offsets and were able to resolve fluctuations in the range of ±7.3Pa (95 % confidence interval). Due to the small pressure amplitudes, especially at low wind speeds, the pressure belt itself may have influenced the flow around the tower. In future experiments, a redesigned belt should therefore be used that is thin enough to only minimally disturb the flow around the tower. A narrower spacing of pressure ports could help to resolve the stagnation point displacement more precisely. This setup can be complemented by a pressure belt mounted on the rotor blade at the same height as the tower-mounted belt. This will allow a simultaneous investigation and comparison of the local flow around both the tower and the rotor blade.

The presented results raise the question of whether future multi-megawatt wind turbines could be designed with reduced tower clearance. To address this question, the investigated turbine would need to be analyzed over a longer observation period and under the most unfavorable wind conditions. In this context, the digital image correlation (DIC) measurements can provide additional insight. The rotor blades of the investigated turbine are also equipped with DIC markers so that the deformation of the blades can be measured during operation. The gained data can be used to quantify the blade–tower clearance and to investigate the blade deformation during tower passage. However, even studies in the literature reporting stronger rotor–tower interaction effects did not observe any blade–tower contact. This suggests that current blade–tower clearance requirements for modern multi-megawatt wind turbines may be conservative, with potential for reduced clearances.

Overall, this study demonstrates that rotor–tower interaction occurs in a modern multi-megawatt wind turbine and can be measured under real operating conditions. The primary contribution of this study is therefore methodological, providing a field demonstration of the applied measurement approach. In future work, a broader evaluation of the available measurement data and accompanying CFD simulations are required to further substantiate the findings.