Journal of Unmanned Vehicle Systems. Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV

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1 Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV Journal: Manuscript ID Manuscript Type: Date Submitted by the Author: Complete List of Authors: Keyword: juvs r1 Article 11-Jun-218 Yeung, Alton; Ryerson University, Aerospace Engineering Bramesfeld, Götz; RYERSON UNIVERSITY, Aerospace Engineering Chung, Joon; Ryerson University, Aerospace Engineering Foster, Stephen; Aventech Research Inc. UAV, Gust, low-altitude, five-hole probe, air-data system Is the invited manuscript for consideration in a Special Issue? : Not applicable (regular submission)

2 Page 1 of 15 1 Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV Alton Yeung, Goetz Bramesfeld, Joon Chung, and Stephen Foster Abstract: A small unmanned aerial vehicle (SUAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes below 5 meters. These gusts have significant impacts on the flight characteristics and performance of SUAVs. The SUAV carried an advanced air-data system that includes a five-hole probe, which was adapted for this specific application. In several flight tests the entire test system was qualified and gust data were recorded. The subsequent experimentally derived gust data were post-processed and compared with turbulence spectra of the MIL-HDBK-1797 von Kármán turbulence model. On the day of the flight test, the experimental results did not fully match the prediction of the von Kármán model. Meanwhile, the wind measuring apparatus were proven to be able to measure gust during flight. Therefore, a broader sampling will be required to generalize the gust measurements and be compared with the existing models. Key words: UAV, Gust, low-altitude, five-hole probe, air-data system. Introduction Small Unmanned Aerial Vehicles (SUAVs) have been gaining popularity over the last couple of decades. The advancements of miniaturization of sensors have led small unmanned aerial vehicles being adopted by the commercial sector, consumer market, and research communities. Many of these small unmanned aerial vehicles are performing missions such as search and rescue, mapping, environmental studies, aerial imaging, and meteorology (Spence et al. 216, Duncan et al. 214, d Oleire- Oltmanns et al. 212, Tilly et al. 216, Bonin et al. 213). These vehicles often operate at less than 5 m above the terrain, due to the nature of their missions, engine output capability, and regulatory limitations. Furthermore, the slower flight speeds and lower flight masses of SUAVs also mean that less intense atmospheric turbulence will have a great affect on their flight dynamics and performance than of those of full-sized aircraft (Spedding et al. 1998). Unpredictable atmospheric gusts can create a challenging environment for SUAVs to operate in safely and efficiently. Therefore, a representative gust model can help to improve our understanding of the environmental factors and lead to better prediction methods of flight characteristics of SUAVs during low-altitude operations. A better understanding of the atmospheric conditions can also lead to significant flight performance improvements for SUAVs. Previous research has been directed towards extracting energy from atmo- Yeung, A., 1 Bramesfeld, G., and Chung, J.. Ryerson University, Toronto, ON M5B-2K3, Canada. Foster, S.. Aventech Research Inc., 756 Huronia Road, Unit 2, Barrie, ON L4N 6C6, Canada. 1 Corresponding author ( alton.yeung@ryerson.ca). unknown 99: 1 15 (218) Proof/Épreuve Published by NRC Research Press

3 Page 2 of 15 spheric turbulence by using gust-soaring. For example, Langelaan has shown the potential for significant range and endurance improvements for SUAVs that use the energy present in gusts (Langelaan 29). He also pointed out that there are little empirical data on gusts available in literature that suitable for SUAV development. Galway has investigated the effect of turbulent wind generated by buildings on SUAVs (Galway 29, Galway 211). The MIL-HDBK-1797 von Kármán model (U.S. Department of Defense 1997) is frequently used to provide the power spectral density of gusts in research papers, despite the fact that this model was originally developed primarily in order to characterize turbulence encounter by full-scale aircraft during cruise (Pisano 29). The mathematical expressions of the von Kármán power spectral density function Φ(Ω) for longitudinal gust and transverse gust (vertical or lateral) are: Φ u (Ω) = σ 2 L π Φ v (Ω) = Φ w (Ω) = σ 2 L π 1 [1 + (1.339LΩ) 2 ] 5/6 (1) (1.339LΩ)2 [1 + (1.339LΩ) 2 ] 11/6 (2) where σ is the turbulence intensity, L the turbulence length scale parameter, and Ω the gust frequency. The MIL-HDBK-1797 outlines empirical expressions for the turbulence intensities at altitude h in feet in the vertical, σ w, and horizontal plane, σ u and σ v, based on the the wind speed at 2 ft above ground, U 2 : σ w =.1U 2 (3) σ w σ u = σ v = ( h).4 (4) The document also outlines the empirical expressions for the turbulence length scale parameters at altitude h in feet: 2L w = h L u = 2L v = h ( h) 1.2 (6) (5) Fig. 1 shows an example of a von Kármán power spectral density plot with vertical turbulence intensity of 1 m/s and length scale parameter of 25 m. The turbulence parameters describe above changes power spectral density curve and the knee of the curve. The knee corresponds to a sharp change on the curve which is modeled by the the von Kármán power spectral density functions. Hence, the von Kármán method is able to model the gusts across different frequencies and intensities with better results than a linear model. 2

4 Page 3 of 15 Fig. 1. Von Kármán gust power spectral density curves, L = 25 m. 1 4 Power spectral density [(m/s) 2 /(cycle/m)] Spatial frequencey ( ) [rad/m] Due to a lack of other validated small-scale aircraft turbulence models, researchers, especially the ones who focus on control system design, adopt the MIL-HDBK-1797 formulations of the von Kármán gust model or similar models, which may not be truly descriptive of the gusty environment that is typically encountered by SUAVs. These existing gust models were not specifically designed for small aircraft and can potentially be enhanced with more gust data captured at low altitudes where most SUAVs operate. In order to conduct such research, an affordable and reliable low altitude gust sensing platform was developed. Furthermore, a regression analysis was applied to the gusts that were measured during the experiment. The subsequent results were compared to data of the MIL-HDBK-1797 gust model. UAVs in general have become major atmospheric research tools as a consequence of the continued advancements of smaller and more capable airborne sensors. This development is further supported by greatly improved autonomous flight-control systems that have made SUAVs highly reliable. Many SUAVs are capable of reaching altitudes of over 1 m with payloads of various shapes and sizes. In general, UAVs provide several advantages over manned platforms, for example, drastically lower operating cost compared to similar missions using manned aircraft. Moreover, electrically powered SUAVs are especially suitable for atmospheric sampling as exhaust gas from internal combustion engine can cause problems with measuring gas compositions. Unmanned vehicles can also perform flights a lot closer to the ground without unnecessarily endangering a crew, which is beneficial to low-altitude gust measurement. 3

5 Page 4 of 15 Fig. 2. Photo of GustAV aerial research platform flying with a fivehole probe mounted on the wing tip. In order to acquire wind gust speed data at low altitudes, a SUAV was developed at Ryerson University. The small airborne platform, GustAV, or Gust Aerial Vehicle, is shown in Fig. 2. It uses an advanced air-data system that includes a five-hole probe that measures atmospheric gusts. The objectives for this work are: 1. To develop an autonomous UAV suitable for low-altitude meteorological sensing. 2. To construct the GustAV and autonomous avionics for autonomous flights. 3. To perform flight experiments in order to measure atmospheric gusts at various altitudes. 4. To extract wind-gust data from the experimental data and compare the results with the MIL- HDBK-1797 von Kármán model. GustAV GustAV is an SUAV that was designed with the particular objective to be suitable for flight tests at various remote locations. Thus, besides having a relatively simple airframe that is sufficiently robust for field handling and can easily be reconfigured for a variety of experiments, GustAV was designed with easy flight and handling qualities in mind. In the current version, the aircraft is equipped with an air-data system in order to measure wind data. Furthermore, an optional autopilot allows for autonomous flight, for example when trying to repeatedly sample wind vectors over the same location. A detailed description of the entire system is provided in (Yeung 217), but this section provides a brief description. Unmanned Aerial Vehicle The main structure of GustAV consists of a wing, landing gear, tail, and motor, which are mounted to a central aluminum frame. A three-view of the aircraft is shown in Fig. 3. The balsa wood main wing is reinforced with an aluminum spar and fiberglass skin in order to minimize bending during flight and reduce the chance of damage during ground handling. The summary of specifications and performance of GustAV are listed in Table 1. GustAV is equipped with a Pixhawk autopilot and controlled using Ardupilot firmware, an opensource flight controller software package (ArduPilot Dev Team 216). The autopilot provides the autonomous flight capability and vehicle system monitoring during the mission via a two-way communication. Yeung and Bramesfeld provide a detailed information of the system integration and data processing of this unmanned vehicle system (Yeung et al. 216). 4

6 Page 5 of 15 Fig. 3. Three-view of GustAV. (All dimensions are in mm) Table 1. Specification and performance of GustAV. Operating Mass 7.52 kg Wing span 2.6 m Wing planform area 1. m 2 Mean aerodynamic chord.41 m Airspeed (cruise/min/max) 15 ms -1 /1 ms -1 /25 ms -1 Endurance 25 mins Motor 9 W electric brushless Main battery 22.2 V, 8 mah Avionics battery 11.1 V, 22 mah Air-Data System The payload of GustAV consists a commercially available air-data system, Aventech AIMMS-3, that was integrated into the airframe. The AIMMS-3 is capable to measure the three-dimensional wind vector in flight. The system was designed by Aventech Research Inc. and it consists of three types of sensors: a wing mounted five-hole probe that measures the three-dimensional wind field, an inertial measurement unit (IMU) that measures linear and angular accelerations and orientation of the vehicle, and a global navigation satellite system (GNSS) unit that measures the position and velocity of the aircraft relative to the inertial reference frame. By subtracting the measurements between the inertial and body reference frames, the wind vector solution can be obtained as illustrated in Fig. 4. With this setup, the air-data system is capable to measure atmospheric gusts of.5 m/s or larger (Aventech Research Inc. 216). 5

7 Page 6 of 15 Fig. 4. Wind measurement reference frames. A Kalman filter was Incorporated in the data acquisition system in order to provide higher accuracy of the velocity vectors of the aircraft. Fig. 5 shows the block diagram of the Kalman filter and data transformation process used on the AIMMS-3 air-data system. The Kalman filter algorithm fused sensor data to estimated error state of the caused by the IMU drift using the GNSS position and velocity measurements. The error state was then being fed back to the IMU time-marched kinematics integration to correct the drift. Fig. 5. GustAV air-data system integration architecture. This method combines the fast sampling frequency of the IMU at 1 Hz while maintaining the accuracy of the IMU measurements with GNSS solutions. In the event of losing GNSS signal, which is required at 1 Hz in order to update the error state, the IMU kinematics integration continues to function without interference. The error state is updated once the GNSS solution becomes available again to the Kalman filter. This ensures robustness of the system and sampling frequency of the data acquisition system. The five-hole probe was placed strategically on GustAV in order to minimize any interference and inaccuracies of the measured flow field due the presence of the vehicle itself. Because of its tractor configuration, mounting the probe in front of the airplane nose was not a viable solution. Therefore, as shown in Fig. 6, the probe was positioned at the wingtip protruding forward in order to measure the 6

8 Page 7 of 15 flow in front of the wing and reduce the disturbance caused by the aircraft. Additionally, the AIMMS- 3 air-data system incorporates a parameterization scheme for static pressure, angle of attack (AoA), and angle of sideslip (AoS) offsets as functions of the local flow angles at the probe tip for the purpose of correcting for these flow effects. A set of nine parameters, three each for static pressure, AoA and AoS corrections, allows for the aerodynamic interference of the host platform to be corrected. These parameters are normally determined from a minimum-variance estimation scheme applied to flight data from a set of special maneuvers, but also can be inferred with aid of computational flow analysis tools. These parameters are considered constant for a given installation once it has been characterized. Further analysis was performed using a potential flow method in order to determine the flow effect that the flow field of the wing causes at the probe location (Yeung 217). Fig. 7 shows the offset that was calculated to be directly proportional to the AoA and AoS of the flight vehicle and similar offsets were observed in the flight data captured by the five-hole probe (Yeung 217). Fig. 6. Five-hole probe mounted on the right wing tip of GustAV. Fig. 7. Angle of attack and angle of sideslip measurement offset across various angles of attack. (Yeung 217) Five-Hole Probe Offset [degree] Angle of Attack Offset Angle of Sideslip Offset Angle of Attack [degree] 7

9 248 Page 8 of 15 Flight Test The flight experiments were performed at TEMAC Field (N W ) that the Toronto Electric Model Aviation Club operates near Stouffville, Ontario, about 4 km north of Toronto, Ontario, Canada. The flying field consists of a paved runway and a flying zone over a farm field with a crop height of less than 2 cm. The flying area measures approximately 3 meters by 4 meters as indicated by the red box in Fig. 8. As part of the planning of the experiments, a digital elevation model was obtained from the government of Ontario and the terrain surrounding of the flying field was studied. As shown in Fig. 8, elevation of the runway is 243 meters above sea level and the farm field that constitutes the flying area has a variation of less than 5 meters in elevation. Fig. 8. Elevation contour map of GustAV flight testing region at TEMAC field Distance North [m] Distance East [m] Prior to performing the actual gust-measurement test flights, GustAV completed 15 system-check flights between September 216 and May 217. The system-check flights were required in order to ensure a safe and reliable operation of the aircraft and its subsystems. The gust measuring mission of GustAV was set out to perform a racecourse pattern that consisted of four waypoints as indicated by the diamond markers in Fig. 9. The flight profile required the aircraft to fly straight-and-level in two opposing directions and allow the air-data system to measure wind with the aircraft flying in either direction. This way, any recorded bias due to aircraft heading and mean wind speed was reduced by comparing the measured results of both flight directions. 8

10 Page 9 of 15 Fig. 9. Flight path and waypoints of the racecourse circuit. Distance North [m] Distance East [m] Fig. 9 and Fig. 1 show the tracks of a typical test flight of GustAV. Takeoff of GustAV was performed under manual control by an RC-pilot. After reaching a safe altitude of 5 m, the autopilot was engaged and climb was resumed with a heading toward the first waypoint until the maximum allowable altitude of 15 m was reached. Once at altitude, the aircraft entered the racecourse pattern and completed two full circuits before descending by 25 m. The flight profile was repeated until the aircraft reached 5 m. After the circuits were completed, the pilot regained control and landed the aircraft. The Aventech AIMMS-3 air-data system records the 3-D wind field data during the flights, and the data were synchronized with the measurement of a ground weather station. The recording of gust data began after the aircraft had reached the required altitude and was maintaining stable flight for about five minutes. The delay was incorporated in order to ensure that the Kalman filter estimator and GNSS receivers had been given sufficient time to have come to a converged solution in order to have an improved accuracy. A 16-minute test flight was conducted using GustAV on the afternoon of 3 June 217. The threedimensional flight profile, altitude profile and airspeed of this flight are shown in Figs. 1 and 11, respectively. The atmospheric wind and its variations were measured during the flight from timestamp 125s to timestamp 175s while GustAV performed the experiment at altitudes of 1 m, 75 m, and 5 m above ground level (AGL) at TEMAC field. The mean wind velocity and direction at each segment are listed in Table 2 and used to extract the wind gust which is the air movements deviate from the mean wind. The subsequently acquired data were post-processed after the flight, which is discussed in the next section. 9

11 Page 1 of 15 Fig. 1. Flight test profile with the gust measurement segment at three different altitudes labeled. Altitude (AGL) [m] Distance North [m] Weather Station m 75m 5m 3 Distance East [m] 4 Fig. 11. Altitude and airspeed profiles of the flight experiment Gust Measurement Altitude (AGL) [m] m 75 m 5 m Airspeed [m/s] Time [s] 1

12 Page 11 of 15 Table 2. Summary of the flight experiment with segments flown at 1 m, 75 m, and 5 m. Reference AGL [m] Start time [s] End time [s] Altitude range [m] Mean wind velocity [m/s] Mean wind bearing [deg] Mean airspeed [m/s] Airspeed range [m/s] 1 m m m Results The gust data from the test intervals indicated in Fig. 11 were converted into power spectral density (PSD) using a fast Fourier transformation (FFT) algorithm in MATLAB (MathWorks Inc. 26). Fig. 12 shows FFT result of the measured gust spectra at a altitude of 5 m. The power spectral densities of the von Kármán turbulence model were plotted over the experimental results as magenta lines. A regression analysis was performed on the flight data in order to create a separate set of intensities and length scales that is compared to the von Kármán prediction. The subsequent experimentally derived power densities are shown in Fig. 12. The green dashed curves were placed to best-fit the measured gust spectra using the new turbulence intensities and length scales. Table 3 lists the results of the regression analysis of the data of three altitudes and the corresponding turbulence parameters that were derived from the experimental data. The knee of the power spatial density curve, that is, a leveling off with lower frequencies, was observed at the spectral frequency of approximately 1 3 of each spectrum, which loosely follows the knee location provided by the von Kármán model (Hoblit 1988). This signifies the frequency range, below which the power special density tapers and remains constant. Fig. 12. Gust spectra (longitudinal, lateral, and vertical) at altitudes of 5 m calculated from the flight data and the von Kármán models (U.S. Department of Defense 1997) Power spectral density [(m/s) 2 /(cycle/m)] Power spectral density [(m/s) 2 /(cycle/m)] Power spectral density [(m/s) 2 /(cycle/m)] MIL-HDBK-1797 Flight Data Fit MIL-HDBK-1797 Flight Data Fit MIL-HDBK-1797 Flight Data Fit Spatial frequencey ( ) [rad/m] Spatial frequencey ( ) [rad/m] Spatial frequencey ( ) [rad/m] Longitudinal gusts. Lateral gusts. Vertical gusts. 11

13 Page 12 of 15 Table 3. Turbulence intensities and length scale parameters calculated using non-linear curve fitting of the data presented in Fig. 12. Reference Longitudinal gust Lateral gust Vertical gust Altitude Length scale Intensity Length scale Intensity Length scale Intensity (AGL) [m] L u[m] σ u[m/s] L v[m] σ v[m/s] L w[m] σ w[m/s] 1 m m m The turbulence intensities of the the von Kármán turbulence model, σ, were determined using the approach that is detailed in MIL-HDBK-1797 (U.S. Department of Defense 1997). This method requires as an input the wind speed measured at the height of 2 feet (6 m). The wind speed at 2 feet was estimated by combining the wind measured by the air-data system at the flight altitude (5 m - 1 m) and the weather ground station (2 m) using the wind profile power law provided in Simiu and Scanlan (Simiu et al. 1978). Fig. 13 shows the resultant curve along with the instantaneous wind speed measured at various altitudes during the experiment shown as scattered dots. The result predicted by the wind power law showed a close relationship with the measured wind speed from the altitude of 1 m all the way down to the ground surface. The parameters that were calculated using the MIL-HDBK method are listed in Table 4. These parameters were applied to the von Kármán turbulence model in order to generate the power special density curve represented by the magenta lines in Fig. 12. Fig. 13. Wind speed estimation curve provided by wind power law and instantaneous wind speed measured at various altitudes during the experiment. 12 U 1m = 5. m/s 1 Altitude (AGL) [m] U 2m =.91 m/s 2 U 2ft = U 6m = 1.46 m/s Wind speed [m/s] 12

14 Page 13 of 15 Table 4. Turbulence intensities and length scale parameters calculated using the method provided in MIL- HDBK-1797 (U.S. Department of Defense 1997). Reference Longitudinal gust Lateral gust Vertical gust Altitude Length scale Intensity Length scale Intensity Length scale Intensity (AGL) [m] L u[m] σ u[m/s] L v[m] σ v[m/s] L w[m] σ w[m/s] 1 m m m When comparing with the von Kármán model, the measured longitudinal and lateral gust spectra match best across the spatial frequencies between 1 3 to 1 1 rad/m with a slight shift to the higher frequencies. This reflects a higher turbulence intensities were measured during the flight test, especially along the longitudinal direction. The result suggests the atmosphere was less stable during the experiments than the idealized atmosphere that formed the basis of the MIL-HDBK-1797 von Kármán turbulence model. As shown in Fig. 12, larger deviations from the model were observed in the vertical gust measurements than observed in the longitudinal and lateral directions. The power spectral density did not taper at the frequency of 1 2, which is shown as the knee on the von Kármán model predictions using the parameters provided by the MIL-HDBK Discussion The turbulence intensities and length scales that were derived from the flight-test results and from the von Kármán model are compared in Fig. 14 (U.S. Department of Defense 1997). Since the flight data was only available from three altitudes on the same day, it was difficult to draw a distinctive conclusion. Nevertheless, the flight test data have trends that one expects in general. For example, the experimentally derived turbulence intensities follows the relative relationships that Etkin reports (Etkin 1981). The turbulence intensities have similar relative magnitudes, that is, from largest to smallest are the longitudinal, lateral, and vertical intensity or σ u > σ v > σ w. Although the turbulence intensities derived from the flight experiments do not exactly follow the values of the von Kármán model, they exhibit similar slopes, especially at higher altitudes, when compared to the theoretical values that are also shown in the plot. The turbulence length scale that was derived using the flight-test data, however, increases at a much faster rate with altitude than the the theoretical model predicts. This higher gust intensities observed during the flight experiment may be caused by better mixing of the atmospheric boundary layer in the afternoon when the experiment took place. The gust intensity results from the flight data suggest that the aircraft encountered stronger gusts during the flight than predicted by the model, which may have been caused by convective activities in the atmospheric boundary layer at the time of the experiment. 13

15 Page 14 of 15 Fig. 14. Comparison between the gust parameters curve-fitted from gust measurements and the empirical models from MIL-HDBK MIL-HDBK-1797 von Karman Flight test fit: u Flight test fit: v Flight test fit: w 12 1 MIL-HDBK-1797 von Karman Flight test fit: L u Flight test fit: L v Flight test fit: L w L w Non-dimensionalized turbulence intensities, /U u, v u v w Turbulence Length scale [m] L u L v L u.1.5 w Altitude [m] 2 L v L w Altitude [m] Conclusions Based on the limited amount of experimental data that were taken, one can conclude that the derived turbulence intensities suggests that there are anisotropic properties in the lower parts of the atmospheric boundary layer in all three directions. Convection in the atmospheric boundary layer during the flight experiment may have contributed to the higher gust intensities that were observed with the flight data in comparison to the MIL-HDBK-1797 model. Future flight tests should take place to further investigate the turbulence levels at different times of the day and different locations. This comparison was a preliminary attempt to validate the published turbulence model at low altitudes for unmanned aerial vehicles research and development. The result from the flight test on the afternoon of 3 June 217 show some disagreement between the von Kármán model and the measured low-frequency vertical gust at altitudes of under 1 m. By repeating the measurement above the same area at different weather and wind conditions will provide a generalized results and be compared with the existing models. A regression analysis of measurements taken under different conditions can produce more accurate solutions to turbulence intensities and length scale parameters for that specific surface terrain. In addition, large-scale surveying over a longer period of time of a variety of terrain features will produce a more generalized model that can compliment the existing MIL-HDBK-1797 von Kármán model with a model more suitable for small unmanned aerial vehicles operating at low altitudes. Future flights should also include higher altitudes to observe if the flight measurements agree with the von Kármán model as the altitude increases. Acknowledgements This research was made possible through support from the Molson Foundation, the Ontario Centres of Excellence and Aventech Research Inc., which provided the air-data system. The authors would also like to express their thanks to Mr. Frank van Beurden and the Toronto Electric Model Aviation Club (TEMAC) for assistance. 14

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