journal of unmanned vehicle systemsing adopted by the commercial sector, consumer market, and...

Draft

Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV

Journal: Journal of Unmanned Vehicle Systems

Manuscript ID juvs-2017-0029.R1

Manuscript Type: Article

Date Submitted by the Author: 11-Jun-2018

Complete List of Authors: Yeung, Alton; Ryerson University, Aerospace EngineeringBramesfeld, Götz; RYERSON UNIVERSITY, Aerospace EngineeringChung, Joon; Ryerson University, Aerospace EngineeringFoster, Stephen; Aventech Research Inc.

Keyword: UAV, Gust, low-altitude, five-hole probe, air-data system

Is the invited manuscript for consideration in a Special

Issue? :Not applicable (regular submission)

https://mc06.manuscriptcentral.com/juvs-pubs

Journal of Unmanned Vehicle Systems

Draft

1

Measuring low-altitude wind gustsusing the unmanned aerial vehicleGustAV

Alton Yeung, Goetz Bramesfeld, Joon Chung, and Stephen Foster

Abstract: A small unmanned aerial vehicle (SUAV) was developed with the specific objectiveto explore atmospheric wind gusts at low altitudes below 500 meters. These gusts havesignificant impacts on the flight characteristics and performance of SUAVs. The SUAVcarried an advanced air-data system that includes a five-hole probe, which was adapted forthis specific application. In several flight tests the entire test system was qualified and gustdata were recorded. The subsequent experimentally derived gust data were post-processed andcompared with turbulence spectra of the MIL-HDBK-1797 von Karman turbulence model.On the day of the flight test, the experimental results did not fully match the prediction ofthe von Karman model. Meanwhile, the wind measuring apparatus were proven to be able tomeasure gust during flight. Therefore, a broader sampling will be required to generalize thegust 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 ofdecades. The advancements of miniaturization of sensors have led small unmanned aerial vehicles be-ing adopted by the commercial sector, consumer market, and research communities. Many of thesesmall unmanned aerial vehicles are performing missions such as search and rescue, mapping, envi-ronmental studies, aerial imaging, and meteorology (Spence et al. 2016, Duncan et al. 2014, d’Oleire-Oltmanns et al. 2012, Tilly et al. 2016, Bonin et al. 2013). These vehicles often operate at less than500 m above the terrain, due to the nature of their missions, engine output capability, and regulatorylimitations. Furthermore, the slower flight speeds and lower flight masses of SUAVs also mean thatless intense atmospheric turbulence will have a great affect on their flight dynamics and performancethan of those of full-sized aircraft (Spedding et al. 1998). Unpredictable atmospheric gusts can create achallenging environment for SUAVs to operate in safely and efficiently. Therefore, a representative gustmodel can help to improve our understanding of the environmental factors and lead to better predictionmethods of flight characteristics of SUAVs during low-altitude operations.

A better understanding of the atmospheric conditions can also lead to significant flight performanceimprovements 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 (e-mail: [email protected]).

unknown 99: 1–15 (2018) Proof/Epreuve Published by NRC Research Press

Page 1 of 15



Draft

spheric turbulence by using ‘gust-soaring’. For example, Langelaan has shown the potential for signif-icant range and endurance improvements for SUAVs that use the energy present in gusts (Langelaan2009). He also pointed out that there are little empirical data on gusts available in literature that suitablefor SUAV development. Galway has investigated the effect of turbulent wind generated by buildingson SUAVs (Galway 2009, Galway 2011). The MIL-HDBK-1797 von Karman model (U.S. Departmentof 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 turbulenceencounter by full-scale aircraft during cruise (Pisano 2009).

The mathematical expressions of the von Karman power spectral density function Φ(Ω) for longi-tudinal gust and transverse gust (vertical or lateral) are:

Φu(Ω) = σ2L

π

1

[1 + (1.339LΩ)2]5/6(1)

Φv(Ω) = Φw(Ω) = σ2L

π

1 + 83 (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 feetin the vertical, σw, and horizontal plane, σu and σv , based on the the wind speed at 20 ft above ground,U20:

σw = 0.1U20 (3)

σu = σv =σw

(0.177 + 0.000823h)0.4(4)

The document also outlines the empirical expressions for the turbulence length scale parameters ataltitude h in feet:

2Lw = h (5)

Lu = 2Lv =h

(0.177 + 0.000823h)1.2(6)

Fig. 1 shows an example of a von Karman power spectral density plot with vertical turbulence in-tensity of 1 m/s and length scale parameter of 2500 m. The turbulence parameters describe abovechanges power spectral density curve and the knee of the curve. The knee corresponds to a sharpchange on the curve which is modeled by the the von Karman power spectral density functions. Hence,the von Karman method is able to model the gusts across different frequencies and intensities withbetter results than a linear model.

2

Page 2 of 15



Draft

Fig. 1. Von Karman gust power spectral densitycurves, L = 2500 m.

10-4 10-3 10-2

Spatial frequencey ( ) [rad/m]

100

101

102

103

104

Pow

er s

pect

ral d

ensi

ty [(

m/s

)2 /(cy

cle/

m)]

Due to a lack of other validated small-scale aircraft turbulence models, researchers, especially theones who focus on control system design, adopt the MIL-HDBK-1797 formulations of the von Karmangust model or similar models, which may not be truly descriptive of the gusty environment that istypically encountered by SUAVs. These existing gust models were not specifically designed for smallaircraft and can potentially be enhanced with more gust data captured at low altitudes where mostSUAVs operate. In order to conduct such research, an affordable and reliable low altitude gust sensingplatform was developed. Furthermore, a regression analysis was applied to the gusts that were measuredduring the experiment. The subsequent results were compared to data of the MIL-HDBK-1797 gustmodel.

UAVs in general have become major atmospheric research tools as a consequence of the continuedadvancements of smaller and more capable airborne sensors. This development is further supportedby greatly improved autonomous flight-control systems that have made SUAVs highly reliable. ManySUAVs are capable of reaching altitudes of over 1000 m with payloads of various shapes and sizes. Ingeneral, UAVs provide several advantages over manned platforms, for example, drastically lower oper-ating cost compared to similar missions using manned aircraft. Moreover, electrically powered SUAVsare especially suitable for atmospheric sampling as exhaust gas from internal combustion engine cancause problems with measuring gas compositions. Unmanned vehicles can also perform flights a lotcloser to the ground without unnecessarily endangering a crew, which is beneficial to low-altitude gustmeasurement.

3

Page 3 of 15



Draft

Fig. 2. Photo of GustAV aerial research platform flying with a five-hole probe mounted on the wing tip.

In order to acquire wind gust speed data at low altitudes, a SUAV was developed at Ryerson Uni-versity. The small airborne platform, GustAV, or Gust Aerial Vehicle, is shown in Fig. 2. It uses anadvanced air-data system that includes a five-hole probe that measures atmospheric gusts. The objec-tives 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 Karman model.

GustAV

GustAV is an SUAV that was designed with the particular objective to be suitable for flight tests atvarious remote locations. Thus, besides having a relatively simple airframe that is sufficiently robustfor field handling and can easily be reconfigured for a variety of experiments, GustAV was designedwith easy flight and handling qualities in mind. In the current version, the aircraft is equipped withan air-data system in order to measure wind data. Furthermore, an optional autopilot allows for au-tonomous flight, for example when trying to repeatedly sample wind vectors over the same location. Adetailed description of the entire system is provided in (Yeung 2017), but this section provides a briefdescription.

Unmanned Aerial VehicleThe 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 wingis reinforced with an aluminum spar and fiberglass skin in order to minimize bending during flight andreduce the chance of damage during ground handling. The summary of specifications and performanceof GustAV are listed in Table 1.

GustAV is equipped with a Pixhawk autopilot and controlled using Ardupilot firmware, an open-source flight controller software package (ArduPilot Dev Team 2016). The autopilot provides the au-tonomous flight capability and vehicle system monitoring during the mission via a two-way commu-nication. Yeung and Bramesfeld provide a detailed information of the system integration and dataprocessing of this unmanned vehicle system (Yeung et al. 2016).

4

Page 4 of 15



Draft

Fig. 3. Three-view of GustAV. (All dimensions are in mm)

2600

419

286

610

229

457

173

1270

484

1690352

431

197

Table 1. Specification and performance of GustAV.

Operating Mass 7.52 kgWing span 2.60 mWing planform area 1.0 m2

Mean aerodynamic chord 0.41 mAirspeed (cruise/min/max) 15 ms-1/10 ms-1/25 ms-1

Endurance 25 minsMotor 900 W electric brushlessMain battery 22.2 V, 8000 mAhAvionics battery 11.1 V, 2200 mAh

Air-Data System

The payload of GustAV consists a commercially available air-data system, Aventech AIMMS-30,that was integrated into the airframe. The AIMMS-30 is capable to measure the three-dimensional windvector in flight. The system was designed by Aventech Research Inc. and it consists of three types ofsensors: a wing mounted five-hole probe that measures the three-dimensional wind field, an inertialmeasurement 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 theaircraft relative to the inertial reference frame. By subtracting the measurements between the inertialand body reference frames, the wind vector solution can be obtained as illustrated in Fig. 4. Withthis setup, the air-data system is capable to measure atmospheric gusts of 0.5 m/s or larger (AventechResearch Inc. 2016).

5

Page 5 of 15



Draft

Fig. 4. Wind measurement reference frames.

A Kalman filter was Incorporated in the data acquisition system in order to provide higher accuracyof the velocity vectors of the aircraft. Fig. 5 shows the block diagram of the Kalman filter and datatransformation process used on the AIMMS-30 air-data system. The Kalman filter algorithm fusedsensor data to estimated error state of the caused by the IMU drift using the GNSS position and velocitymeasurements. The error state was then being fed back to the IMU time-marched kinematics integrationto correct the drift.

Fig. 5. GustAV air-data system integration architecture.

This method combines the fast sampling frequency of the IMU at 100 Hz while maintaining the ac-curacy of the IMU measurements with GNSS solutions. In the event of losing GNSS signal, which isrequired at 1 Hz in order to update the error state, the IMU kinematics integration continues to functionwithout interference. The error state is updated once the GNSS solution becomes available again to theKalman filter. This ensures robustness of the system and sampling frequency of the data acquisitionsystem.

The five-hole probe was placed strategically on GustAV in order to minimize any interference andinaccuracies of the measured flow field due the presence of the vehicle itself. Because of its tractorconfiguration, mounting the probe in front of the airplane nose was not a viable solution. Therefore, asshown in Fig. 6, the probe was positioned at the wingtip protruding forward in order to measure the

6

Page 6 of 15



Draft

flow in front of the wing and reduce the disturbance caused by the aircraft. Additionally, the AIMMS-30 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 purposeof correcting for these flow effects. A set of nine parameters, three each for static pressure, AoA andAoS corrections, allows for the aerodynamic interference of the host platform to be corrected. Theseparameters are normally determined from a minimum-variance estimation scheme applied to flightdata from a set of special maneuvers, but also can be inferred with aid of computational flow analysistools. 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 thatthe flow field of the wing causes at the probe location (Yeung 2017). Fig. 7 shows the offset that wascalculated to be directly proportional to the AoA and AoS of the flight vehicle and similar offsets wereobserved in the flight data captured by the five-hole probe (Yeung 2017).

Fig. 6. Five-hole probe mounted on the right wing tip ofGustAV.

Fig. 7. Angle of attack and angle of sideslip measurement offsetacross various angles of attack. (Yeung 2017)

-5 0 5 10 15

Angle of Attack [degree]

-1

0

1

2

3

4

5

Fiv

e-H

ole

Pro

be O

ffset

[deg

ree]

Angle of Attack OffsetAngle of Sideslip Offset

7

Page 7 of 15



Draft

Flight Test

The flight experiments were performed at TEMAC Field (N4357′11.9′′ W7919′15.1′′) that theToronto Electric Model Aviation Club operates near Stouffville, Ontario, about 40 km north of Toronto,Ontario, Canada. The flying field consists of a paved runway and a flying zone over a farm field witha crop height of less than 20 cm. The flying area measures approximately 300 meters by 400 metersas indicated by the red box in Fig. 8. As part of the planning of the experiments, a digital elevationmodel was obtained from the government of Ontario and the terrain surrounding of the flying field wasstudied. As shown in Fig. 8, elevation of the runway is 243 meters above sea level and the farm fieldthat constitutes the flying area has a variation of less than 5 meters in elevation.

Fig. 8. Elevation contour map of GustAV flight testing re-gion at TEMAC field.

239

239

240

240

241

241

241

241

241242

242

242

242

243

243

243

243

244

244

244 244

245

245

245

245

246

246

246

246246

247

247

247

248

248

24

8

249 249

250

250

250

251

251252

252

253

254255

256

257 258

-200 -100 0 100 200 300 400 500

Distance East [m]

-400

-300

-200

-100

0

100

200

300

400

Dis

tan

ce

No

rth

[m

]

Prior to performing the actual gust-measurement test flights, GustAV completed 15 system-checkflights between September 2016 and May 2017. The system-check flights were required in order toensure a safe and reliable operation of the aircraft and its subsystems. The gust measuring mission ofGustAV was set out to perform a ‘racecourse’ pattern that consisted of four waypoints as indicated bythe diamond markers in Fig. 9. The flight profile required the aircraft to fly straight-and-level in twoopposing directions and allow the air-data system to measure wind with the aircraft flying in eitherdirection. This way, any recorded bias due to aircraft heading and mean wind speed was reduced bycomparing the measured results of both flight directions.

8

Page 8 of 15



Draft

Fig. 9. Flight path and waypoints of the ‘racecourse’ circuit.

-200 -100 0 100 200 300 400

Distance East [m]

-250

-200

-150

-100

-50

0

50

100

150

200

Dis

tanc

e N

orth

[m]

Fig. 9 and Fig. 10 show the tracks of a typical test flight of GustAV. Takeoff of GustAV was per-formed under manual control by an RC-pilot. After reaching a safe altitude of 50 m, the autopilot wasengaged and climb was resumed with a heading toward the first waypoint until the maximum allow-able altitude of 150 m was reached. Once at altitude, the aircraft entered the ‘racecourse’ pattern andcompleted two full circuits before descending by 25 m. The flight profile was repeated until the aircraftreached 50 m. After the circuits were completed, the pilot regained control and landed the aircraft.

The Aventech AIMMS-30 air-data system records the 3-D wind field data during the flights, andthe data were synchronized with the measurement of a ground weather station. The recording of gustdata began after the aircraft had reached the required altitude and was maintaining stable flight forabout five minutes. The delay was incorporated in order to ensure that the Kalman filter estimator andGNSS receivers had been given sufficient time to have come to a converged solution in order to havean improved accuracy.

A 16-minute test flight was conducted using GustAV on the afternoon of 3 June 2017. The three-dimensional flight profile, altitude profile and airspeed of this flight are shown in Figs. 10 and 11,respectively. The atmospheric wind and its variations were measured during the flight from timestamp1250s to timestamp 1750s while GustAV performed the experiment at altitudes of 100 m, 75 m, and50 m above ground level (AGL) at TEMAC field. The mean wind velocity and direction at each segmentare listed in Table 2 and used to extract the wind gust which is the air movements deviate from the meanwind. The subsequently acquired data were post-processed after the flight, which is discussed in thenext section.

9

Page 9 of 15



Draft

Fig. 10. Flight test profile with the gust measurement segment at three different altitudes la-beled.

02550

300

75100

Alti

tude

(A

GL)

[m]

125150

200

100400Distance North [m]

0 300-100 200

Distance East [m]100-2000

-300-100

Weather Station

50m75m100m

Fig. 11. Altitude and airspeed profiles of the flight experiment.

800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800

Time [s]

0

25

50

75

100

125

150

175

200

A

ltitu

de (

AG

L) [m

]

0

5

10

15

20

25

30

35

Airs

peed

[m/s

]

Gust Measurement

100 m

75 m

50 m

10

Page 10 of 15



Draft

Table 2. Summary of the flight experiment with segments flown at 100 m, 75 m, and 50 m.

ReferenceAGL [m]

Starttime [s]

Endtime [s]

Altituderange [m]

Mean windvelocity [m/s]

Mean windbearing [deg]

Meanairspeed [m/s]

Airspeedrange [m/s]

100 m 1330 1460 96.7 - 107.1 5.00 287.3 21.6 19.5 - 23.875 m 1485 1615 71.3 - 81.0 3.72 284.2 21.5 18.0 - 24.250 m 1630 1730 46.0 - 56.9 3.21 294.5 21.6 18.1 - 25.1

Results

The gust data from the test intervals indicated in Fig. 11 were converted into power spectral den-sity (PSD) using a fast Fourier transformation (FFT) algorithm in MATLAB (MathWorks Inc. 2006).Fig. 12 shows FFT result of the measured gust spectra at a altitude of 50 m. The power spectral densitiesof the von Karman 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 intensi-ties and length scales that is compared to the von Karman prediction. The subsequent experimentallyderived power densities are shown in Fig. 12. The green dashed curves were placed to best-fit the mea-sured gust spectra using the new turbulence intensities and length scales. Table 3 lists the results of theregression analysis of the data of three altitudes and the corresponding turbulence parameters that werederived from the experimental data.

The knee of the power spatial density curve, that is, a leveling off with lower frequencies, wasobserved at the spectral frequency of approximately 10−3 of each spectrum, which loosely follows theknee location provided by the von Karman 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 50 m calculated from the flight data andthe von Karman models (U.S. Department of Defense 1997).

10-4 10-3 10-2 10-1


10-2

10-1

100

101

102

103

Pow

er s

pect

ral d

ensi

ty [(

m/s

)2 /(cy

cle/

m)]

MIL-HDBK-1797Flight Data Fit

Longitudinal gusts.

10-4 10-3 10-2 10-1


10-2

10-1

100

101

102

103

Pow

er s

pect

ral d

ensi

ty [(

m/s

)2 /(cy

cle/

m)]


Lateral gusts.

10-4 10-3 10-2 10-1


10-2

10-1

100

101

102

103

Pow

er s

pect

ral d

ensi

ty [(

m/s

)2 /(cy

cle/

m)]


Vertical gusts.

11

Page 11 of 15



Draft

Table 3. Turbulence intensities and length scale parameters calculated using non-linear curve fitting of thedata presented in Fig. 12.

Reference Longitudinal gust Lateral gust Vertical gustAltitude Length scale Intensity Length scale Intensity Length scale Intensity

(AGL) [m] Lu[m] σu[m/s] Lv[m] σv[m/s] Lw[m] σw[m/s]

100 m 655.30 0.34 694.57 0.30 1033.49 0.2375 m 411.98 0.48 496.59 0.35 608.83 0.2750 m 148.63 0.51 323.67 0.41 132.41 0.26

The turbulence intensities of the the von Karman turbulence model, σ, were determined usingthe approach that is detailed in MIL-HDBK-1797 (U.S. Department of Defense 1997). This methodrequires as an input the wind speed measured at the height of 20 feet (6 m). The wind speed at 20 feetwas estimated by combining the wind measured by the air-data system at the flight altitude (50 m -100 m) and the weather ground station (2 m) using the wind profile power law provided in Simiu andScanlan (Simiu et al. 1978). Fig. 13 shows the resultant curve along with the instantaneous wind speedmeasured at various altitudes during the experiment shown as scattered dots. The result predicted bythe wind power law showed a close relationship with the measured wind speed from the altitude of 100m all the way down to the ground surface. The parameters that were calculated using the MIL-HDBK-1797 method are listed in Table 4. These parameters were applied to the von Karman turbulence modelin 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 lawand instantaneous wind speed measured at various altitudes during theexperiment.

0 2 4 6 8 10

Wind speed [m/s]

0

20

40

60

80

100

120

Alti

tude

(A

GL)

[m]

U100m

= 5.00 m/s

U2m

= 0.91 m/s

U20ft

= U6m

= 1.46 m/s

12

Page 12 of 15



Draft

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 gustAltitude Length scale Intensity Length scale Intensity Length scale Intensity

(AGL) [m] Lu[m] σu[m/s] Lv[m] σv[m/s] Lw[m] σw[m/s]

100 m 505.17 0.21 252.58 0.21 50.0 0.1575 m 418.39 0.21 209.2 0.21 37.5 0.1450 m 310.79 0.32 155.39 0.32 25.0 0.20

When comparing with the von Karman model, the measured longitudinal and lateral gust spectramatch best across the spatial frequencies between 10−3 to 10−1 rad/m with a slight shift to the higherfrequencies. This reflects a higher turbulence intensities were measured during the flight test, especiallyalong the longitudinal direction. The result suggests the atmosphere was less stable during the exper-iments than the ‘idealized’ atmosphere that formed the basis of the MIL-HDBK-1797 von Karmanturbulence model. As shown in Fig. 12, larger deviations from the model were observed in the ver-tical gust measurements than observed in the longitudinal and lateral directions. The power spectraldensity did not taper at the frequency of 10−2, which is shown as the knee on the von Karman modelpredictions using the parameters provided by the MIL-HDBK-1797.

Discussion

The turbulence intensities and length scales that were derived from the flight-test results and fromthe von Karman model are compared in Fig. 14 (U.S. Department of Defense 1997). Since the flightdata was only available from three altitudes on the same day, it was difficult to draw a distinctiveconclusion. Nevertheless, the flight test data have trends that one expects in general. For example, theexperimentally derived turbulence intensities follows the relative relationships that Etkin reports (Etkin1981). The turbulence intensities have similar relative magnitudes, that is, from largest to smallest arethe longitudinal, lateral, and vertical intensity or σu > σv > σw. Although the turbulence intensitiesderived from the flight experiments do not exactly follow the values of the von Karman model, theyexhibit similar slopes, especially at higher altitudes, when compared to the theoretical values that arealso 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 gustintensities observed during the flight experiment may be caused by better mixing of the atmosphericboundary layer in the afternoon when the experiment took place. The gust intensity results from theflight data suggest that the aircraft encountered stronger gusts during the flight than predicted by themodel, which may have been caused by convective activities in the atmospheric boundary layer at thetime of the experiment.

13

Page 13 of 15



Draft

Fig. 14. Comparison between the gust parameters curve-fitted from gust measurements and the empirical modelsfrom MIL-HDBK-1797.

0 20 40 60 80 100 120

Altitude [m]

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Non

-dim

ensi

onal

ized

turb

ulen

ce in

tens

ities

, /U

20

u,

v

w

u

v

w

MIL-HDBK-1797 von KarmanFlight test fit:

u

Flight test fit: v

Flight test fit: w

0 20 40 60 80 100 120

Altitude [m]

0

200

400

600

800

1000

1200

Tur

bule

nce

Le

ngth

sca

le [m

]

Lu

Lv

Lw

Lu

Lv

Lw

MIL-HDBK-1797 von KarmanFlight test fit: L

u

Flight test fit: Lv

Flight test fit: Lw

Conclusions

Based on the limited amount of experimental data that were taken, one can conclude that the derivedturbulence intensities suggests that there are anisotropic properties in the lower parts of the atmosphericboundary layer in all three directions. Convection in the atmospheric boundary layer during the flightexperiment may have contributed to the higher gust intensities that were observed with the flight datain comparison to the MIL-HDBK-1797 model. Future flight tests should take place to further inves-tigate the turbulence levels at different times of the day and different locations. This comparison wasa preliminary attempt to validate the published turbulence model at low altitudes for unmanned aerialvehicles research and development. The result from the flight test on the afternoon of 3 June 2017 showsome disagreement between the von Karman model and the measured low-frequency vertical gust ataltitudes of under 100 m. By repeating the measurement above the same area at different weatherand 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 accuratesolutions to turbulence intensities and length scale parameters for that specific surface terrain. In addi-tion, large-scale surveying over a longer period of time of a variety of terrain features will produce amore generalized model that can compliment the existing MIL-HDBK-1797 von Karman model witha model more suitable for small unmanned aerial vehicles operating at low altitudes. Future flightsshould also include higher altitudes to observe if the flight measurements agree with the von Karmanmodel as the altitude increases.

Acknowledgements

This research was made possible through support from the Molson Foundation, the Ontario Centresof Excellence and Aventech Research Inc., which provided the air-data system. The authors wouldalso like to express their thanks to Mr. Frank van Beurden and the Toronto Electric Model AviationClub (TEMAC) for assistance.

14

Page 14 of 15



Draft

References

ArduPilot Dev Team. 2016. SITL Simulator (Software in the Loop). http://ardupilot.org/dev/docs/sitl-simulator-software-in-the-loop.html. [accessed October 2017].

Aventech Research Inc. 2016. AIMMS-30 Technical Brochure. http://aventech.com/products/aimms30.html. [accessed May 2017].

Bonin, T. et al. 2013. “Observations of the Early Evening Boundary-Layer Transition Using a SmallUnmanned Aerial System”. In: Boundary-Layer Meteorology 146.1, pp. 119–132. DOI: 10.1007/s10546-012-9760-3.

d’Oleire-Oltmanns, S. et al. 2012. “Unmanned aerial vehicle (UAV) for monitoring soil erosion inMorocco”. In: Remote Sensing 4.11, pp. 3390–3416. DOI: 10.3390/rs4113390.

Duncan, B. A. and Murphy, R. R. 2014. “Autonomous Capabilities for Small Unmanned Aerial Sys-tems Conducting Radiological Response: Findings from a Highfidelity Discovery Experiment”. In:Journal of Field Robotics 31.4, pp. 522–536. DOI: 10.1002/rob.21503.

Etkin, B. 1981. “Turbulent Wind and Its Effect on Flight”. In: Journal of Aircraft 18.5, pp. 327–345.DOI: 10.2514/3.57498.

Galway, D. 2009. Urban Wind Modeling with Application to Autonomous Flight.Galway, D. 2011. “Modeling of Urban Wind Field Effects on Unmanned Rotorcraft Flight”. In: Journal

of Aircraft 48.5, pp. 1613–1620.Hoblit, F. M. 1988. Gust loads on aircraft : concepts and applications. American Institute of Aeronau-

tics and Astronautics, p. 306.Langelaan, J. W. 2009. “Gust Energy Extraction for Mini and Micro Uninhabited Aerial Vehicles”. In:

Journal of Guidance, Control, and Dynamics 32.2, pp. 464–473. DOI: 10.2514/1.37735.MathWorks Inc. 2006. Periodogram power spectral density estimate. https://www.mathworks.

com/help/signal/ref/periodogram.html. [accessed May 2017].Pisano, W. J. 2009. The development of an autonomous gust insensitive unmanned aerial vehicle.Simiu, E. and Scanlan, R. H. 1978. Wind effects on structures : an introduction to wind engineering.

Wiley, p. 458.Spedding, G. R. and Lissaman, P. B. S. 1998. “Technical Aspects of Microscale Flight Systems”. In:

Journal of Avian Biology 29.4, pp. 458–468. DOI: 10.2307/3677165.Spence, C. and Mengistu, S. 2016. “Deployment of an unmanned aerial system to assist in mapping an

intermittent stream”. In: Hydrological Processes 30.3, pp. 493–500. DOI: 10.1002/hyp.10597.Tilly, N. et al. 2016. “Geomorphological Mapping With Terrestrial Laser Scanning And Uav-based

Imaging”. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial In-formation Sciences 41.July, pp. 591–597. DOI: 10.5194/isprsarchives-XLI-B5-591-2016.

U.S. Department of Defense. 1997. U.S. Military Handbook MIL-HDBK-1797. Tech. rep.Yeung, A. 2017. Wind Gust Measuring at Low Altitude Using an Unmanned Aerial System.Yeung, A. and Bramesfeld, G. 2016. “Measuring Atmospheric Gusts at Low Altitude”. In: 35th AIAA

Applied Aerodynamics Conference. Denver, Colorado.

15

Page 15 of 15



journal of unmanned vehicle systemsing adopted by the commercial sector, consumer market, and...

Documents