Experimental Investigation of Optimal Aerodynamics of a Flying Wing UAV(Link)

EXPERIMENTAL INVESTIGATION OF

OPTIMAL AERODYNAMICS OF A

FLYING WING UAV

Baba Kakkar

A final year project submitted in partial fulfilment for the

degree of Masters in Aerospace Engineering

University of Bath

April 2016

Experimental Investigation of Optimal Aerodynamics of a Fly-

ing Wing UAV

Department of Mechanical Engineering

University of Bath

Supervisor: Dr. David Cleaver

Assessor: Dr. Zhijin Wang

April 2016

Abstract

There is currently a growing interest in UAV’s, due to their applications in numerous markets.

The application of UAV in low Reynolds number creates several challenges in order to maintain

stable flight under harsh weather conditions, especially for a flying wing concept. Previous work

by aerodynamicist have concentrated on blended wing configurations in civil and transonic flights,

which limits the understanding at low Reynolds numbers. This concept is usually chosen due to

its advantages with improved aerodynamic performance. However, as flying wings generally have

high sweep and low aspect ratio to compensate for control, stall behaviour can be a great challenge

especially at the tip, which is highly loaded. Wing tip stall is a big challenge. As the aircraft

looses lift at the tip during turbulent weather conditions, it starts to roll with the opposite tip

rising leading into a dive.

In this project the optimal aerodynamic planform is experimentally investigated focusing on three

aspects: aerodynamic performance, stall behaviour and longitudinal stability. It was highlighted

from the literature review, that during the design phase, four planform characteristics are directly

effected; aspect ratio, taper ratio, geometric and aerodynamic twist. The objectives for this

study was then identified as; design and build the test rig of a half span model and record

steady state measurements of force, moments and pressure. Incorporate wing planform changes,

looking at variation in aspect ratio, linear washout and aerodynamic twist. Finally, to make the

necessary changes to the flying wing concept, which will then be entered into the IMechE UAS

competition.

Results presented in this report, demonstrate that the aerodynamic performance, stall and con-

trol behaviour improvements can be achieved. Higher aspect ratios, increased the aerodynamic

performance of the aircraft but the stall behaviour was directly effected. On the other hand,

washout improved the stall behaviour, but not eliminated and aerodynamic performance was re-

duced. However, it was found that changing the camber of the wing, to have a thicker airfoil at

the tip, increased the aerodynamic performance as well as the stall behaviour.

From this study, the optimum aerodynamic planform was found, which was changing the airfoil

from MH45 at the root to a more stable airfoil, S822 at the tip. The first two objectives were

accomplished, which were set out for this project. The final objective will be achieved, upon the

completion of Skyseeker, which will then be entered in to the 2016 IMechE competition.

III

Acknowldgements

I would like to acknowledge the valuable assistance of the following individuals, as without their

continued support and assistance this work would not have been possible:

First and foremost, I would like to start by thanking my supervisor and assessor Dr. David Cleaver

and Dr. Zhijin Wang, who has been the backbone of my work. Without their endless patience,

advice and guidance this work would not have been possible.

I have faced many challenges in order to complete this project; the advice, guidance and help

from the electronics and material technicians, Vijay Rajput and Steve Thomas at the university

of Bath made this project possible.

I would also like to thank all Team Bath drones colleagues and our supervisors. We have faced

many challenges along the way, but working collaboratively with talented individuals ensured this

project was executed as smoothly as possible.

Last but by no means least, I would like to thank my parents and Priya Popat who have provided

support throughout this project.

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Table of Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Skyseeker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Team Bath Drones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Unmanned Aerial Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.2 Types and Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Flying Wings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2.2 Cruise Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2.3 Static Stall Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Improving Aerodynamic Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 72.3.1 Pre-design Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3.2 Post-design Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Flow Visualisation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.4.1 Smoke and Vapour Flow Visualisation . . . . . . . . . . . . . . . . . . . . . 142.4.2 Oil Film Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4.3 Wall Tufts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3 Aims and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Experimental Methodology and Instrumentation . . . . . . . . . . . . . . . . . . 18

4.1 Airfoil Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.1.1 MH45 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.1.2 S822 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Experimental Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.4 Wing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.4.1 Manufacturing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.5 Force and Moment Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.6 Pressure Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.7 Tuft Flow Visualisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.8 Experimental Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.8.1 Reynolds Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.8.2 Tunnel Interference affects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.9 Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5 CFD and Reynold Number Comparison . . . . . . . . . . . . . . . . . . . . . . . . 33

5.1 Lift and Drag Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6 Aspect Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

V

6.1 Force Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366.2 Longitudinal Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396.3 Aerodynamic and Power Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 416.4 Stall Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7 Geometric Twist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49


8 Aerodynamic Twist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61


9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

10 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

A Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

B Wind Tunnel Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

C Manufactured Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

D Force Sensor Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

E Data Analysing Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

F CFD Pressure Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

G Wind Tunnel Interference affects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

H Bending Moment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

VI

List of Figures

Figure 1.1.1 CAD model of Skyseeker, flying wing concept [4] . . . . . . . . . . . . . . . 2

Figure 2.2.1 Northrop’s design of the Grumman B-2 and Horten brother’s Ho 229 flying

wing designs [15] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Figure 2.3.1 Change in induced drag at 2◦ to 6◦ washout at 3 taper planforms [25] . . . 9

Figure 2.3.2 Results of optimum spanwise lift distribution of a blended wing body. Illus-

trating the lift distribution, twist distribution and (t/c) distribution. [26] . . . . . 9

Figure 2.3.3 Stall and lift characteristics of a swept back wing [23] . . . . . . . . . . . . 10

Figure 2.3.5 Airfoil selection for aerodynamic twist [29] . . . . . . . . . . . . . . . . . . . 11

Figure 2.3.6 Stall strip design and position on a wing [30] . . . . . . . . . . . . . . . . . 12

Figure 2.3.7 Design of vortex generators design and position on wing surface [30] . . . . 12

Figure 2.3.8 The affects on lift characteristics with vortex generators and stall strips . . 13

Figure 2.3.9 The effects and design of wing stall fences on lift characteristics . . . . . . . 13

Figure 2.3.10The effects and flow visualisation of stall fences on lift characteristics . . . . 13

Figure 2.4.1 Wind tunnel setup of a smoke and laser sheet to visualise flow on the upper

surface of a wing [36] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 2.4.2 Use of smoke technique to show vortex systems in a wake of a group of three

cylinders [40] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 2.4.3 Oil film technique used on a orbital model to visualise flow pattern [37] . . 15

Figure 2.4.4 Fluorescent mini-tufts used on a car moving past a stationary camera [41] . 16

Figure 4.1.1 Wing cross section showing the MH45 airfoil chosen for the fuselage and

wing root . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 4.1.2 Comparison between the root and tip airfoils of the MH45 and S822 . . . . 20

Figure 4.1.3 Wing cross section showing the S822 airfoil chosen at the tip for aerodynamic

twist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Figure 4.3.1 Similar schematic of the University of Bath closed return wind tunnel [46] . 22

Figure 4.3.2 Schematic of the wind tunnel setup showing turntable, scanivalve, pressure

tube, force sensor and direction of free stream velocity . . . . . . . . . . . . . . . . 22

Figure 4.5.1 Correlations between the raw body forces in Fx and time at low and high

angle of attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 4.5.2 Six axis force and torque sensor with the reference position in wind tunnel

setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

VII

Figure 4.6.1 Instrumentation scheme for pressure taps, adapted from Sanz, A and Vogt

[50] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 4.6.2 Wing and fuselage model highlighting pressure taps, tubes and carbon fibre

stiffeners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 4.7.1 Baseline wing model with 56 fluorescent tufts attached on upper surface . . 28

Figure 4.9.1 Lift coefficient uncertainties for the baseline model at angle of attack of 0◦

to 18◦ compared at two Reynolds numbers [51] . . . . . . . . . . . . . . . . . . . . 30

Figure 4.9.2 Drag coefficient uncertainties for the baseline model at angle of attack of 0◦

to 18◦ compared at two Reynolds numbers [51] . . . . . . . . . . . . . . . . . . . . 31

Figure 4.9.3 Pressure coefficient uncertainties for the baseline model at normalised span

position η at angles of attack of 0◦ to 18◦ . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 5.1.1 Comparison of lift coefficient with CFD, panel and theoretical methods and

Reynolds number [49] [51] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Figure 5.1.2 Comparison of drag coefficient with CFD, panel and theoretical methods

and Reynolds number [49] [51] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 6.1.1 Time averaged lift coefficient at AoA of 0◦ to 18◦, 370,000 Reynolds number,

4◦ washout and different aspect ratios . . . . . . . . . . . . . . . . . . . . . . . . . 36

Figure 6.1.2 Time averaged lift coefficient versus drag coefficient for 370,000 Reynolds

number, 4◦ washout and different aspect ratios . . . . . . . . . . . . . . . . . . . . 37

Figure 6.1.3 Time averaged lift coefficient versus induced drag coefficient for Reynolds

number of 370,000, 4◦ washout and different aspect ratios . . . . . . . . . . . . . . 38

Figure 6.2.1 Time averaged lift coefficient versus pitching moment for Reynolds number

of 370,000, 4◦ washout and different aspect ratios . . . . . . . . . . . . . . . . . . . 39

Figure 6.2.2 Angle of attack versus normalised COP chord position for Reynold number

of 370,000, 4◦ washout and different aspect ratios. XCP of 0 indicates the leading

edge and 1 indicates the trailing edge of the root chord . . . . . . . . . . . . . . . . 41

Figure 6.2.3 Normalised COP spanwise position versus angle of attack for Reynold num-

ber of 370,000, 4◦ washout and different Aspect ratios. ηCP 1 indicates the wing

tip and 0 indicates the root chord . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Figure 6.3.1 Aerodynamic efficiency ratio at angle of attack of 0◦ to 18◦, Reynolds number

of 370,000, 4◦ washout and different aspect ratios . . . . . . . . . . . . . . . . . . . 42

Figure 6.3.2 Power efficiency ratio for angle of attack of 0◦ to 18◦, Reynolds number of

370,000, 4◦ washout and different aspect ratios . . . . . . . . . . . . . . . . . . . . 43

Figure 6.4.1 Pressure contour map at several spanwise taps for angle of attack of 0◦ to

18◦, Reynolds number of 370,000, 4◦ washout and different aspect ratios at 10%

chord. Top left at AR5, top right AR5.5, bottom left AR 6 and bottom right AR 7 44

Figure 6.4.2 Surface tuft visualisation for AR 5 with 4◦ washout. Top left 0 AoA, top

right start of tip separation and bottom left start of root separation . . . . . . . . 46

VIII

Figure 6.4.3 Surface tuft visualisation for AR 5.5 with 4◦ washout. Top left 0 AoA, top






Figure 7.1.1 Time averaged lift coefficient for angle of attack of 0◦ to 18◦, 370,000

Reynolds number, AR 5.5 and 3◦ to 6◦ washout . . . . . . . . . . . . . . . . . . . . 49

Figure 7.1.2 Time averaged lift coefficient versus drag coefficient at 370,000 Reynolds

number, AR 5.5 and 3◦ to 6◦ washout . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 7.1.3 Time averaged lift coefficient versus induced drag coeffiecient at Reynolds

370,000, AR5.5 and 3◦ to 6◦ washout . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 7.2.1 Time averaged lift coefficient versus pitching moment for Reynolds number

of 370,000, AR 5.5 and washout of 3◦ to 6◦ . . . . . . . . . . . . . . . . . . . . . . 52

Figure 7.2.2 Angle of attack versus normalised COP chord position for Reynolds 370,000,

AR5.5 and washout from 3◦ to 6◦. XCP of 0 indicates the leading edge and 1

indicates the trailing edge of the root chord . . . . . . . . . . . . . . . . . . . . . . 53

Figure 7.2.3 Normalised COP spanwise position versus angle of attack for Reynolds

370,000, AR5.5 and washout from 3◦ to 6◦. ηCP of 1 indicates the wing tip and 0

indicates the trailing edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Figure 7.3.1 Aerodynamic efficiency ratio versus angle of attack of 0◦ to 18◦, Reynolds

number of 370,000, AR5.5 for washout of 3◦ to 6◦ compared to baseline model . . 55

Figure 7.3.2 Power efficiency ratio versus angle of attack of 0◦ to 18◦, Reynolds number

of 370,000, AR5.5 for washout of 3◦ to 6◦ compared to baseline model . . . . . . . 55

Figure 7.4.1 Pressure contour map of several spanwise taps for angle of attack of 0◦ to

18◦, Reynolds number of 370,000, AR5.5 and washout of 3◦ to 6◦. Top left at 3◦,

top right 4◦, bottom left 5◦ and bottom right 6◦ of washout . . . . . . . . . . . . . 57

Figure 7.4.2 Surface tuft visualisation of 3◦ washout with AR 5.5. Top left 0 AoA, top








Figure 8.1.1 Time averaged lift coefficient for angle of attack of 0◦ to 18◦, 370,000

Reynolds number, 4◦ washout compared with baseline model and aerodynamic twist 61

IX

Figure 8.1.2 Time averaged lift coefficient versus time averaged drag coefficient at 370,000

Reynolds compared with the baseline design and aerodynamic twist . . . . . . . . 62

Figure 8.1.3 Time averaged lift coefficient versus induced drag coefficient at 370,000

Reynolds compared with the baseline design and aerodynamic twist . . . . . . . . 62

Figure 8.2.1 Time averaged lift lift coefficient versus pitching moment for Reynolds

370,000 compared with baseline design and aerodynamic twist . . . . . . . . . . . . 63

Figure 8.3.1 Aerodynamic efficiency ratio versus angle of attack of 0◦ to 18◦, Reynolds

number of 370,000 of the aerodynamic twist planform compared to the baseline

planform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Figure 8.3.2 Power efficiency ratio versus angle of attack of 0◦ to 18◦, Reynolds number

of 370,000 of the aerodynamic twist planform compared to the baseline planform . 65

Figure 8.4.1 Pressure contour map of several spanwise taps for angle of attack of 0◦ to

18◦, Reynolds number of 370,000, comparing the baseline planform (left) and the

aerodynamic twist planform (right) . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Figure 8.4.2 Surface tuft visualisation of the baseline. Top left 0 AoA, top right start of

tip separation and bottom left start of root separation . . . . . . . . . . . . . . . . 66

Figure 8.4.3 Surface tuft visualisation of aerodynamic twist planform. Top left 0 AoA,

top right start of tip separation and bottom left start of root separation . . . . . . 67

Figure B.0.1Calibration of the wind tunnel to set a AoA of 0 degrees . . . . . . . . . . . 80

Figure C.0.1Fuselage model used in wind tunnel testing used in the wind tunnel . . . . 81

Figure C.0.2Upper surface of baseline planform wing model used in the wind tunnel . . 81

Figure C.0.3Lower surface of baseline planform wing model used in the wind tunnel . . 82

Figure C.0.4Wing models with different aspect ratios used in the wind tunnel . . . . . . 82

Figure E.0.1Data processing work flow for force, moment and pressure measurements . . 84

Figure F.0.1Pressure distribution of the Skyseeker using CFD analysis at stall angle

during cruise [49] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Figure H.1.1Bending moment coefficient versus angle of attack at 0◦ to 18◦, fixed Reynolds

number of 370,000, 4◦ washout and different ARs . . . . . . . . . . . . . . . . . . . 89


number of 370,000, AR 5.5 and different washouts . . . . . . . . . . . . . . . . . . 90


number of 370,000 compared with baseline model and aerodynamic twist . . . . . . 90

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List of Tables

1.1.1 Skyseeker preliminary design specification [3] . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Team Bath Drone’s technical and management role breakdown [2] . . . . . . . . . 3

4.2.1 Experimental parameters and the uncertainties involved . . . . . . . . . . . . . . . 20

4.2.2 Test matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.4.1 Table of operations to manufacture a single wing model . . . . . . . . . . . . . . . 24

4.5.1 Range and resolution of the commercial iCub force and torque sensor . . . . . . . . 25

4.9.1 Uncertainties in lift and drag compared at two Reynolds numbers . . . . . . . . . . 31

5.2.1 Comparison of wind tunnel results with CFD, panel and theoretical methods . . . 35

6.1.1 Effects of AR on CL,max, lift curve slope dCLα and stall speed Vs . . . . . . . . . . 37

6.1.2 Drag polars and efficiency factors for different aspect ratios . . . . . . . . . . . . . 39

6.2.1 Normalised mean aerodynamic chord and centre positions for different aspect ratios 40

6.3.1 Aerodynamic and power efficiencies compared to the baseline model for different

aspect ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.4.1 Angle of flow separation observed at the tip, comparing pressure distribution and

tuft flow visualisation for different aspect ratios . . . . . . . . . . . . . . . . . . . . 48

7.1.1 Effects of washout on CL,max, lift curve slope dCL/dCα and stall speed VS . . . . 50

7.1.2 Drag polars for washout of 3◦ to 6◦ . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

7.2.1 Aerodynamic centre of wing planform with different washouts . . . . . . . . . . . . 53

7.3.1 Aerodynamic and power efficiencies compared to the baseline model for different

washouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

7.4.1 Angles of flow separation at the tip identified by the pressure distribution and tuft

flow visualisation for different washouts . . . . . . . . . . . . . . . . . . . . . . . . 60

8.1.1 Drag polars for the baseline model and aerodynamic twist . . . . . . . . . . . . . . 63

8.2.1 Normalised mean aerodynamic chord and centre positions . . . . . . . . . . . . . . 64

D.0.1Specification of the 6 channel force sensor obtained from the manufacturer . . . . . 83

XI

Abbreviations and Numenclature

b Induced drag factor

CB Time averaged bending moment coefficient

CD Time averaged drag coefficient

CDi Induced drag coefficient calculated from time average drag and

lift coefficient

CDi,ell Induced drag coefficient of elliptical distribution

CD0 , CDmin Zero lift drag coefficient

CL Time averaged lift coefficient

CLα Time averaged Lift curve slope

CL,max Time averaged maximum lift coefficient

CM Time averaged pitching moment coefficient

CMx Pitching moment at the location of support

CP Pressure coefficient

c̄ Mean aerodynamic chord

D Time averaged drag, N

FA, FB Time average body force in set A and B, n

FU , FL Maximum and minimum raw body force in a single set, N

Fx, Fy, Fz Raw body forces in the direction of force sensor, N

k Induced efficiency factor

L Time averaged lif, N

P Pressure, Pa

Q Aerodynamic constant, Pa

R Reynolds number

RAE Aerodynamic efficiency ratio

RPE Power efficiency ratio

Tx, Ty, Tz Raw body torque in the direction of force sensor, Nm

U∞ Free stream velocity

Vs Stall speed, m/s

α Angle of attack, deg, rad

η Normalised span position

µ Absolute Viscosity, Ns/m2

ρ Air density, kg/m3

σ Standard deviation

XII

Abreviation

AoA Angle of Attack

AR AR

AT Epoxy Laminating Resin and Hardener

BWB Blended Wing Body

CFD Computational Fluid Dynamics

CNC Computer Numerical Control

EL Epoxy Laminate

F/T Force and Torque

GA General Assembly

IMechE Institution of Mechanical Engineers

L/D Lift to Drag Ratio

MAV Micro Air Vehicle

NACA National Advisory Committee for Aeronautics

NASA National Aeronautics and Space Administration

TBD Team Bath Drones

UAS Unmanned Aerial System

UAV Unmanned Aerial Vehicle

USD United Sates Dollar

UD Uni-directional

USD United States Dollars

VG Vortex Generators

WP Work Package

XIII

Outline of Project

This project describes an experimental study of a flying wing concept design for an unmanned

aerial vehicle, as a method to improve the aerodynamic performance by assessing stall behaviour

and control.

First, the project description will be highlighted. This will identify the need to conduct the

research and its relevance to team bath drones. This will include a brief summary of the team

and their roles and the concept aircraft chosen.

Chapter 2 will provide an overview of UAV. Current research and state of the art designs of flying

wings will be explained. An overview of different types and applications of UAV’s and their new

emerging markets. The advantages of the flying wing design will then be identified, regarding the

aerodynamic performance and issues with stall behaviour and control. The next two subsections

then identify the two different type of methods to improve the aerodynamic performance; pre-

design and post-design improvements.

Chapter 3 will identify the aims and objectives of the study, which is required in order to find the

optimal aerodynamics planform for the flying wing UAV.

Chapter 4 describes the experimental apparatus and instrumentation methods. The wind tunnel

setup, manufacturing process and force, moment and pressure measurements will then be covered.

Flow visualisation technique, experimental conditions and uncertainty associated with these results

will be discussed.

Chapter 5 will look at affects of Reynolds number on the lift and drag characteristics. The wind

tunnel results will also be validated against CFD, theoretical and panel code predictions, to asses

their similarity.

Chapter 6,7 and 8 will highlight the results identifying the improvement opportunity with vari-

ation in AR washout and aerodynamic twist. Planform changes will be assessed to optimise the

aerodynamic performance, longitudinal stability and stall behaviour.

Chapter 9 summarises the conclusion from all previous chapters, following references and appen-

dices.

XIV

Chapter 1

Introduction

This project will investigate fundamental issues with steady state stall behaviour and aerodynamic

performance of flying wings at low Reynolds number, so that optimum planform can be selected

for the 2016 TBD aircraft. Current research focuses on transonic flights of flying wings, which

limits the understanding of aerodynamic characteristics at low Reynolds numbers. AR, taper

and sweep will be selected for optimal subsonic performance [1]. An experimental study will be

conducted on aerodynamic efficiency and control behaviour, which will be compared against CFD

and theoretical methods, to give a better understanding of flying wings.

This project will be in collaboration with TBD, who will be conducting research in specific areas,

which will be discussed in the following sub sections. The flying wing aircraft will be built, tested

and entered into the 2016 UAS IMechE competition, which will be a proof of the TBD UAV

concept, assessing its viability and performance to complete its designed mission.

1.1 Skyseeker

The Skyseeker is a flying wing concept designed by a group of final year design students as part

of the group business design project, shown in figure 1.1.1. It was designed to target agricultural

monitoring, aerial mapping and wildlife conservation market segments. The key design specifica-

tion include, a maximum take-off weight of 7kg and the ability to drop two payloads separately

onto a designated drop zone and be fully autonomous. The aerodynamic performance and ge-

ometry was optimised by vortex lattice methods and the key results are shown in table 1.1.1

[2].

1

Table 1.1.1: Skyseeker preliminary design specification [3]

Skyseeker

Wing Area 1.135m2

Wing Span 2.5m

AR 5.5

Taper ratio 0.3

1/4 chord sweep 23◦

Dihedral 4◦

Washout 4◦

L/D at cruise 24

CL,max 0.9

However, the theory used is limited to the prediction of profile drag and boundary layer affects,

due to its complexity. It therefore needs to be supported by experimental results. This can help

determine the affect of various features in a design. The design can then be modified, which is

safe, quick and relatively cheap.

Figure 1.1.1: CAD model of Skyseeker, flying wing concept [4]

Figure 1.1.1 shows the general assembly of the Skyseeker. The Skyseeker is a highly swept wing,

low AR with high taper ratio. This imposes challenges on stall and aerodynamic performance at

cruise conditions.

The Skyseeker will be applied at a low Reynolds number, therefore subjected to laminar flow,

which is more prone to separation, especially at the tip, which is highly loaded [5]. As a result,

control surfaces lose effectiveness and due to the local lift loss and the large sweep, the aerodynamic

centre shifts forward to cause a nose-up pitching moment [6]. Therefore, stall is a serious issue,

especially at low flight speeds typical of a UAV [7].

2

1.2 Team Bath Drones

The Skyseeker will be manufactured and built during a four month period from February to May

2016. The technical roles associated with each member is described in table 1.2.1, which will be

carried forward through the manufacture and testing phase of the project.

Table 1.2.1: Team Bath Drone’s technical and management role breakdown [2]

Name Technical Role Management Role

McMahon, B Landing Control Project Manager

Kakkar, B Aerodynamics Workshop Manager

Gilespie, O Landing Flight Safety

Kucera, J Structures Airframe Integrator and Pilot

Mok, T Stability and Control IMechE Co-ordinator

Patel, P System Control Equipment and Purchasing

Patra, S Propulsion Business Manager

Turner, H Sensing and Vision System Integrator

Whitely, B Radar Marketing

Wright, D Trajectory and Mission Planning Flight Test Operations

3

Chapter 2

Literature Review

2.1 Unmanned Aerial Vehicle

The UAV market has shown great interest in recent years and is expected to grow over the next

decade. The current value is predicted as $6 billion USD and is expected to reach $12 billion.

Military applications have dominated the market, but civil applications are expected to grow [8][9].

UAV have already proved to be successful in field operations however, further research can enable

UAV’s to be used in intelligence gathering, such as stealth and combat operations [34].

A UAV can be defined as a reusable powered aircraft (drone) that does not carry a human

operators. It also does not carry any passengers and can operate autonomously or remotely, and

be expendable or recoverable [10].

2.1.1 History

The interest in UAV’s has been observed since 1916, when the first modern unmanned aircraft

was invented, Hewitt’s UAV. This was a result of Sperry’s work, on the flight stabilisation using

gyroscope devices, which provided flight stabilisation [11]. This attracted the interest of the US

Navy however, due to technical difficulties the research and work in automatic planes was lost. In

1933 the Royal Navy Queen bee’s target drone was operated for the first time and the potential

of UAV’s was understood, but it still required perfection of remote operations. Reginald Denny

then developed the successful target drone RP-2, during WWII using radio control [12].

During the Cold war, the development in reconnaissance missions increased and the first recon-

naissance UAV was developed, called the MQM-57 Falconer [13]. Not long after, the Ryan Model

147 was launched, which was the first unmanned aircraft which is known as an UAV today. In

conclusion, the importance and usefulness of UAV was demonstrated over the years and is now

being further researched, focusing on longer endurance UAV’s and MAV’s [12].

4

2.1.2 Types and Uses

UAV’s can be divided into three distinct classes; long endurance aircraft, long range aircraft,

medium range/tactical aircraft and close range/battlefield aircraft.

Long Endurance, Long Range aircraft

Long endurance aircraft follow the shape of an conventional layout, that have a rear mounted

propulsion unit with horizontal and vertical tail surfaces. The aircraft are designed to withstand

a minimum of 5000km range and 24 hour endurance, however most UAV’s in this class are able

to withstand longer hours. Typical examples of these classes are Global Hawk and Predator B,

which have a wing span of 40m and 20m and can hold a maximum payload of 1360kg and 230kg

respectively [14].

Medium Range, Tactical aircraft

Medium range aircraft are primarily used for two applications; reconnaissance and artillery. These

UAV’s usually take form into two main configurations: fixed and rotary wings. However, this

depends on the type of market and application, and may be designed to take off and land vertically

from ships or restricted areas. Examples of each configuration type are the Seeker 2 and Sea Eagle.

Typical endurance of these types of UAV’s are 6 hours and 150km [14].

Close Range, Battlefield aircraft

Close range UAV’s are usually designed for multiple applications, such as military, paramilitary

and civil. These UAV’s have typical missions involving low altitude with high response time, this

proposes design challenges in launch and recovery. As before, these UAV’s can be divided into

fixed and rotary wings, with a typical range and endurance of 2 hours and 25km. Examples of

these type of UAV’s are the Phoenix and Aviation Sprite [14].

2.2 Flying Wings

A flying wing is defined as: a type of airplane in which ’all of the functions of a satisfactory

flying machine are disposed and accommodated within the outline of the airfoil itself’ [15]. It has

numerous advantages over the conventional UAV configurations, which will be discussed in further

subsections.

5

2.2.1 History

Germany, England and the United states have all contributed to more than a century worth of

research and development, in the flying wing concept. Horten Brothers, Geoffrey Hill and John

Northrop have achieved great success in their designs of the flying wing [16]. Famous examples

of flying wing concepts are; Northrop’s design of the Grumman B-2 and the Horten brother’s Ho

229 fighter, shown figure 2.2.1.

Recently, a blended wing body aircraft concept was also studied by Boeing and NASA for civil

applications, which proved to have successful performance in wind tunnel testing. Taranis, is

another design developed by BAE, that is a highly swept unmanned combat air vehicle that

uses stealth technology. Interest in flying wing designs for both UAV’s and larger scale civil

applications, are now being revisited.

(a) Grunman B-2 (b) Horten Ho 229

Figure 2.2.1: Northrop’s design of the Grumman B-2 and Horten brother’s Ho 229flying wing designs [15]

2.2.2 Cruise Performance

The aerodynamic performance of a flying wing has several advantages, over the conventional

aircraft at cruise. With the integration of the wing and the fuselage, aerodynamic performance

has been shown to improve due to: reduced wing loading, decreased wetted area and hence

increased lift-to-drag ratio. Similar to tailless aircraft, flying wings do not have an horizontal tail

and therefore, the horizontal tail friction and induced drag are eliminated [17]. Interference drag

is also reduced, as no sharp edges exist between the centre body and the wing. Overall, at least

20% improvement in (L/D) can be achieved when compared to conventional designs. Blended

wing bodies are also very beneficial at transonic speeds as design planform allows for higher cruise

mach numbers. A large amount of valuable research has been conducted on the blended wing

body at high mach numbers, due to its benefits at these speeds [18].

6

2.2.3 Static Stall Performance

Stall behaviour on aircraft depend on the airfoil section and the wing planform. Stall over a wing

can cause a reduction in the ability to lift, due to the separation of air flow over the wing. As

the flow separates, the suction over the leading-edge and forward force are lost. Hence, the force

acting normal to wing increase the rearward drag [19]. Stall is a known problem in flying wings,

especially when trimming the aircraft where the wing tip stalls before the root, which is discussed

in more detail in the following subsection.

2.2.3.1 Wing Tip Behaviour

Wing tip stall occurs, when one of the tips of the wings stall before the root, then the tip will start

to drop increasing its affective angle of attack. The aircraft will start to roll with the opposite

wing tip rising and also starts to yaw due to drag [19]. This behaviour was witnessed by Jack

Northrop during the flight test in 1942 with the XB-35, which was a swept flying wing design.

The aircraft would tend to stall at the wing tips, losing the elevon effectiveness. In May 1943 it

claimed its first casualty as the aircraft entered a spin [20].

An aerodynamic study conducted on a blended wing body by N. Qin showed that high lift existed

on the outer wing especially at increasing angle of attacks, where stalling was observed. This

research led to an important observation which highlighted that the aerodynamic loading should

be moved inboard, to improve the aerodynamic performance and minimise bending moment at

the outer wing and to avoid tip stall [21].

2.3 Improving Aerodynamic Performance

To analyse the aerodynamic issues and stall behaviour of a flying wing at cruise, two approaches

will be discussed in the following subsections;

• Pre-design improvements Design improvements which are analysed in the design phase.

Wing planform characteristics which have affect on stall behaviour and control include;

– Geometric twist/washout

– Sweep

– Taper

– Aerodynamic twist

• Post-design improvements Design and mechanisms that can be implemented after the

design phase, include;

– Stall strips

7

– Vortex generators

– Wing stall fences

2.3.1 Pre-design Improvements

2.3.1.1 Geometric Twist/Washout

Geometric twist or washout is referred to when the wing tip angle of attack is lower then the root

angle of attack. Introducing twist to a wing design is mainly to achieve two goals;

• Avoid tip stall

• Modification of the lift distribution to a more elliptical distribution

The negative impact of twist is reduction in lift, which is due to the outer wings producing negative

lift at 0 AoA [22]. However, it is a very affective planform change for moderate taper wings, but

provides minimum benefits for high taper wings [23].

In comparison to Northrop’s design which used linear twist from the centre to the wing tip, Horten

brothers used slight wash-in at the centre section and strong washout at the tip. N. Qins research

highlighted that an optimum positive twist at centre section and then negative twist at the outer

sections gives the most optimum aerodynamic performance [21]. Albion H. Bowers, a researcher

in NASA also confirmed these results in his study [24].

A study conducted by R. Anderson looked at planform changes in taper ratio and washout to

avoid tip stalling. The results are shown in figure 2.3.1, which describe how increasing washout at

tip also increases induced drag. Therefore, this concludes that twist should be implemented only

to a degree where tip stall is avoided [25].

8

Figure 2.3.1: Change in induced drag at 2◦ to 6◦ washout at 3 taper planforms [25]

In addition, a study was conducted by N. Qin to optimise the spanwise lift distribution of a

blended wing body, for civil applications. The analysis was completed using an aerodynamic model

based on panel methods and (RANS) solver. The research concluded that the most optimised

distribution which would increase the aerodynamic performance was an averaged elliptical and

triangular distribution [21]. A very similar investigation was also conducted by Zhouhie Lyu, but

focused on a range of speeds. This study also supported N. Qin’s results. The normalised lift

distribution can be seen in figure 2.3.2 [26].

Figure 2.3.2: Results of optimum spanwise lift distribution of a blended wing body.Illustrating the lift distribution, twist distribution and (t/c) distribution. [26]

9

These studies conclude the optimum spanwise distribution, but no experimental research has been

conducted at low Reynolds numbers with observation of stall behaviour.

2.3.1.2 Sweep

Wing sweep is only considered when compressibility affects are an issue. At low speed applications,

the boundary layers at the tip region thicken, causing wing tip stall for swept back wings. Highly

swept wings also have aeroelastic affects, as the wings bend upwards at high angle of attacks and

therefore, produce a nose-up pitching moment. Boundary layer on swept back wings thicken near

the trailing edge of the outer wing, therefore causing separation and wing tip stall [27].

Figure 2.3.3 shows how the load on tip increases with sweep. However, this is beneficial at cruise

as the aircraft increases its CL,max and decreases its stall angle [23].

(a) Stall pattern on swept back wings (b) Lift distribution for swept and unswept wings

Figure 2.3.3: Stall and lift characteristics of a swept back wing [23]

The lift and stall characteristics of swept wings at different Reynolds numbers was investigated by

D. S. Woodward. The research concluded that for a swept wing at low Reynolds number results in

poor stall characteristics [28]. Therefore, this suggests that sweep should only be used on aircraft

to relocate the position of the centre of gravity, to permit stability of the aircraft.

2.3.1.3 Taper

Taper ratio is defined as the ratio of the tip to the root chord. Tip stall is directly affected by

increasing taper ratio and therefore, it is likely a higher taper ratio results in tip stall. This is due

to a higher local lift coefficient near the tip compared to the root, due to the decreasing spanwise

lift distribution [23]. This can be seen in figure ??, which demonstrates how increasing taper ratio,

the stall progression moves away from the root and tends towards the tips.

In addition, a study of lift and stall characteristics of different tapered wings was investigated by

R. Anderson. The results showed that as taper increases, Reynolds number decrease near the tip,

10

thus decreasing maximum lift coefficient and causing flow separation. He also concluded that an

optimum wings have a taper ratio of 1/2 or 1/3 [25].

2.3.1.4 Variable Camber / Aerody-

namic Twist

Figure 2.3.5: Airfoil selection for aerody-namic twist [29]

To overcome stall at the tip, a more sta-

ble aerofoil is preferred in this section, which

means the aircraft can climb at higher angle of

attacks. In addition, stability is also increased

due to the stable tip aerofoil reducing tip vor-

tices and yaw control.

Aerodynamic twist has been used on a popu-

lar light GA aircraft, Cessna 152 with a NACA

0012 aerofoil on the outer wing and a NACA

2214 on the centre wing section. With this se-

lection of aerofoils, the wing tip stalls at 18◦

AoA, compared to the centre section which

stalls at 14◦ AoA [23]. The stall behaviour can

be seen in figure 2.3.5 [29].

2.3.2 Post-design Improvements

When an aircraft faces issues with stall or control after the design phase after the design phase,

post-design improvements can be used. This allows aircraft to progress and evolve with better

aerodynamic performance.

2.3.2.1 Stall Strips

Stall strips are devices placed at the leading edge, which vary across the span of the wing. Their

purpose is to correct certain undesired stall behaviour, shown in figure 2.3.6 [30].

A study was conducted by W. Newsom to explore the affects of leading edge slats on stall be-

haviour. Two sizes of stall strips were used during the test and showed little aerodynamic im-

provement. Results shown in figure 2.3.8 (a), show that the maximum angle of attack increases

by 5◦, however the maximum lift coefficient decreases [31].

11

Figure 2.3.6: Stall strip design and po-sition on a wing [30]

This result was also confirmed by Z. Federik, who

conducted a CFD analysis. However Federik’s re-

sults concluded, that the 7mm stall strip posi-

tioned below the leading edge had the greatest

aerodynamic improvement [32].

2.3.2.2 Vortex Generators

Vortex generators are small, low AR plates placed

vertically at a specific angle of attack on the wing

surface. VG’s are placed at the edge of turbulent

boundary layer ahead of flow separation, which

produce tip vortices to re-energise the flow. This therefore increases the adverse pressure gradient

and delays flow separation, which can be seen in figure 2.3.7. However, VG’s can increase cruise

drag [30].

Figure 2.3.7: Design of vortex generators de-sign and position on wing surface [30]

A study was conducted by W. H. Wentz

to investigate the lift characteristics of vor-

tex generators. The study concluded that

by adding vortex generators at an angle

of 40◦ increased the maximum lift coeffi-

cient by 0.2 and increased the maximum

angle of attack by 3◦ at either position of

elevons, however drag was increased. At

higher angle of attacks, the delay of flow

separations resulted in significant drag re-

ductions. These results can be seen in figure 2.3.8 (b) [33].

2.3.2.3 Wing Stall Fences

Stall fences on swept wings are used to stop the boundary layer from tending towards the tips,

which allows wing tips to stall at a higher angle of attack. Wing stall fences are shown in figure

2.3.9 [30].

Wing fences have been used for many years and are still being used, even on low speed aircraft

such as the SB-13, which is a swept wing tailless glider. K. Bill suggest that the most affective

locations to position wing fences are within 40% and 60% of the wing span or between the front

and inner edge control systems. This provides good control behaviour and improves maximum

angle of attack [34].

12

(a) affects of Stall strips on lift characteristics[30]

(b) affects of Vortex generators on lift charac-teristics [33]

Figure 2.3.8: The affects on lift characteristics with vortex generators and stall strips

(a) affects of lift coefficient with stall fences on swept wings[30]

(b) Wing fence designs and placement on aerofoils [30]

Figure 2.3.9: The effects and design of wing stall fences on lift characteristics

(a) Flow direction on wing surface at 13◦ AoA, with and

without stall fences [35]

(b) affects of stall fences on lift characteristics in wind

tunnel testing [35]

Figure 2.3.10: The effects and flow visualisation of stall fences on lift characteristics

13

A study conducted by M. Williams shows wind-tunnel visualisation of a wing fences vortex. The

results indicate that the stall fences significantly slow the flow separations at angles above 13◦,

below this angle small differences were noticed. Figure 2.3.10 shows how the flow varies over the

wing surface at 13◦ AoA and lift characteristics [35].

2.4 Flow Visualisation Techniques

Fluid flow is an important field of research and has been used for centuries in wind tunnel testing

and flight tests. Visualisation of fluid flow allows a better understanding of the flow pattern and

its behaviour [36]. Flow visualisation can be split into three main categories; 1. Addition of

foreign matter to the fluid. 2. Optical methods to visualise flow 3. Introducing energy in the field

[37].

However, this literature will mainly focus on visualisation techniques, which are applicable to

closed throat wind tunnels. The techniques used to understand the state of boundary layer and

transition regions, will be discussed in the following sub sections.

2.4.1 Smoke and Vapour Flow Visualisation

Smoke visualisation is one of the oldest techniques and is still widely used in wind tunnel experi-

ments [36]. Smoke generators use hydrocarbon oils, such as kerosene, which have smaller particle

size, vaporisation temperature and are inflammable. The problem arises in closed return, when

the wind tunnel is covered in smoke. Instead tracer visualisation is used, allowing the fog to

disappear [38].

Another method to visualise smoke is, lasers to observe the flow and its structure. This technique

has been applied in several experiments as seen in figure 2.4.1 [39].

Figure 2.4.1: Wind tunnel setup of a smokeand laser sheet to visualise flow on the uppersurface of a wing [36]

An example of a photographic image using

smoke visualisation is shown in figure 2.4.3,

where vortices, wakes and separated flows

can be visualised. However these images

are obtained at low velocities and turbu-

lence without the use of laser [40].

14

Figure 2.4.2: Use of smoke technique to show vortex systems in a wake of a group ofthree cylinders [40]

2.4.2 Oil Film Techniques

Oil film technique has been used to visualise for as a standard technique for many experiments

which allows visualisation of the flow pattern close to the surface [37].

The surface is coated with paint consisting of a oil and powdered pigment. Frictional forces and

the air stream carry the oil along the surface, which gives an visualisation of the flow pattern.

This observation can indicate the transition between laminar and turbulent flow. However the oil

film affects the boundary conditions of the free stream air and can cause errors in instrumentation

[37]. Therefore this technique can not be used at various angles of attack or in conjunction with

force and pressure measurements. It would require re-applying of the pigment and re testing at

different angles of attack.

Typical oils which are used are; kerosene, light diesel oil, light transformer oil and also alcohol at

low Reynolds numbers. An example of this technique is shown in figure 2.4.3 of an orbital model

from a research by NASA Ames research centre [37].

Figure 2.4.3: Oil film technique used on a orbital model to visualise flow pattern [37]

15

2.4.3 Wall Tufts

A more simple visualisation technique is tufts that give an impression on the direction of air flow

close to the surface. Tufts can be attached using glue or a fixing mechanism. As the air flow

transitions from laminar to turbulent, tufts experience a unsteady motion. A more aggressive

motion of the tufts indicate a separated region on the surface of the model [37].

A study by Slavica Ristic indicated that the diameter of the tufts should be larger then 0.1mm

[36]. Tufts can be made from yarn or nylon, however the fixing devices need to be designed so that

they do not create errors in flow pattern. Crowder studied how these affects can be minimised and

the solution was found as mini tufts, which were made from fluorescent nylon monofilament. The

tufts had a diameter of about 20 µm and the visualisation was improved by observing through

UV lamp [41]. Tufts ares able identify flow pattern such as vortex shredding, boundary layer

separations and flow separated regions [36].

Figure 2.4.4: Fluorescent mini-tufts used on a car moving past a stationary camera[41]

16

Chapter 3

Aims and Objectives

The literature review highlights two areas of study to optimise the aerodynamic performance of

a flying wing UAV. As the Skyseeker is currently in the design phase, the first approach of pre

design improvements will be considered, in order to improve the aerodynamic performance and

stall behaviour.

The aim of the current study is to optimise the Team Bath Drones 2016 UAV aircraft by refin-

ing and improving aerodynamic design experimentally, assessing aerodynamic performance, stall

behaviour and control. This will be achieved through the following objectives:

1. Design and build the test rig facility capable of varying the AoA of a half span, 0.8 scale

UAV models and record steady state measurements of force, pressure and moments.

2. Incorporate wing planform changes, such as AR, washout and aerodynamic twist, to the

UAV model and compare experimental results with theoretical and CFD predictions.

3. Assess optimal wing planform characteristics for the Bath 2016 UAV aircraft which will then

be entered in the IMechE UAS challenge.

17

Chapter 4

Experimental Methodology and

Instrumentation

4.1 Airfoil Selection

The selection of the airfoils will be discussed in the following subsections. The MH45 was used as

the primary airfoil and the S822 airfoil, which was used for the model with aerodynamic twist at

the tip.

4.1.1 MH45

The airfoil chosen for the flying UAV was the MH45 by the aerospace business group design

project. It was required to have a pitching moment coefficient which was positive as flying wings

have no tail surfaces. The airfoil was selected by comparing CL,max, stall characteristics and

moment coefficient. The airfoil was required to have a large thickness to chord ratio, for the

electrical systems and payloads at the centre section [3]. The cross-sectional profile of the MH45

is shown in 4.1.1. It has a positive camber at the forward section, which provides good lift and

drag characteristics and slightly negative camber at the rear section to create a negative pitching

moment.

18

Figure 4.1.1: Wing cross section showing the MH45 airfoil chosen for the fuselageand wing root

4.1.2 S822

The S822 airfoil was selected as the tip airfoil for the test model with aerodynamic twist. The

airfoil was selected due its thickness and high stall characteristics, which had a very similar lift

gradient as the root airfoil MH45 shown in figure 4.1.3. Another airfoil considered was the S1223,

which also had a high stall angle but a higher CL,max. It was highly cambered with a strong

downturned trailing edge, which would create manufacturing issues and the behaviour of the two

combined could be very unpredictable. Whereas the S822 had a similar lift curve and a similar

cross-section.

The S822 and S823 family of airfoils were mainly designed for two applications, wind turbines and

UAV’s. In the wind turbine application, the S822 airfoil is used at the tip and the S823 at the

root and is designed for Reynolds number of 600,000. The skyseeker will be cruising at 870,000

and the Reynolds number at the tip would be 300,000. The S822 was experimentally investigated

by Pohilippe Gigeure, who concluded that these thick family airfoils are not sensitive to roughness

and the lift hysteresis is not affected at Reynolds numbers under 200,000 [42].

The lift and drag coefficients are compared in figure 4.1.2, which show how the two airfoils vary.

The graphs were plotted using XFLR5, which is programme created to analyse airfoils, wings and

aircraft at low Reynolds numbers by Mark Drela as an MIT project. The analysis is based on lifting

line theory, vortex lattice method and 3D panel method. The program has been thoroughly tested

against other software and wind tunnel results with moderate success. However, the methods tend

to underestimate the decrease in lift at high AoA [43].

The S822 airfoil has a lower CLmax but a higher stall angle. The MH45 stalls at 14◦ compared

to the S822 which stalls at 18◦. The drag coefficients for the S822 is also minimum compared to

the MH45 and therefore should not further increase drag on the aircraft. The higher CLα of the

S822 signifies it produces more lift, however with a four degree washout, it was expected to drop

below the root airfoil.

19

-1.5

-1

-0.5

0

0.5

1

1.5

-20 -10 0 10 20

Li#Coe

fficien

t,Cl

AngleofA1ack(deg)

MH45,Re=870kMH45,Re=360kS822,Re=860kS822,Re=370k

(a) CL versus α comparing MH45 and S822

0

0.1

0.2

0.3

0.4

-20 -10 0 10 20

DragCoe

fficien

t,Cd

AngleofA3ack(deg)

MH45,Re=870kMH45,Re=360kS822,Re=860kMH45,Re=360k

(b) CD versus α comparing the MH45 and S822

Figure 4.1.2: Comparison between the root and tip airfoils of the MH45 and S822

Figure 4.1.3: Wing cross section showing the S822 airfoil chosen at the tip for aero-dynamic twist

4.2 Experimental Parameters

Force, pressure and moments were measured on the flying wing half span UAV models, which

were mounted vertically in the closed loop wind tunnel. The experimental parameters and their

ranges are shown in table 4.2.1.

Table 4.2.1: Experimental parameters and the uncertainties involved

Variable Range Uncertainty

Reynolds Number 200, 000− 370, 000 +/− 15,000

Angle of Attack 0◦ - 18◦ +/− 0.5

Aspect Rato 5− 7 +/− 1.2%

Washout 4◦ - 6◦ +/− 1%

These parameters were tested against eight wing planforms, seven which varied in AR and washout

20

and one with aerodynamic twist, as shown in table 4.2.2. It was decided to use AR 5.5 and 4◦

washout as the baseline design model, which was theoretically proven to be the most optimum

planform [3]. This would allow a comparison to be made and critical informations could be passed

on to TBD members.

The variation in AR was decided so that the Reynolds number, taper angle and sweep were kept

constant. Therefore this meant that the area and taper ratio were varied. However this would be

normalised with the lift coefficient and therefore the wings could be fairly compared.

The uncertainty was calculated using the analytical methods described by Moffat, [44]. Velocity,

lift and drag coefficient were found in a relatively straightforward manner. Details regarding

uncertainties will be discussed in later section and can also be found in appendix A.

Table 4.2.2: Test matrix

Geometric Twist

-3 -4 -5 -6

AR

5 X

5.5 X X X X

6 X

7 X

Aerodynamic Twist, AR 5.5, Twist -4 S822

It was required to take 40 measurements on 8 different wing planforms. This would require to

test 2 wings per day. Force, pressure and moment were measured at each parameter state. It

was calculated that each recording would last 3 mins +− 30 seconds at each angle of attack. For

18 positive angle of attacks and 2 velocities, 80 measurements were required, and therefore each

wing would take, 160 mins +− 20 mins. Each day would therefore require 320 mins +− 40 mins,

which is considering time taken to replace pressure tubes and increasing angle attack and Reynolds

number.

4.3 Experimental Setup

The experiments were conducted in a large closed return wind tunnel at the university of Bath. The

wind tunnel is capable of maximum flow speeds of 40m/s with a free stream turbulence intensity

of 0.1% [45]. The tunnel has a rectangular working sections of dimensions 2.1m x 1.5m and 2m

length. A similar schematic of the wind tunnel is shown in figure 4.3.1. The primary purpose of this

wind tunnel is to conduct research on unsteady aerodynamics of airfoils and wings and flow control.

The free stream velocity was controlled thorough the dynamic pressure mounted on the floor of

the wind tunnel upstream of the leading edge. The temperature fluctuations were accounted for

by the dynamic pressure and the drift in the temperatures was always minimum.

21

Figure 4.3.1: Similar schematic of the University of Bath closed return wind tunnel[46]

The wing model was scaled to 80% Reynolds number in order to fit in the wind tunnel. The

wing model had dimensions of 0.56m root chord, tip chord and span which varied from 0.1m to

0.2m and 0.9m to 1.2m, respectfully. The wing was placed vertically in the wind tunnel and the

clearance between the walls was kept minimum, in order to reduce wall interference effects. The

force sensor was attached to an aluminium rod at the quarter chord of the root, then attached to

the turntable outside of the working section. The turntable controlled the angle of attack using a

frequency controller, as seen in figure 4.3.2. The output from the force balance was a USB port

and all pressure tubes were kept away from the force sensor in order to reduce and minimise the

noise and errors in force and moment readings.

Figure 4.3.2: Schematic of the wind tunnel setup showing turntable, scanivalve, pres-sure tube, force sensor and direction of free stream velocity

22

The wind tunnel was calibrated so that wings models were at zero AoA, two weights were attached

from the leading edge and trailing edge of fuselage and the turntable was adjusted to form a straight

line. Further details of calibrating the wind tunnel can be found in appendix B.

4.4 Wing Model

The experimental models were designed and manufactured so that the surface finish would be

similar to that of the final aircraft, in order to increase the accuracy. The process and tools used

during the process is discussed in the following sections.

4.4.1 Manufacturing Process

The wing was made with Styrofoam using the CNC hot wire foam cutter. It was then wet laid

with fibre glass and stiffened to avoid fluttering at high Reynolds number.

4.4.1.1 CNC Hot Wire Cutter

A XL1 machine was used, which is a heavy duty hot wire CNC foam cutter made by ’rcfoamcutter’.

The setup included a 4 axis electronic box and variable hot wire power supply. To control the CNC

machine, Mach3 ®programme was used, which controls the motion of motors stepper and servo

by a G-Code. The G-Code was generated using MATLAB®, which was completed by defining

two airfoil profiles and extrapolating to the two extreme ends of the CNC hot wire cutter. Each

of the four axis was calibrated by inputing a fixed distance and measuring the actual distance

travelled. This was looped until both values matched. It was found that the heat from the CNC

machine took off 0.5mm of the foam around the airfoil. So the G-Code was developed by increasing

the airfoil profiles.

4.4.1.2 Plastic Film and Peel Ply Techniques

Two types of composite manufacturing techniques were investigated; plastic film finish and peel

ply finish. It was found that using vacuum bagging could cause the foam to crush and change the

airfoil shape, and therefore avoided. The plastic film finish was found to have better surface over

the wing, however it caused problems during sanding and CNC cleanup / machine maintenance.

Peel ply technique was then investigated, which slightly rougher finish. After sanding the wing,

the surface finish improved to that of the plastic film.

Table 4.4.1 describes each major operation in the manufacturing process of a single wing model,

however due to the time available, four wings were manufactured in parallel. Overall, it took eight

days to manufacture a complete wing model with variation of two days due to manufacturing

23

errors and re-work. It took 20 days to manufacture all 8 models, which included 4 day to rectify

any errors. All steps of the wing model were inspected visually after each operation. Photographs

of examples of the manufacturing methods can be found in appendix C.

Table 4.4.1: Table of operations to manufacture a single wing model

Operation Details Equipment Time (hrs)

01 Cut wing

profile

- Generate G-code using MATLAB

- Input parameters and place foam on the

CNC place holder

Hot wire

CNC

2

02 Sand wing - Upper and lower surface sanded using 80 -

600 grit sand paper

Emery cloth

sand paper

2

03 Cut pres-

sure panel

- Pressure panel foam cut Hot wire

CNC

2

04 Lay UD

carbon

fibre

- Wet lay carbon tape on the trailing edge and

at centre of twist and cure overnight

Laminating

epoxy, slow

hardener

13

05 Lay upper

surface

- Wet lay upper surface using one layer of 120g

glass fibre and peel ply

Laminating

epoxy, slow

hardener

14

06 Lay bottom

surface

- Wet lay bottom surface using one layer of

120g glass fibre and peel ply

Hit wire

CNC

14

07 UD Carbon

fibre

- Wet lay UD carbon fibre on the trailing edge

and inside the pressure panel at the centre of

twist

Laminating

epoxy, slow

hardener

13

08 Lay leading

edge

- Lay leading edge with 120g of glass fibre at

45◦ of orientation and peel ply

Laminating

epoxy, slow

hardener

13

09 Sand wing - Sand upper and lower surface, and leading

edge using 80 to 600 grit

Emery cloth

sand paper

2

10 Drill pres-

sure holes

- Drill pressure hole at 10% chord perpendic-

ular to the surface

1.6m drill bit 1

11 Assemble

tube

- Cut hyper-dermic tubes and lay flat on the

upper wing surface.

Guillotine

machine

2.5

24

4.5 Force and Moment Measurements

Wings and fuselage models were connected to a commercial 6 axis force sensor by an aluminium

fixture at the wing root quarter chord. Range and typical resolutions quoted by the manufacturing

company are found in table 4.5.1 [47]. Detailed specifications of the force sensor can be found

in appendix D [47]. LABVIEW 7.1®was used to post process the data and time voltage was

converted into time average force through voltage calibration curves. The post data processing

details can be found in appendix E.

Table 4.5.1: Range and resolution of the commercial iCub force and torque sensor

Fx, Fy (N) Fz (N) Tx, Ty (Nm) Tz (Nm}

Range 2000 2000 40 30

Resolution 0.25 0.25 0.0049 0.00307

Prior to any testing, a systematical procedure was setup so that accurate readings were obtained.

The following procedure was undertaken before and after each test run:

1. Before each run with the wind tunnel off, the force sensor was reset, by taking a average

after 60 readings, over a minute. The standard deviation between the mean was found to

be <1%. This is shown in figure 4.5.1 at the two extreme angles of attack.

2. At each test speed 2 sets of readings were recorded to ensure the precision and accuracy of

the data.

3. After each test, the wind tunnel was shut off and the drift in the force sensor was measured,

this was later found to be negligible.

153.3153.35153.4

153.45153.5

153.55153.6

153.65153.7

0 10 20 30 40 50 60

RawBod

yForceSignals

Time(s)

(a) Raw body forces at 0◦ AOA versus time

157

157.5

158

158.5

159

159.5

0 10 20 30 40 50 60

RawBod

yForceSignals

Time(s)

(b) Raw body forces at 18◦ AOA versus time

Figure 4.5.1: Correlations between the raw body forces in Fx and time at low andhigh angle of attacks

After post processing of the data, the body forces were converted into lift and drag. As the force

sensor was relative to the wind tunnel, the following equations were derived from resolving the

25

forces along a single alpha.

L = Fx cosα− FY sinα (4.5.1)

D = Fx cosα+ FY sinα (4.5.2)

The time-averaged force and moment measurements were non dimensionalised through the fol-

lowing relationships [48].

CF =F

0.5ρbcU2(4.5.3)

CT =T

0.5ρbcU2c̄(4.5.4)

The force sensor relative to the model is shown in figure 4.5.2, which highlights the axis of the

force and moments.

Figure 4.5.2: Six axis force and torque sensor with the reference position in windtunnel setup

4.6 Pressure Measurements

The wing pressure instrumentation was located at sixteen span positions and one chord position,

concentrating more along the tip of the wing. This allowed a closer observation of the flow

characteristics, in order to understand the flow behaviour. Due to instrumentation procedure and

wing size, more chord wise positions could not be located. Therefore, the chord position was

located assessing the pressure distribution from the CFD results obtained by J. Barber [49]. 10%

chord position was select where the flow first started to separate at the stall angle. The CFD

results can be found in appendix F.

26

The taps were connected to the scanivalve using urethane tubes and hyperdermic tubing, which

were embedded in the wing upper section. The wing was instrumented with 0.2mm pressure

taps. The taps were positioned so that the flow features on the top surface were unchanged. The

hyperdermic tubing was then bent to a radius of 90◦, to allow for the minimal thickness near the

tip. The instrumentation of the pressure taps can be seen in figure 4.6.1.

Figure 4.6.1: Instrumentation scheme for pressure taps, adapted from Sanz, A andVogt [50]

The scanivalve was set up with a dummy transducer linked to a 6 millibar pressure transducer. The

transducer was selected by calculating the maximum pressure on the wing at the highest Reynolds

number, which was found to be 4 millibar. The transducer was then connected to a data acquisition

card and LABVIEW 7.1®was used to record the data. The wing and fuselage model is shown in

figure 4.6.2, highlighting the pressure taps, carbon rods and pressure instrumentation.

Figure 4.6.2: Wing and fuselage model highlighting pressure taps, tubes and carbonfibre stiffeners

Prior to any testing, an systematical procedure was set to ensure the system was free from leaks

and that all pressure taps were reading accurately:

1. Before each run with the wind tunnel off, the pressure sensors at each tap were reset, by

27

taking a average of the each tap reading. The standard deviation between the mean was

<0.1%.

2. At each test speed at settling time of 2 seconds was set in order to determine the pressure

over the wing surface, ambient pressure and the dynamic pressure.

3. After each test, the wind tunnel was shut down and the drift in the pressures were measured,

which was found to negligible.

It was found that tap 0 of the scanivalve had a leak and was not used during the experiment. The

averaged pressures were non dimensionalised though the following relationship [48]:

CP =P

12ρU

2(4.6.1)

4.7 Tuft Flow Visualisation

Tuft flow visualisation was used to observe the flow pattern over the surface of each wing model.

This was considered after all measurements were taken so forces, moments and pressures were not

affected. As described in the literature in section 2.4, fluorescent mini-tuft were used concentrating

more on the tip surface of the wing. The tufts were cut to 2.5cm long pieces of yarn attached

with 2.5cm spacing to the suction side of the wing. A high definition video camera was used to

record the flow pattern. The tuft flow visualisation was only observed at the highest Reynolds

number at 370,00 through angles of 0◦ to 18◦. The baseline model fitted with 56 tufts and purple

fluorescent dye as shown in figure 4.7.1.

Figure 4.7.1: Baseline wing model with 56 fluorescent tufts attached on upper surface

28

4.8 Experimental Conditions

4.8.1 Reynolds Number

The Reynolds number for this study will be maintained at 370,000, although other values were

also considered at 200,000. The Reynolds number selected will allow an understanding of the

results and provide a conclusion on the most optimum planform.

4.8.2 Tunnel Interference affects

The flow around a model in the wind tunnel varies from that for the UAV in air. These effects are

due to the distortion in the working section and due to the wires and struts used for supporting

the model. The wind tunnel interference effects were calculated using the Pankhurst and Holder

methods [15]. These effects can be divided into five sources, which are discussed in the following

sub sections. Further details regarding the calculations of the interference affects are discussed in

appendix G.

4.8.2.1 Solid Blockage

Solid blockage creates an increase in velocity due the wing model restricted in the working section.

In the a case of three dimensional flow of a wing these affects would induce a solid blockage of

11.3%, compared to the cross sectional area of the wind tunnel. This induces a 0.2% error in the

tunnel to free flight velocity at the highest Reynolds number of 370,000.

4.8.2.2 Wake Blockage

Wake blockage effects creates a decrease in lift in the working section as the tunnel walls limit flow

streamlines, especially in the case of a wing model. A stationary model at 18◦ AoA produced the

most drag, and it was found that the difference between the free air and closed loop tunnel was

<1.2% . The force and moments coefficient were then corrected using the solid and wake blockage

as 2.74%.

4.8.2.3 Lift Effects

Lift affects accounts for the lift which is limited in the wind tunnel due to the restricted working

section walls. It was calculated that the difference between stationary and AoA of 18◦ was 1.74%

in the free stream air velocity.

29

4.8.2.4 Static-Pressure Gradient

Static pressure gradient may arise throughout the length of the tunnel due to acceleration of the

fluid created by both the wake and the developing tunnel-wall boundary later. The drag force was

measured on the force balance should therefore be corrected accordingly.

4.8.2.5 Wall Boundary-Layer Interference

The boundary layers created by the two side walls if turbulent interferes with the flow over the

surface of the wing sections. For the current setup the end plates began 1m upstream of the

leading edge and the required distance for transmission under these conditions would be 1m based

on a critical Reynolds number of 2×105. However boundary layer theory predicts that its thickest

turbulent boundary will be at 30mm. At this point the lift on the fuselage will be minimum

and the first pressure tap was located 100mm from the end plate and therefore the effect can be

ignored.

4.9 Uncertainty Analysis

The uncertainty associated with the force and moment measurements was calculated using the

methods described by Moffat [44]. This methods analyses all source of errors including calibrations,

standard deviation and instrumentation errors, for further details see appendix A. The time

averaged lift and drag uncertainties for the MH45 wing planforms, with AR 5.5 and 4◦ washout,

angle of attack from 0◦ to 18◦ is shown in figures 4.9.1 and 4.9.2.

Figure 4.9.1: Lift coefficient uncertainties for the baseline model at angle of attackof 0◦ to 18◦ compared at two Reynolds numbers [51]

30

Figure 4.9.2: Drag coefficient uncertainties for the baseline model at angle of attackof 0◦ to 18◦ compared at two Reynolds numbers [51]

At higher angles of attack the uncertainties increases, which is due to the increasing fluctuation

in the force sensor. It was found during the analysis that the highest uncertainty was in velocity

with +− 1m/s. This was due to the uncertainty in measuring the free stream velocity in the wind

tunnel, due to the accuracy of the dynamic pressure of 0.5 pascals. This affected the uncertainty

in the aerodynamic constant Q by 6% and affecting CL by 1.6%. These uncertainties are constant

throughout each wind model for all measurements. The same experiment carried out, using

the exact same instrumentation at a smaller Reynolds numbers of 140,000 by P. Patel, a team

bath drones member [51]. The lift coefficient can be seen to have a significant difference in the

uncertainty bounds, with a clearance gap between most plots. The variation in uncertainties are

shown in table 4.9.1.

Table 4.9.1: Uncertainties in lift and drag compared at two Reynolds numbers

Reynolds no. CL, 0◦ AoA Uncertainty, 0◦ AoA CL, 18◦ AoA Uncertainty, 18◦ AoA

140,000 -0.01 5% 0.60 26%

370,000 0.11 2% 0.84 11%

CD, 0◦ AoA Uncertainty, 0◦ AoA CD, 18◦ AoA Uncertainty, 18◦ AoA

140,000 0.03 12% 0.25 22%

370,000 0.01 7% 0.37 8%

The difference in the maximum and minimum pressure coefficients for the wing planform, with

AR 5.5 and 4◦ washout, angle of attack from 0◦ to 18◦ is shown in figure 4.9.3.

31

Figure 4.9.3: Pressure coefficient uncertainties for the baseline model at normalisedspan position η at angles of attack of 0◦ to 18◦

The uncertainties associated with CP do not fluctuate as much as the force and moment, as the

instrumentation uncertainty was 0.01% and the calibration and drift uncertainty were also always

below 1%.

It is clear from the uncertainty analysis that at higher Reynolds numbers the uncertainties de-

creases as the force becomes larger. Therefore, it is more reliable to analyse the data at the highest

Reynolds number.

32

Chapter 5

CFD and Reynold Number

Comparison

5.1 Lift and Drag Comparison

Time-averaged lift and drag coefficients for the wing planform, with AR5.5 and 4◦ washout was

compared to CFD, panel and theoretical methods, at angles of attack from 0◦ to 18◦ is shown in

figures 5.1.1 and 5.1.2.

Figure 5.1.1: Comparison of lift coefficient with CFD, panel and theoretical methodsand Reynolds number [49] [51]

33

Figure 5.1.2: Comparison of drag coefficient with CFD, panel and theoretical methodsand Reynolds number [49] [51]

The difference between lift and drag coefficient are significant when compared to the theoretical

and panel predictions. Panel methods is unable to predict boundary layers and flow separation

and therefore only comparable away from the stall regions, while theoretical prediction, does not

predict any stall behaviour.

The wind tunnel results were also compared against J. Barbers CFD results for the Skyseeker at

cruise [49]. It is noticed that CLα is slightly similar to that of the CFD results. Higher stall angle

and CLmax is due the difference in Reynolds number. The stall angle increases from 14◦ to 16◦

according to CFD.

Wind tunnel data were also compared at lower Reynolds number, which was measured by P.

Patel, as part of stability and control project for team bath drones [51]. The decrease in Reynolds

number shows that the stall angle decreases from 14◦ to 10◦ and the CLmax from 0.9 to 0.7. The

gradient of both curves are also very similar, which is expected.

Moreover, at 140,000 Reynolds number a laminar separation bubble can be observed with a peak

in the drag coefficient at 4◦ AoA. At the low Reynolds number, the separation is caused by a strong

adverse pressure gradient and its inability to transition from turbulent flow, therefore creating a

laminar separation bubble. The increasing thickness of the boundary layer, increases the drag.

This effect is however eliminated at the higher Reynolds number.

34

5.2 Conclusion

It is clear from the comparison that there is a significant difference between the wind tunnel results

and theoretical and panel code, due to efficiency in modelling near the stall region. However, the

results compared to CFD and lower Reynolds number shows a very similar relationship, shown

in table 5.2.1. Testing at Reynolds number of 370,000 would therefore be significant enough to

understand the stall and control behaviour, which can be further analysed to fulfil the aims and

objectives. It is also clear that the wing stalls in the region of 10◦ - 16◦ and testing any further

will not fulfil the objectives and aims of this study. It is clear that a Reynolds number of 370,000

is high enough to avoid laminar separation bubble, which was present at 140,000 Reynolds.

Table 5.2.1: Comparison of wind tunnel results with CFD, panel and theoreticalmethods

CL,max Lift curve slope, CLα Stall angle, αs CL0

Re, 145,000 0.69 4.48rad−1 10◦ -0.01

Re, 370,000 0.94 4.36rad−1 14◦ 0.11

CFD, 860,000 1.01 4.58rad−1 18◦ 0.023

Panel method 0.9 4.28rad−1 14◦ -0.05

Theoretical ∞ 3.99rad−1 ∞ -0.039

35

Chapter 6

Aspect Ratio

6.1 Force Measurements

Time averaged lift coefficient at angle of attack of 0◦ to 18◦, fixed Reynolds number 370,000,

washout of 4◦ and a different AR is shown in figure 6.1.1.

Figure 6.1.1: Time averaged lift coefficient at AoA of 0◦ to 18◦, 370,000 Reynoldsnumber, 4◦ washout and different aspect ratios

The CL versus α graphs shows that increasing AR increases the lift curve slope. dCLdα increases

from 3.90 rad−1 to 5.04 rad−1 at the two extreme planforms. This results in a higher CL,max,

which varies from 0.99 to 0.90. As the AR increases the lift curve of the three-dimensional wing

starts getting closer to its two dimensional airfoil section. This is due to the reduction of the

influence of wing tip vortex. The flow near the tip curls around to the top surface, being forced

from the high pressure region underneath to the low pressure region on top. As the AR increases

36

the linear lift region is reduced and stall angle αs decreases. The stall angle is maximum at AR

5 with 14◦ and minimum at AR 7, at 11◦. In addition, AR 6 and 7 notice a small drop in lift

just before the stall angle. This indicates the loss of lift at the tip of the wing. As higher AR

have lower taper ratios the stall progresses towards the root, as described in the literature. The

CL versus α graph also indicates no abrupt lift characteristics, therefore suggests that all wing

models have a trailing edge stall, as the decrease in lift is smooth.

Taking the design weight of the UAV obtained from J.Barber as 7kg, the stall speed was calculated

for each wing, assuming the weight is constant at 7kg. However, an increase in AR would increase

the weight of the wings in order to carry the higher load. The results from the graph are shown in

table 6.1.1. Stall speed is not an issue at take-off for UAV’s but more for landing and manoeuvre

and therefore the performance is increased if kept minimum.

Table 6.1.1: Effects of AR on CL,max, lift curve slope dCLα and stall speed Vs

AR CL,maxdCLdα VS , (m/s) Taper Ratio

5 0.90 3.90 11.88 0.19

5.5 0.94 4.21 11.44 0.26

6 0.96 4.99 11.19 0.30

7 0.99 5.04 10.57 0.34

Time-average lift coefficient versus drag coefficient for angle of attack at 0◦ to 18◦, fixed Reynolds

number 370,000, washout of 4◦ and different aspect ratios is shown in figure 6.1.2.

Figure 6.1.2: Time averaged lift coefficient versus drag coefficient for 370,000Reynolds number, 4◦ washout and different aspect ratios

The drag polar was used to determine, the induced drag for fixed Reynolds number of 370,000

37

and different AR shown in figure 6.1.3. This was found by calculating the lift dependent and

independent drag. The drag polar is illustrated in table 6.1.2 for each AR. As the wing airfoil has

camber and twist, the CDo is not the same at CD,min, and this effect was ignored when calculating

the induced drag [52].

CD = CD0 + bC2L (6.1.1)

Figure 6.1.3: Time averaged lift coefficient versus induced drag coefficient forReynolds number of 370,000, 4◦ washout and different aspect ratios

The drag polar indicates that increasing AR, L/D also increases as the design lift coefficient moves

further up, with maximum at AR 7. As expected from the literature, increasing AR decreases

the induced drag, from 30% to 50% of the total drag at higher angles of attack. The lift vector

produced by the downwash at the wing tips increases for the smaller wings, hence smaller AR. it

is therefore desirable to have a larger AR to minimise induced drag. The parasitic drag however

increases at higher AR due to an increase in frontal area.

The degree of efficiency was also calculated for each AR as the k-factor. The greater the k factor

the worse the related lift distribution with respect to the induced drag. This is found by the

following relationship, described by Edward Arnold [53].

K = CDi/CDi,ell (6.1.2)

The results shown in table 6.1.2 that the k factor reduces with AR. At AR of 5, 23% has lost in

performance due to induced drag, but only 10% lost at AR 7. An elliptical wing produces least

induced drag for a given planform.

38

Table 6.1.2: Drag polars and efficiency factors for different aspect ratios

AR CDo b k-factor

5 0.015 0.078 1.23

5.5 0.016 0.068 1.21

6 0.017 0.059 1.17

7 0.02 0.050 1.10

6.2 Longitudinal Stability

Time averaged lift coefficient versus pitching moment for fixed Reynolds number of 370,000, 4◦

washout and different aspect ratios is shown in figure 6.2.1.

Figure 6.2.1: Time averaged lift coefficient versus pitching moment for Reynoldsnumber of 370,000, 4◦ washout and different aspect ratios

Increasing AR reduces the cruise angle, as the aircraft requires more force to create a zero pitch-

ing moment. The cruise angle required for zero pitching moment varies from 5◦ to 3◦, at the

two extreme planforms. As the separation reaches the leading edge the wing becomes more sta-

ble. Downwash at the wing decreases as the wing gives up lift causing the centre of pressure to

move rearward, therefore becoming more stable. This is a good characteristic, especially for stall

recovery. Larger AR have a larger non minimum phase control response.

The aerodynamic centre was found by the Cm versus CL graph using the following equation.

CMa.c = CMx − CL(xc̄− δ

c̄

)(6.2.1)

39

Assuming the pitching moment at the aerodynamic centre is constant;

d(CMx)

d(CL)=x

c̄− δ

c̄(6.2.2)

Where x/c̄ and δ/c̄ are the normalised position of the fixture and aerodynamic centre of the mean

aerodynamic chord. The aerodynamic centre of each wing model is shown in table 6.2.1. It is

understood from the table that the aerodynamic centre is further aft then expected of the design

of 0.25c̄. This means even though the aircraft is stable, it requires a higher force to be able to

trim during cruise, especially with increasing aspect ratio.

Table 6.2.1: Normalised mean aerodynamic chord and centre positions for differentaspect ratios

Aspect Rato c̄ xc̄

dCmxdCL

δc̄

5 0.403 -0.347 0.076 0.423

5.5 0.392 -0.357 0.081 0.438

6 0.389 -0.360 0.077 0.437

7 0.379 -0.370 0.080 0.45

The centre of pressure spanwise was found using the pitching moment, CL and bending moment

and the equation below was used to calculate the centre of pressure chordwise, which is shown in

figures 6.2.2 and 6.2.3. The bending moment results are shown in appendix H.

XCP

c̄=

1

4− CMP

CL(6.2.3)

As AR increases the bending moment increases. At higher AR the span gets longer and therefore

the wing weight and bending moment also grow larger. This creates a higher moment at the

wing root, which requires more stiffness. With increasing angle of attack more stress is being

transferred to the root compare to the lower AR wings. This forces the centre of pressure more aft

creating a unsteady pitching motion. This behaviour occurs earlier at higher AR and the curve

is delayed at lower ARs. It is expected that the centre of pressure moves aft rearwards in the

chordwise position after stall. At the smallest AR the curves turns back dramatically. The centre

of pressure lies at around 40% span. Comparing the movement of centre of pressure spanwise and

chordwise, it can be seen that the centre pressure does not tend to move rearwards after stall has

been reached, as the tip is no longer affective and corresponding to any lift. At around 12◦ the

centre of pressure has completely moved inboard and does not move back, which can be a issue

at higher angles of attack, especially in aerobatic manoeuvre.

40

Figure 6.2.2: Angle of attack versus normalised COP chord position for Reynoldnumber of 370,000, 4◦ washout and different aspect ratios. XCP of 0 indicates theleading edge and 1 indicates the trailing edge of the root chord

Figure 6.2.3: Normalised COP spanwise position versus angle of attack for Reynoldnumber of 370,000, 4◦ washout and different Aspect ratios. ηCP 1 indicates the wingtip and 0 indicates the root chord

6.3 Aerodynamic and Power Efficiency

UAV’s require flying for several hours in all weather conditions, requiring higher power efficiency

and lift to drag ratio, as described in the literature. Therefore the power needs to be kept minimum

to increase the endurance. From the breguet range equation [52] it can be seen that to achieve

41

maximum range and endurance, (CL/CD) and (CL3/2/CD) needs to be maximised.

R =η(L/D)

gc

(W1

W2

)(6.3.1)

Power required to keep a fixed wing in the air [52];

P = W

(CD

C3/2L

)√2

ρ

W

S(6.3.2)

The aerodynamic and power efficiency ratios versus angle of attack of 0◦ to 18◦, fixed at Reynolds

number of 370,000, 4◦ washout and different aspect ratios compared to the baseline design is

shown in figures 6.3.1 and 6.3.2. The following equations were used to determine the aerodynamic

and power efficiency ratios.

RAE =(Cl/Cd)Models

(Cl/Cd)Baseline(6.3.3)

RPE =(C

3/2L /CD)Models

(C3/2L /CD)Baseline

(6.3.4)

Figure 6.3.1: Aerodynamic efficiency ratio at angle of attack of 0◦ to 18◦, Reynoldsnumber of 370,000, 4◦ washout and different aspect ratios

42

Figure 6.3.2: Power efficiency ratio for angle of attack of 0◦ to 18◦, Reynolds numberof 370,000, 4◦ washout and different aspect ratios

At lower angles of attack the aerodynamic and power efficiencies fluctuate, which may be due

to the high uncertainty associate at the low values. It is therefore more clear to see the ratios

at higher angles of attack when the uncertainty is reduced, above 2◦. Interestingly the lift to

drag ratio seem fairly similar for all wing models but a substantial increase in power efficiency at

regions between 2◦ to 14◦ is noticed at higher AR. This is more typical cruise condition for the

UAV. The baseline design has a maximum (L/D) of 17, and the improvement in the aerodynamic

and power efficiency are shown in table 6.3.1.

Table 6.3.1: Aerodynamic and power efficiencies compared to the baseline model fordifferent aspect ratios

AR (L/D) improvement Power Improvement

5 -25% -30%

6 10% 15%

7 20% 25%

43

6.4 Stall Behaviour

Pressure coefficient at normalised span location at angles of attack of 0◦ to 18◦, fixed at Reynolds

number of 370,000, 4◦ washout, 10% chord and different aspect ratios is shown in figure 6.4.1.

Figure 6.4.1: Pressure contour map at several spanwise taps for angle of attack of 0◦

to 18◦, Reynolds number of 370,000, 4◦ washout and different aspect ratios at 10%chord. Top left at AR5, top right AR5.5, bottom left AR 6 and bottom right AR 7

44

The pressure contours show really interesting results. It was expected to identify the span location

at which the flow starts to first separate. However, it cannot be identified when the flow separates

unless we have more chordwise pressure taps. At the 10% chord the pressure starts decreasing with

increasing angle of attack, while the velocity increases in the boundary layer along the surface.

After minimum pressure has been reached, CP starts to increase along the surface and velocity

decreases. At this adverse-pressure region, dp/dx > 0, the flow is in risk of separation. Indication

of flow separation can be assumed when the pressure gradient is constant. It can be seen from

the pressure contours that for AR 5, the adverse pressure is seen to continue to decrease at higher

angles of attack to around 14◦, which then becomes constant indicating the point of separation.

However, this can be confirmed with tuft flow visualisation, described further.

Moreover, the pressure maps do indicate the progression of laminar flow from the tip to the root.

It can be seen that increasing the taper ratio and AR the transition from laminar progressing

towards the root of the wing slows down. This creates serious control issues as the tip, which is

highly loaded compared to the entire wing. At 10◦, the flow is still laminar at the whole span for

AR 5 and 5.5. However, for AR 6, 5% of the outer wing has moved inboard and more than 20%

for AR7. Increase in AR severely affects the progression in the pressure field from the tip to the

root.

Surface tuft technique was used to visualise the flow pattern, which also reflects the lift charac-

teristics shown earlier. The first image is at zero angles of attack, where steady flow is observed,

as the tufts are generally directed towards the rear and are motionless. The tufts then progress

to unsteady flow as the tufts oscillate through a range of 45◦ from the chord direction. The sec-

ond image shows tufts oscillating wildly about all directions such as pointing forward at the tip

and being raised off the surface. The third image then indicates the angle which the root first

progresses to unsteady flow [54].

45

Figure 6.4.2: Surface tuft visualisation for AR 5 with 4◦ washout. Top left 0 AoA,top right start of tip separation and bottom left start of root separation

Figure 6.4.3: Surface tuft visualisation for AR 5.5 with 4◦ washout. Top left 0 AoA,top right start of tip separation and bottom left start of root separation

46



At high angles of attack it can be observed that the tufts at the tip reattach, which could be due

47

to attached vortex. This region stays attached even at 20◦ AoA. Table 7.4.1 shows a summary

of when of flow separation is observed at the tip, comparing the pressure coefficient and tuft flow

visualisation. The tuft flow and pressure distribution all reflect each other very well and the stall

regions identified from tuft flow visualisation lie within the pressure regions.

Table 6.4.1: Angle of flow separation observed at the tip, comparing pressure distri-bution and tuft flow visualisation for different aspect ratios

AR Pressure Distribution Tuft Flow visualisation

5 12◦ - 16◦ 14◦

5.5 12◦ - 14◦ 13◦

6 10◦ - 12◦ 12◦

7 8◦ - 12◦ 11◦

6.5 Conclusion

Aerodynamic performance and stall behaviour was analysed for models with various aspect ratios

with 4◦ washout and Reynolds number of 370,000. It was found that by increasing AR, aerody-

namic and power efficiency, CL,max and dCL/dα were improved. However a combination of higher

AR and lower taper ratio, created undesired stall behaviour, especially near the tip. Considering

aerodynamic performance, the optimum planform would lie between AR 5.5 and AR 7. On the

other hand, considering stall behaviour the optimum planform would lie between AR of 6 and

5. Any further increase of AR, would increase the chance of tip stall at even lower angles of

attack.

48

Chapter 7

Geometric Twist


Time averaged lift coefficient at angle of attack of 0◦ to 18◦, fixed at Reynold number of 370,000,

AR of 5.5 and washout of 3◦ to 6◦ is shown in figures 7.1.1.

Figure 7.1.1: Time averaged lift coefficient for angle of attack of 0◦ to 18◦, 370,000Reynolds number, AR 5.5 and 3◦ to 6◦ washout

The CL versus α graph indicates that increasing the geometric twist, lift curve slope remains fairly

constant, at 4.21 rad−1. The fluctuation of dCL/dα at the low angle of attack is less than 2%,

which is within the uncertainty bounds. The main change which was expected from the literature

review, was an increase in angle of attack at zero lift with lower geometric twist, if the lift curve

was assumed to continue in the similar manner at negative angle of attack [55].

49

The stall angle is maximum at washout of 4◦ at 14◦ and minimum at washout of 6◦, at 11◦.

The effect of washout with sweep and taper moves the point of maximum lift coefficient inboard.

Therefore, CL,max decreases with higher washout angles, which is true for all washouts, except

for washout of 3◦. A sharp fall in lift is noticed at 9◦. This suggests that there is a sudden loss

in lift, which maybe located at tip. This will be identified further subsections looking at pressure

and surface tufts visualisation. As the wing is linearly decreasing in angle, the larger washouts

actually feel a negative lift. The results in higher CL,max, which varies from 0.90 to 0.78. The

stall behaviour for all washouts are very similar and gradually decrease in lift as the separation

moves towards the leading edge from the trailing edge, therefore suggests that all wing models

have a trailing edge stall, as the decrease in lift is smooth.

The results in table 7.1.1 indicate that the stall is speed is increased at higher washouts due a

decreases in maximum lift coefficient.

Table 7.1.1: Effects of washout on CL,max, lift curve slope dCL/dCα and stall speed VS

Washout CL,maxdCLdα VS , (m/s) Taper Ratio

3◦ 0.89 4.85 11.22 0.26

4◦ 0.93 4.21 10.98 0.26

5◦ 0.85 4.50 11.48 0.26

6◦ 0.82 4.90 11.70 0.26

Time-averaged lift coefficient versus drag coefficient, fixed at Reynold number of 370,000, AR 5.5

and different washouts of 3◦ to 6◦ is shown in figure 7.1.2. The drag polar was used to determine,

the induced drag shown in figure 7.1.3.

Figure 7.1.2: Time averaged lift coefficient versus drag coefficient at 370,000 Reynoldsnumber, AR 5.5 and 3◦ to 6◦ washout

50

Figure 7.1.3: Time averaged lift coefficient versus induced drag coeffiecient atReynolds 370,000, AR5.5 and 3◦ to 6◦ washout

It is clear from the drag polar that with larger washouts, L/D maintains fairly constant, as

the design lift coefficient is unchanged. As expected from the literature review it can be seen

that larger washouts produces more induced drag, from 30% to 40% of the total drag at higher

angles of attack. The change from 4◦ washout to twist 6◦ show a increase in induced drag.

The wing planform washout of 3◦ does not follow this trend, as identified earlier the lift curve

shows a early stall with this model and therefore the drag is increased substantially. As usual

component of induced drag, which is proportional to C2L, a wing with washout produces induced

drag proportional to the washout squared and gives rise to induced drag at zero net lift [56].

The k factor increases with higher washout. At washout 6◦, 32% has lost in performance due

to induced drag, but only 12% lost at washout 3◦. Therefore washout should be kept minimum

to increase the performance. However, induced drag is not directly affected by washout, which

indicates the degrading in lift distribution.

Table 7.1.2: Drag polars for washout of 3◦ to 6◦

Washout CDo b k

3◦ 0.012 0.080 1.12

4◦ 0.016 0.054 1.21

5◦ 0.035 0.059 1.22

6◦ 0.04 0.065 1.32

51


Time averaged lift coefficient versus pitching moment for fixed Reynold number of 370,000, AR

5.5 and washout of 3◦ to 6◦ is shown in figure 7.2.1.

Figure 7.2.1: Time averaged lift coefficient versus pitching moment for Reynoldsnumber of 370,000, AR 5.5 and washout of 3◦ to 6◦

The pitching moment shows that larger washouts requires a higher value of CL for cruise, which

is expected from the literature as the aircraft produces negative lift at at the tip of the wing,

creating a larger pitch down moment. The cruise angle required varies from 2◦ to 5◦, at 3◦ and

6◦ washout respectively.

The wing planform with 3◦ washout does not follow this behaviour, which maybe due to the

uncertainty error bounds and due to the wing tip loss noticed from the lift curve earlier. The wing

planforms with higher washout notice no gradual decrease in pitching but suddenly pitches up

and then back down, indicating the washout has delayed the stall at the tip and transferred to the

root. The gradual increase in pitching moment can be seen with a washout of 3◦, as from 0.6 CL,

the linear region has stopped and tends to pitch up. This could due to stall starting develop over

the tips. When, stall is developed against the span the wing model pitches down again. At higher

washouts the pitching moment seems to more linear due to the late stall at the tip, however the

angle which the root stalls has decreased and creates a unstable response.

The aerodynamic centre of each wing model is shown in table 7.2.1. It is understood from the

table that the aerodynamic centre is further aft then expected of the design of 0.25c̄. This means

even though the aircraft is stable, it requires a higher force to be able to trim during cruise. As

the mean aerodynamic chord is similar and no change in pitching moment, all wing models have

52

the same mean aerodynamic centre.

Table 7.2.1: Aerodynamic centre of wing planform with different washouts

Washout c̄ xc̄

dCmxdCL

δc̄

3◦ 0.392 -0.357 0.081 0.438

4◦ 0.392 -0.357 0.081 0.438

5◦ 0.392 -0.357 0.081 0.438

6◦ 0.392 -0.357 0.081 0.438

The centre of pressure chordwise and spanwise was found using the similar approach as described

earlier and is shown in figures 7.2.2 and 7.2.3.

Figure 7.2.2: Angle of attack versus normalised COP chord position for Reynolds370,000, AR5.5 and washout from 3◦ to 6◦. XCP of 0 indicates the leading edge and1 indicates the trailing edge of the root chord

53

Figure 7.2.3: Normalised COP spanwise position versus angle of attack for Reynolds370,000, AR5.5 and washout from 3◦ to 6◦. ηCP of 1 indicates the wing tip and 0indicates the trailing edge

For all washouts the COP remains fairly linear. The centre of pressure lines at around 20% - 40%

span and then decreases at high angles of attack when there has been a loss in lift. Comparing the

movement of centre of pressure spanwise and chordwise, it can be seen that the centre pressure

does not tend to move rearwards after stall has been reached for lower washouts, as the tip is

no longer affective and corresponding to any lift. At around 12 ◦s the centre of pressure has

completely moved inboard.

54


Aerodynamic efficiency ratio at angle of attack of 0◦ to 18◦, fixed at Reynolds number of 370,000,

AR5.5 and washout of 3◦ to 6◦ is shown in figures 7.3.1 and 7.3.2.

Figure 7.3.1: Aerodynamic efficiency ratio versus angle of attack of 0◦ to 18◦,Reynolds number of 370,000, AR5.5 for washout of 3◦ to 6◦ compared to baselinemodel

Figure 7.3.2: Power efficiency ratio versus angle of attack of 0◦ to 18◦, Reynoldsnumber of 370,000, AR5.5 for washout of 3◦ to 6◦ compared to baseline model

The decrease in aerodynamic and power efficiency at lower angles of attack is due to the aircraft

producing negative lift at higher washouts and therefore producing more drag. At higher angles

55

of attack the aerodynamic efficiency starts to increase while the wing planform with 3◦ washout

starts to decrease at angles of attack of 2◦ to 10◦. Washout starts to adjust the lift distribution

so that the tip is not highly loaded and therefore flow is kept laminar for a longer period. In

addition, washout of 5◦ seems to remain fairly constant in terms of aerodynamic and power

efficiency, suggesting there is no change in the lift distribution. It can also be noticed that at

angles of attack after 10◦, the aerodynamic and power efficiency start to degrade, which could be

do the flow separation progressing to the root faster.

The baseline design has a maximum L/D of 17, and the improvement in the aerodynamic and

power efficiency are shown in table 7.3.1.

Table 7.3.1: Aerodynamic and power efficiencies compared to the baseline model fordifferent washouts

Washout (L/D) improvement Power Improvement

3◦ 20% 30%

5◦ -1% -1%

6◦ -5% -3%

7.4 Stall Behaviour


number of 370,000, AR 5.5, 10% chord and different washouts is shown in figure 7.4.1.

As identified earlier we can not determine the point of flow separation.Therefore, indication of

flow separation can be assumed when the pressure gradient is constant. In addition, with change

in washout, it can also be seen that the start of decreasing pressure is also moved further aft. Due

to a change in lift distribution, the pressure distribution over the wing has also been shifted and

therefore the taps would need to be relocated.

The results indicate the opposite affect of increasing AR. The pressure distribution can be seen

to move inboard at lower angles of attack for larger washouts. Therefore the tip is less loaded

compared to the root which improves the tip stall behaviour. The pressure maps do indicate the

progression in laminar flow from the tip to the root and can be seen that increasing washout the

transition from laminar, progressing towards the root of the wing is delayed.

56

Figure 7.4.1: Pressure contour map of several spanwise taps for angle of attack of 0◦

to 18◦, Reynolds number of 370,000, AR5.5 and washout of 3◦ to 6◦. Top left at 3◦,top right 4◦, bottom left 5◦ and bottom right 6◦ of washout

Table 7.4.1 shows a summary of when flow separation is observed at the tip, comparing the pressure

coefficient and tuft flow visualisation. The tuft flow and pressure contour all reflect each other very

well and the stall regions identified from tuft flow visualisation lies within the pressure regions.

It can be seen that increasing the washout does allow the tip to stall at a later angle of attack

but to some extent. After washout of 5◦, tip stall did not improve and instead the root stalled

at a earlier angle. As the twist is linear from the root, this could be due to the manufacturing of

the wing through the CNC foam cutter. At high angles of attack it can also be observed that the

tufts at the tip reattach, which could be due to attached vortex. This region stays attached even

at 18◦ AoA.

57

Figure 7.4.2: Surface tuft visualisation of 3◦ washout with AR 5.5. Top left 0 AoA,top right start of tip separation and bottom left start of root separation


58



59

Table 7.4.1: Angles of flow separation at the tip identified by the pressure distributionand tuft flow visualisation for different washouts

Washout Pressure Distribution Tuft Flow visualisation

3◦ 12◦ 8◦

4◦ 12◦ - 14◦ 13◦

5◦ 12◦ - 15◦ 12◦

6◦ 8◦ - 15◦ 12◦

7.5 Conclusion

To conclude, it is understood that increasing washout has negative impact on the cruise perfor-

mance of the aircraft. This was seen by a decrease in lift to drag ratio, power index, lower CL,max

and a lower stall angle. The cruise performance indicates that the optimum planform lies between

4◦ and 5◦ washout. However it was identified that 3◦ washout had severe affects on tip stall and

therefore performance due to drag. Considering the stall behaviour it can be seen that larger

washouts change the lift distribution of the wing. The wing improves its stall behaviour especially

at the tip of the wing but it is not completely avoided. Therefore we would need to consider higher

washouts of up to 15◦. It can be concluded that the wing planform with optimum stall behaviour

lies between 4◦ to 5◦ washout.

60

Chapter 8

Aerodynamic Twist


Time averaged lift for the baseline wing planform and aerodynamic twist, at fixed Reynolds number

of 370,000, angles of attack from 0◦ to 18◦ is shown in figures 8.1.1.

Figure 8.1.1: Time averaged lift coefficient for angle of attack of 0◦ to 18◦, 370,000Reynolds number, 4◦ washout compared with baseline model and aerodynamic twist

The CL versus α graphs shows that by having a variable camber along the span, with the S822

airfoil at the tip the lift gradient remains fairly constant at 4.21 rad−1, but the maximum lift

coefficient is increased. The linear region and stall behaviour is also comparable. With an increased

CL,max, the stall speed has decreased from 11.4m/s to 10.6m/s. This is due to the reduction of

the influence of wing tip vortex, due to a thicker wing tip.

61

The drag polar was used to determine, the induced drag coefficient for fixed reynolds number

of 370,000 compared with the baseline design and aerodynamic twist shown in figures 8.1.2 and

8.1.3.

Figure 8.1.2: Time averaged lift coefficient versus time averaged drag coefficient at370,000 Reynolds compared with the baseline design and aerodynamic twist

Figure 8.1.3: Time averaged lift coefficient versus induced drag coefficient at 370,000Reynolds compared with the baseline design and aerodynamic twist

The drag polar shows close similarity between the two models with a slight increase in the design

lift coefficient. Near the maximum lift coefficient, the drag coefficient diverges to the baseline

design. It is known from airfoil aerodynamics that increasing the thickness of the airfoil, increases

it maximum lift coefficient but also its viscous drag. However, the results show that with MH45

62

at the root and S882, the minimum drag is the same, therefore further increasing the aerodynamic

performance.

The results shown in table 8.1.1 show that the k-factor reduces with aerodynamic twist. With

the baseline design, 21% has lost in performance due to induced drag, but only 8% lost with

aerodynamic twist.

Table 8.1.1: Drag polars for the baseline model and aerodynamic twist

CDo b

Baseline 0.016 0.054

Aerodynamic twist 0.016 0.049


Time averaged lift coefficient versus pitching moment compared with the baseline planform and

aerodynamic twist, fixed at Reynolds number of 370,000 shown in figure 8.2.1.

0

0.2

0.4

0.6

0.8

1

1.2

-0.08-0.06-0.04-0.0200.02

Li#Coe

fficien

t,Cl

PitchingMoment,Cm

AeroTwistRe=370kBaselineRe=370k

Figure 8.2.1: Time averaged lift lift coefficient versus pitching moment for Reynolds370,000 compared with baseline design and aerodynamic twist

The pitching moment indicates the aerodynamic twist model would require a smaller cruise angle

and the slope of the curve indicated a less stable wing compared to the baseline design. The

behaviour of each wing is very similar at high lift coefficients, which indicates instability after

stall.

The aerodynamic centre of each wing model is shown in table 8.2.1. It is understood from the

table that the aerodynamic centre is further aft then expected of the design of 0.25c̄. This means

63

even though the aircraft is stable, it requires a higher force to be able to trim during cruise, with

both wing models which have a slight difference in aerodynamic centre.

Table 8.2.1: Normalised mean aerodynamic chord and centre positions

c̄ xc̄

dCmxdCL

δc̄

Baseline 0.392 -0.357 0.081 0.438

Aerodynamic twist 0.392 -0.357 0.082 0.439


The aerodynamic and power efficiency ratios versus angle of attack of 0◦ to 18◦, fixed at Reynolds

number of 370,000, of the baseline planform compared to the aerodynamic twist model is shown

in figures 8.3.1 and 8.3.2.

Figure 8.3.1: Aerodynamic efficiency ratio versus angle of attack of 0◦ to 18◦,Reynolds number of 370,000 of the aerodynamic twist planform compared to thebaseline planform

64

Figure 8.3.2: Power efficiency ratio versus angle of attack of 0◦ to 18◦, Reynolds num-ber of 370,000 of the aerodynamic twist planform compared to the baseline planform

The aerodynamic performance of the aerodynamic twist planform is clearly seen to have a slight

improvement especially at angles above the 4◦, The power and aerodynamic efficiency is shown to

increase by 10% with aerodynamic twist. Due to the more stable tip, at higher angles of attack

the lift produced by the wing seems to be more in attached to the wing compared to the baseline

model.

8.4 Stall Behaviour


number of 370,000, 4◦ washout and AR 5.5 compared to aerodynamic twist, at 10% chord is shown

in figure 8.4.1.

65

Figure 8.4.1: Pressure contour map of several spanwise taps for angle of attack of 0◦

to 18◦, Reynolds number of 370,000, comparing the baseline planform (left) and theaerodynamic twist planform (right)

It can be seen from the pressure contours that for the baseline planform, the adverse pressure is seen

to continue to decrease at higher angles of attack to around 14◦, when it starts to become constant

indicating the point of separation. With aerodynamic twist, it can be seen that the pressure keeps

decreasing to an angle of around 16◦, which indicates the separation region between 14◦ to 16◦.

However, this is shown more clearly with tuft flow visualisation. The model with aerodynamic

twist seems to have better properties, as the tip seems to be more in contact compared to the

root. This gives it better tip stall characteristics, but does not eliminate this issue.

Figure 8.4.2: Surface tuft visualisation of the baseline. Top left 0 AoA, top rightstart of tip separation and bottom left start of root separation

66

Figure 8.4.3: Surface tuft visualisation of aerodynamic twist planform. Top left 0AoA, top right start of tip separation and bottom left start of root separation

Surface tufts was used to understand the separation, which also reflects the lift characteristics

shown earlier. The image are in order as described in earlier sections.

The lift curve indicates a stall region of 14◦ and pressure distribution indicates a stall region

between 14◦ - 16◦. However, no movement in the tufts can be seen until 16◦. This lies within

the pressure distribution prediction but does not reflect with the lift curve. This could be due to

higher uncertainty at the larger angle, which would mean further lift improvement.

8.5 Conclusion

To conclude, it is understood that by varying the camber of the wing with a thicker airfoil at

the tip, aerodynamic performance has been improved due to flow separation at the tip being

delayed to a higher angle of attack. Tip stall has still not been eliminated but delayed more than

increasing washout. Moreover, this has been achieved without increasing the induced drag and

also increasing the aerodynamic efficiency. Therefore we can conclude that the most optimum

planform, which has the highest efficiency and also performs better with stall at the tip is the

planform model with aerodynamic twist.

67

Chapter 9

Conclusion

The test rig was designed and built, which was able to vary the Skyseeker half span model.

Successful measurements were then recorded for force, moment and pressure. In addition, tuft

flow visualisation technique was used to understand the flow pattern and make predictions of

the stall region and flow behaviour. Results from measurements and flow visualisation showed

that significant improvement in aerodynamic performance and stall behaviour were achievable. In

addition, the literature review supported the results that were recorded from the wind tunnel, to

some extent.

At the Reynolds number of 370,000 significant performance improvement were possible with in-

creasing AR. This included higher CL,max, aerodynamic and power index and decreased induced

drag. Consequently this created undesired stall behaviour, with decreasing stall angle and wing

tip flow separation. It was therefore decided that the optimum AR was between AR 5.5 and 6.

The variation in washout was then considered. It was found that increasing washout did help

prevent tip stall but was not completely avoided and therefore required testing at larger washouts.

The aerodynamic performance was highly degraded at higher washouts and it was found that a

minimum washout 4◦ should be used. Therefore, combining two aspects, the optimum washout

was found to be between 4◦ and 5◦. Finally, aerodynamic twist was considered, which used a

more stable, thicker airfoil at the tip, which meant the wing varied in camber along the span.

This was compared with the baseline design which was found to be the optimum planform. Force

and moment measurements and surface tufts showed improvement in aerodynamic performance

and stall behaviour.

The first objective of this study was completed by designing the wind tunnel fixture and recording

all measurements, with success. The second objective was then completed by analysing the data

and finding the optimum planform, which was found to be the wing model with aerodynamic

twist with the airfoil S822, at the tip. The final objective will be to incorporate the optimal wing

planform on to the Skyseeker, conduct several flight tests and enter the flying wing concept in the

IMechE competition

68

Chapter 10

Future work

Further investigation needs to be conducted at higher Reynolds number of 870,000 to understand

the Skyseeker’s behaviour at cruise. This would require a stronger manufacturing method to

avoid any vibration and flutter in the model. This could be completed using stronger composite

materials, such as carbon fibre, which consists of ribs and spars.

Variation in aspect ratio can not be further developed as the maximum taper ratio was tested, any

further increase would result in a negative tip chord. However, wing morphing technique could

be used, to assess the performance on non linear twist. A study conducted by Roelof Vos, showed

that morphing a wing with non linear twist not only eliminates wing tip stall behaviour but also

increases the aerodynamic performance and static and dynamic stability [57].

The interest in UAV’s is now being primarily focused in military applications. With technologies

being developed in stealth, combat and intelligence. Therefore, to generalise this concept, force

and moments measurements can be investigated at high Reynolds numbers as well as high angles

of attack.

69

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73

Appendices

74

Appendix A

Uncertainty AnalysisThe force and moment uncertainties were calculated based on the method of moffat. This method

analyses the uncertainties by considering all variables, so that if the experiment was repeated the

vales would lie in between these values.The main equation of this method is shown below.

δR =

{(∂R

∂x1δx1

)2

+

(∂R

∂x2δx2

)2

+ ......

(∂R

∂xiδxi

)2} 1

2

(A.0.1)

A.1 Reynolds Number

The main equation to determine the Reynolds number is:

Re =ρUd

µ(A.1.1)

Considering all variables and there uncertainties the equation can be developed into the form;

δRe =

{(∂Re∂ρ

δρ

)2

+

(∂Re∂U

δU

)2

+

(∂Re∂d

δd

)2

+

(∂Re∂µ

δµ

)2} 1

2

(A.1.2)

The uncertainty in pressure was determined by the measure equipment in the wind tunnel, which

was only accurate to only 0.5 pascals. The uncertainty associated with the velocity was found

to be 0.1 m/s and the distance can only be visualised to 0.5mm. Therefore the uncertainty in

Reynolds is found to be;

δRe =

{(ρd

µ× 0.4

)2

+

(Ud

µ× 0.01

)2

+

(ρU

µ× 0.0005

)2} 1

2

(A.1.3)

A.2 Lift and Drag Coefficients

CL and CD can be combined to calculate the uncertainty in the force measurements. The non

dimensionalised form used is;

75

Cf =F

12ρbcU

2=F

Q(A.2.1)

This can then be re-arranged using Moffat’s methods ad;

δR =

{(∂Cf∂F

δF

)2

+

(∂Cf∂Q

δQ

)2} 1

2

=

{(1

QδF

)2

+

(F

Q2δQ

)2} 1

2

(A.2.2)

The two terms δF and δQ can therefore be further investigated to determine their uncertain-

ties.

A.2.1 Force Uncertainty δF

The uncertainties associated with this force are described below.

A.2.1.1 Force Sensor Resolution

The commercial force sensory used had a force resolution of 0.025N

δfx = 0.025

A.2.1.2 Averaging uncertainty

The uncertainty of the averages obtained over a time can be estimated through:

δN =1.96× σ(V )

N0.5(A.2.3)

The 1.96 was selected for 95% confidence interval bounds.

A.2.1.3 Calibration Uncertainty

The calibration uncertainty is present as the mean value was used in the results and therefore the

upper and lower limit need to determined to asses the fluctuation.

δC =FU − FL

2(A.2.4)

76

A.2.1.4 Drift Uncertainty

Once the tests were complete a measurement was run again, in which the measure of the two

mean values was measure, which bring the final source of uncertainty.

δD =FB − FA

2(A.2.5)

These four uncertainty combine to give;

δF =

{(∂F

∂NδN

)2

+

(∂F

∂CδC

)2

+

(∂F

∂DδD

)2

+

(δfx

)2} 1

2

=

{(1.96× σ(V )

N0.5

)2

+

(FU − FL

2

)2

+

(FB − FA

2

)2

+

(0.25

)2} 1

2

(A.2.6)

A.2.2 Aerodynamic Constant Uncertainty δQ

A.2.2.1 Density Uncertainty

The uncertainty of density was calculated through the limiting case of the pitot tube which

displayed pressure within a range of 0.05 pascals.

δρ = 0.01

A.2.2.2 Velocity Uncertainty

The uncertainty associated with the free stream velocity is determined as the limiting case of the

pressure measurement.

δU = 0.4

A.2.2.3 Span Uncertainty

The span can only be measured to 0.5mm.

δb = 0.0005

A.2.2.4 Chord Uncertainty

The chord can only be measured to 0.5mm.

δc = 0.0005

77

These four uncertainty combine to give

δQ =

{(∂Q

∂ρδρ

)2

+

(∂F

∂UδU

)2

+

(∂F

∂cδc

)2

+

(∂F

∂bδb

)2} 1

2

=

{(1

2bcU2 × 0.01

)2

+

(ρbcU × 0.4

)2

+

(1

2ρbU2 × 0.0005

)2

+

(1

2ρcU2 × 0.0005

)2} 1

2

(A.2.7)

A.3 Pitching Moment Coefficient

The pitching and bending moment uncertainties are analysed in a similar manner.

Cf =M

12ρbcU

2c=

F

Q2(A.3.1)

δR =

{(∂Cf∂M

δM

)2

+

(∂Cf∂Q2

δQ2

)2} 1

2

=

{(1

Q2δF

)2

+

(F

Q22

δQ2

)2} 1

2

(A.3.2)

A.3.1 Moment Uncertainty

The moment uncertainties used the same equation for the following errors;

1. Force sensor resolution

2. Averaging uncertainty

3. Calibration uncertainty

4. Drift uncertainty

These four uncertainty combine to give;

δM =

{(∂M

∂NδN

)2

+

(∂M

∂CδC

)2

+

(∂M

∂DδD

)2

+

(δm

)2} 1

2

=

{(1.96× σ(V )

b

N0.5

)2

+

(MU −ML

2

)2

+

(MB −MA

2

)2

+

(0.049

)2} 1

2

(A.3.3)

78

A.3.2 Aerodynamic Constant Uncertainty δQ2

As before the uncertainties associated with δQ2 are;

1. Density uncertainty

2. Velocity uncertainty

3. Span uncertainty

4. Chord uncertainty

These four uncertainty combine to give

δQ2 =

{(∂Q2

∂ρδρ

)2

+

(∂Q2

∂UδU

)2

+

(∂Q2

∂cδc

)2

+

(∂Q2

∂CδC

)2

+

(∂Q2

∂bδb

)2} 1

2

=

{(1

2bcU2C × 0.01

)2

+

(ρbcUC × 0.4

)2

+

(1

2ρbcU2 × 0.0005

)2

+

(1

2ρbU2C × 0.0005

)2

+

(1

2ρcCU2 × 0.0005

)2} 1

2

(A.3.4)

79

Appendix B

Wind Tunnel CalibrationThe wind tunnel was calibrated by attaching two weights on a thread at the leading edge and

trailing edge of the fuselage. The turntable was then adjusted by measuring the weights from the

wall of the working section, until they were both at same distances. The image below shows the

weights on the trailing and leading edge of the fuselage.

Figure B.0.1: Calibration of the wind tunnel to set a AoA of 0 degrees

80

Appendix C

Manufactured ModelsThe following figures highlights the models used in the wind tunnel facility, with all relevant

features annotated.

Figure C.0.1: Fuselage model used in wind tunnel testing used in the wind tunnel

Figure C.0.2: Upper surface of baseline planform wing model used in the wind tunnel

81

Figure C.0.3: Lower surface of baseline planform wing model used in the wind tunnel

Figure C.0.4: Wing models with different aspect ratios used in the wind tunnel

82

Appendix D

Force Sensor SpecificationThe specification of the force sensor are described below, which have been obtained from the

manufacturer.

FTSens 6 axis torque and force sensor with CAN Bus communication

Table D.0.1: Specification of the 6 channel force sensor obtained from the manufac-turer

Specification

Power supply 5V +/− 10%, current consumption max 100mA, provided from

CAN Bus connector

Communication CAN Bus 2.0B, 1Mbps

Channels Six, 3 torques (Tx, Ty, Tz) and 3 forces (Fx, Fy, Fz)

Measure Range 2000N (Fx, Fy, Fz) 40 Nm (Tx, Ty) 30Nm (Tz)

Resolution 0.25N (Fx, Fy, Fz) 0.049Nm (Tx, Ty) 0.037Nm (Tz)

Output Data 16bit, 6 channels, up to 1K messages/sec

Micro-controller dsPIC40F4013 16bit, 30MIPS, 48K Flash, 2K RAM, CAN, SPI

Alarms CAN communication, memory, ADC and PGA

Digital Filter 6 independent 5th order IIR

A/D Converter 16 bit, 250ksps

Gain Settings Fixed analog gain

Offset Correction Digital offset correction

Utilities In field reprogramming, device configuration, graphical data anal-

ysis

Operating Conditions 0 to 50 ◦s, humidity <85% without condensation

Dimensions 45x18mm

Weight 122g

83

Appendix E

Data Analysing Procedure

Raw Data

Fx, Fy, Tz, P(v)

Apply Calibration

Offset

Determine steady

tunnel speed

Calibrate Wind

tunnel AOA

Record data for 1

minute, Change

AOA by 1 degrees

Reached +/- 20

degrees AOA?

No

Yes

Completed 2 sets of

Measurements?

No

Measure Drift in

Force Sensor

Measure Drift in

Pressure Transducer

Calculate Means

and Standard

deviation

Results

Yes

Figure E.0.1: Data processing work flow for force, moment and pressure measure-ments

84

Appendix F

CFD Pressure DistributionThe CFD results were conducted by James Barber, and figure F.0.1, shows the tip location at the

stall angle. It can be identified that the adverse pressure gradient starts really close to the leading

edge. It was therefore decided to 10%, where the pressure can be seen to separate.

Figure F.0.1: Pressure distribution of the Skyseeker using CFD analysis at stall angleduring cruise [49]

85

Appendix G

Wind Tunnel Interference affectsThe tunnel interference affects were calculated using the methods provided by Pankhurst and

Holder, [58]. The boundary constraints were analysed by dividing the affects into 5 components,

which were then combined.

G.1 Solid Blockage

The solid blockage for three dimensional flow was determined by a mixture of R and M, 1984,

which is proven to be more preferable in closed loop wind tunnels. The equation for the blockage

factor εs was given by;

εs =

√π

2τV

C3/2(G.1.1)

Where V is the volume of the wing model in the working section and C is the cross sectional area

of the working section. τ is the factor depending on the basic of he working section and was found

using;

√π

2τ = 0.36

(hb

+b

h

)(G.1.2)

Where h and b is the heigh and breadth of the tunnel and with respect to the model being

tested.

The error in the free stream velocity can then be calculated using the following relationship;

UF = UT (1 + εs) (G.1.3)

G.2 Wake Blockage

The wake blockage was calculated for a three dimensional flow supported along the tunnel axis.

The wake blockage factor, εw is found using the following relationship;

εw =1

4

S

bhCD (G.2.1)

86

Where, S is the surface of the area of the wing model, which contributes to the drag coefficient.

This can then be used to calculate the error in the free stream velocity as described before;

UF = UT (1 + εw) (G.2.2)

G.3 Application of Solid Blockage and Wake Blockage

The solid blockage and wake blockage factors can be used to find the corrected lift coefficient,

CFT ;

CFT = CF (1− 2ε) (G.3.1)

Where ε is the sum of the wake and solid blockage correction factors, εs + εw and CF is the

umcrrected lift coefficient.

G.4 Lift affects

The correction in lift was applied by the incident of wing due to the wall boundary affects, using

the following relationship.

αF − αT = δS

CCL (G.4.1)

Where αF − αT is the affective change in the angle due to free stream velocity. δ can be found

from tables illustrated in Pankhurst and Holder and is based on the working section of the wind

tunnel, in this case it was found to be 0.112.

G.5 Static Pressure Gradient

As the tunnel speed was measured from the upstream of the leading edge and therefore is not

affect by the growth of the boundary layer on the walls. This affect can therefore be ignored for

the setup of the wind tunnel in the experiment.

G.6 Wall Boundary Layer

As the wing model was positioned in the middle of the working section, the laminar flow was

transitioned to turbulent at 0.5m. This was calculated through the critical reynolds number of a

87

flat plate Rc;

Rc =ρud

µ(G.6.1)

The turbulent wall boundary was then calculated by calculating the distance of the laminar region

and the distance of the turbulent region. This was then used to calculate the boundary layer

thickness.

88

Appendix H

Bending Moment ResultsThe bending moment coefficient versus angle of attack are shown below for different aspect ratio,

washout and aerodynamic twist. These results were used to calculate the spanwise centre of

pressure.

H.1 Aspect Ratio

Figure H.1.1: Bending moment coefficient versus angle of attack at 0◦ to 18◦, fixedReynolds number of 370,000, 4◦ washout and different ARs

89

H.2 Geometric Twist

Figure H.2.1: Bending moment coefficient versus angle of attack at 0◦ to 18◦, fixedReynolds number of 370,000, AR 5.5 and different washouts

H.3 Aerodynamic Twist

Figure H.3.1: Bending moment coefficient versus angle of attack at 0◦ to 18◦, fixedReynolds number of 370,000 compared with baseline model and aerodynamic twist

90