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![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
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![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
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![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
Figure H.2.1Bending moment coefficient versus angle of attack at 0◦
to 18◦
, fixed Reynolds
number of 370,000, AR 5.5 and different washouts . . . . . . . . . . . . . . . . . . 90
Figure H.3.1Bending moment coefficient versus angle of attack at 0◦
to 18◦
, fixed Reynolds
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
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![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].
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![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].
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![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
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![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].
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![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.
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![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 229
flying 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].
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![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
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![– 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].
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![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]
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![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-27-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-28-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-29-2048.jpg)
![(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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-30-2048.jpg)
![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 smoke
and laser sheet to visualise flow on the upper
surface 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-31-2048.jpg)
![Figure 2.4.2: Use of smoke technique to show vortex systems in a wake of a group of
three 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-32-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-33-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-37-2048.jpg)
![Figure 4.1.1: Wing cross section showing the MH45 airfoil chosen for the fuselage
and 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-38-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-40-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-41-2048.jpg)
![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.
153.3
153.35
153.4
153.45
153.5
153.55
153.6
153.65
153.7
0 10 20 30 40 50 60
Raw Body Force Signals
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
Raw Body Force Signals
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 and
high 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
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![forces along a single alpha.
L = Fx cos α − FY sin α (4.5.1)
D = Fx cos α + FY sin α (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
0.5ρbcU2¯c
(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 wind
tunnel 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-45-2048.jpg)
![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 and
Vogt [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 carbon
fibre 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-46-2048.jpg)
![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
1
2 ρU2
(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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-47-2048.jpg)
![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.
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![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 attack
of 0◦
to 18◦
compared at two Reynolds numbers [51]
30](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-49-2048.jpg)
![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]
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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-50-2048.jpg)
![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 methods
and Reynolds number [49] [51]
33](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-53-2048.jpg)
![Figure 5.1.2: Comparison of drag coefficient with CFD, panel and theoretical methods
and 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-54-2048.jpg)
![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 + bC2
L (6.1.1)
Figure 6.1.3: Time averaged lift coefficient versus induced drag coefficient for
Reynolds 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-59-2048.jpg)
![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
Figure 6.2.3: Normalised COP spanwise position versus angle of attack for Reynold
number of 370,000, 4◦
washout and different Aspect ratios. ηCP 1 indicates the wing
tip 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-62-2048.jpg)
![maximum range and endurance, (CL/CD) and (CL
3/2
/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
C
3/2
L
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)
RP E =
(C
3/2
L /CD)Models
(C
3/2
L /CD)Baseline
(6.3.4)
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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-63-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-66-2048.jpg)
![Chapter 7
Geometric Twist
7.1 Force Measurements
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◦
, 370,000
Reynolds 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-71-2048.jpg)
![Figure 7.1.3: Time averaged lift coefficient versus induced drag coeffiecient at
Reynolds 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 C2
L, 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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-73-2048.jpg)
![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](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-93-2048.jpg)
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![Appendix F
CFD Pressure Distribution
The 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 angle
during cruise [49]
85](https://image.slidesharecdn.com/brkkk20fyp-160910180024/75/Experimental-Investigation-of-Optimal-Aerodynamics-of-a-Flying-Wing-UAV-Link-110-2048.jpg)
![Appendix G
Wind Tunnel Interference affects
The 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
2
τ
V
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
2
τ = 0.36
h
b
+
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
bh
CD (G.2.1)
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Experimental Investigation of Optimal Aerodynamics of a Flying Wing UAV(Link)
- 1. EXPERIMENTAL INVESTIGATION OF OPTIMALAERODYNAMICS 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
- 3. Experimental Investigation ofOptimal Aerodynamics of a Fly- ing Wing UAV Department of Mechanical Engineering University of Bath Supervisor: Dr. David Cleaver Assessor: Dr. Zhijin Wang April 2016
- 4. Abstract There is currentlya 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
- 5. Acknowldgements I would liketo 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. IV
- 6. Table of Contents 1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Skyseeker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Team Bath Drones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Unmanned Aerial Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 Types and Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Flying Wings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.2 Cruise Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.3 Static Stall Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Improving Aerodynamic Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3.1 Pre-design Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3.2 Post-design Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Flow Visualisation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4.1 Smoke and Vapour Flow Visualisation . . . . . . . . . . . . . . . . . . . . . 14 2.4.2 Oil Film Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4.3 Wall Tufts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3 Aims and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4 Experimental Methodology and Instrumentation . . . . . . . . . . . . . . . . . . 18 4.1 Airfoil Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.1.1 MH45 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.1.2 S822 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Experimental Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.4 Wing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.4.1 Manufacturing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.5 Force and Moment Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.6 Pressure Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.7 Tuft Flow Visualisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.8 Experimental Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.8.1 Reynolds Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.8.2 Tunnel Interference affects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.9 Uncertainty Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5 CFD and Reynold Number Comparison . . . . . . . . . . . . . . . . . . . . . . . . 33 5.1 Lift and Drag Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 6 Aspect Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 V
- 7. 6.1 Force Measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 6.2 Longitudinal Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 6.3 Aerodynamic and Power Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.4 Stall Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 7 Geometric Twist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 7.1 Force Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 7.2 Longitudinal Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 7.3 Aerodynamic and Power Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 7.4 Stall Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 8 Aerodynamic Twist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 8.1 Force Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 8.2 Longitudinal Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 8.3 Aerodynamic and Power Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 8.4 Stall Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 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
- 8. List of Figures Figure1.1.1 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
- 9. Figure 4.6.1 Instrumentationscheme 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
- 10. Figure 6.4.3 Surfacetuft 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 Figure 6.4.4 Surface tuft visualisation for AR 6 with 4◦ washout. Top left 0 AoA, top right start of tip separation and bottom left start of root separation . . . . . . . . 47 Figure 6.4.5 Surface tuft visualisation for AR 7 with 4◦ washout. Top left 0 AoA, top right start of tip separation and bottom left start of root separation . . . . . . . . 47 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 right start of tip separation and bottom left start of root separation . . . . . . . . 58 Figure 7.4.3 Surface tuft visualisation of 4◦ washout with AR 5.5. Top left 0 AoA, top right start of tip separation and bottom left start of root separation . . . . . . . . 58 Figure 7.4.4 Surface tuft visualisation of 5◦ washout with AR 5.5. Top left 0 AoA, top right start of tip separation and bottom left start of root separation . . . . . . . . 59 Figure 7.4.5 Surface tuft visualisation of 6◦ washout with AR 5.5. Top left 0 AoA, top right start of tip separation and bottom left start of root separation . . . . . . . . 59 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
- 11. Figure 8.1.2 Timeaveraged 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 Figure H.2.1Bending moment coefficient versus angle of attack at 0◦ to 18◦ , fixed Reynolds number of 370,000, AR 5.5 and different washouts . . . . . . . . . . . . . . . . . . 90 Figure H.3.1Bending moment coefficient versus angle of attack at 0◦ to 18◦ , fixed Reynolds number of 370,000 compared with baseline model and aerodynamic twist . . . . . . 90 X
- 12. List of Tables 1.1.1Skyseeker 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
- 13. Abbreviations and Numenclature bInduced 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 ¯c 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 RP E 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
- 14. Abreviation AoA Angle ofAttack 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
- 15. Outline of Project Thisproject 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
- 17. Chapter 1 Introduction This projectwill 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
- 18. Table 1.1.1: Skyseekerpreliminary 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
- 19. 1.2 Team BathDrones 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
- 21. Chapter 2 Literature Review 2.1Unmanned 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
- 22. 2.1.2 Types andUses 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
- 23. 2.2.1 History Germany, Englandand 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 229 flying 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
- 24. 2.2.3 Static StallPerformance 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
- 25. – 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
- 26. Figure 2.3.1: Changein 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
- 27. These studies concludethe 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
- 28. thus decreasing maximumlift 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
- 29. Figure 2.3.6: Stallstrip 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
- 30. (a) affects ofStall 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
- 31. A study conductedby 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 smoke and laser sheet to visualise flow on the upper surface 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
- 32. Figure 2.4.2: Useof smoke technique to show vortex systems in a wake of a group of three 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
- 33. 2.4.3 Wall Tufts Amore 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
- 35. Chapter 3 Aims andObjectives 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
- 37. Chapter 4 Experimental Methodologyand 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
- 38. Figure 4.1.1: Wingcross section showing the MH45 airfoil chosen for the fuselage and 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
- 39. -1.5 -1 -0.5 0 0.5 1 1.5 -20 -10 0 10 20 Li# Coefficient, Cl Angle of A1ack (deg) MH45, Re = 870k MH45, Re = 360k S822, Re = 860k S822, Re = 370k (a) CL versus α comparing MH45 and S822 0 0.1 0.2 0.3 0.4 -20 -10 0 10 20 Drag Coefficient, Cd Angle of A3ack (deg) MH45, Re = 870k MH45, Re = 360k S822, Re = 860k MH45, 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
- 40. and one withaerodynamic 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
- 41. Figure 4.3.1: Similarschematic 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
- 42. The wind tunnelwas 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
- 43. 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
- 44. 4.5 Force andMoment 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. 153.3 153.35 153.4 153.45 153.5 153.55 153.6 153.65 153.7 0 10 20 30 40 50 60 Raw Body Force Signals 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 Raw Body Force Signals 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 and high 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
- 45. forces along asingle alpha. L = Fx cos α − FY sin α (4.5.1) D = Fx cos α + FY sin α (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 0.5ρbcU2¯c (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 wind tunnel 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
- 46. The taps wereconnected 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 and Vogt [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 carbon fibre 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
- 47. taking a averageof 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 1 2 ρU2 (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
- 48. 4.8 Experimental Conditions 4.8.1Reynolds 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
- 49. 4.8.2.4 Static-Pressure Gradient Staticpressure 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 attack of 0◦ to 18◦ compared at two Reynolds numbers [51] 30
- 50. Figure 4.9.2: Dragcoefficient uncertainties for the baseline model at angle of attack of 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
- 51. Figure 4.9.3: Pressurecoefficient uncertainties for the baseline model at normalised span 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
- 53. Chapter 5 CFD andReynold 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 methods and Reynolds number [49] [51] 33
- 54. Figure 5.1.2: Comparisonof drag coefficient with CFD, panel and theoretical methods and 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
- 55. 5.2 Conclusion It isclear 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 theoretical methods 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
- 57. Chapter 6 Aspect Ratio 6.1Force 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 Reynolds number, 4◦ washout and different aspect ratios The CL versus α graphs shows that increasing AR increases the lift curve slope. dCL dα 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
- 58. the linear liftregion 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,max dCL dα 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 370,000 Reynolds 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
- 59. and different ARshown 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 + bC2 L (6.1.1) Figure 6.1.3: Time averaged lift coefficient versus induced drag coefficient for Reynolds 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
- 60. Table 6.1.2: Dragpolars 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 Reynolds number 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 x ¯c − δ ¯c (6.2.1) 39
- 61. Assuming the pitchingmoment at the aerodynamic centre is constant; d(CMx ) d(CL) = x ¯c − δ ¯c (6.2.2) Where x/¯c and δ/¯c 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 0.25¯c. 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 different aspect ratios Aspect Rato ¯c x ¯c dCmx dCL δ ¯c 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 ¯c = 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
- 62. Figure 6.2.2: Angleof 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 Figure 6.2.3: Normalised COP spanwise position versus angle of attack for Reynold number of 370,000, 4◦ washout and different Aspect ratios. ηCP 1 indicates the wing tip 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
- 63. maximum range andendurance, (CL/CD) and (CL 3/2 /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 C 3/2 L 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) RP E = (C 3/2 L /CD)Models (C 3/2 L /CD)Baseline (6.3.4) 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
- 64. Figure 6.3.2: Powerefficiency ratio for angle of attack of 0◦ to 18◦ , Reynolds number of 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 for different aspect ratios AR (L/D) improvement Power Improvement 5 -25% -30% 6 10% 15% 7 20% 25% 43
- 65. 6.4 Stall Behaviour Pressurecoefficient 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
- 66. The pressure contoursshow 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
- 67. Figure 6.4.2: Surfacetuft 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
- 68. Figure 6.4.4: Surfacetuft visualisation for AR 6 with 4◦ washout. Top left 0 AoA, top right start of tip separation and bottom left start of root separation Figure 6.4.5: Surface tuft visualisation for AR 7 with 4◦ washout. Top left 0 AoA, top right start of tip separation and bottom left start of root separation At high angles of attack it can be observed that the tufts at the tip reattach, which could be due 47
- 69. 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
- 71. Chapter 7 Geometric Twist 7.1Force Measurements 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◦ , 370,000 Reynolds 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
- 72. The stall angleis 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,max dCL dα 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 Reynolds number, AR 5.5 and 3◦ to 6◦ washout 50
- 73. Figure 7.1.3: Timeaveraged lift coefficient versus induced drag coeffiecient at Reynolds 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 C2 L, 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
- 74. 7.2 Longitudinal Stability Timeaveraged 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 Reynolds number 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 0.25¯c. 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
- 75. the same meanaerodynamic centre. Table 7.2.1: Aerodynamic centre of wing planform with different washouts Washout ¯c x ¯c dCmx dCL δ ¯c 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 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
- 76. Figure 7.2.3: NormalisedCOP 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 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
- 77. 7.3 Aerodynamic andPower Efficiency 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 baseline model 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 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
- 78. of attack theaerodynamic 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 for different washouts Washout (L/D) improvement Power Improvement 3◦ 20% 30% 5◦ -1% -1% 6◦ -5% -3% 7.4 Stall Behaviour Pressure coefficient at normalised span location at angles of attack of 0◦ to 18◦ , fixed at Reynolds 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
- 79. Figure 7.4.1: Pressurecontour 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
- 80. Figure 7.4.2: Surfacetuft 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 Figure 7.4.3: Surface tuft visualisation of 4◦ washout with AR 5.5. Top left 0 AoA, top right start of tip separation and bottom left start of root separation 58
- 81. Figure 7.4.4: Surfacetuft visualisation of 5◦ washout with AR 5.5. Top left 0 AoA, top right start of tip separation and bottom left start of root separation Figure 7.4.5: Surface tuft visualisation of 6◦ washout with AR 5.5. Top left 0 AoA, top right start of tip separation and bottom left start of root separation 59
- 82. Table 7.4.1: Anglesof flow separation at the tip identified by the pressure distribution and 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
- 83. Chapter 8 Aerodynamic Twist 8.1Force Measurements 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◦ , 370,000 Reynolds 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
- 84. The drag polarwas 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 at 370,000 Reynolds compared with the baseline design and aerodynamic twist Figure 8.1.3: Time averaged lift coefficient versus induced drag coefficient at 370,000 Reynolds 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
- 85. at the rootand 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 8.2 Longitudinal Stability 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 -0.02 0 0.02 Li# Coefficient, Cl Pitching Moment, Cm Aero Twist Re = 370k Baseline Re = 370k Figure 8.2.1: Time averaged lift lift coefficient versus pitching moment for Reynolds 370,000 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 0.25¯c. This means 63
- 86. even though theaircraft 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 ¯c x ¯c dCmx dCL δ ¯c Baseline 0.392 -0.357 0.081 0.438 Aerodynamic twist 0.392 -0.357 0.082 0.439 8.3 Aerodynamic and Power Efficiency 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 the baseline planform 64
- 87. Figure 8.3.2: Powerefficiency 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 Pressure coefficient at normalised span location at angles of attack of 0◦ to 18◦ , fixed at Reynolds 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
- 88. Figure 8.4.1: Pressurecontour 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) 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 right start of tip separation and bottom left start of root separation 66
- 89. Figure 8.4.3: Surfacetuft visualisation of aerodynamic twist planform. Top left 0 AoA, 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
- 91. Chapter 9 Conclusion The testrig 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
- 93. Chapter 10 Future work Furtherinvestigation 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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- 100. Appendix A Uncertainty Analysis Theforce 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 ∂x1 δx1 2 + ∂R ∂x2 δx2 2 + ...... ∂R ∂xi δxi 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
- 101. Cf = F 1 2 ρbcU2 = F Q (A.2.1) Thiscan then be re-arranged using Moffat’s methods ad; δR = ∂Cf ∂F δF 2 + ∂Cf ∂Q δQ 2 1 2 = 1 Q δF 2 + F Q2 δQ 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
- 102. A.2.1.4 Drift Uncertainty Oncethe 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 ∂N δN 2 + ∂F ∂C δC 2 + ∂F ∂D δD 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
- 103. These four uncertaintycombine to give δQ = ∂Q ∂ρ δρ 2 + ∂F ∂U δU 2 + ∂F ∂c δc 2 + ∂F ∂b δb 2 1 2 = 1 2 bcU2 × 0.01 2 + ρbcU × 0.4 2 + 1 2 ρbU2 × 0.0005 2 + 1 2 ρcU2 × 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 1 2 ρbcU2c = F Q2 (A.3.1) δR = ∂Cf ∂M δM 2 + ∂Cf ∂Q2 δQ2 2 1 2 = 1 Q2 δF 2 + F Q2 2 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 ∂N δN 2 + ∂M ∂C δC 2 + ∂M ∂D δD 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
- 104. A.3.2 Aerodynamic ConstantUncertainty 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 ∂ρ δρ 2 + ∂Q2 ∂U δU 2 + ∂Q2 ∂c δc 2 + ∂Q2 ∂C δC 2 + ∂Q2 ∂b δb 2 1 2 = 1 2 bcU2 C × 0.01 2 + ρbcUC × 0.4 2 + 1 2 ρbcU2 × 0.0005 2 + 1 2 ρbU2 C × 0.0005 2 + 1 2 ρcCU2 × 0.0005 2 1 2 (A.3.4) 79
- 105. Appendix B Wind TunnelCalibration The 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
- 106. Appendix C Manufactured Models Thefollowing 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
- 107. Figure C.0.3: Lowersurface 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
- 108. Appendix D Force SensorSpecification The 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
- 109. Appendix E Data AnalysingProcedure Raw Data Fx, Fy, Tz, P(v) Apply Calibration Offset Determine steady tunnel speed CalibrateWind 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 ForceSensor 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
- 110. Appendix F CFD PressureDistribution The 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 angle during cruise [49] 85
- 111. Appendix G Wind TunnelInterference affects The 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 2 τ V 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 2 τ = 0.36 h b + 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 bh CD (G.2.1) 86
- 112. Where, S isthe 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, CF T ; CF T = 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 = δ S C CL (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
- 113. 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
- 114. Appendix H Bending MomentResults The 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◦ , fixed Reynolds number of 370,000, 4◦ washout and different ARs 89
- 115. H.2 Geometric Twist FigureH.2.1: Bending moment coefficient versus angle of attack at 0◦ to 18◦ , fixed Reynolds 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◦ , fixed Reynolds number of 370,000 compared with baseline model and aerodynamic twist 90