Uav Dissertation

Elliot Newman @00320195
University Of Salford
School of Computing Science and Engineering
Balloon Bot UAV
Final Year Undergraduate Project
Final Report
6th
of May 2014
Name: Elliot Newman @00320195
Course Title: BEng Aeronautical Engineering
Course Code: AE/F1
Supervisor: Dr Theo Theodoridis

University Of Salford
School of Computing Science and
Engineering
Name of Student: Elliot Newman
Course Code: AE/F1
Title of Project: Balloon Bot UAV
I certify that this report is my own work. I have properly acknowledged all
material that has been used from other sources, references etc.
Signature of Student: Date:
Official Stamp:
Submission Date (to be entered by relevant School Office staff):

Abstract
As more automated and artificially intelligent systems become operational, further
research and development is likely to be undertaken. In this project the design,
manufacture and testing of an omni directional balloon UAV is discussed,
focusing on the aerodynamic aspects of the venture. By initially deliberating the
theory to be applied to the product, an understanding of the forces and effects at
hand was attained. Practical tests and computational fluid dynamics have been
conducted to witness the theory in a visual light and generate results that can be
applied during the design process. These results will allow for conclusive
decisions to be made on the UAV’s aspect orientation in the pursuit of peak
performance, with the final product evaluated. As the UAV’s operational ability
became temperamental, certain advancements were unable to occur and have been
illustrated as potential future work.

Acknowledgements
First and foremost I would like to thank the other members of the group whose
dedication and attention to detail, coupled with hard work made the project
possible. Also there willingness to step beyond there project criteria to enable
constant progress to take place.
Also my supervisor, Dr. Theodoridis and the robotics technicians, most notably
Andy, whose patience and expertise was paramount to the advancements attained
during the project.
Finally, I would like to acknowledge my friends, family and girlfriend whose
constant support and advice helped maintain my focus and work ethic over the
course of the year.

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Contents
1. Introduction ....................................................................................................1
2. Literature Review............................................................................................3
3. Model Description ...........................................................................................9
3.1 Buoyancy.................................................................................................. 10
3.1 Drag.......................................................................................................... 12
3.2 Propulsion................................................................................................. 15
4 Theoretical/Experimental Calculations ............................................................ 17
4.1 Buoyancy Experiment................................................................................ 17
4.2 CFD Simulations ....................................................................................... 20
4.2.1 Balloon Simulation............................................................................. 21
4.2.2 Nacelle Simulation ............................................................................. 22
5. Data Analysis ................................................................................................ 26
5.1 Buoyancy Experiment................................................................................ 26
5.2 CFD Simulations ....................................................................................... 27
5.2.1 Balloon Simulation............................................................................. 27
5.2.2 Nacelle Simulation ............................................................................. 28
6. Further Experiments ...................................................................................... 30
6.1 Drag Force Prediction ................................................................................ 30
7. Further Analysis ............................................................................................ 32
7.1 Drag Force Prediction ................................................................................ 32
8. Conclusions .................................................................................................. 33
8.1 Discussion................................................................................................. 33
8.1.1 Buoyancy Experiment......................................................................... 33
8.1.2 CFD Simulations ................................................................................ 34
8.1.2.1 Balloon Simulation ......................................................................... 34
8.1.2.2 Nacelle Simulation.......................................................................... 35
8.1.3 Drag Force Prediction ......................................................................... 36
8.2 Outcomes.................................................................................................. 37

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8.2.1 Rotational analysis.............................................................................. 38
8.3 Future Work.............................................................................................. 39
8.3.1 Balloon Development ......................................................................... 39
8.3.2 Structural Advancements .................................................................... 41
8.4 Conclusion ................................................................................................ 42
9. References .................................................................................................... 44
10. Appendices ................................................................................................... 46
10.1 Tables....................................................................................................... 46
10.2 Figures...................................................................................................... 51

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1. Introduction
As the world’s needs and desires advancement, so does the search for technology
to meet them. In the aerospace industry that need manifests itself as a vision for
greater accuracy, flexibility and most paramount, greater safety. With the
advancements in artificial intelligence the simplest way to achieve this directive is
to remove the human occupant from the situation completely, flying the aircraft
remotely from a ground based location. This dissertation is in line with these
views and as a result, during the course of this project, the aim is to produce an
Omni directional UAV (Unmanned Air Vehicle) capable of sustained indoor
flight. A venture such as this will require meticulous planning and design with
many areas from inception and manufacture coming together to produce a
successful outcome. To this end, by completing this project, an aptitude in
research, design and construction will have been displayed, as well as working
cohesively within a group.
In engineering circles, the design process is a practiced one and therefore has a
structured format and one which will aid the development of the Balloon UAV.
Initially beginning with the concept, in this case, the vision to produce a lighter
than air UAV capable of sustained indoor flight. Next on the agenda is the
research, which comes in the form of a literature review, investigating projects
with transferable lessons and functionalities to narrow the subject region. Then
using this research during the design, test and refine stage, taking the lessons to
formulate estimated designs and iterating them based on data analysis from
experimental procedures. Through these steps the final model can be portrayed
with the knowledge that each component has been subjected to stringent
examination to produce a successful prototype.

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As a paper of this magnitude requires significant personal work, specific areas of
the process will be allocated to an individual within the topic area, such as;
Aerodynamic, Mathematical modelling etc. This format will allow for the
demonstration of the specific skill sets needed to successfully navigate an
assignment such as this. This paper shall investigate the aerodynamic nature of the
UAV, testing and analysing the balloons interaction with the fluid around it, as
well as aiming to maximise the propulsion of the system.
The report is self-contained and shall begin with a view of the project as a whole,
moving forward, a review of the relevant literature obtained during preliminary
research will highlight the key topics and procedures that will be encountered and
have to be overcome. The literature will then be put to use to help make informed
decisions about the UAV’s directional progress, as well as enabling an efficient
design. Completing this, theory behind the review shall be investigated and
experimentally tested, through physical and computational means, thus generating
a greater noesis, leading to more apt design choices.
Finally, due to the congested nature of the product timeline, any future work to
further improve the UAV shall be postulated, including any improvements in
design selection process that have been made in hindsight that could be employed
in the future.

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2. Literature Review
In order for any object to attain flight many aspects must first be considered, modelled
and authenticated such as; power to weight, aerodynamics, its physical structure and
potential future applications. To achieve a successful operation, first the relevant
information must be sourced, collated and understood by investigating projects and
areas with similar fundamental characteristics and behaviours. As indoor balloon
robots are quite rare, adjacent fields will be examined, with the applicable material
extracted to shed light on the aerodynamic challenges that will be faced. In this
literature review research will be correlated to potential problems that may be faced
and then summarised in order to give a broader noesis of the topic at hand.
As with any body functioning in a fluid, its own aerodynamic profile will become its
most paramount feature as it directly impacts on potential performance, structural,
stability and the general feasibility of the rig. A sound profile will allow for more
flexibility in these areas as they will have room to manoeuvre and their structural
locations will not be dictated purely on stability.
Hydrostatics dictates that, any object immersed in a fluid will still experience a force
even if there is no relative movement (Anderson 1991: 27). This principle is known as
‘buoyancy’. The hydrostatic equation illustrates that the pressure on a body changes
with relation to height and only acts vertically:
𝑑𝑝 = −𝑔𝜌 𝑑𝑦

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In theory, these vertical pressure forces all act through the objects centre of gravity and
due to the horizontal pressures being at the same height, these cancel out and therefore
apply no perceivable moments. Yet when applied practically to the early designs this
will not be the case and consequently will have to be factored into the modelling
process. Buoyancy is a key principle for balloon and other lighter than air systems and
is felt as an upward force proportional to the amount of fluid the body displaces.
𝐵𝑢𝑜𝑦𝑎𝑛𝑐𝑦 𝑓𝑜𝑟𝑐𝑒 𝑜𝑓 𝑎 𝑓𝑙𝑢𝑖𝑑 = 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑓𝑙𝑢𝑖𝑑 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑑 𝑏𝑦 𝑏𝑜𝑑𝑦
None mechanically powered balloons exploit this idea by displacing huge amounts of
air in order to achieve a large enough force to maintain flight and therefore this
relationship can be applied to the ‘balloon bot’ during periods of minimal movement [1].
During motion, having a large buoyancy will create a trade-off against drag which will
compromise the stability and motion of the UAV. Due to the easy nature of conducting
buoyancy tests, the procedure can be repeated and differing sizes tested in order to
achieve the perfect compromise. During the moments of powered flight, the other forces
in effect, for instance lift and drag, will need to be calculated and modelled in order to
reveal the magnitude of their influence.
Due to the circular nature of the balloon shape, the fluid flow over the surface will also
need to be investigated. The fluid in contact with the surface, also known as the
boundary layer, suffers a decelerating effect due to the amount of skin friction
encountered and the viscosity of the fluid. Under certain pressures the airflow can be
brought to rest, which in turn leads to separation. This greatly reduces the aerodynamic
effect of the body as the fluid flow, still influenced by the adverse pressure, begins to
fold backwards creating reverse flow and turbulent air [2]. The readiness of the flow to
undertake this action is associated to the Reynolds number of the body, which links of

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the ratio of inertial forces to viscous forces. This value affects the flow at the boundary
layer, in particular its thickness and point of separation. As the ‘balloon bot’ will have a
high Reynolds Number, a more turbulent boundary layer will be present, delaying the
point of separation. The Reynolds Number is also a dimensionless quantity, which will
prove an advantage when coming to make scale models for aerodynamic testing [3].
Mathematically modelling the aerodynamic effects of size, shape, velocity and mass
will also prove potentially vital, as it is paramount to effectively quantify each
individual element before models are built and tested. There are various ways of
efficiently doing this, one of which is the Euler-Lagrange equations, which are used to
help with optimization and finding when a particular problem is at its maximum. It
employs the proven theory that at any point that the function is at a maximum or
minimum then its derivative is therefore zero. Then whether the point is a maxima or
minima is determined by using the second derivative. These are linked into Newtonian
laws and can therefore also be used to help predict motion [4]. Another effective way of
doing this is by using Navier-Stokes equations. By adopting these it is easier to
eliminate unnecessary values and focus on the unknowns with the greatest effect. These
can also then be used to allude to the turbulent nature of the flow beyond the point of
contact in the balloons wake [5].
Due to the rarity of indoor flying balloon robots, it may also be of some use to explore
other forms of aerial vehicles which enjoy similar characteristics to the proposed design.
An example is the Bosch SASS lite, a non-rigid helium filled balloon with a similar
engine mounting position to that predicted of the ‘balloon bot’. The non-rigid nature of
the design brings into question the stability of the craft whilst in motion, which was
overcome in this case by highly pressurising the balloon’s skin and keeping speeds of
travel low on the Bosch. With stability whilst moving a huge factor for any balloon

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based aircraft due to the effect of rolling, a centrally mounted engine also combatted
any potential issues that could have arisen during flight, by focusing the largest
percentage of its mass through its centre of gravity [6]. Also with the indoor nature of the
balloon bot, gusting and varying wind direction can be neglected during the tests,
leading overall to a much more stable set of test parameters. There are also theoretical
procedures to help calculate the stability of an object in fluid flow.
Due to the lighter than air nature of the balloon bot, investigating micro UAV’s will
also yield some benefit, as they too will also employ a high thrust to weight ratio and
also have a tendency to operate indoors or confined environments. Interestingly many of
the advancements in this area have come from studying insects of similar
characteristics, which include; higher Reynolds Numbers, high thrust to weight, low
overall weight, relatively low velocity range and being able to stably fly in its own
turbulent air flow [7].
The low velocity and powered nature of the UAV have led to the conclusion that
adopting a nacelle with a convergent nozzle would aid in boosting the net thrust of the
system, allowing greater manoeuvrability and velocity range. A convergent nozzle, also
known as a ‘propelling nozzle’ constricts the flow and thus maximises the exit velocity.
By taking advantage of the incompressible nature of the flow, mass flow rate is
uniform, relinquishing the equation:
𝑉1 𝐴1 = 𝑉2 𝐴2
Therefore by reducing the outlet area, the output velocity will rise proportionally with
its decline [8].
During the development of any new concepts how the design phases are undertaken
shall essentially decide the success or failure of the project. The process of; concept,

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research, design, test, refine design and then finalise is a model that has been widely
acknowledged as the accepted practice of any design tasks. Employing this procedure
effectively to the balloon UAV will enable a smoother design curve towards the final
product [9].
In design orientated projects operating in scarcely ventured territory, simulated testing
known as ‘computational fluid dynamics,’ abbreviated to CFD in engineering circles,
has become a paramount feature, vastly reducing costs and increasing the accuracy of
the initial models created. Using programs such as SolidWorks, with its addition of
FlowXpress, parameters such as; flow speeds, pressure stagnation, wake distribution
and aerodynamic inefficiencies can be monitored and addressed before production even
begins, inherently increasing the likelihood of success of the opening model [10].
Also a key aspect of any design task is how the data is interpreted and employed
moving forwards to create more improved iterations of the model. Through a
comprehensive and thorough examination of test data, anomalies and inefficiencies can
be designed out and any fundamental errors rectified. Furthermore, through scouring the
results, the test its self can be altered to ensure that the correct parameters are being
illuminated successfully and are being effectively measured. There are many forms of
presenting results so that the lessons of the data are more glaring which can be;
graphically, so that trend lines can be plotted highlighting the data drift, Tabular, the
results are inherently more organised reducing the risk of oversights and visual
programs, such as flow diagrams illustrating the physical behaviour of the data [11].
Continuing with data analysis, ensuring the accuracy of both practical and theoretical
values is of paramount importance during any research venture. The accuracy’s
significance is amplified by the fact that there isn’t much information to directly

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compare with our own discoveries. To this end, performing error analysis between
theoretical and practical parameters will highlight if any mistakes or miscalculations
have been made. Zuljian and Grum (n.d) demonstrate this practice and its practical
applications [12]. Furthering its importance is that during testing, scale models shall be
used and by proving the mathematics is applicable to the scale model will allow
extrapolation to the full scale final design.

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3. Model Description
A balloon UAV is comprised of many differing materials and intellectual practices
which, when cohesively brought together, realise the aim of a successfully flying
balloon robot. These aspects will now be investigated and discussed to achieve a greater
understanding and appreciation of the task undertaken.
Beginning with an overall vision of the UAV itself, there will be a large balloon, helium
filled and around 8ft in diameter providing the lift, seated into a stabilising structure
with the electrical aspects housed and connected below. This will consist of all the
electronics, from the motherboard to the control devises. Attached to the outside of the
housing will be the propulsion system, in the form of two propellers with channelling
convergent exhausts driven by motors, providing both the horizontal and vertical
motion, orientated by a servo that rotates the rig. A simplistic design is depicted below:
Figure 1: Initial Balloon Design

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3.1Buoyancy
As would be expected of a ‘balloon bot’, the most important aerodynamic devise is the
balloon itself, all the lift and the majority of the drag will be produced by the balloon as
its passes through the air. Bodies submerged in fluids are fundamentally at the mercy of
their own design, most paramount of which is that of its aerodynamic profile. The way
that the object interacts with the surrounding air will drastically affect its potential
performance once functional. As a balloon is the only source of aerodynamic lift, then
exploiting the buoyancy force has become integral to the success of the project. As
Anderson (1991: 27) demonstrated, ‘any object immersed in a fluid will still experience
a force even if there is no relative movement.’ This is known as the Hydrostatic
Equation:
𝑑𝑝 = −𝑔𝜌 𝑑𝑦
and is in relation to the pressure force changing as the height of the body is changed.
The negative sign illuminates that any useful, effective lift will happen in the same
plane as gravity, but in the opposite direction and therefore up. A major benefit of this
law is that it perfectly illustrates that there will also be no moment forces acting on the
body, as all forces act through the objects centre of gravity and all horizontal forces
cancel each other out by notion of them being equal. This will enable a more naturally
statically stable structure uninfluenced by rotational inertial forces. Underwater this
effect is amplified and can be felt with a simple experiment of trying to push a ball or
balloon under the surface. A resistive force acts vertically and attempts to return the
object to its equilibrium point above the surface. If the force pushing down does not
maintain the balance, then this eventuality comes to the fore. In this situation, the

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density of the water is far greater than that of the air inside the object and therefore this
creates an upward force known as ‘Buoyancy’.
𝐵𝑢𝑜𝑦𝑎𝑛𝑐𝑦 𝑓𝑜𝑟𝑐𝑒 𝑜𝑓 𝑎 𝑓𝑙𝑢𝑖𝑑 = 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑓𝑙𝑢𝑖𝑑 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒𝑑 𝑏𝑦 𝑏𝑜𝑑𝑦
In the air, the principle is still the same, with gravity taking on the role of the
submergence force. All that needs to change is that the air inside the balloon needs to be
lighter than that of the air around it in order to create a perceivable buoyancy force. In
order to do this, then the conventional air inside the balloon must be substituted with a
more concentrated mix lighter elements. With the known volatility of Hydrogen, the
most common and practical solution is to create a body filled primarily with Helium, as
much as 90%, thus spawning a ‘lighter than air’ craft. As the variance between the two
densities is far less than the water based example, then to fully capitalise on this
theorem, the balloons have to displace vast volumes of air to generate the required
buoyancy force to carry useful payloads and ratify their functionality. This phenomenon
is reduced slightly in the case of the ‘balloon bot’ due to its indoor nature; the projected
altitude limits are very low, maintaining the density of the air at its maximum, thus
creating the largest possible void between the density of the air and that of inside the
balloon. These basic principles will have to be taken into account when the selection of
prospective equipment process begins.
A practical experiment was deduced to physically witness and more notably, measure
the force produced. The balloon would be inflated up to its operational size, then a
harness attached by string, which would carry the load to be added. Moving forward,
masses would be added in small intervals until the buoyancy force became equal to that
of the weight and equilibrium reached. Then using a scale the total rig would be added

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and the simple process of measuring the total physical mass, minus the reading on the
scale would provide a practical value for the buoyancy force.
This experiment would be carried out multiple times and on differing dates in order to
reduce the error as much as possible. In this test the error manifests itself in many
forms, such as human error when applying the weights, although this is very minimal,
they would need to be applied gently to negate and momentum forces when added.
Also, the pressure in the room can vary depending on the weather; therefore conducting
the test under varying conditions would provide an unbiased vision of which conditions
would be the best to conduct the final flight test under.
3.1Drag
Buoyancy and therefore lift may be the primary focus in the preliminary stages of
design, but they come with a harsh trade off manifested as drag; lift induced, form and
skin frictional. Drag is a parasitic force which always opposes the direction of motion,
acts in the plane of relative fluid flow and is proportional to the velocity of that flow [2].
Usually in the case of airborne vehicles, this is taken into account during the early
design of the aerofoil and its effective impact minimised. Yet, as the balloon is a non-
rigid structure, and its hull integrity maintained only by pressurisation from the inside,
manipulating its shape isn’t an option. This shortcoming leads us to that all of the drag
will be classed as profile drag, the sum of form and skin friction, with most of the
potential drag likely to be encountered will be form, around 90%. The rest is left to skin
friction as there isn’t any lift induced drag because of the uniform shaped nature of the
balloon [13]. Form drag is produced solely because of the profile of the object, with the
larger the presented cross-section producing the most drag. The specified balloon for the
UAV has a very large cross sectional area of 4.909𝑚2
, yet, in this project, the usually

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damaging size shouldn’t be irrevocable. The reason for this is that the profile drag of an
aerofoil is proportional to its velocity squared:
𝐹𝐷 =
1
2
𝜌𝑣2
𝐶 𝐷 𝐴
And as the UAV will not be attaining speeds of more than 1 − 2𝑚𝑠−1
as it flies
indoors, then the drag value will be dramatically lowered.
In order to fully maximise this equation, a value for the coefficient of drag (𝐶 𝐷) must be
approximated. This parameter has ties with the Reynolds number of the balloon, a
dimensionless quantity that links the viscosity of the surrounding fluid to the inertial
forces of the balloon. This quantity can also be used to estimate any potential
disturbances in the wake of the UAV, with characterised wake patterns being linked to
values with different profiles. The equation to calculate this constraint is:
𝑅 𝑒 =
𝑉𝐷 𝐻
𝑣
With 𝑉 being the relative velocity of the fluid flow at a distance away from the object as
so the flow is undisturbed. 𝐷 𝐻 is a quantity known as the hydraulic diameter, in the case
of the sphere is simply its diameter, and finally 𝑣, the kinematic viscosity of the
adjacent fluid [14]. Bysimple inspection of the order of magnitudes of each parameter, it
becomes abundantly clear that the driving value behind the formula will be the
kinematic viscosity and as an estimation, that 𝑅 𝑒 is inversely proportion to 𝑣, which
leads to the idea that the design specifications of the balloon UAV ultimately will have
little effect on the final Reynolds number and that the external stimuli of the operational
surroundings dictates final practical performance levels.

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Figure 2: Reynolds number flow predictions.
Through using this formula and inputting readily available data, a value attained is:
𝑅 𝑒 =
2∗2.50
15.11𝑥10−6 = 3.31𝑥105
As an operational value, this is extremely high, fundamentally controlled with the low
performance expectations of the balloon UAV. The primary use of this parameter is to
estimate the wake disturbances and flow patterns around the immersed object, allowing
the design to be tailored further still before any physical parts are made. Through
employing the Reynolds numbers predictive nature it is expected that the balloon will
have laminar flow at the boundary layer up until separation, generating a turbulent
wake, as illustrated in figure 2:
This prediction comes with inherent unreliability and to this end, computational fluid
flow simulations shall be run in order to confirm the true nature of the flow patterns.
The aptly named, FloXpress, is a simulation tool that makes up part of the SolidWorks
program package shall be employed to gain a visual appreciation of the balloon’s
interaction with the surrounding fluid.
Upon obtaining the confirmation of the Reynolds number flow, estimations about the
likely drag levels can be made through the relationship of Reynolds number to
coefficient of drag. NASA research into drag coefficients of a sphere relinquished a

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comprehensive set of results, with the trend clearly illustrated in graphical form (figure
12:?). Through extrapolation to the x-axis and assuming the balloon surface is smooth,
which at maximum inflation will be the case, it is clear that the 𝐶 𝐷 of a sphere carrying
our Reynolds number is approximately 0.5 [15]. The implications are that theoretical
calculations can now be made of drag levels at an array of velocities. Then, by plotting
this trend, allow for informed choices to be made about the operational range of the
UAV.
Although the values attained will not be the absolute values, there are certain
advantages to having a value to work with before the critical testing phases. The first of
these being that it will enable the mathematics and the theory to be verified if the
theoretical and practical values come comparatively close. This helps eradicate any
mistakes that many have crept in during the design process, whilst also giving
confidence to pursue more complex solutions. Secondly, understanding these
characteristics will allow for the design of the final stabilising structure to be tailored to
the specific requirements of the rig, as the maximum values of payload and velocity will
be agreed. Finally, they will assist in the final testing procedures, as any anomalous
results from miss-calibration of the wind tunnel will be effective caught and rectified.
3.2 Propulsion
The propulsion of any aircraft is the fundamental driving factor behind its raw
performance, furthering capabilities in not only velocity, but; range, altitude and
payload. With this in mind propulsion can also be aerodynamically exploited in order to
achieve the maximum thrust available from the propellers. One avenue to pursue this is
to add a nacelle, encasing the propellers, with a convergent exhaust nozzle for the

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Figure 3: Diagram of potential nacelle design
propeller to blow air through. As the flow is forced through the conical nozzle, the air
will be accelerated and thus providing extra propulsion.
In order to adopt this approach, the flow must be assumed to be incompressible, which
due to the very low speeds anticipated will be the case for the balloon UAV. With this
in mind, the mass flow rate into the cone will be equal to that out of it, therefore
allowing density changes to be ignored, simplifying the mass flow rate equation to:
𝑉1 𝐴1 = 𝑉2 𝐴2
This explicitly states that if the area of the exit nozzle is smaller than that of the
entrance then the velocity of the airflow will increase [16]. Initially the velocity of the
air at the inlet had to be calculated, which, using the rpm (3000) and propeller pitch
(3in) was achieved to yield a value of 3.81𝑚𝑠−1
. Then applying this value to the mass
flow rate equation, a value of 0.0824𝑘𝑔𝑠−1
was achieved. These hand calculations now
allow for the system to be theoretically and computationally verified in the same vein as
the flow predictions, providing a solid foundation on which final predictions can be
made of the overall performance characteristics of the UAV.
V1
A1
V2
A2

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4 Theoretical/Experimental Calculations
The production of any conceptual design requires a series of critical path tasks to have
been completed before manufacture can proceed, with the next step being theoretical
and experimental validation of the UAV. Before assembling any aspects of the final
design, it is imperative to understand each components performance characteristics and
how they complement each other towards the final objective. Theoretically determining
these parameters and then practically testing reduces wastage during the process, but
more importantly, creates a more accurate preliminary designs, reducing the number of
iterations before optimising the model.
4.1 Buoyancy Experiment
By beginning with the balloon itself, a key limitation of the UAV’s performance will be
its proficiency at carrying payloads, enabling a more complex stabilising structure and
heavier, more powerful motors. Therefore, by calculating this, a solid base prediction
can be made of secondary components. For testing, scale models of the final balloon
had to be used, primarily to save expense, in balloons and helium, and time spent
inflating.
The buoyancy can be found by, ‘the weight of fluid displaced by the body (Anderson:
1984: 27).’ Using this information, calculating the buoyancy is a formality as it will be:
( 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑑𝑖𝑠𝑝𝑙𝑎𝑐𝑒 𝑎𝑖𝑟 − 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 ℎ𝑒𝑙𝑖𝑢𝑚 𝑖𝑛𝑠𝑖𝑑𝑒 𝑏𝑎𝑙𝑙𝑜𝑜𝑛)
− 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑎𝑙𝑙𝑜𝑜𝑛

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The collective buoyancy calculations are all below:
𝑉𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑎 𝑠𝑝ℎ𝑒𝑟𝑒 = 4
3⁄ 𝜋𝑟3
= 4
3⁄ 𝜋0.453
, 𝑟 = 0.45𝑚
𝑉𝑠𝑝ℎ𝑒𝑟𝑒 = 0.3817𝑚3
𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑑𝑠𝑝𝑙𝑎𝑐𝑒𝑑 𝑎𝑖𝑟 = 𝑉𝑠𝑝ℎ𝑒𝑟𝑒 𝜌 𝑎𝑖𝑟
= 0.3817(1.225), 𝜌 𝑎𝑖𝑟 = 1.225𝑘𝑔𝑚3
𝑊𝑎𝑖𝑟 = 0.4676𝑘𝑔
𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝐻𝑒𝑙𝑖𝑢𝑚 𝑏𝑎𝑙𝑙𝑜𝑜𝑛 = 𝑉𝑠𝑝ℎ𝑒𝑟𝑒 𝜌ℎ𝑒𝑙𝑖𝑢𝑚
= 0.3817(0.1785), 𝜌ℎ𝑒𝑙𝑖𝑢𝑚 = 0.1785𝑘𝑔𝑚3
𝑊ℎ𝑒𝑙𝑖𝑢𝑚 = 0.0681𝑘𝑔𝑚3
𝑃𝑎𝑦𝑙𝑜𝑎𝑑𝑡𝑜𝑡𝑎𝑙 = [(𝑊𝑎𝑖𝑟 − 𝑊ℎ𝑒𝑙𝑖𝑢𝑚 ) − 𝑊𝑏𝑎𝑙𝑙𝑜𝑜𝑛 ]
= (0.4676 − 0.0681)− 0.010, 𝑊𝑏𝑎𝑙𝑙𝑜𝑜𝑛 = 0.010𝑘𝑔
𝑃𝑎𝑦𝑙𝑜𝑎𝑑𝑡𝑜𝑡𝑎𝑙 = 0.3889𝑘𝑔
𝐵𝑢𝑜𝑦𝑎𝑛𝑐𝑦 𝑡𝑜𝑡𝑎𝑙 = 𝑃𝑎𝑦𝑙𝑜𝑎𝑑𝑡𝑜𝑡𝑎𝑙 ∗ 9.81
= 0.3889 ∗ 9.81
𝐵𝑢𝑜𝑦𝑎𝑛𝑐𝑦 𝑡𝑜𝑡𝑎𝑙 = 3.8151𝑁
Density values [17].

P a g e | 19
This value gains significance upon completion of the experiment to practically measure
the buoyancy of the balloon, then the efficiency of the system can be quantified. Also, it
can be used to gauge the success of the practical and allow any errors to be rectified and
the mistake not integrated further.
The practical experiment was detailed previously, page 11, where the test balloon would
be inflated to its stated maximum and mass increments added in order to achieve an
operational value for the buoyancy.
The test began initially by taking readings of the pressure, in mmHg, and the
temperature, in kelvin, enabling a calculation for the air density at that moment. This
would further allow for a theoretical value for the lift to be postulated based on these
parameters and the volume at which the balloon is inflated too. Then filling the balloon
to the predetermined of radius 0.45𝑚 with pressurised helium from the container. Due
to the volatility of a highly pressurised balloon and for overall safety, latex gloves were
worn at all times to avoid bursting the subject. Inflation finished once the desired size
was reached, maximum and the knot tied to seal the balloon. String, pre-attached to a
mass harness was then tied the knot at the base of the balloon ready for the increments
to be added.
Before the masses additions could begin, the scales had to be calibrated and zeroed,
ensuring accuracy. With the practical now ready to begin, the system placed on the
scale and the masses added in 100g steps, subtracting the reading from the computed
system weight at each occasion, an approached repeated with scale readings from
631.5 – 931.5 grams.
Then, the average difference between these readings illustrates the balloons payload
capabilities, which, when multiplied by gravity, yields the buoyancy force.

P a g e | 20
Also deduced from this information was the density of the helium inside the balloon,
which, when the value is studied will allude to the concentration and purity found
inside.
A full copy of the acquired data can be found as table 1 page 46 with the vital
information tabulated below:
Helium density Lift
0.236079611 0.330325
4.2 CFD Simulations
A number of experiments were conducted using CFD as it enables metaphysical tests to
be conducted and results collated to a high degree of accuracy. The main subjects were
the design and testing of two different nacelle designs, with the vision of increasing
overall propulsion of the UAV and the balloon, to generate flow and wake simulations
to plot the disturbance made through the air.
Theoretical
Circumference(cm) Radius V L
0.439 0.354391 0.351617
Experimental
Readings
Scale reading
Without Balloon
(g)
Scale reading with
Balloon (g)
Total
lift(lift +
spring
mass)
631.5 300.8 330.7 330.7
731.5 399.9 331.6 331.6
831.5 502 329.5 329.5
931.5 602 329.5 329.5

P a g e | 21
4.2.1 Balloon Simulation
Beginning with the balloon, the aim was to see how the air reacted to the subject
traveling through at a predicted 2ms-1. The results would firstly, either confirm or deny
the wake predictions form using the Reynolds number, secondly, illustrate pressure
build up on the balloon with the view to passing on the information to the structural
division to aid stability and finally, clearly demonstrate the flow characteristics at the
boundary layer over the surface of the balloon.
The selected program used was SolidWorks, due to its comprehensive flow simulations
addition FloXpress. To begin, the design has to be fabricated in the software, with the
case of the balloon, a simple sphere with the volumetric conditions identical to that of
the physical object.
Then by loading this into the programs FloXpress add on, computational domains can
be selected, allowing the temperature, pressure and flow speed to be dictated. Then by
meshing this the solver can compute the iterations of the system, yielding data on many
system parameters, though the ones applicable here were the:
 Flow patterns
 Iso-pressure images
The Reynolds number calculation had been conducted with a predicted velocity of
2𝑚𝑠−1
and for continuity and for direct comparison, this value was used as part of the
input domain for the simulation, along with room temperature of 293.15𝐾 and ISA sea
level pressure 101325𝑃𝑎.
The results and images of the relinquished data fields, velocity and pressure can be seen
below (figures 4 and 5: 22):

P a g e | 22
Figure 4: Velocity simulation results. Figure 5: Pressure simulation results.
More comprehensive images are displayed as appendices 4, 5 and 13, pages 53 and 57
and the complete set of results tabulated as table 2, page 47.
4.2.2 Nacelle Simulation
CFD was also used to design and test the nacelle propeller housing which followed a
similar process to that of the balloon by initially having to input the geometry
specifications of each design in order to comparatively test them. Two designs were
tested, with more iterations enabling the correct design would be selected. One, a
conventional cone shape, tapering with straight line to the exit (figure 6:23) the other,
curved, reducing in area dramatically before remaining more constant towards the exit
(figure 7:23), both are illustrated below with images directly from the SolidWorks
program, with larger images as appendices 6 and 7, page 54:

P a g e | 23
Then using the principle alleviated to earlier the:
𝑉1 𝐴1 = 𝑉2 𝐴2
The velocity output computed.
It is clear to see that, 𝑉2 ∝ 𝑟2
, with 𝑟 being the radius of the outlet. With this
information estimations were made as to the scale of the reduction in 𝑟, finally settling
on a third. The reasons for this are that although the flow is simulated as
incompressible, further tapering the exit nozzle would practically cause certain levels of
stagnation pressure build up as the flow is forced outwards, which in turn would reduce
the overall efficiency. Also due to the low powered nature of the propellers, flow
‘eddying’ in the outlet could severely lower performance.
This hand calculated information now allowed the system to be simulated using
FloXpress from SolidWorks. Two conical nozzles were modelled, the first with curved
tapering edges, the second with a straight tapered nozzle, both demonstrated with
drawings from SolidWorks, (Figures 6 and 7) had an inlet size of 150mm to encase the
propeller, then converging to 50mm at the exhaust exit.
Figure 6: Straight taped nacelle. Figure 7: Curved nacelle

P a g e | 24
The only unknown, was alluded to earlier, 𝑉1 = 3.68𝑚𝑠−1
, with the theoretical
calculations shown:
𝑟𝑝𝑚 ∗ 60 = 𝑟𝑝ℎ
3000 ∗ 60 = 180,000𝑟𝑝ℎ
180,000 ∗ 3 = 540,000 𝑖𝑛𝑐ℎ𝑒𝑠 𝑝ℎ , 𝑤ℎ𝑒𝑛 𝑝𝑟𝑜𝑝𝑒𝑙𝑙𝑒𝑟 𝑝𝑖𝑡𝑐ℎ = 3 𝑖𝑛𝑐ℎ𝑒𝑠
Then thorough conversions:
𝑉1 = 3.81𝑚𝑠−1
By then using this information in the equation, we generate an expected exit flow
velocity of:
3.81(0.01767) = 𝑉2(0.001963)
𝑉2 = 34.30𝑚𝑠−1
This now needs to be validated by the simulation where it was observed that for the
same inlet velocity, the airflow speed was greater when using the curved tapered
nacelle, yielding a velocity of 36.214𝑚𝑠−1
, compared to that of 34.2202𝑚𝑠−1
for
alternate profile. A condensed results table is depicted below:
Results
Name – Curved Nacelle Unit Value
Maximum Velocity m/s 36.214
Name – Tapered Nacelle Unit Value
The complete tabulated results are displayed as tables 3 and 4, pages 48 and 49.

P a g e | 25
The advantage of CFD is that the simulations provide fully developed motion footage,
allowing for a visual appreciation of the flow and pressure dynamics. Figures 8 and 9
depict the velocity flow through the nacelle, complete with colour representation in the
key:
Full size images can be found as appendices 8 and 9, page 55.
Figure 8: Straight tapered nacelle simulation results. Figure 9: Curved nacelle simulation results.

P a g e | 26
5. Data Analysis
A fundamental aspect of conducting simulations, tests and experiments is that the
results have to be thoroughly analysed, generating a greater noesis of the parameters
being monitored. From the literature, Lewis-Beck, dictates that through in depth data
analysis the model can be either conclusively supported or that changes need to be
made.
The advantage of theoretical calculations before the tests come to the fore during
analysis also, as the validity of the test and the acquired data can now subjected to
scrutiny and any anomalous results removed from the data selection and retested.
5.1 Buoyancy Experiment
Upon initially conducting the data analysis for the experiment, it has significant benefit
to first, compare the theoretical to practical values, with 3.8151𝑁 theoretically and
3.2402𝑁 during the experiment. This discrepancy is to be expected as theoretical
calculations are based on an idealised set of parameters and most importantly, 100%
helium concentration. As shown during the test, the helium density value of
0.2361𝑘𝑔𝑚3
, compared to a pure value of 0.1785𝑘𝑔𝑚3
illustrates that its purity level
is much lower than idealised having a large impact on the important air to balloon
weight differential.
A secondary aspect that will have impacted the absolute lift value was that, on safety
grounds, we were advised not to inflate the balloon to its maximum as the risk of
puncture exponentially increases. To this end, the radius of the experimental balloon is
0.439, and if this was factored into the theoretical calculations the buoyancy would
immediately be lowered to 3.4826𝑁, 0.3326𝑁 lower, further closing the gap and

P a g e | 27
increasing the reliability of both values. This new force can be used to estimate the
helium purity value and indicates a value close to 93%.
The success and inherent correlation between the two values means that a prediction for
the buoyancy of the full size balloon, where 𝑟 is 1.25𝑚, can be made and also carry the
93% purity value forwards to give a more accurate representation of the practical value
yielding a theoretical buoyancy of 82.5180𝑁, then applying the purity factor generates
a final practical buoyancy value of 76.7418𝑁, effectively allowing a payload of
7.823𝑘𝑔.
5.2 CFD Simulations
The CFD simulations obviously use computer programs to compute the desired
scenarios and generate the data spread in many formats; video simulations, iso -pressure
images or tabular form, making their analysis a far more interesting prospect.
5.2.1 Balloon Simulation
The primary focus during this simulation was to investigate the flow and wake patterns
when disrupted by the balloons presence and then compare these to the theoretical
interpretation previously depicted.
Beginning with the wake flow analysis, the predicted flow pattern was generated from
use of the Reynolds number and issued an estimation of the disturbance (figure 2:14).
Through controlling the computational domain of the simulation and selecting the same
size sphere and velocity of motion, the program was able to render a moving image
demonstrating the reaction of the fluid, demonstrated by figures 4 and 5 (page22) and a
further rendered image in the appendix as figure 13 page 57.

P a g e | 28
Firstly, the visual comparison of the two predictions are identical, even to the point of
separation. The boundary layer remains intact, known as the coanda effect – a fluids
tendency to attach to a smooth surface [18], until the adhesive force is overcome by the
force from the direction of travel, at which point the separation air flow constricts the
disturbed eddying flow between it, most clearly demonstrated on figure 4 (page 22).
The disrupted air then begins a helical flow pattern (figure 13:57), most likely caused
from sideslip off the cascading geometry of the balloon. Also the wake signals a
stagnation of low pressure to the rear of the balloon as the airflow begins to converge
after separation before stabilising as the pressure returns closer to that of the ambient.
This all signifies the reliability of the calculated Reynolds number, and from that, the
coefficient of drag, which now allows a secondary experiment to predict the drag level
faced by the UAV at an array of velocities.
A further simulation was of the pressure stagnation upon the balloons surface and the
image (figure 5:22) clearly illustrates that the area under the largest pressure is the face
perpendicular to the motion of travel. The pressure gradually reduces until it reaches its
minima, parallel to motion as the airflow is slightly accelerated by the balloon as it
travels over its largest radius.
5.2.2 Nacelle Simulation
The first note of comparison, is that the simulated velocities are close to the theoretical
value, which indicates the credibility of the test and that human error, in the form of
data input, did not occur. This success now allows the comparison of the two proposals
to be analysed with greater credibility and further reinforces the technique of theoretical
validation.

P a g e | 29
The implications of the simulation reaching a definitive result made the choice of the
most advantageous option for the UAV simple and also provided proof that the design
orientation was the correct one. Also for the curved convergence, the airflow reaches
the maximum speed earlier in the nozzle, stabilising before exit, which has added
propulsive benefit with a directionally uniform airflow.
In order to endorse the results from the simulation, a practical test will take place, were
the physical effect can be witnessed. Also the assumption of uniform, incompressible
flow had been made, which in practice would not be the case as slight compression
would take place towards the exit of the nozzle, in turn marginally constricting the fluid
flow and slightly reducing the nacelles effectiveness.

P a g e | 30
6. Further Experiments
Completing the data analysis on the first stage of experimental testing alluded to the
success and viability of parameters that had been calculated. Now, as stated in the
literature by Ertas, next in the design process is to refine and test again. However the
initial tests ended in positive results, leaving the only consideration to be the level of
drag likely to be encountered during operation.
6.1 Drag ForcePrediction
Having an accurate prediction of the operational drag forces will significantly aid in
beginning to understand the complexity of the model when in motion. Managing to
attain a value for the drag at a range of values will enable the system to be more
effectively modelled by mathematics. Through using the formula:
𝐹𝐷 =
1
2
𝜌𝑣2
𝐶 𝐷 𝐴
The magnitude of the drags influence can be examined at a range of velocities, a sample
calculation takes the form of:
𝐹𝐷 =
1
2
(1.225) 𝑣2(0.5)(19.635), 𝜌 = 1.225, 𝐶 𝐷 = 0.5, 𝐴 = 19.635
The 𝐶 𝐷was the value that was proven from the balloon wake simulations, as this
reinforced that Reynolds number and from the association between the coefficient of
drag and the Reynolds number for a sphere investigated by NASA (2010) and illustrated
in graphical form (Figure 12:57) [15].
Now through varying 𝑣 between 0 and 2𝑚𝑠−1
, the relationship between the two can be
appreciated, depicted by figure 10 (page 31).

P a g e | 31
Figure 10: Drag vs Velocity graph
The complete table of results is in the appendix, table 5, page 50.
0
1
2
3
4
5
6
7
0 0.5 1 1.5 2
DragForce(N)
Velocity (ms-1)
Drag ForceVs Velocity
Drag

P a g e | 32
7. Further Analysis
Analysis in to the secondary experimentation will illuminate the need to carry out any
more investigations into yet alluded to parameters, whilst also increasing the
understanding of previous experimental data sets.
7.1 Drag ForcePrediction
The velocity range of the UAV is likely to be low, and therefore the drag calculations
were only modelled up to 2𝑚𝑠−1
.
Through visual analysis of the graph, it is clear to see the exponential rise in drag in
accordance to the increase in the velocity. The understanding behind this is that the drag
force 𝐹𝐷 ∝ 𝑣2
causing the exponential gradient rise. The drag is also dependant on the
cross sectional area and therefore also proportional to 𝑟2
, whereas the buoyancy,
𝐵 ∝ 𝑟3
, thus creating a trade-off between buoyancy and drag, as seen in the literature.
The drag force prediction is a vital part of the design process as it is one of the four
fundamental forces that drive motion; lift, weight, thrust and drag. In the UAV’s case,
the lift and the weight forces will remain constant at all times due to design
specifications, therefore promoting thrust and drag forces to an increased level of
prominence. Being able to definitively know the obstructive force at any velocity will
allow for the thrust levels to be designed to exceed the resistive forces of drag and drive
the system forwards.

P a g e | 33
8. Conclusions
In this section the implications of the work carried out shall be conducted, focusing on
how this will affect the model and procedures moving forwards and any future work
that could be conducted if time allowed. Initially, discussions about each experiments
evaluation shall transpire.
8.1 Discussion
Analysed results only highlight discrepancies within the data stream, to fully understand
and therefore utilise the findings, demonstrating the reasons for these incongruences
shall alleviates the results. Through discussing the differences and then comparing these
back to the literature, the success, reliability and validity of the experiments can be
proven.
8.1.1 Buoyancy Experiment
During the buoyancy experiment, it was demonstrated that from the theoretical value,
0.2424𝑁 of lift performance was lost, generating an error of:
𝐸𝑟𝑟𝑜𝑟 =
𝑇ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙−𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙
𝑇ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙
∗ 100
=
3.4826 −3.2402
3 .4826
∗ 100
𝐸𝑟𝑟𝑜𝑟 = 6.96%
As the literature depicted, this is seen as a very acceptable level of error, although
aiming to improve this value will inevitably improve the UAV’s performance and ease
computational design reliability. As established in the analysis, the purity of the helium
wasn’t perfect at 93%, and increasing this would further reduce the statistical error. One
method of inducing this would be, before helium inflation, to pump all the residual air

P a g e | 34
out of the balloon, seal the base, then attach it to the helium tanks, thus improving purity
and ultimately the buoyancy force inducted.
This aside, the levels of lift confirmed by the balloon will mean there will definitely be
a surplus once the motor construction and stabilising structure are added, which gives
the distinct advantage of being able to add ballast to the system. The benefit of this is
that we get to decide where we want to the concentration of weight to be, and to
increase the stability of the UAV as a whole. Ideally the ballast would need to be
located centrally to deter the system from any kind of pitch and roll during motion.
8.1.2 CFD Simulations
Computational fluid dynamics is a relatively new technology that has vastly improved
the art of design and simulation, allowing multiple variations, some only altered by
millimetres, to be tested in accordance with one another at no physical cost. This not
only reduces the cost, but use of materials, wastage, time and man power, allowing
design focus to view the peripherals, encompassing and integrating the whole system
together as they are designed simultaneously.
8.1.2.1 Balloon Simulation
Conducting the simulations on the balloon allowed for an understanding to be gained of
aspects that cannot easily be physical monitored. Using a wind tunnel is one of few
ways to measure the airflow over the surface of objects, yet when that object is a
balloon, the situation becomes more complex as fixing the balloon to achieve a
consistent reading would be almost impossible.
The aim of the experiment was to confirm the flow was behaving in the predicted way,
thus confirming the Reynolds number for the system, which in turn confirms the

P a g e | 35
coefficient of drag promoting further work, as supported by the design process from the
literature.
The results of the perceived wake of the balloon was exactly as expected, supported by
the theory, pointed towards a conclusive success. Through images of the simulation
(figures 4, 5:22) the expected laminar flow, primarily due to low air speed, was
perceived up until the separation point, where the viscous nature of the flow becomes
overawed by the force of the flow and breaks away from the boundary layer on the
surface of the balloon and becoming turbulent in the wake.
8.1.2.2 Nacelle Simulation
A similar case to the simulation of the balloon, the investigation was conducted using
computational means and the flow patterns simulated. The aim of the design was to
induce an air flow velocity increase as the profile of the nacelle narrows, a hypothesis
strongly supported by the relevant literature, most notably Cohen and Rodgers.
As seen in the results data, one design accelerated the air more efficiently than its
counterpart down to it sleeker profile design. Also, as the profile converged more
aggressively before levelling out, the air flow was accelerated most initially, stabilising
before the exit nozzle. This in itself is more advantages, as a more uniform flow would
provide greater thrust capacities in the desired direction, in comparison to the where the
air flow will likely be turbulent on exit and therefore lose efficiency.
The obvious success of the experiment made progressing forwards with the design
process much simpler, and also supports the statements made in the literature regarding
design techniques.
Unfortunately, due to strict time constraints and limited apparatus, the planned physical
conduction of experiments with the nacelle wasn’t able to take place, where the effects

P a g e | 36
on the airflow from the simulation would be mirrored and results compared. From the
literature, it was clear to not expect the exemplary performance characteristics from the
computational test, as these are an idealised set of circumstances and inefficiencies such
as; pressure stagnation, eddying currents and skin friction from the model are neglected.
8.1.3 Drag Force Prediction
This experiment became valid directly from the success of the balloon simulation test
discussed earlier (page 27). Progression in this manner is supported in the literature and
depicts a success project and allows secondary parameters to be indirectly monitored, in
this case the drag.
Aforementioned, again during the balloon simulation section, (page 34) physically
measuring airflow characteristics of a balloon in a wind tunnel would be problematic
and therefore many parameters have had to be modelled theoretically. The implications
of this are that, with any theoretical calculation, the attained value will always be an
idealised situation, where certain inefficiencies aren’t taken into account to reduce the
complexity of the formulas.
Here, the material of the balloon is neglected and to this end, skin friction isn’t factored
into the final drag value. Fortunately, according to Shapiro (1961) around 90% of the
drag encountered will be form drag, with the other 10% determined by skin friction.
The levels of drag predicted were only of very small intensities due to the low velocities
attained and therefore this discrepancy will have little effect of on the final output.
Further reducing its importance is that, from the results yielded from the nacelle
simulations, there is an abundance of thrust that far exceeds the resistive forces like to
be encountered.

P a g e | 37
8.2 Outcomes
The aim of the project was to design a functioning balloon UAV and in this section the
final processes of manufacturing the robot shall be discussed with explanation, analysis
and discussion of issues faced.
Upon the group completing the necessary research into their designated fields, the time
came to complete the physical entity of the ‘balloon bot’. Two structural design were
produced and therefore we created two versions of the robot:
The initial design had the structural casing seated centrally below the balloon. The
concept mirrored that of a hot air balloon, an air vehicle with similar characteristics,
with the propulsion system hung in suspension below the balloon in line with its
aerodynamic centre for maximum stability and performance. The propellers were then
mounted at 300mm from the central point on a 3mm steel rod.
The second again had all the main electrical components housed in the structural casing,
although this was physically attached to the base of the balloon, rather more like the
traditional blimps depicted in the literature such as the Bosch SASS lite. The arms on
which the propellers are then connected was reduced for the second iteration to induce
aspects with comparative testing capabilities. They were mounted at 150mm from the
centre, which overall had many performance benefits.
Between the two designs, it was initially clear to see that form a stability aspect, the
second design would be the most beneficial allowing for smoother control and reducing
the amount of induced roll and yaw upon power application. This was due to the free
hanging nature of the first design where, as power was applied the housing would rock
and then the moment translated into the balloon harming overall performance.

P a g e | 38
8.2.1 Rotational analysis
Completing the UAV allows for the opportunity to perform practical experiments that
would otherwise prove problematic. One such test was a rotational analysis,
investigating the varying performance characteristics of the two designs during rotation.
The hypothesis is that, the wider placed motors will reduce the rotation period, as the
turning moment will be increased due to the extra perpendicular distance [19]:
𝑀𝑜𝑚𝑒𝑛𝑡 ( 𝑁𝑚) = 𝐹𝑜𝑟𝑐𝑒 ∗ 𝑃𝑒𝑟𝑝𝑒𝑛𝑑𝑖𝑐𝑙𝑎𝑢𝑟 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
Understanding this area will allow for more apt designs to be devised during future
work, including providing a stable rotational time period that allows the user to react to
the manoeuvre.
Of the two designs, the main difference was the location of the propellers, with the first
placed 300mm from the centre, with this distance reduced to 150mm for the second
design. Then through monitoring the time taken to alter the orientation of the UAV to
varying angles the following data set was acquired with the complete data set found as
table 6, page 51 in the appendix:
300mm arms
Rotation
Degree
Time (s)
(Average)
0 0
90 0.7
180 1.3
360 2.1
150mm arms
0 0
90 1
180 1.4
360 2.6

P a g e | 39
From the attained results it is clear to see the hypothesis is correct as the large moment
arm induced significantly reduced rotation times, between 30-40% during most
rotations. Although in most cases obtaining peak performance characteristics is the
projected endeavour, in this case, achieving stable operational performance is the key
requirement. Observed during the experiment was that the smaller arm offered this
prerequisite, allowing for greater ease of control, increasing the reaction window to
accurately steering the robot. The slower turning circle also lends itself to the relay lag
between remote input into the program and the instruction then being carried out by the
audrino.
Due to the conclusive nature of the result and analysis, the shorter moment arm design
was sanctioned as the final structural rig to be permanently operated.
8.3 Future Work
As with any design and manufacturing project with a finite development timescale,
there are avenues that couldn’t be fully explored, the complexity of the project ensured
that there are a multitude of an areas to investigate and therefore subject prioritisation
had to take place. Discussing future work can alleviate important facets of potential
development directions, also once the design is fully functional, improving efficiency
becomes an important subject of exploration.
8.3.1 Balloon Development
A key area of development would be investigating potential shapes for the balloon, as
the overall intricacy of the design was substantial, simplification in certain areas
allowed for the design to fit the allotted timeframe. A spheres crude shape allowed for
structural designs to retain a more modest approach and provide completed models.

P a g e | 40
Developing a proficiency in this area will decrease the most resistive force faced during
operation, drag, which as illustrated in the literature (Shapiro, 1961) manifests itself
mainly as profile drag, 90%, thus by inhibiting the profile of the object in question
interacting perpendicular to the airflow, parasitic drag forces shall be reduced. The
major gains will be through reducing the drag encountered, thus improving not only
operational efficiency, but also peak performance characteristics furthering acceleration,
top speed and manoeuvrability.
Altering the shape would induce the necessity for secondary research into other areas
such as helium diffusion. As helium diffuses through the surface of the balloon. If the
shape was anything other than perfectly uniform, a sphere, then its profile would alter
slightly and this would have to be investigated. Circumstances such as this would place
an operational life span on the UAV before certain aspects would have to be serviced.
Also, the final structural design was far lighter than anticipated, coming in at around
500𝑔, as a result the balloon had payload capabilities far beyond what was necessary
and during testing the balloon was not fully inflated. To this end, using the buoyancy
calculations undertaken previously, it becomes apparent only 6.39% of the original
payload capacity is required (after efficiency factors applied) and, to reduce the amount
of required ballast, a balloon with a radius of 0.5𝑚 would satisfy.
The advantages go further than reducing ballasts complications, as the reduced size has
secondary benefits in reducing the induced drag from the balloons profile. A gain such
as this would lower the peak drag at 2𝑚𝑠−1
from 6𝑁 to 0.96𝑁, significant progress
encountering 84% less restive force. Again, this is only taking form drag into the

P a g e | 41
scenario, so the practical value would be slightly higher once skin friction is taken into
account. The new information is illustrated in the graph below (figure 11):
Figure 11: Revised drag prediction.
8.3.2 Structural Advancements
Another significant area would be to perform aerodynamic analysis on the equipment
housing which attaches to the base of the balloon. The final design would cause a
substantial amount of drag, and through a joint venture with the structural design team,
focusing on surfaces whose profile interacts directly with the air flow, creating more
aerofoil like surfaces to generate a more laminar flow.
The advantages of progression in this area are profound and demonstrated in vast
amounts of literature (Batchelor, 1973) and although the surfaces lift production will be
minimal, it’s a capacity that isn’t required with the abundance being produced through
buoyancy. The effects of the drag reduction will be very similar to that of the balloon
increasing the almost all efficiency and manoeuvrable aspects of the UAV.
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5 2
DragForce(N)
Velocity (ms-1)
Drag

P a g e | 42
Although, advancements in this sector must be approached with slight trepidation, the
structures primary function is to house the externals of the robot and becoming too
aggressive with aerodynamic pursuits could jeopardise the integrity of the structure.
8.4 Conclusion
As a projects lifecycle comes to an end, an important task to carry out is reflection, to
ensure that lessons have been understood and can be carried forwards and applied to the
next situation.
It was observed that the advancement of technology was leading the world towards
automating many of the previously human operated equipment. This is clearly visible in
other engineering sectors, most notably manufacturing, where applying users to
practical applications has become obsolete and replaced by more accurate, efficient
computational systems. With the development of artificial intelligence (AI) the cross
over between where a humans judgement and a computers analysis is most effective is
becoming more hazy, couple this with the added requirement for casualty minimisation
and stringent safety applications, the aerospace industry is heavily investing and
developing unmanned aerial vehicles.
With this in mind, this paper has aligned itself with these views and transcribes the
undertaking of the design, manufacture and testing of such an aircraft. The ‘balloon bot’
UAV applies the previously learnt lessons of unmanned flight to produce the final
indoor flying robot. The venture was partially successful with several controlled
airborne successes occurred during closed door tests, although ultimately no sustained
flight was verified.

P a g e | 43
There were many difficulties faced and overcome during the project such as;
information black spots, testing difficulties and temperamental apparatus. The lessons
of the delicacy of testing with volatile equipment, as one major burden during testing
was the sensitivity of the balloons which regularly popped halting progress. This
highlighted the difficulties faced in the industry of practical design, demonstrating the
timescales of such tasks and inconveniences of facet failures. Further complications
arose from the sporadic material on the subject, meaning most of the work was primary
research.
Moving forwards, certain improvements could be made to the critical path in order to
achieve a more successful outcome, by potentially beginning construction and testing
earlier to allow contingency periods in the event of process failures.
Overall, as robotics was an uncharted academic subject, the advancement demonstrated
during the project term illustrates the success and progression of the group. By working
together cohesively and relying on each other to complete tasks to move forwards, an
operational product was produced and individual understand of such matters enhanced.
The rarity of directly applicable subject matter increased the difficulty of the project, yet
this was overcome soundly. The literature on model designs mirrors that of which we
applied, depicting the overall successful nature of the study, even if in fact the final
product still required further work.

P a g e | 44
9. References
1. Anderson, J. Fundamentals of aerodynamics. New York: McGraw-Hill. 1984.
2. Lachmann, G. Boundary layer and flow control. New York [u.a.]: Pergamon
Press. 1961.
3. Kermode, A., Barnard, R. and Philpott, D. Mechanics of flight. Harlow:
Longman. 1996.
4. Hazewinkel, M. Lagrange equations (in mechanics), Encyclopedia of
Mathematics. Berlin: Springer. 2001.
5. A.R. Vasel-Be-Hagh, R. Carriveau, D.S.-K. Ting, Numerical simulation of flow
past an underwater energy storage balloon, Computers & Fluids, Volume 88, 15
December 2013, Pages 272-286.
6. Munson, K. Jane's unmanned aerial vehicles and targets. Coulsdon, Surrey,
UK: Jane's Information Group. 1995.
7. Zufferey, J.C. Bio-inspired Flying Robots: Experimental Synthesis of
Autonomous Indoor Flyers. Lausanne: EPFL Press. 2008.
8. Cohen, H., Rodgers, G.F.C and Saravanamuttoo, H.I.H. Gas Turbine Theory.
Brunt Mill, Harlow, Essex: Longman Scientific & Technical. 1987.
9. Ertas, A. and Jones, J. C. (1996). The Engineering Design Process. New York:
John Wiley & Sons, Inc. 1993.
10. Tu, J., Yeoh, G. H. and Liu, C. Computational Fluid Dynamics. Amsterdam:
Butterworth-Heinemann. 2008.
11. Lewis-Beck, M. S. Data Analysis. Thousand Oaks, Calif: Sage Publications.
1995.
12. Zuljian, D. and Grum, J. n,d. An Introduction to Error Analysis, the Study of
Uncertainties in Physical Measurements/ John R Taylor.

P a g e | 45
13. French, A. Newtonian mechanics. New York: W.W. Norton. 1971.
14. Shapiro, A. Shape and flow. Garden City, N.Y.: Anchor Books. 1961.
15. Grc.nasa.gov. Drag of a Sphere. [online] Available at:
http://www.grc.nasa.gov/WWW/k-12/airplane/dragshpere.html [Accessed: 26
Mar 2014].
16. Batchelor, G. An introduction to fluid dynamics. 1st ed. Cambridge: University
Press. 1973.
17. Engineeringtoolbox.com. Air Properties. [online] Available at:
http://www.engineeringtoolbox.com/air-properties-d_156.html [Accessed 15
Mar 2014].
18. Tritton, D. Physical fluid dynamics. 1st ed. Oxford [England]: Clarendon Press.
1988.
19. Serway, R. A. and Jewett, Jr. J. W. Physics for Scientists and Engineers. 6th Ed.
Brooks Cole. 2003.

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10. Appendices
10.1 Tables
Table 1 – Buoyancy Test Data.
Temperature 297.65
Pressure 99791.7928
Density 1.16817267
String Mass 0
Theoretical
Circumference(cm) Radius V L
0.439 0.354391 0.351617
Experimental
Readings
Scale reading 1 Scale reading 2
Total
lift(lift +
spring
mass)
631.5 300.8 330.7 330.7
731.5 399.9 331.6 331.6
831.5 502 329.5 329.5
931.5 602 329.5 329.5
Helium density Lift
0.236079611 0.330325

P a g e | 47
Table 2 – Balloon Simulation Results.
Min/Max Table
Name Minimum Maximum
Pressure [Pa] 101323 101327
Temperature [K] 293.198 293.202
Velocity [m/s] 0 2.74006
X – Component of Velocity
[m/s]
-0.224311 2.7299
Y – Component of Velocity
[m/s]
-1.32104 1.32313
Z – Component of Velocity
[m/s]
-1.32037 1.32294
Fluid Temperature [K] 293.198 293.202
Mach Number [ ] 0 0.0079846
Shear Stress [Pa] 0 0.0111969
Heat Transfer Coefficient
[W/m^2/K]
0 0
Surface Heat Flux [W/m^2] 0 0
Density [kg/m^3] 1.20368 1.20373

P a g e | 48
Table 3 – Straight Tapered Nacelle Results.
SolidWorks FloXpress Report
Model
Model Name: F:Balloon Robotstraight cone with lids.SLDASM
Smallest Flow Passage
Smallest flow passage:0.145666421 m
Fluid
Air
Inlet Mass Flow 1
Type Mass Flow Rate
Faces <1 >
Value Mass Flow Rate: 0.0783 kg/s
Temperature: 293.2 K
Environment Pressure 1
Type Environment Pressure
Faces <1 >
Value Environment Pressure:101325 Pa
Results
Name – Tapered Nacelle Unit Value

P a g e | 49
Table 4 – Curved Nacelle Simulation Results.
SolidWorks FloXpress Report
Model
Model Name: F:Balloon Robotcurved cone completed.SLDASM
Smallest Flow Passage
Smallest flow passage:0.145666421 m
Fluid
Air
Inlet Mass Flow 1
Type Mass Flow Rate
Faces <1 >
Value Mass Flow Rate: 0.0783 kg/s
Environment Pressure 1
Type Environment Pressure
Faces <1 >
Value Environment Pressure:101325 Pa
Results
Name – Curved Nacelle Unit Value

P a g e | 50
Table 5 – Drag/Velocity Data.
density 1.225
A 4.909
Cd 0.5
drag (N)
v (ms-
1)
0 0
0.015034 0.1
0.060135 0.2
0.135304 0.3
0.240541 0.4
0.375845 0.5
0.541217 0.6
0.736657 0.7
0.962164 0.8
1.217739 0.9
1.503381 1
1.819091 1.1
2.164869 1.2
2.540714 1.3
2.946627 1.4
3.382608 1.5
3.848656 1.6
4.344772 1.7
4.870955 1.8
5.427206 1.9
6.013525 2

P a g e | 51
Table 6 – Rotational Analysis.
Rotational Analysis
300mm arms Readings
Rotation
Degree
Time (s)
(Average)
0 0 0 0 0 0 0
90 0.7 0.67 0.69 0.72 0.71 0.71
180 1.3 1.33 1.28 1.34 1.31 1.27
360 2.1 2.08 2.1 2.11 2.11 2.09
150mm arms
0 0 0 0 0 0 0
90 1 0.95 1.03 1.01 0.98 1
180 1.4 1.36 1.3 1.41 1.43 1.43
360 2.6 2.55 2.59 2.66 2.6 2.61
10.2 Figures
Figure 1 – Original Balloon Design.

P a g e | 52
Figure 2 – Reynolds Number Flow Prediction.
Figure 3 – Propulsion Diagram.
V1
A1
V2
A2

P a g e | 53
Figure 4 – Balloon Velocity Simulation Results.
Figure 5 – Balloon Pressure Simulation Results.

P a g e | 54
Figure 6 – Straight Tapered Nacelle.
Figure 7 – Curved Nacelle.

P a g e | 55
Figure 8 – Straight Tapered Nacelle Simulated Results.
Figure 9 – Curved Nacelle Simulated Results.

P a g e | 56
Figure 10 – Drag vs Velocity Graph.
Figure 11 – Revised Drag Prediction.
0
1
2
3
4
5
6
7
0 0.5 1 1.5 2
DragForce(N)
Velocity (ms-1)
Drag
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5 2
DragForce(N)
Velocity (ms-1)
Drag

P a g e | 57
Figure 12 - NASA Drage of a Sphere Graph.
Figure 13 Balloon Simulation Flow Pattern

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