Real World Design Challenge 2013
Submitted by the CCA Innovators
Team Member
*Team Leader*
Athena Kao
Alexis Amelotte
Devin Slaugenhaupt
Courtney Thurston
Ian Cavanaugh
Jacob Antonio
Rick Ciora
Ann Camp, Coach,
CCA Teacher
Mentor Name
Bob Camp
*National Mentor list*
Andrew Kacmar
*National Mentor list*
Joe Gruenenwald
Grade
12
Age
16
12
11
10
9
10
11
-
18
17
15
15
16
17
-
Address
Phone #
717-486-6066
Email
bcamp@vectron.com
815-404-0066
andrew.kacmar@gmail.com
100 Watts
Street
251 Edwards
Street
n/a
814-797-0240
Email
skieslily@gmail.com
shakespeareanlover1215@yahoo.com
awesomeflyer1@yahoo.com
thurscon@gmail.com
technomageapprentice@gmail.com
turtle3@windstream.net
rickciora@gmail.com
acamp@connectionsacademy.com
n/a
Matthew Del Buono
n/a
n/a
mpdelbuono@gmail.com
Grant Schneemann
n/a
n/a
grant.schneemann@faa.gov
Commonwealth Connections Academy
4050 Crums Mills Road
Harrisburg, PA 17112 (717) 245-0342
March 16, 2016
Team/Coach Validating Signatures: A Camp Coach
Before & after, coach, and progress surveys: all completed A Camp
Objective Function Value: 311100.52 dollar hours, Search Time: 20.493 minutes
1
Executive Summary
The goal for this challenge is to design an Unmanned Aircraft System (UAS) consisting
of one or more Unmanned Aircraft Vehicles (UAV). The UAS has to be capable of
searching for and identifying a child lost somewhere within the given two-mile radius
search area of Philmont Ranch.
In the conceptual design phase, most of our activities involved research. Team
members examined other unmanned aircrafts that are used in the real world to find
models efficient enough to provide us with an idea of the kind of UAV we would like to
develop. We then began to search for airfoils and constructed basic designs that
incorporated the concepts we had discovered.
For the preliminary design, we continued our research and began to flesh out the
designs with the provided worksheets and computer-aided design software. We first
identified the combination of sensor payloads that would provide a wide scanning
camera footprint. Next, we created the UAV model that could contain all the needed
components in the fuselage. We searched for airfoils that would provide high lift for our
aircraft, and then started to test each option to find which would be most suitable for our
design. It was here that we began to significantly consider the performance viability of
our aircraft. We did the performance analysis on each design, which determined our
selection of airfoil and wing configuration to move onto the detailed design phase.
Finally, we strove to perfect our chosen design in the detailed phase. Members worked
on the business planning and mission planning revolving around our detailed design,
while the design team focused on perfecting our UAV and conducting mission analysis
to ensure efficiency and minimize the objective function. We made the choice to reduce
cost and design an environmentally friendly, quiet UAS by changing our propulsion
system to the E-20 electric engine. Our two final designs are:
Wing Planform Area (in )
Wing MAC (in)
Wmtow (lbf)
Wbattery (lbf)
Alphastall (deg)
Maximum Lift Coefficient
Vmin at 8000 ft MSL(mph)
Engine Max Power (hp)
Power Loading (lbf/hp)
Propeller Efficiency
Static Thrust (lbf)
Design 1 (X1000 + X3000)
540
8.25
34.96
3.70
12.198
1.837
50.253
2.414
14.483
0.8
13
Design 2 (X3000 only)
540
8.25
34.41
3.70
12.198
1.837
49.856
2.414
14.255
0.8
13
Propulsion System Selection
E-20 engine
E-20 engine
Engine(motor) Efficiency
Wing Loading (lbf/ft2)
Distance to Clear 50ft
Obstacle
0.96
9.3227
189.995
0.96
9.176
184.492
2
2
Table of Contents
1 Team Engagement ..................................................................................................... 5
1.1 Team Formation and Project Operation ................................................................. 5
1.2 Acquiring and Engaging Mentors ........................................................................... 6
1.3 State the Project Goal ............................................................................................ 8
1.4 Tool Set-up / Learning / Validation ...................................................................... .10
1.5 Impact on STEM .................................................................................................. 12
2 Document the System Design ................................................................................. 13
2.1 Conceptual, Preliminary, and Detailed Design ..................................................... 13
2.1.1 Conceptual Design (Many Solution Candidates) ........................................... 13
2.1.2 Preliminary Design (Few Solution Candidates) ............................................. 22
2.1.3 Detailed Design (One Solution Candidate Refined) ....................................... 32
2.1.4 Describe lessons learned .............................................................................. 34
2.1.5 Describe project plan updates and modifications .......................................... 35
2.2 Detail the Aerodynamic Characterization ............................................................. 36
2.3 Selection of System Components ........................................................................ 41
2.3.1 Propulsion System ......................................................................................... 41
2.3.2 Sensor Payload Selection .............................................................................. 42
2.3.3 Ground Station Equipment Selection ............................................................. 43
2.3.4 Addition UAV/UAS Equipment ...................................................................... 45
2.4 Aircraft Geometric Details .................................................................................... 46
2.4.1 Wing Configuration ........................................................................................ 46
2.4.2 Tail Configuration........................................................................................... 47
2.4.3 Fuselage ........................................................................................................ 48
2.5 System and Operational Considerations .............................................................. 49
2.6 Component and Complete Flight Vehicle ............................................................. 50
2.7 Structural Analysis ............................................................................................... 50
2.8 Maneuver Analysis ............................................................................................... 51
2.9 CAD models ......................................................................................................... 52
3
3 Document the Mission Plan .................................................................................... 55
3.1 Search Pattern................................................................................................................. 55
3.2 Camera footprint………………………………………………………………………………….56
3.3 System Detection and Identification………………………………………….........................60
3.4 Example Mission ............................................................................................................. 63
3.5 Mission Time and Resource Requirements ......................................................... 67
Objective Function Calculation………………..………………...……………………68
4 Document the Business Case…………………………………………………………...69
Overview .................................................................................................................... 69
4.1 Identify targeted commercial applications ............................................................ 69
4.2 Amortized System Costs ...................................................................................... 69
4.2.1 Initial Costs .................................................................................................... 69
4.2.2 Direct Operational Cost per Mission .............................................................. 70
4.2.3 Amortization ................................................................................................... 71
4.3 Market Assessment.............................................................................................. 73
4.4 Cost / Benefit Analysis and Justification............................................................... 74
Works Cited ................................................................................................................. 77
4
1. Team Engagement
1.1 Team Formation and Project Operation
Our team is comprised of students with either strong backgrounds or enthusiastic
interest in science, technology, engineering, and math:

Athena is a senior, and has been a competitor in the Real World Design
Challenge for 4 years. She serves as the team leader.

Rick is a junior, and has participated in RWDC for 3 years. He enjoys working on
the design components of the challenge.

Devin is a junior and has been a member of our team for 2 years. He worked to
develop our business case.

Courtney is a sophomore and has also been on our team for 2 years. She
worked on documenting our progress throughout the year.

Jacob is a sophomore and also in his 2nd year of RWDC participation. He loves
working on the math problems involved with the challenge.

Ian is a freshman and this is his 1st year as a member of the team. Ian is
interested in design, and worked with Rick to develop several preliminary UAV
designs.

Alexis is a senior and this was also her 1st year in the Real World Design
Challenge. She enjoys reading and writing.
We attend an online virtual charter school, so we only met virtually in an Adobe Connect
“LiveLesson” room to collaborate every week. Although we initially viewed this situation
as a setback and potential detriment, we feel that it has actually become an asset to
strengthening our communication skills. Engineering is a field where crystal clear,
specific, and frequent communication must be maintained to ensure the development
and optimization of a project, so we consider ourselves fortunate to have learned these
skills through the Real World Design Challenge. We needed to become extra aware of
each member’s progress in order to ensure the completion of each component of our
design.
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This did make it very difficult to find a meeting time that suited all of our schedules, so
we met on Friday evenings when everyone could attend. As the challenge progressed,
we added meetings on Sunday afternoons and Tuesday evenings to ensure that we
allocated enough time to discuss what tasks we had remaining on our itinerary. We also
kept in contact via the internal webmail system at our school. Over webmail or at
meetings, we would talk about tasks that needed to be completed by the next week and
any new developments in our progress.
In the conceptual design phase, most of our tasks involved research. For the
preliminary design, roles expanded out into continued research, designs, and some
testing. Finally, in the design phase, members worked on the business case and
mission planning revolving around our detailed design, while the design team focused
on perfecting our UAV and conducted testing to guarantee efficiency and minimize the
objective function. We found that our method of goal setting was very effective for our
environment as we were able to share our documents and collaborate by utilizing
Windchill, Dropbox, and a feature in our “LiveLesson” room known as the “Sharepod”.
1.2 Acquiring and Engaging Mentors
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We as a team knew that it would be best if we chose a mentor that was technical,
professional, and could assist us in areas where we weren’t inherently as strong. We
brainstormed some traits that we needed to find in our mentor figure. We wanted them
to be knowledgeable with airplanes and their designs, physics, and aerodynamics.
Also, because of the team’s cyber environment, it was important for us to find a mentor
that would adapt to this kind of environment to help the team by dedicating time to
communicate with the team virtually. If we chose a mentor that would not be willing to
dedicate his or her time to help, the effort we put in to finding great mentors would be
futile. Because of this, we chose mentors that were willing to offer their free time to
mentor us, and who would be able to communicate frequently with the team throughout
the duration of the challenge.
We felt that it was very important to have somebody who would commit to working with
us through the completion of the challenge without diminishing contact with us. With
these criteria in mind, we chose two mentors off of the National List and obtained two
further mentors. We retained Mr. Camp, who was a great asset to our team last year,
because of his engineering experiences and knowledge of software and cost analysis.
Another mentor of ours, Andrew Kacmar, is an aeronautical engineer who also happens
to be a nephew of our coach. We were happy to take him on as a mentor given his
expertise and experience in the field. We also approached three further mentors, Dr.
Gruenwald and Matthew Del Buono, who some of our team members are fortunate
enough to call friends. Dr. Gruenwald has extensive knowledge of business and
marketing, and we felt as though these skills would assist us greatly as we developed
our business case. Matt Del Buono works for Boeing Insitu, one of the world’s leading
UAV manufacturers, as a software engineer for embedded systems of unmanned
aircraft, specializing in flight automations, fault detection isolation & recovery (FDIR),
and built-in tests (BIT). Finally, we gained FAA employee Grant Schneemann as a
mentor as well.
Due to our unusual method of solely virtual collaboration throughout the project, we felt
that having many mentors would complicate communication. Therefore, we found five
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mentors that specialized in areas where we needed assistance and were also willing to
devote their time by means of online contact. After discussion, the team decided to
commit to just these five mentors. In addition to these mentors, we are also fortunate to
have an extremely knowledgeable and skilled coach, who we consider to be just as
much of an asset and resource to us as a mentor.
We made contact with these mentors as soon as possible, speaking with Mr. Camp and
Mr. Kacmar before the challenge release in September on the first day of school and
again after its release in late October, when we also began contacting Mr. Del Buono
and Mr. Schneemann. We engaged Dr. Gruenwald as soon as we began working on
the business case. As a team, we maintained frequent contact and interacted with our
mentors about 2 or 3 times per week. Whether it was through webmail, email,
LiveLessons, or through our coach, we communicated with each of them regularly and
maintained contact throughout the challenge. As work on the challenge became more
involved, contacts with mentors increased substantially throughout the week.
1.3 State the Project Goal
Objective Function
Part of the state and national project goal was to minimize the objective function. The
objective function combines the technical and business aspects of the challenge to
analyze the efficiency of the design solution.
In this formula:
T represents the total time (in hours) required to find the missing child for the
example mission, given the worst-case scenario that the search area must be
completely scanned by the UAS (using one or more UAVs to achieve total coverage).
During the search, the UAS completes an “object detection” event followed by an
“object confirmation” event and a successful confirmation of the child. One of each of
the three false-matches must occur in each zone. The successful confirmation of the
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child must occur in Zone 3. Total time also includes the time to complete any required
refueling, but not the time required to return to base and land. The 50-yard radius
“search center” is the operations area and does not need to be searched by the UAS.
C is the “fully loaded” cost of building the system and completing the above
mission fifty times. The “fully loaded” cost is initial cost + 50 * operational time * cost per
hour.
Design Variables

Wing and tail geometry including area, aspect ratio, taper ratio, sweep, dihedral,
twist, airfoil selection.

Propulsion system selection.

Fuselage layout, including location of sensor payload(s), transmitter(s), fuel tank,
battery, autopilot, and other electronics.

Structural design and material selection.

Sensor payload and telemetry selection.

UAV(s) search pattern.

Mission computer selection and other ground-based components.

Number of UAVs designed.
These design variables contribute to the time and/or cost components of the objective
function, and the specifications of each must not violate the constraints of the challenge
that is detailed below.
Aircraft Constraints

The sUAS will comply with RWDC FAA Technical Readiness Guidelines

A maximum gross takeoff weight (including fuel) of not more than 55 pounds per
UAV. There is no minimum weight requirement.

Antennas on-board the UAV must be separated by a minimum of 18 inches to
avoid destructive interference.
9

Search operations are conducted at an altitude of 150 - 1,000 feet above local
ground level (assume that ground level is equal to the ground station altitude,
8,000 ft MSL).

Our choice of flight control hardware, sensor selection, video datalinks and
associated ground hardware is limited to cataloged items to be provided. A
catalog of propulsion options is provided, but substitutions are allowed.

The aircraft must be able to take-off and clear a 50 foot obstacle within 300 linear
feet from the starting position.
1.4 Tool Set-up / Learning / Validation
Set-up
In previous years, our team members encountered many issues with the set-up of tools.
Some members were entirely unable to download the tools off the PTC website, so CDs
with the programs on them were sent out to the members affected. This took quite a
while in some cases because our team is spread out across Pennsylvania. To resolve
the issue for this year, we decided to image our machines ahead of time so that the
executable files needed for set-up would already be located on the laptops. All we had
to do from there was enter product keys where applicable. This worked very well for our
team and resolved most our issues with Mechanica, FloEFD, and Creo Elements. We
did still have to download MathCad from the internet, but we did not encounter any
problems with setting that up. We did encounter issues with signing up for and
participating in Windchill, as documented in “Validation.”
Learning
We met in our LiveLesson room every morning for a week before the State challenge
was issued to learn how to use the tools. Our coach guided us through the process of
using the CAD software to create a variety of objects including cakes, brownies,
doughnuts, and a rough sketch of a fuselage. We also experimented with the software
in our free time to practice creating objects of our choice. This helped all team
members, returning and new, to become accustomed to Creo Elements. To learn how
to use MathCad, Mechanica, OpenVSP, and FloEFD, our team attended the Webinar
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sessions. Team members became well acquainted with the software required for the
challenge during the State competition, and our skills only improved further throughout
the National challenge.
Validation
1. Windchill
One of our members held “pending” status in Windchill for several months, but this did
not prohibit him from uploading files or participating in discussions. Another member
was initially unable to accept the invitation; they were able to join Windchill, but their
account was not automatically connected to our project. After persistent login attempts
by this user from the invitation link, the issue appeared to have resolved itself.
2. MathCad
We noticed that if the airfoil file was changed to a different name, even if the change
was made in the wing definition sheet, MathCad would crash when the performance
analysis sheet was run. If only the wing definition sheet is running, this conflict does not
occur.
3. FloEFD
We experienced issues with FloEFD for Creo where the program was producing invalid
calculations when compared with the published wind-tunnel experiment results. We
contacted support regarding this issue and was informed there was an issue in FloEFD,
so we decided to use the result from JavaFoil for our airfoil.
4. JavaFoil
We used JavaFoil as a substitution for FloEFD, which was not working for our team. We
used this tool to search for high-life airfoils in addition to utilizing it to perform our
aerodynamic characterization testing.
5. Mechanica
We did not experience any issues with this software tool.
6. Creo Elements
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One member was not able to use the PIM Installer to validate his license for Creo during
the National Challenge. We contacted PTC Support regarding this issue.
1.5 Impact on STEM
Most team members had prior interest in science, technology, engineering, or math, but
participating in the Real World Design Challenge has deepened and broadened that
interest for all members. Many of us signed up for the team wanting to fulfill one specific
role, but we came to appreciate many different aspects of the challenge instead. For
example, a member who was very interested in dealing with the mathematical
components of the challenge realized that he also had an interest in designing and
engineering. Team members come from a variety of backgrounds; some from families
of engineers while others are the only member of their family interested in STEM. The
Real World Design Challenge has had a profound impact on the potential career paths
of our team members; all of our team members are considering rigorous careers with
applications to STEM fields:

Athena would like to become a software engineer.

Rick is going to pursue chemical engineering.

Devin is going to attend Westpoint to become a Navy SEAL.

Courtney would like to become a systems engineer.

Jacob wants to work as an orthopedic surgeon.

Ian would like to eventually work in some engineering discipline.

Alexis wants to become a pediatric oncologist.
Our team is comprised of very self-motivated, determined, and ambitious students.
These qualities have served us well when applied to school activities, and our
experience as participants in the Real World Design Challenge has significantly
contributed to our drive and determination. All of our members are involved in Advanced
Placement (AP) courses of some sort, many revolving around math and science
courses. Other members are eager to pursue these rigorous courses in fields of
particular interest to them, and are currently involved in a plethora of honors and
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advanced courses across all subjects. We strive to challenge ourselves and become
well-rounded individuals by exploring a variety of difficult curricula, believing that those
who excel in STEM are apt at balancing their roles as both visionaries and technicians.
Unfortunately, engineering and related disciplines are often perceived as cold and
calculating. While calculations are certainly integral to project development, STEM fields
are in no way cold. We consider ourselves fortunate to have now had the experience of
working on such a large scale project, where it is truly beautiful to watch all the
components of the project come together to form a coherent and successful system.
A direct result of participation as competitors in the Real World Design Challenge, team
members have been inspired to become involved in other extracurricular activities
related to science, technology, engineering, math, and critical thinking globally. These
include First Lego League, First Robotics, CyberPatriot, Odyssey of the Mind, and the
Stock Market Game. Several members also regularly dedicate their free time to learning
programming languages, graphical design, and computer animation.
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2. Document the System Design
2.1 Conceptual, Preliminary, and Detailed Design
2.1.1 Conceptual Design (Many Solution Candidates)
Our first step in the conceptual design phase was to research what the mission would
have to complete and what our constraints would be. To gain this understanding, we
thoroughly read the Challenge Statement, FAA Technical Readiness Criteria, FAA rules
for public sUAS Operation in the National Airspace System, and the Mission Planning
Flight Guidelines. We also watched the Challenge Introduction webinar to gain an
overview of expectations and work flow for the National Challenge.
We approached the second step of the conceptual design phase by conducting
research. We examined the definition of an unmanned aircraft, typical range and fuel
types, sources of power, propulsion and retrieval systems, and other critical design
factors. We looked specifically for aircraft that would fit within our design constraints due
to the fact that our design may only weigh up to 55 pounds. Examining models that are
on the market today helped us to understand what market expectations for UAVs exist,
which assisted us in developing a competitive product. We learned quite a lot from our
research, and these lessons greatly impacted the development of our solution at the
State level, which we built upon and refined for the National Challenge. However, we
made sure we did not rush directly into a detailed design based off of our initial
observations. This resolution allowed us to make modifications to our project with ease
as new announcements and changes to the challenge occurred.
Our initial conceptual design was influenced greatly by the following research into
existing concepts and models on the market, which taught us much about what a welldesigned UAV should be able to accomplish with a specific purpose, budget, and other
resources in mind;
14
We began our research of existing models by looking at the RQ-21 Integrator that is
currently being developed by Insitu. One of our mentors, Matthew Del Buono, works on
this project as a software engineer, so we were fortunate to have a resource very
familiar with the development of unmanned aircraft for reconnaissance missions. We
discovered that this aircraft features a very interesting propulsion and retrieval system,
one that propels the aircraft very effectively and is able to automatically recover it. The
autonomy provided by these measures help to reduce over mission duration for the
human operators, thus making missions more cost effective. This craft also features
excellent payload capacity. While the RQ-21 weighs far more than 55 pounds, what we
learned from it assisted us later on when we had to make a sensor payload and
propulsion system selection.
15
We also came across the TU-150, pictured above, when doing our research. This UAV
is notable for combining the capabilities of a helicopter with a fixed-wing aircraft. As a
result of this design, the TU-150 is capable of completing a variety of missions across
many different environments. From this, we recognized that we wanted to design an
aircraft that was dynamic enough to successfully complete missions in plain, forested,
and mountainous environments such as those at Philmont Ranch, per the challenge.
16
One idea we had entertained as a team for quite some time has been that of including
solar panels on our design in hopes of making it more environmentally friendly. We
recognized that something along the lines of the UAV pictured above, the X-48C, would
provide optimal surface area to implement this idea. However, we later discovered that
the surface area required would make the aircraft much too heavy and the power
required to support the aircraft could be easily provided by batteries and would exceed
the power generated by the solar panels.
17
Aircrafts that have implemented the solar panel concept, such as the NASA Helios
depicted above, are unable to complete tactical reconnaissance missions such as the
one the challenge prompt requires of us; another reason why we ruled this prospect out
in the conceptual design phase.
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The team also considered developing a UAS, an unmanned system consisting of
several UAVs, rather than simply a single UAV. We considered using a number of
microcosm UAVs to achieve this, and began to do research on what developing a micro
aircraft entails. We discovered that Aerovironment is a leader in this field, and that they
have several tiers of aircraft that weigh just fractions of a pound. These four tiers are
referred to as the “Wasp” generation (pictured first above), and some are even
manufactured to resemble flying insects or birds. Other products are slightly heavier,
such as the RQ-11 Raven pictured second. While we found the micro UAV concept
fascinating, we quickly realized that such small aircrafts would never be able to support
the sensor payloads we would need to complete our mission.
The team focused on developing a well-rounded conceptual idea of what we would like
to design during this phase. Through our research, we were able to compile a list of
different existing aircrafts and learn from those currently out on the market. We
narrowed down these ideas into several potential solutions that we carried forth into the
preliminary design phase to flesh out further. Once we had a good concept of what we
wanted to accomplish, we began to examine potential airfoils in search of one that
would provide excellent lift for our UAV.
Keeping in mind what we learned from this research, our next step was to read the
Sensor Payload Selection Guidelines document and to select a few sensor payloads
19
from the Sensor Payload Catalog in order to analyze the pros and cons of using each in
our conceptualized design.
X1000
normal view angle
(degrees)
zoom
roll / pitch (deg)
Slew (deg/sec)
cost
X2000
X3000
X4000
X5000
40
80
55
64
60
1
30
50
$8,000.00
2
85
100
$25,000.00
10
80
200
$38,000.00
16
85
200
$42,000.00
30
70
250
$75,000.00
tan view angle/2
max alt for conf – wide
(feet)
tan zoom view angle /2
max alt for conf – zoom
(ft)
0.364
302
0.839
131
0.521
211
0.625
176
0.577
190
0.364
302
0.364
302
0.048
2,285
0.035
3,143
0.017
6,288
opt alt for scan – wide (ft)
max alt for scan – zoom
(ft)
1,407
1,407
610
1,407
984
10,659
819
14,662
887
29,332
The above table demonstrates how we analyzed the sensor payloads provided as
options in the catalog. We decided that the X3000 was the most efficient single payload,
and we began to consider the idea of utilizing multiple sensor payloads on our design.
In our State solution, we utilized three X1000s for the purpose of detection and a single
X3000 for identifying the object detected as the child we were searching for. We wanted
to minimize our objective function further for the National Challenge, so this would
involve decreasing cost as well as the duration of the reconnaissance mission.
There are two main cost penalties associated with utilizing multiple sensor payloads on
one UAV. The first of these is the initial purchase price of the sensor itself, and the
second is the cost of required additional UAV components per payload. For example,
the Configurator spreadsheet requires the purchase of one Video Datalink UAV
Transmitter ($200) and one Onboard Video Receiver ($600) per sensor payload. If we
intended to use multiple sensor payloads on a single UAV, this would get extremely
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expensive very quickly. As a result, we recognized that we needed to use as few
sensors as possible while still retaining optimal mission efficiency.
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Our fourth step was to consider how we would like our conceptualized design to
ultimately conduct missions. We reviewed the Mission Flight Planning guidelines and
began to think about potential search patterns and sensor payload distributions across
the aircraft to maximize camera footprint and thus minimize mission duration. This
pattern would depend greatly on how many UAVs we decided to use, however, so we
resolved to develop a spreadsheet to calculate the objective function output of using a
number of UAVs (1-8) with a variety of sensor combinations in the three portions of the
given search area (tall trees, short trees, no trees). This allowed us to consider the
relative objective function and total cost over 50 missions, as yielded by these
combinations.
As a result of these considerations, we were left with a variety of potential candidate
solutions. We left the conceptual design phase debating whether or not to use one or
multiple sensor payloads and, if the latter option, how many. We quickly recognized that
the inclusion of multiple payloads might require us to utilize a more powerful and
heavier propulsion system, while fewer sensors could be supported by a less powerful
propulsion system.
2.1.2 Preliminary Design (Few Solution Candidates)
To prepare for the preliminary design phase, we watched the Aviation Design webinar
as a team and began to utilize the FY13 Aviation National Challenge Toolset. Our first
step in the preliminary design phase was to implement what we had learned regarding
each sensor payload into our considerations. We recognized that our sensor selection
would impact the rest of the design, including propulsion system selection and mission
planning, so selecting our sensors was our first step.
All sensor models feature a resolution of 640x480. As it requires 4 pixels for object
detection, we noticed that the wider view angles offered in the more expensive models
actually require the UAV to fly at a lower altitude, which yields a smaller camera
footprint. Our first priority when choosing the sensor payload was to obtain as much
camera footprint as possible within the maximum detection distance circle. For the State
22
challenge, we recognized that combining three X1000 sensor payloads would be a cost
effective way to dramatically increase the camera footprint. For identifying the object -in the issued challenge, a small child -- the X3000 was adequate because of its 10x
telescopic lens. The idea was to let the three X1000 tilted up so they could detect the
object forward. As the object was detected, the X3000 then identified the object.
However, we wanted to optimize our design for the National challenge. This would
involve minimizing both mission time and cost. From the analysis of camera
configuration, we realized that using multiple sensor payloads over the tall-tree area
does not help reduce the search time. To drastically minimize mission duration, we
recognized that we would have to use more than one UAV in our unmanned aircraft
system (UAS). With this in mind, we decided that we could not use the same sensor
payload combinations as we had previously, as that payload combination works well
only over the no-tree area and would be too expensive to yield a low objective function.
Though we had a general idea that using more UAVs might yield a lower value of
objective function, we needed a quantitative analysis to find how many UAVs could
theoretically yield the best result.
The first quantitative analysis we performed was to estimate the search time for a single
UAV in all three zones. The search time is determined by two factors; air speed and
scanning width. Due to the required 0.5 seconds for detecting an object and 5 seconds
for identifying an object, the air speed and scanning width are not independent factors.
As illustrated by the graph below, when the width of camera footprint is larger than the
diameter of the maximum detection circle, the maximum scanning with Ws is related to
air speed v and the diameter D of the maximum detection circle as:
23
Detection and Identification
𝑤𝑠 = √𝐷2 − (5.5𝑣)2
Detection Only
𝑤𝑠 = √𝐷2 − (0.5𝑣)2
With a formula to calculate the scanning width, we were able to find the best scanning
widths for the chosen sensor payloads in each zone. After carrying out some
calculations, we realized that reasonable scanning width can be obtained for detection
and identification preformed in sequence only at a no-tree zone. Over the short-tree
zone and tall-tree zone, (especially over the tall-tree zone) scanning width for detection
and identification preformed in sequence is rather small and thus increases search time
dramatically. To achieve an acceptable search time, we decided to use the turn-around
identification strategy over the short-tree and tall-tree zones.
Over the No-Tree zone, adding sensor payload can effectively widen the scanning
width. We decided to combine X1000 and X3000 sensor payloads for searching the NoTree Zone. At the altitude 680 AGL, this sensor payloads combination has a scanning
width of 1182 ft.
Over the short-tree zone, we figured out the best navigation altitude is 794 ft AGL where
the Max Detection Distance Circle for X1000 overlaps with the circle with short-tree
zone Line of Sight. For the combined sensor payloads of X1000 and X3000, at 80 mph,
it has a scanning width of 914 ft if the turn-around identification strategy is used.
We also considered the case that only X3000 sensor payload would be used. With the
field of view of X3000 adjusted to 41.5/31.125 degrees such that the camera footprint
24
rectangle touch the Max Detection Distance Circle, the sensor payload could have a
scanning width of 624 ft.
Over the tall tree area, the search is severely limited by the 15-degree Line of Sight. We
recognized that it is preferable to have a sensor payload with narrower field of view and
navigate at a higher altitude. With X3000 sensor payload zooming to 30/22.5 degrees
field of view from the altitude of 1000 AGL, the effective scanning width at 80 mph is
about 532 ft if the turn-around identification strategy is used.
Knowing the scanning width, the next step was to estimate the search time. Though
there is a Mission Planning Excel Worksheet provided in the Design Kit, it is not suitable
for estimation as it requires almost all the design variables of the whole UAS which we
wouldn’t have at the preliminary design phase. We decided to develop our own Excel
Worksheet to estimate the search time in each zone. Besides the scanning width and
air speed, the search pattern is another factor that has great impact on the search time.
For example, comparing the two search pattern illustrated below, the Ray-SearchPattern will need twice the time needed by the Shell-Search-Pattern because there is
overlap in the center when searching with the Ray-Search-Pattern.
Ray-Search-Pattern
Shell-Search-Pattern
25
With the Shell-Search-Pattern, the total travel distance can be estimated (time needed
for turn-around is neglected) as
𝑁
∑ 𝜃 ∙ (𝑟 +
𝑖=0
𝑊
+ 𝑖 ∙ 𝑊)
2
, where N is the integer no less than (R-r)/W. The search time can then be calculated by
dividing the total travel distance by the air speed.
Shown below is a screen capture of a spreadsheet we developed for estimating the
search time with various air speeds.
Once we were able to estimate the search time, we started to consider the effect to the
objective function when adding more UAVs. Shown below is a screen capture of a
spreadsheet that we developed to calculate initial cost, operational cost per hour,
search time (hrs), total operation time (search time + 8 hours, rounded to the nearest
hour), total cost of 50 missions, and the objective function output. We then charted the
relative objective function vs. number of UAVs to verify the worthiness to employ more
UAVs:
26
Relative Objective Function Value
1.2
1
0.8
0.6
0.4
0.2
0
0
2
4
6
8
10
Number of UAVs
The chart demonstrated to us that the more UAVs we used, generally, the lower the
objective function was despite the increase in cost. However, the effect of lowering
objective function diminished quickly when there were more than four UAVs used.
Furthermore, we noticed that the Mission Planning spreadsheets provided in the
National Challenge Toolset only allowed for up to 4 UAVs to be used in the UAS. Still,
this portion of our preliminary design was meant to determine how many UAVs we
should use to reduce flight time, and found that the answer was basically as many as
we can feasibly support.
Previously, we had utilized three X1000s for detection and one X3000 for identification.
With the information above in mind, we examined the sensor payload specs to see if
there might be a way that the X3000 could fulfill both roles, as the X1000 certainly is not
adequate for both. The X3000 sensor payload features continuous zoom between 5 and
55 degrees, and we realized that we would have to zoom out to approximately 40
degrees to detect the object in the two zones with limited line of sight.
27
For searching the tall trees zone, we decided to use only this sensor payload on two out
of our four UAVs because the addition of an X1000 payload would not increase the
scanning width to reduce the search time, but adding the cost of that sensor.
We had to ensure that the aircraft we would be designing was large enough to support
this payload, so our next step was to estimate gross weight of the combined payload,
which were 2.6 pounds for the design with two sensors (X1000 & X3000) and 2.1 for the
one utilizing only the X3000. This left us quite a lot of room under the 55 pounds
maximum weight limit given in the issued design constraints, giving us the ability to later
choose from many potential propulsion systems.
We knew that we would need a high lift airfoil for the low stall speed and smaller wing
planform area. Unfortunately, a high-lift airfoil is generally accompanied by high drag,
which can be a concern when it comes to fuel efficiency. Even despite this tradeoff, the
constraint outlined above reminded us that we absolutely would have to find an airfoil
with high lift. We found high-lift, low Reynolds number airfoils by searching the UIUC
database and the three volumes of Summary of Low-Speed Airfoil Data. We then tested
the selected airfoils with JavaFoil to find their aerodynamic characteristics. We
evaluated the following airfoils with a preliminary run of the provided MathCAD
Performance worksheet:
28

S1223

S2027
29

SD7037

SD7062
We found that S1223 was the airfoil with the highest Cl max (>2) when the data of
coefficient of lift was observed visually. However, the Performance MathCAD worksheet
fit the data with a curve having a much lower maximum. The airfoil SD7062 has a Clmax
above 1.8 which was an adequate fit for the needed lift. The following charts illustrated
two important aerodynamic characteristics of SD7062:
30
Lift Coefficient vs. Drag Coefficient
Lift Coefficient vs. Angle of Attack
In fact, we found that by using the SD7062, the wing planform area could be reduced to
600 in2.
31
At this point, we input our sensor payload, propulsion system, and additional component
selections into the Configurator Excel worksheet to examine costs. Reviewing the
following output values for our two designs, helped us to evaluate the efficiency of our
aircraft:
Design A
Design B
Fuel Tank
$12.50
$12.50
Batteries
$35
$35
Electrical Wiring
$5
$5
Wing
$409.45
$409.45
Horizontal Tail
$76.54
$76.54
Vertical Tail
$27.09
$27.09
Fuselage
$1164.25
$1164.25
Sensor Payload(s)
$46000
$38000
Propulsion System
$545
$545
Video Datalink UAV
$400
$200
$300
$300
Flight Control System
$2000
$2000
Total cost when without
$50,974.82
$42,774.82
Petro Fuel
$5
$5
Total cost with fuel
$50,979.82
$42,779.82
Transmitter
Command Datalink UAV
Transceiver
fuel
We continued testing by running a preliminary performance analysis to ensure the
viability of our design up to that point. We reviewed the Mission Flight Planning
Guidelines and then tested the endurance and total search time of our UAV, two very
crucial factors to our mission plan. We evaluated our objective function value output, as
given in the Mission Planning Excel sheet and Mission Analysis MathCAD worksheet.
2.1.3 Detailed Design (One Candidate Solution Refined)
Our goal for the detailed design phase was refine and optimize the design that emerged
successfully from the preliminary design phase. To accomplish this, we first watched the
32
Aviation Design Process (Part II) webinar as a team to gain perspective. Then, we had
to identify our selections for all the components required to assemble the UAV. These
included our sensor payloads, video datalink transceiver/video recorder, control datalink
transceiver, associated antennae, propulsion system, required batteries for all
electronics, and the fuel tank. We ensured these components were input into the
Configurator successfully, as these cost figures contributed significantly to the final
revisions of our business plan, which had to incorporate—among other components—a
working market assessment and amortization of incurred costs, including initial and
iterative operation throughout the 50 missions.
After identifying all selections, we assembled all the Creo Part files for the components
and sized our fuselage accordingly to handle this payload. We then placed all
components inside of the fuselage. When these tasks had been completed, we used the
Configurator spreadsheet to produce weight and balance analysis for our UAV. We then
used JavaFoil to analyze our airfoil selection, SD7062, and input the results of this airfoil
analysis into the provided AeroData document, which yielded the aerodynamic
coefficient data. We also performed a zero-lift drag estimate analysis for the UAV using
the Performance Mathcad worksheet. We found that we could reduce the planform area
from 600 in2 to 540 in2, so we did so to reduce weight and drag, effectively increasing
efficiency.
One constraint given in the challenge was that the UAV had to be capable of flying over
a 50 feet high obstacle within 300 feet after take-off. This means that we should have a
reasonably powerful propulsion system as well as a high-lift airfoil. We had previously
used the GL25 engine to serve this purpose as well as minimize mission duration
overall. At the end of preliminary design, we had an UAS consisting of four UAVs
powered by GL25 propulsion system that could finish search under 21 minutes. This
relative short search time lured us to consider the possibility of utilizing electric
propulsion system which could make the fuselage more streamlined and possibly
reduced the weight of the UAV. We found E-20 from the provided list of electric
propulsion systems which had equivalent power output as GL25 and a much smaller
33
size/weight. We went ahead to redesign the fuselage and found the reduced weight in
fuselage can offset the weight of added batteries.
With the new design, all four of the UAVs are under 35 lbs and have more 30% fuel
remained after the mission. The specifications of our UAVs, which incorporate two
designs, are:
2
Wing Planform Area (in )
Wing MAC (in)
Wmtow (lbf)
Wbattery (lbf)
Alphastall (deg)
Maximum Lift Coefficient
Vmin at 8000 ft MSL(mph)
Engine Max Power (hp)
Power Loading (lbf/hp)
Propeller Efficiency
Static Thrust (lbf)
Engine(motor) Efficiency
Wing Loading (lbf/ft2)
Distance to Clear 50ft
Obstacle
Design 1 (X1000 + X3000)
540
8.25
34.96
3.70
12.198
1.837
50.253
2.414
14.483
0.8
13
0.96
9.3227
189.995
Design 2 (X3000 only)
540
8.25
34.41
3.70
12.198
1.837
49.856
2.414
14.255
0.8
13
0.96
9.176
184.492
2.1.4 Describe Lessons Learned
We learned quite a lot about the engineering design process in each design phase. In
the conceptual design phase, most of our activities involved research. Team members
examined other unmanned aircrafts that are used in the real world to find models
efficient enough to provide us with an idea of the kind of UAV we would like to develop.
Based off of what we had learned, we then began to search for airfoils and constructed
basic designs that incorporated the concepts we had discovered.
For the preliminary design, we continued our research and began learning how to flesh
out the designs with the provided worksheets and computer-aided design software. We
first identified the most cost-effective combination of sensor payloads that would ensure
an adequate camera while and minimizing cost and time. Next, we created the UAV
model that could contain all the needed components in the fuselage. We searched for
34
airfoils that would provide high lift for our aircraft, and then started to test each option to
find which would be most suitable for our design. It was here that we began to
significantly consider the performance viability of our aircraft. We learned quite a lot
about cost benefit analyses, as we had to perform a performance analysis on each
design, which determined our selection of airfoil and the wing configuration to move
onto the detailed design phase.
Finally, we attempted to perfect our chosen design in the detailed phase. Members
were more experienced at this point, and used our learned cost-benefit analysis and
research skills to work on the business case and mission planning revolving around our
detailed design. We also made a final propulsion system selection and focused on
perfecting our UAV and conducting mission analysis to ensure efficiency by minimizing
our objective function as much as possible.
2.1.5 Describe project plan updates and modifications
We took an agile development approach with our project, something that we had
learned about from one of our mentors who works as a software engineer and is also a
Scrum practitioner. Scrum is an iterative and incremental agile development framework
for managing product development. It focuses on "a flexible, holistic product
development strategy where a development team works as a unit to reach a common
goal" as opposed to a "traditional, sequential approach”. Essentially, we resolved issues
as they arose through the process of iterative and incremental development throughout
the project. This ensured that time we used working on the project was very effective as
we would not have to face loads of problems at the end of the design phase because
we had addressed these issues and modified our design as they arose. This gave us
flexibility when updates to the RWDC Aviation Design Kit or Worksheets were modified,
for example.
Our environment as students of an online, virtual school helped us significantly. We are
all accustomed to having to take the responsibility of meeting deadlines, and tools that
we need to do that are at our fingertips. Our Adobe Connect room provides us with a
35
modular version of Dropbox, called the “Sharepod”, where we could share files quickly
with each other. We could also text and voice chat in the room. Engineering requires
that all parties involved communicate well and specifically to begin with, but because we
did not meet physically even once to complete this challenge, we had to communicate
exceptionally well with each other and our unique situation as online collaborators only
helped to foster these skills. Each week during all design phases, and the preliminary
phase in particular, we set goals to accomplish for the next week. By keeping up with
these and organizing our documents using Dropbox, the Sharepod, and Windchill, we
were able to successfully complete each benchmark on time.
2.2 Detail the Aerodynamic Characterization
We used the provided Performance MathCAD worksheet to analyze our aerodynamic
characterization. Our geometric input into the sheet was our wing planform area, wing
aspect ratio, UAV wetted area (without wing), and wing MAC incidence to fuse axis. To
obtain the aerodynamic data needed for the Performance MathCAD worksheet, we first
preformed CFD analysis using FloEFD as demonstrated in the webinar video.
Unfortunately, the result obtained deviated significantly from both the wing-tunnel
experiment result as well as JavaFoil. We contacted RWDC support and were informed
that there was issue in FloEFD and we should proceed with the calculated result from
JavaFoil. The input values of our aerodynamic data for SD7062 airfoil are as follows:
36
This table displays the coefficient of lift, drag, and moment experienced by each
degree section of our wing.
The analysis results for our aerodynamic characterization are:
37
Cl
Having a high coefficient of lift was important to us to ensure that we would meet
the constraint that demands we be able to clear an obstacle 50 feet tall within 300
feet after takeoff, and is also necessary to ensure adequate altitude over each zone.
Essentially, the higher the coefficient of lift the better the wing is working.
Cd
The more drag, the more thrust is required to keep the UAV flying. Our coefficient of
drag was important because we wanted to minimize drag on our aircraft as much as
possible, which is why we were excited to reduce our planform area from 600in2 to
540in2.
38
Cm
Coefficient of moment describes our angle of attack, which we wanted to optimize
for our design to ensure minimal drag.
Our calculations to find our minimum airspeed at maximum takeoff weight are as
follows:
The minimum velocity is only slightly over 50 mph which empowers our UAVs to make
tight turn when needed.
39
Wing loading is the aircraft weight per unit of wing area. A high wing loading equates to
a high minimum speed. Our result as output by the provided Performance Mathcad
sheet:
Aircraft must be trimmed for flight by resolving unbalanced pitching moments by
balancing tail loads. This is crucial because a design that is not well balanced is
penalized by a high trim drag from the horizontal tail. The following is our review of
balancing tail loads impact on lift and drag:
The most efficient airspeed can be found at the maximum velocity times lift divided by
drag. We changed the altitude, flight weight, center of gravity, and airspeed in the
provided Performance Mathcad sheet to analyze how the best cruise speed varies for
our configuration versus altitude. We input the following details…

Velocity: 75 mph

Altitude: 8000 ft

Flight weight: 35 lbf

Flight Center of Gravity: 23.124 in
…this allowed us to find our most efficient airspeed:
One constraint issued as a part of the National challenge was that the UAV designs
must be able to clear a 50 foot tall obstacle within 300 feet after takeoff. To verify that
40
our two designs could accomplish this, we analyzed the following result from the
provided Performance Mathcad sheet:
This result confirmed that we had met the requirement of that constraint.
Next, we ran a cycled, iterative set of loiter speed calculations until we were able to find
the ground speed that maximizes lift over drag:
This speed, 77 mph, is close to the maximum allowed speed 80 mph which our UAVs
will be flying at most of time.
2.3 Selection of System Components
2.3.1 Propulsion System
We chose the E-20 electric engine as our propulsion system during the detailed design
phase. We felt that this selection as most appropriate as the power output of 1800 watts
generates enough thrust to clear an obstacle 50 feet high within 300 feet after takeoff as
well as maintain an appropriate speed and altitude over each zone of the Philmont
ranch. As we have learned in previous years as competitors in the Real World Design
Challenge, electric engines generally tend to be slower than diesel alternatives. With
this in mind, we had previously used the GL25 engine. With the introduction of the three
distinct zones (tall trees, short trees, no trees), however, we had to change our search
pattern to ensure that all of our UAVs had adequate time to detect and identify objects
throughout the zones in search of the child. For half of our UAVs, this meant detecting
and immediately zooming in to identify, while for the other half it meant detecting an
object and then identifying it after turning around. Our search pattern is illustrated below
for your reference:
41
Given the line of sight limitations—30 degrees from vertical in the short trees zone and
15 degrees from vertical in the tall trees zones—in addition to our sensor payload
selection (2/4 designs use one X3000 for both detection and identification while the
other 2/4 designs use one X1000 for detection and one X3000 for identification), we
were more concerned with overall flight efficiency over just speed. For us, this meant
ensuring that our sensor payload selection and flight path were optimal, as well as
ensuring that there was minimal—if any at all—camera overlap near the edges of the
zones. Because speed itself was no longer our first priority, the E-20 propulsion system
suited our needs best.
2.3.2 Sensor Payload Selection
Initially, we considered using three X1000s for detection and one X3000 for
identification. Our thought with this configuration was that that using the three detection
payloads would help to maximize our camera footprint and thus speed up the mission.
However, because each sensor is so expensive, we examined the sensor payload
specs to see if there might be a way that the X3000 could fulfill both roles, as the X1000
certainly is not adequate by itself for identification purposes. The X3000 sensor payload
features continuous zoom between 5 and 55 degrees, and we realized that we would
42
have to zoom out to approximately 40 degrees to identify the object in the two zones
with limited line of sight.
We decided to use only this single X3000 sensor payload on two out of our four UAVs.
These two would search the tall trees area, where the addition of an X1000 payload
would not increase the scanning width at all. In the zones with no-trees and short trees,
the other two UAVs are equipped with one X1000 and one X3000 because we found
that the addition of the X1000 could increase the scanning width to speed up the search
significantly to justify the $8000 cost of that sensor.
Design A, 2 UAVs search the no trees area
Equipped with the X1000 and X3000
and move to search the short trees.
payloads.
Design B, 2 UAVs search the tall trees zone.
Equipped only with an X3000 payload.
2.3.3 Ground Station Equipment Selection
With our design defined and our propulsion system and sensor payload selections out
of the way, we then went through the Configurator spreadsheet to according to the
provided ground station requirements (detailed below in the table) to pick the necessary
Ground Station equipment that we would need to suit the needs of our UAS. Our
selections are as follows:
43
Ground Station Options
Per Item Cost
Component
Required
Quantity
Quantity
Total
1 per UAV
1 per UAV
4
4
$800
$6000
1 per set of 4
sensor
payloads
$300.00 1 per UAV
2
$24000
4
$1200
$400.00 1 per sensor
payload
6
$2400
1
1
$14000
$12712.73
$61,112.73
Safety Pilot Flight Box
Operational Pilot Workstation
Computer
$200.00
$1,500.00
Sensor Payload Workstation
Computer - Version B
$12,000.00
Command Datalink Ground
Transceiver
Video Datalink Ground
Receiver
Shelter/Trailer - Fleet
Launch Catapult/Snag Line
Ground Station Total
$14,000.00 1 per sUAS
$12,712.73 1 per sUAS
In addition to physical equipment, we also had to determine our “per-hour” cost. We did
this by following the personnel requirements listed in the Configurator spreadsheet, and
went about selecting the appropriate personnel for our UAS. We found that we would
not need any “data analysts” due to our utilization of computer software. Our personnel
selections are as follows:
Operational Personnel
Resource
Payload Operator
Ground Search
Personnel
Range Safety/ Aircraft
Launch & Recovery/
Maintenance
Safety Pilot
Operational Pilot
Personnel Total
Resource
Cost Per
Hour
Number
of
Positions
Required
$150.00
$0.00
2
3
$175.00
1
$100.00
$150.00
$1,475.00
4
4
44
2.3.4 Additional UAV Components
In addition to determining our ground station equipment and personnel selections, the
Configuration also required the selection of some additional UAV components relative to
each design. For our Design A, which features two sensor payloads, our selections are:
Component
Video Datalink UAV
Transmitter
Command Datalink
UAV Tranceiver
Flight Control
System
Total Additions
Additional UAV Components for Design A
Fuselage Moment Weight
Per Item
Required
Station
(inch(lbs)
Cost
Power
(inches)
lbs)
(Watts)
30.00
3.0
0.05
$200.00
0.4
53.00
5.3
0.10
$300.00
0.3
50.00
5.0
0.10
$2,000.00
0.1
44.33
13.3
0.30
$2,700.00
1.20
Required
Quantity
Quantity
Up to 1
per
sensor
payload
Up to 1
per UAV
1 per UAV
2
1
1
We had the option of including an onboard video receiver, as can be seen above, but
the data recorder requires you to land, unload data and fly back out - time penalty is not
justified by the cost reduction. The Video Datalink Transmitter and Transceivers were
necessary to fulfill the requirement of communication between the UAS in flight and the
ground station, and our selection of the Flight Control System was required by the
Configurator spreadsheet.
For our Design B (which features only one sensor payload) our selections—made by
following the same selection criteria—are as follows:
Component
Video Datalink
UAV Transmitter
Command
Datalink UAV
Tranceiver
Flight Control
System
Total Additions
Fuselage
Station
(inches)
30.00
Additional UAV Components for Design B
Moment Weight
Per Item
Required
(inch(lbs)
Cost
Power
lbs)
(Watts)
1.5
0.05
$200.00
0.4
53.00
5.3
0.10
$300.00
0.3
50.00
5.0
0.10
$2,000.00
0.1
47.20
11.8
0.25
$2,500.00
0.80
Required
Quantity
Quantity
Up to 1
per
sensor
payload
Up to 1
per UAV
1
1 per
UAV
1
1
45
2.4 Aircraft Geometric Details
2.4.1 Wing Configuration
We chose the SD7062 airfoil given its high-lift properties that we needed. We found that
by using the SD7062, the wing planform area could be reduced from 600 in 2 to 540 in2.
We input the following into the provided Wing Definition Mathcad sheet to calculate wing
parameters, generate a plot of the wing planform, and send root and tip section
coordinates to output data files:
Our design inputs, taken from airfoil analysis spreadsheets we developed, output the
following:
We used this information to define our wing and generate a plot of the wing planform,
where the aerodynamic center is denoted. We then used this plot and the output wing
coordinates, also calculated in the Mathcad sheet, to create our wing in Creo Elements.
We imported the two generated coordinate files for our root and tip into Creo to create
point references, and then used the features of Creo itself to create solid wing
geometry.
46
2.4.2 Tail Configuration
With a wing defined, the next step was to define our tail. We defined two sections in our
tail configuration; the vertical and horizontal tails. To calculate tail specifications, we first
had to input wing details (planform area, span, mean aerodynamic chord, aerodynamic
center) into the Vertical Tail Definition Mathcad sheet. We also input the following
details:
The resultant calculations sized the tail arm at 32.481 inches, the tail volume coefficient
as 0.02, planform area as 21.854 in2, aspect ratio 3.5, taper ratio 0.5, and a sweep of 15
degrees. The vertical tail design outputs are:
Like when we were defining our wing, this sheet also generated coordinate files and a
plot of our planform that we used to create our CAD model.
Our next step was to define our horizontal tail. We input the same wing and fuselage
details as listed above, and the output calculations defined an aspect ratio of 4, taper
ratio of 0.7, sweep angle of 15 degrees, and declared dihedral, incidence, and washout
angles at 0, and planform area of 70.754 in2. The design outputs are as follows:
47
With this information and the generated coordinate files, we were finally able to
construct all the components of our tail in Creo.
2.4.3 Fuselage
For both UAV designs, we used the same fuselage which is 60 inches long with the
widest cross-section being a 5 in. x 5.5 in. ellipse. We chose this size because not only
is it capable of supporting our current propulsion system, it can also support a larger
electric engine or more batteries for further range. This size was also sufficient to
support additional components, such as sensors and wiring. Using the same fuselage
gave us to flexibility to add or remove the X1000 sensor payload based on the mission
requirement.
48
The equipment layout is shown in the picture below:
2.5 System and Operational Considerations
The UAS with four UAVs we have designed is targeted to reach lowest objective
function value while maintain the total cost under one million dollars. Though based on
our analysis, adding more UAVs can reduce the search time and lower the objective
function, it also adds complexity in mission planning. We have found it was difficult to
plan mission for four UAVs with balanced search load. As discussed in our business
plan later, the added cost may not appeal to the customer as the effect in the timesaving is not significant.
If a much larger area is to be searched, we would reconsider the selection of propulsion
system. The choice of E-20 electric engine is geared for the goal set for the National
Challenge. Using GL-25 gas engine will be more practical in general business practice.
49
2.6 Component and Complete Flight Vehicle Weight and Balance
Design A
Component
Batteries
Fuselage
Station
(inches)
Moment
Weight
(inch-lbs)
45
166.5
24
Electrical Wiring
Wing
Design B
Moment
Weight
(lbs)
Fuselage
Station
(inches)
(inch-lbs)
(lbs)
3.7
45
166.5
3.7
0.5
24
12
24.87
188
56
Horizontal Tail
12
7.56
24.87
1.32
56
73.9
57
Vertical Tail
0.5
188
7.56
1.32
73.9
0.47
57
26.8
0.47
26.8
Fuselage
15.34
267.1
17.41
15.34
267.1
17.41
Total Airframe
Sensor Payload Model
23.72
734.3
30.96
23.72
734.3
30.96
X1000
19
9.5
0.5
X3000
23
48.3
2.1
23
48.3
2.1
Total Sensor Payload
Propulsion Model
22.23
57.8
2.6
22.23
57.8
2.1
E-20
2.75
3
1.1
2.75
3
1.1
2.75
3
1.1
2.75
3
1.1
30
3
0.05
30
3
0.05
Command Datalink UAV
Transceiver
53
5.3
0.1
53
5.3
0.1
Flight Control System
50
5
0.1
50
5
0.1
44.33
13.3
0.3
44.33
13.3
0.3
23.12
808.4
34.96
23.17
797.3
34.41
Total Electric Propulsion
Additional Component
Video Datalink UAV
Transmitter
Total Additions
At MaxTakeoff Weight
2.7 Maneuver Analysis
The performance analysis result shows that the distance to clear 50 ft obstacle are
190.0 ft and 184.5 ft respectively for both UAV designs we have. In our mission
planning for all of the four UAVs, the highest rate of climb is about 740 fpm. With E-20
electric engine, the needed power output is about 50% of what E-20 can offer.
50
Way
Point
A
Ground Speed
(mph)
Rate of Climb/
Decent (fpm)
80
80
740
740
Distance
Traveled
(miles)
0
1.226
Elapsed Time
(minutes)
0
0.9195
Altitude
(feet)
8000
8680.4
Percent
Power
100
50.643
Percent Petro
Remaining
Percent
Battery
Remaining
100
100
100
93.082
Therefore, our propulsion selection is very capable of accomplishing the mission.
Regarding to the turn-around at the edges of the zone, we adjusted the speed to make
sure no warnings logged in the mission calculation output. As stated in the webinar, the
minimum turn-around radius was calculated with angle of bank capped at 30 degrees
angle of bank. However, in our analysis which will be discussed in later section, it is
possible to have greater angle of bank at high speed.
2.8 CAD Models
Design A: with X1000 & X3000 Sensor Payloads
51
Design B: with only X3000 sensor payload
2.9 Three View of Final Design
Top View:
52
Front View:
Side View:
53
Vehicle Flight Dimensions:
54
3. Mission Planning
3.1 Search Pattern
As stated in the preliminary design section, we found the shell-search-pattern as
illustrated below is an efficient way to search through each zone.
We decided to let two UAVs with only the X3000 sensor payload to search tall-tree area
first and then move to search the short-tree area. Meanwhile, the other two UAVs—
equipped with both X1000 and X3000 sensor payloads—search the no-tree area first
and then search the short-area.
Over the Tall-tree and Short-tree areas, after detecting the object, the UAV turns around
to identify the object. Over the No-tree area, after the UAV detects the object, the X3000
sensor payload zooms in immediately to identify the object.
For using the turn-around identification strategy, it is necessary to estimate the time
needed for turn-around. In the later “System Detection and Identification” section, the
time needed for turn-around will be discussed in detail.
The graph below depicts the mission path of our UAVs through the three zones, and
labels each turn in the search pattern;
55
This graph, as output by the provided Mission Planning Excel spreadsheet, was a cause
of concern for us as it illustrated that there may be some minimal—but still existent—
uncovered area along the edge of each zone where the UAV turns.
3.2 Camera Footprint
Our sensor payload selections and resultant camera footprints over each search zone
are as follows:
56
No-Tree Area (with one X1000 and one X3000 sensor payloads):

Altitude: 680 AGL

X1000 Field of View (HFOV/VFOV): 40/30 (deg.)

X3000 Field of View (HFOV/VFOV): 41.5 / 31.125 (deg.)

X1000 Pointing (Roll right/Pitch up): +20/0 (deg)

X3000 Pointing (Roll right/Pitch up): –20/0 (deg)

Total Camera Footprint Width: 1182 (ft)

Detection Area Radius: 615 ft
57

Effective Scanning Width: 1182 (ft)
Short-Tree Area (with one X1000 and one X3000 sensor payloads):
With one X1000 and one X3000 sensor payloads

Altitude: 794 AGL

X1000 Field of View (HFOV/VFOV): 40/30 (deg.)

X3000 Field of View (HFOV/VFOV): 41.5 / 31.125 (deg.)

X1000 Pointing (Roll right/Pitch up): +20/0 (deg)

X3000 Pointing (Roll right/Pitch up): –20/0 (deg)

Effective Scanning Width at 80 mph: 914 (ft).
58
With only one X3000 sensor payload:




Altitude: 794 AGL
Field of View (HFOV/VFOV): 41.5 / 31.125 (deg.)
Camera Pointing (Roll right/Pitch up): 0/0 (deg)
Camera Footprint Width/Length: 624/472 (ft)
59
Tall-Tree Area (with only one x3000 payload):

Altitude: 1000 AGL

Field of View (HFOV/VFOV): 30 / 22.5 (deg.)

Camera Pointing (Roll right/Pitch up): 0/0 (deg)

Camera Footprint Width/Length: 546/412 (ft)

Zone 3 Line-of-sight radius: 268 ft.

Effective Scanning Width at 80 mph: 532 ft.
3.3 System Detection and Identification
The search time is determined by two factors; air speed and scanning width. Due to the
required 0.5 seconds for detecting an object and 5 seconds for identifying an object, the
air speed and scanning width are not independent factors. As illustrated by the graph
below, when the width of camera footprint is larger than the diameter of the
maximum detection circle, the scanning with Ws is related to air speed v and the
diameter D of the maximum detection circle as:
60
Detection and Identification
Detection Only
𝑤𝑠 = √𝐷2 − (5.5𝑣)2
𝑤𝑠 = √𝐷2 − (0.5𝑣)2
The red rectangles in the diagrams above represent the effective area for detection and
identification (or just detection) with the maximum scanning width. As discussed in
preliminary design section, we realized that reasonable scanning width can be obtained
for detection and identification preformed in sequence only at a no-tree zone. Over the
short-tree zone and tall-tree zone, (especially over the tall-tree zone) scanning width for
detection and identification preformed in sequence is rather small and thus increases
search time dramatically. To achieve an acceptable search time, we decided to use the
turn-around identification strategy over the short-tree and tall-tree zones.
At 80 mph (7040 ft/min), with a 30 degree angle of bank, the turn radius is 741 ft. The
turn-around time is 2**741/7040 = 0.661 minute. However, at 80 mph, the UAV is
capable of turning at 65 degree angle of bank which has the turn radius of only 199 ft.
In this case, the turn-around time is 0.178 minute. Mathematically, the turn-radius and
turn-around time can be expressed by the following equations:
𝑣2
𝑇𝑢𝑟𝑛 𝑅𝑎𝑑𝑖𝑢𝑠 𝑟 =
g tan 𝛼
𝑇𝑢𝑟𝑛 − 𝑎𝑟𝑜𝑢𝑛𝑑 𝑡𝑖𝑚𝑒 𝑡 =
2𝜋𝑟
2𝜋𝑣
=
𝑣
g tan 𝛼
61
For the same air speed, the larger the angle of bank, the shorter the turn-around time.
However, the angle of bank is limited because the only part of the lift is can be used to
counter the weight of the aircraft
For the aircraft to maintain altitude,
𝑊𝑒𝑖𝑔ℎ𝑡 = 𝐿𝑖𝑓𝑡 ∙ cos 𝛼
The stall turning speed ustall is related to vstall as
𝑢𝑠𝑡𝑎𝑙𝑙
𝐿𝑖𝑓𝑡
1
√𝐿𝑖𝑓𝑡
=
=√
=
𝑣𝑠𝑡𝑎𝑙𝑙 √𝑊𝑒𝑖𝑔ℎ𝑡
𝑊𝑒𝑖𝑔ℎ𝑡 √cos 𝛼
𝑢𝑠𝑡𝑎𝑙𝑙 =
𝑣𝑠𝑡𝑎𝑙𝑙
√cos 𝛼
For a given turning speed u, the maximum angle of bank is
𝑣𝑠𝑡𝑎𝑙𝑙
√cos 𝛼𝑚𝑎𝑥 =
𝑢
𝑣𝑠𝑡𝑎𝑙𝑙 2
cos 𝛼𝑚𝑎𝑥 = (
)
𝑢
𝑣𝑠𝑡𝑎𝑙𝑙 2
𝛼𝑚𝑎𝑥 = cos −1 (
)
𝑢
Assuming 𝑣𝑠𝑡𝑎𝑙𝑙 = 52 𝑚𝑝ℎ,
62
The shortest turn-around time at different speed is
𝑡𝑚𝑖𝑛 =
2𝜋𝑟
2𝜋𝑢
=
𝑢
g tan 𝛼𝑚𝑎𝑥
𝑣𝑠𝑡𝑎𝑙𝑙 2
2𝜋𝑢
(
2𝜋𝑢
𝑢 )
=
=
4
4
√1 − (𝑣𝑠𝑡𝑎𝑙𝑙 )
√1 − (𝑣𝑠𝑡𝑎𝑙𝑙 )
𝑔
𝑢
𝑢
𝑔
𝑣𝑠𝑡𝑎𝑙𝑙 2
( 𝑢 )
The above graph indicates that the shortest turn-around time can be achieved at air
speed 80 mph, which is well under 0.2 minute.
3.4 Example Mission
First, we would first need to arrive at Philmont Ranch. The drive there would take us 3.5
hours each way, with .5 hours allotted for set-up and tear down. Our operational time,
63
as a result, would be 9 hours total. Our search time itself takes only 20.493 minutes. All
UAVs end the search with 30% or more power remaining.
An example mission with four UAVs is illustrated in details in the tables below.
Basically, UAV#1 and UAV#2 search the no-tree zone first and then search the shorttree zone while UAV#3 and UAV#4 search the tall-tree zone first and then search the
short-tree zone.
UAV #1:
Waypoints
A
B
C
D
E
F
G
H
I
J
K
L
M
M
O
P
Q
R
UAV Position
Distance
Angle from
from Search
Search
Center
Center
(miles)
(degrees)
1.890
1.890
1.669
1.670
1.447
1.447
1.088
1.088
1.420
1.420
1.752
1.752
0.413
0.413
0.177
0.177
0.059
0.059
250
90
90
250
250
90
90
0
0
90
90
0
0
90
90
0
0
90
Segment
Type
(S, P) for
Straight,
Polar
Waypoint
Corner
Turn
Radius
(feet)
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Camera Ground
Footprint Speed
Width At (mph)
Ground
(feet)
0
1178
1178
1178
1178
1178
0
910
910
910
910
910
910
910
910
910
910
910
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
52
52
80
Rate of
Climb/
Decent
(fpm)
Refuel
(Y, N)
480
0
0
0
0
0
423
0
0
0
0
0
0
0
0
0
0
0
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
64
UAV #2
Waypoints
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
UAV Position
Distance
Angle from
from Search
Search
Center
Center
(miles)
(degrees)
Segment
Type
(S, P) for
Straight,
Polar
Waypoint
Corner
Turn
Radius
(feet)
1.226
1.226
1.005
1.005
0.784
0.784
0.563
0.563
0.341
0.341
0.120
0.120
1.917
90
250
250
86
86
253
253
70
80
270
265
85
0
S
P
S
P
S
P
S
P
S
P
S
P
S
0
0
0
0
0
0
0
0
0
0
0
0
0
1.917
1.586
1.586
1.254
90
90
-1
-1
P
S
P
S
0
0
0
0
1.254
0.912
0.912
0.295
0.295
91
91
-2
-2
90
P
S
P
S
P
0
0
0
0
0
Camera Ground
Footprint Speed
Width At (mph)
Ground
(feet)
0
1178
1178
1178
1178
1178
1178
1178
1178
1178
1178
1178
0
910
910
910
910
910
910
910
910
910
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
Rate of
Climb/
Decent
(fpm)
Refuel
(Y, N)
740
0
0
0
0
0
0
0
0
0
0
0
79
0
0
0
0
0
0
0
0
0
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
65
UAV #3
Waypoints
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
UAV Position
Distance
Angle from
from Search
Search
Center
Center
(miles)
(degrees)
1.952
1.952
1.753
1.753
1.554
1.554
1.354
1.354
1.155
1.155
0.956
0.956
0.757
0.757
0.557
0.557
0.259
0.259
0.649
0.649
0.531
0.531
1
-111
-111
2
2
-112
-112
3
3
-113
-113
3
3
-113
-113
4
4
-115
90
0
0
90
Segment
Type
(S, P) for
Straight,
Polar
Waypoint
Corner
Turn
Radius
(feet)
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Camera Ground
Footprint Speed
Width At (mph)
Ground
(feet)
0
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
0
624
624
624
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
80
65
70
80
Rate of
Climb/
Decent
(fpm)
Refuel
(Y, N)
683
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-308
0
0
0
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
66
UAV#4
Waypoints
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
UAV Position
Distance
Angle from
from Search
Search
Center
Center
(miles)
(degrees)
1.852
1.852
1.653
1.653
1.454
1.454
1.255
1.255
1.055
1.055
0.856
0.856
0.657
0.657
0.458
0.458
0.358
0.358
0.159
0.159
0.059
0.059
0.767
0.767
2
-112
-112
3
3
-113
-113
4
4
-114
-114
5
5
-115
-115
6
6
-116
-116
7
7
-130
90
0
Segment
Type
(S, P) for
Straight,
Polar
Waypoint
Corner
Turn
Radius
(feet)
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
S
P
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Camera Ground
Footprint Speed
Width At (mph)
Ground
(feet)
0
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
528
0
624
80
80
80
80
80
80
80
80
80
80
80
80
80
80
65
60
60
55
55
52
52
52
80
80
Rate of
Climb/
Decent
(fpm)
Refuel
(Y, N)
720
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-338
0
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
3.5 Mission Time and Resource Requirements
We calculated the time it would take for our UAS to finish searching each zone, and as
the UAVs are searching concurrently, the maximum time is the total mission duration
overall. The table with our calculations, and the total mission time highlighted, is shown
below:
67
UAV#
1
2
3
4
Sensor Payloads
X1000 + X3000
X1000 + X3000
X3000
X3000
Searched Area
No-Tree, Short-Tree
No-Tree, Short-Tree
Tall-Tree, Short-Tree
Tall-Tree, Short-Tree
Time (minutes)
20.2163
20.4502
20.4927
20.1471
In conclusion, the search mission can be accomplished with 21 minutes including the
time needed for turn-around identification.
68
4. Document the Business Case
4.1 Identify targeted commercial applications
Innovative Solution’s target market will consist of the international, federal, and state
government, armed forces and military endeavors, local law enforcement agencies, as
well as private corporations and individuals. Innovative Solutions will market its
specialized, efficient programs to all of these persons in varying capacities to suit their
individualized UAS needs, with an emphasis on acquiring contracts with governmental
agencies and other parties with considerable, significant demand. By offering our
services to a variety of parties, we seek to maximize and diversify our potential clientele
to ensure both maximum profit and stability as a company.
Eager to capitalize on the relatively new field of UAS in search and discovery, we are
prepared to penetrate the space and compete with established unmanned aircraft
manufacturers for market share. Innovative Solutions will strive to provide the best
services on the market at the lowest costs possible in order to keep customer
satisfaction high and to ensure that the integrity, quality, and efficiency of our UAV
designs are unwavering. We will be looking to incorporate new and innovative
technologies into our design going forward, with the hope of minimizing cost while
maximizing efficiency for our clients.
We recognize that each potential customer, whether coming from the private or public
sector, has their own distinct needs and specifications. We would look to offer our
services to as many parties as possible by utilizing the technology that would allow us to
do so. Early on in our conceptual design phase, we entertained the idea of utilizing the
cloud to allow a variety of customers to access our systems. Unfortunately, the potential
for lag and other technical issues was too great at this point in time for us to seriously
consider this as a business strategy. We are, however, closely following research being
conducted at the Johns Hopkins Applied Physics Lab in Maryland, where researchers
are looking to drastically reduce these technical issues specifically in regards to
communication between UAVs and ground information systems. This is just one
example of a possible technological advance that we would look to take advantage of
69
eventually. Innovative Solutions would also look into possibly adding thermal sensors
into our payload.
This year, the Real World Design Challenge prompt revolved around the search and
rescue of mission persons. According to the National Institute of Justice, approximately
2,300 American adults are reported missing on a daily basis; this indicates an average
of around 839,500 missing American adults throughout the year. This is, clearly, a
shocking and very disturbing number. However, what is perhaps even more disturbing
is that approximately 800,000 children in the United States are reported missing
annually (this figure being distinct from the aforementioned figure of adults who are
reported missing every year). We feel that we can lend a helping hand to this pressing
issue, and through Innovative Solutions, we seek to ensure that more of these lost
individuals will be found and returned safely to their loving families than ever before.
Unmanned vehicles equipped with the capabilities of our design constitute promising
tools for conducting reconnaissance tasks in both military and humanitarian conditions,
due to the removal of any direct risk to human life when compared to a 'manned'
aircraft. The applications of such aircraft are vast, and stretch far beyond the domestic
search and rescue missions of mission persons. There is even the possibility that in the
future our drones will be able to be contracted to the government or American military
as a way to search for missing servicemen and women. Also, in the future Innovative
Solutions would likely have the opportunity to expand to overseas markets, allowing for
much more revenue to be brought in as well as for many more peoples’ lives to be
saved.
4.2 Amortized System Costs
4.2.1 Initial Costs
Our total initial cost is $247,099.13 dollars. This figure was found in the following
manner:
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The personnel cost is not included in this figure since the personnel total is our
operational cost, and thus not needed to construct the system itself. We can break
down our initial cost into a table such as this:
Initial Cost
2 x Design A UAVs =
$101,193.20
2 x Design B UAVs =
$84,793.20
Ground Station total =
$61,111.53
Initial Total Cost =
$247,097.93
4.2.2 Direct Operational Cost per Mission
Operational Personnel
Resource
Resource Cost Per
Hour
Number of
Total Cost Per-9
Positions Required Hour
Payload Operator
$150.00
2
$2,700.00
Range Safety/Aircraft
Launch &
Recovery/Maintenance
Safety Pilot
$175.00
1
$1,575.00
$100.00
4
$3,600.00
Operational Pilot
$150.00
4
$5,400.00
Total Cost Per-Hour
$13,275.00
The direct operational cost per mission is, in our case, our personnel selection cost. We
arrived at the number of 9 hours due to the fact that we will be driving 3.5 hours each
way, .5 hours for set-up and tear-down, plus our search time of 20.5 minutes. Thus, this
rounds up to 9 hours we will have to pay our personnel per mission. Also, the cost perhour would be $1,475.00. We are using the E-20 electric engine so we incur not fuel but
rather battery costs, which have already been accounted for under the airframe
component of our system initial cost.
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4.2.3 Amortization
To ensure that we recoup our funds and are able to meet our business goals, we must
ensure that we charge enough per mission that we are able to gradually able to pay off
our initial system cost. To find this amortized cost, we can use the following formula:
Here, we add the initial system cost to the operational cost per-hour over 50 missions,
and then divide this sum by 50. The resultant number is our amortized cost per mission.
In our case:
247097.93 + (1475 * 9)*(50)
______________________ = 18,216.96
50
The result of this calculation means that we would have to charge clients at least
$18,216.96 per mission to pay off our initial system cost over 50 missions even though
cost incurred to us per mission (“operational cost”) is only $13,275 assuming the
average time for recovery missions may be 9 hours including time to drive to and from
the launch site.
If we charge $18,216.96, it will only take fifty missions for Innovative Solutions to cover
its initial systems costs along with all personnel costs. It is very likely that Innovative
Solutions will be able to complete these fifty missions within a year, as 2,300 people go
missing in the United States every day, and in Colorado alone 108 search and rescue
missions were performed in 2012, often at costs over $20,000. It will only cost
$663,750.00 to execute 50 missions which last 20.5 minutes not including driving time,
which is a very reasonable amount.
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4.3 Market Assessment
After profuse research about the search and rescue market, we arrived at a variety of
conclusions. The first such conclusion is that search and rescue missions can be very
costly. The average cost for a mission lasting only a couple hours is upwards of
$17,000.00. However, some searches which last for more than twelve hours can cost
up to $337,000.00. This is a very large amount. The cost to use equipment such as
planes and boats alone is quite a large sum of money. It costs $6,625.00 per hour for
the Coast Guard to use a rescue helicopter in a search and rescue mission, and it costs
$7,600.00 per hour for the use of a C-130 turboprop search airplane. However, the cost
for these vehicles for a search and rescue mission will be higher than represented by
these numbers. While it may take less time for the C-130 or the helicopter to arrive at
the search destination, they also have to do safety checks and fuel up the vehicles. This
takes a long time, and while they are doing this, we will be able to be driving to our
destination already. Also, our per-hour cost for our UAV includes travel time for up to
three hours each way plus three hours of search time.
Besides just looking at the overall cost of search and rescue missions, we also decided
to research the costs of individual vehicles used in search and recovery missions. The
MC-12W intelligence, surveillance and reconnaissance plane costs $17,000,000.00 for
one unit. The Scan Eagle UAV system costs $3,200,000.00 per unit. And finally, the
HazMat Bot search and recovery robot costs $150,000.00. After comparing the cost of
our UAVs to the costs of these units, it is obvious that our system is far less expensive
all while exceeding expectations of efficiency.
While it may take a combination of any of the abovementioned vehicles, volunteers,
search dogs, etc. many hours to find a missing person, it will only take our system 20.5
minutes. When someone is stranded in the wild they often don’t have hours, and every
minute counts towards their survival. Also, these searches often cost above $16,000.00.
So not only is our service faster, it is also more cost effective per-hour, making it the
clear choice for any customer seeking rapid, effective, and relatively inexpensive help.
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Also worth noting is that our location greatly aids us in attracting potential business. As
we are based in Colorado Springs, Colorado, there are twenty-five major state and
national parks within 3 ½ hours of our location, including Rocky Mountain National Park,
Cheyenne Mountain State Park, and the Golden Gate Canyon State Park. The cost for
these missions, as well as the mission time including driving, will be the same as for the
fifty missions originally performed at Philmont Ranch, New Mexico. Thousands of
people go missing in national and state parks every year, and as such, we will be able
to save more people as well as to gain reinvestment capital from the large amount of
missions we would be able to perform from a location such as this.
The Innovative Solutions UAV systems are much more advanced and suited to the job
of search and recovery than that of competitors. The first UAV system uses one piece of
X1000 sensor payload and one piece of X3000 sensor payload. The second UAV
system uses one piece of X3000 sensor payload. This combination of UAV systems
greatly improves the efficiency of our drones in locating an objective. The X1000 sensor
is for the detection of an objective, and the X3000 is for the identification of that object.
Even going at a high speed of 80 mph, a drone can still identify an object in only five
seconds. In addition to this, the drone has a flight time of 9 hours. This lengthy span of
time coupled with the technology utilized in sensory payload equipment makes the
Innovative Solutions drone much more advanced and suited for the job than those of
the competitors.
Another advantage of the Innovative Solutions UAV system is that it is also much more
environmentally friendly than any of the other vehicular options available for search and
recovery. Rather than using gasoline, our UAVs are powered by an electric engine. This
is a very quiet engine, and as such it will not bother people if it must fly over areas
which are populated. Also, since the UAV is powered by electric it has a very small
carbon footprint. So, not only is our UAV environmentally friendly in this regard, but it is
also a much more environmentally friendly option if we must search in a national park or
otherwise where a diesel engine would pollute the air. Finally, as our UAV is powered
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by just electricity, its per-mission cost is quite low, allowing us to engage in many
missions for a much lower cost than any other vehicle.
4.4 Cost / Benefits Analysis and Justification
Previously, we had utilized one UAV design which used three X1000 sensor payloads
for detection and one X3000 sensor payloads for identification. Because each sensor is
so expensive, we examined the sensor payload specs to see if there might be a way
that the X3000 could fulfill both roles, as the X1000 certainly is not adequate for both.
The X3000 sensor payload features continuous zoom between 5 and 55 degrees, and
we realized that we would have to zoom out to approximately 40 degrees to identify the
object in the two zones with limited line of sight.
We decided to employ the use of four UAVs in order to complete our search operations
for a variety of reasons. The first reason is that this number is relatively low which would
allow the launch of these drones to be quite rapid. Our response time could be quite
rapid, as we can reach many places where people go missing within a three hour drive
from our location in Colorado Springs, Colorado. The second reason is that we have
determined that in searching a high-tree area with two of the drones and searching the
low/no-tree area with two the total search time would take only twenty and one-half
minutes. Another reason we chose this number is that this relatively small number of
UAVs does not take a large amount of people to control them. This being the case
Innovative Solutions is able to save money on additional personnel costs and use this
money to improve the services offered to the customer. Also, if we had to search an
open area then we could potentially use less than four UAVs which would decrease the
cost even more. Finally, we decided to go with four UAVs rather than a slightly larger
number such as eight because we determined that while the search time would go
down, the cost would go up so much that it would counter any benefit gained from the
lower search time.
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This graph is to show why we decided to use four UAVs rather than 30. While this
amount would decrease the search time, it would also greatly increase the cost, thus
making four our best option for minimizing our objective function.
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