Critical Design Review
Submitted to:
Inst. Kadri Parris
GTA Jin Yang
Created By:
Table L
Emily Clapper
Sydney Gravitt
Petar Lukacevic
Ricky Taulker
Engineering 1182
The Ohio State University
Columbus, OH
April 22, 2016
Executive Summary
After the death star was destroyed the galactic empire has begun
to rebuild their army. The rebel alliance must prepare for attack
from the galactic empire by preparing their planets for battle
without the enemy knowing. The power is scarce on these remote
planets so in order to transport R2 units very little energy must
be used. An Advanced Energy Vehicle(AEV) must be used to transport
to these remote planets. The AEV runs on a monorail system and runs
on its own battery power enabling it to run in places without any
outside power source. The engineering team must design an AEV that
runs smoothly and consistently while minimizing costs and
maximizing efficiency.
The team’s ultimate goal was to create an efficient vehicle that
operates properly while completing all of the necessary tasks. Many
steps were taken to ensure the best final product. Initial designs
were made from previous knowledge of the team members. Then various
tests of propellers and code efficiencies were completed to better
understand the way the AEV runs. Screening and scoring were helpful
in the process of design analysis of the original AEV designs.
After two designs were selected the data analysis tool was used to
compare the efficiency of the two designs on the track. Once a
final design was chosen the team focused on improving the
efficiency of the code for that particular design. Several types of
code were compared. The team created a motorSpeed-focused code, a
celerate code and a burst code. The team compared the efficiency of
each code and then used that data to determine which final code to
use. Adjustments to the code were made using trial and error in
testing. The team worked together when observing the code to
determine why the vehicle was performing in certain ways. Based on
all of these tests a final product was constructed and the code was
adjusted accordingly.
In Performance Tests 1-3 information and data were collected to
help improve the overall behavior of the AEV. In performance test 1
the two designs, Concept 1 and Concept 2, that passed screening and
scoring were evaluated to pick the final design used. Both concept
1 that evolved from team member Sydney’s design and Concept 2 that
evolved from the team bottle design were compared by running them
on the track with the same code. The vehicle with the higher
efficiency was then used for further testing and improvement.
In Performance test 2 the team compared two different codes. A
code that used only motorSpeed commands as a means for propulsion
was created and a code that used only celerate commands for power
was created. A code was created using each of these codes for half
of the track. Concept 1, the design that passed Performance Test 1,
was then run on half the track(to the R2) with each code and
analyzed. The data analysis tool was used from Matlab to
quantitatively compare the energy usage from each run. The team
found the motorSpeed command to be more efficient.
Then in Performance Test 3 the team compared the efficiency
rates of the motorSpeed code versus another code named the “burst”
code. The burst code also used the motorSpeed command but supplied
power to the AEV in large short spurts of energy. Both of these
codes were constructed to run a quarter of the track(to the gate)
to obtain quantitative comparable data. The burst code was found to
be more efficient than the motorSpeed code and was thus used in
Performance Test 4/final testing.
This final code was adjusted using the trial and error on the
track. Final testing went perfectly. The code performed three out
of the four times consistently. In the future to improve the code,
advanced coding techniques could be added to improve consistency
such as a while loop.
Table of Contents
Introduction
..………………………………………………………………………………………………………………………………………… 3
Experimental Methodology
..………………………………………………………………………….……………………………………… 3
Results
..……………………………………………………………………………...………………………………………………………………… 4
Discussion
..……………………………………………………………………...……………………………………………………………….… 16
Conclusion & Recommendations
…………………………………………………………………………………………………………. 19
References
………………………………………………………………………………………………………………………………………….. 21
Appendix
..……………………………………………………………………...………………………………………………….……………….. 22
Introduction
In the final performance tests the code was improved to maximize
efficiency for the chosen concept 1 design. In performance test 2
the motorSpeed and celerate codes were compared. Both codes were
constructed to run the Advanced Energy Vehicle (AEV) on half of the
track for comparison. The code that provided the better efficiency
was then compared with the burst code in performance test 3. The
code with the best efficiency was then chosen for final testing of
the the AEV. In these performance tests the team also added
components to the design of the AEV so the vehicle adhered to the
Star Wars theme.
Experimental Methodology
The initial process of this project required each team member to
brainstorm and create a unique design. Then these individual design
ideas were then combined to create an initial team design using
provided equipment. Each individual concept was screened and scored
compared to a provided reference vehicle provided by the
instructional team. From this qualitative data analysis the team
concluded from the results which initial design they would continue
to develop and test.
The team then attached reflective sensors to the designs to
start testing the arduino code. The sensors were tested by rotating
the wheels to ensure that the sensors registered the correct
direction and then adjustments were made as needed. The team
created a simple AEV code that would be used to observe the
behavior of the reflectance sensors and the AEV’s overall
performance on the track.
To improve the efficiency of the vehicle the team tested two
different propeller configurations. The propellers tested were of 3
inch diameter and 2.5 inch diameter (3030 and 2510) and had two
different configurations (pusher and puller). Upon observation of
the wind tunnel results the team then made a decision on which
propeller to use based on the one with the higher propulsion
efficiency.
After the AEV was constructed, the code was then observed to
improve the overall efficiency of the vehicle. The code was
constructed to run the AEV for a quarter of the track(the start
position to the gate). Then the team collected the voltage,
current, time, and marks travelled in order to calculate the power
used by the AEV and made note of the trends. This data was analyzed
and the energy was broken down into phases to analyze each
command.
The team used trial and error to observe the performance of the
code in order to make necessary adjustments to improve the
efficiency and consistency. The process of improving the efficiency
of the AEV was made simpler with utilizing the Design Analysis tool
which provided the team with the energy the AEV used in each run,
the time of the AEV run, and the power per time plots and power per
distance plots. This tool allowed for the team to quickly evaluate
the energy used in each phase of the AEV’s operation. The team then
tested Concept 1 and Concept 2. During testing, the team collected
energy, time, and power per time plot for both concepts in order to
conclude which design was the most efficient in performance test
1.
Once the most efficient design was chosen, the team turned its
focus to the programming aspect of the project so that the
efficiency of the AEV could be further improved. In each of the
following tests performed the team utilized the design analysis
tool in order to record information such as energy used by the AEV,
time of operation, and distance the AEV traveled in each run. This
tool also generated power versus time and distance graphs in which
the team used to evaluate each line of code and compare between the
commands used in each variation of the code.
First the team evaluated the use of celerate versus motorSpeed
commands and the resulting impact on efficiency in performance test
2. A code was developed to run the AEV with both variations until
it reached the gate from the starting position. The motorSpeed
command accelerated the AEV at some high motor power for a brief
amount of time and then maintained a smaller motor power until the
AEV braked. The celerate code sped up the AEV to a desired speed
within a predefined time and maintained that speed until the AEV
braked. When the AEV was braked for both these variations, the
motors reversed at a predefined number of marks and initiated a
motorSpeed command at some power setting for some amount of time
resulting in the AEV stopped at the gate. The team chose to move
forward with the motorSpeed command due to its higher
efficiency.
The team then evaluated the previous motorSpeed code and a new
code the team refers to as the “Burst” code in performance test 3.
The motorSpeed code followed the same setup as described
previously. The new “Burst” code accelerated the AEV at some high
power, utilizing the motorSpeed command, for a brief time and then
the motors were turned off. After the initial high power of the
Burst code and the motors were turned off, the AEV glided until it
reached a specific mark and then applied a motorSpeed command at
some power setting for some amount of time resulting in the AEV
stopping at the gate. With this evaluation, the team settled on the
the “Burst” code having the best efficiency of all the codes
tested.
The Burst code was then further developed and tested until it
completed a full cycle consistently and with the best efficiency
the team could obtain. This code was used in the final testing of
the AEV.
Results
Below Figure A and Figure B are the concepts that were tested in
Performance Test 1 and were developed from the preceding labs.
Figure A: Concept 1
Figure B: Concept 2
Concept 1 used an x-shaped base and right trapezoids that act as
angled wings with the T-shaped wheel arm and weighed 281 grams.
Concept 2 used a rectangular base with the same wheel arm and wing
structures and weighed 271 grams. Both concepts come with different
potential benefits. Concept 1 required less power due to more
momentum, and Concept 2 was made with a compact design to increase
stability and speed. These concepts were chosen because they
offered improvements over the original designs that maximize
efficiency and minimize inconsistency of the AEV .
Table 1: Design Screening New Concepts vs. Initial Designs
Success Criteria
Reference
Design A
Design B
Design C
Design D
Design E (Group)
Concept 1
Concept 2
Stability
0
0
1
0
-1
0
0
1
Aerodynamics
0
0
1
-1
1
0
1
0
Adherence to Theme
0
0
1
1
1
1
1
0
Cost
0
0
-1
-1
-1
-1
-1
0
Durability
0
0
-1
0
0
0
1
0
Weight Distribution
0
1
-1
1
-1
0
1
1
Speed
0
0
0
0
0
-1
0
0
Efficiency
0
0
-1
0
-1
-1
1
0
Total
0
1
0
0
-2
-2
4
2
Continue?
No
Yes
No
No
No
No
Yes
Yes
The goal of the Design Screening matrix (see Table 1) was to
make qualitative observations about potential designs. Specific
criteria was chosen by the group for comparison to the reference
design. A score of zero meant the design was comparable to the
reference for that category. A score of one or negative one meant
the design was hypothesized to perform better or worse than the
reference, respectively, in that category. When the group first
screened the original designs A - E, it was found that Design A,
Sydney’s design, was expected to perform better than the group
design E seeing as it received a score of 1 versus -2. Once changes
were made to Design A to make Concept 1 and Design E to make
Concept 2, they were compared to the reference and original
designs. The changes made to Concept 1 improved the efficiency,
aerodynamics, adherence to theme, and durability though the cost
suffered. It received a score of 4. The changes made to Concept 2
improved the stability, weight, cost, speed, and efficiency. It
received a score of 2.
Table 2: Design Scoring New Concepts vs Initial Designs
Reference
Concept 1
Concept 2
Success Criteria
Weight
Rating
Weighted Score
Rating
Weighted Score
Rating
Weighted Score
Stability
10%
3
0.3
3
0.3
4
0.4
Aerodynamics
10%
3
0.3
4
0.4
3
0.3
Adherence to Theme
5%
3
0.15
4
0.2
3
0.15
Cost
15%
3
0.45
3
0.45
2
0.3
Durability
15%
3
0.45
4
0.6
4
0.6
Weight Distribution
20%
3
0.6
4
0.8
4
0.8
Speed
10%
3
0.3
3
0.3
4
0.4
Efficiency
15%
3
0.45
4
0.6
3
0.45
Total
3
3.65
3.4
Continue?
No
Develop
No
Once the designs were screened, the three designs that had the
best scores were compared using a Design Scoring matrix(see Table
2). This assigned a value to each criteria so that when the designs
were rated, their final score would reflect which design would best
meet the MCR guidelines. Weight distribution, efficiency,
durability and cost were given the highest values ranging from 15%
- 20%. When considering the importance of each criteria, Concept 1
was found to have the highest score compared to Concept 2 and the
reference design. Concept 1 was considered to be more efficient,
mostly due to its more aerodynamic design. The general shape of
Concept 2 is the same as the reference so it was considered to be
no more aerodynamic. Both concepts were found to be have a better
weight distribution, the most valued criteria, than the
reference.
Figure C: Propulsion Efficiency vs. Advance Ratio
There were two different propellers to choose from, one with a
3.0 inch diameter (3030) and a 2.5 inch (2510). These propellers
could also be oriented in either a puller or pusher configuration.
Tests were done in wind tunnels to determine which propeller would
be used on the AEV. The Advance Ratio of the propellers, and
Propulsion Efficiency at incremental motor power percentages. A
general trend was that at lower Advance Ratios, the Propulsion
Efficiency was higher; the instance with the largest efficiency of
20% occurred at about a ratio of 0.6. However the 3030 propeller in
the puller configuration is seen to provide the most efficiency
overall.
During Performance Test 1, the group chose which concept to
forward with by analyzing the physical performance and energy usage
of each design. Below are the results from runs through a quarter
of the track.
Figure C: Concept 1 Power vs. Distance(Quarter Track)
Figure C shows the power used over the distance travelled of
Concept 1. According to Table 4 in the Appendix, which has the
breakdown of energy usage for this run, it completed a quarter
circuit in 9.661 seconds and used a total of 45.055J. Figure C
provides that there was significant drop in power at about 0.34
meters along the track. This drop occurred when the Arduino code
commanded the motors to switch power percentage from 45% to 30%. A
big spike in power was at about 16.33 watts at a position a little
more than 4.0 meters from the start position. This peak was
directly related to the increase in power percentage of the AEV
from 30% to 40%. The initial switch in power causes the graph to
spike but then level out. After the graph levels out it is still
using significantly more power than before at 11.67 watts.
Figure D: Concept 2 Power vs. Distance(Quarter Track)
Figure D shows the power used over the distance travelled by
Concept 2. According to Table 5 in the Appendix, which has the
breakdown of energy usage for this run, it completed a quarter
circuit in the same amount of time as Concept 1 however it used a
total of 67.46J. Figure D provides that there was a significant
drop in power at about 0.34 meters along the track. This drop is
the same as in Figure C but the power the vehicle dropped to about
7 watts versus 4.67 watts in Figure C. A big spike in power was
about 16.33 watts at a position a little more than 4.0 meters from
the start position. This peak was directly related to the increase
in power percentage of the AEV from 30% to 40%. This is the exact
same as Figure C. The initial switch in power causes the graph to
spike but then level out. After the graph levels out it is still
using significantly more power than before of a little less than
11.67 watts.
Once Concept 1 was chosen as the more efficient design,
Performance Test 2 was used to develop a code that would take the
AEV through a full circuit. Below are the results found for half
the circuit used to build the code. The code structure that used
the celerate command to accelerate the AEV was called the celerate
code and the structure that used motorSpeed was called the
motorSpeed code.
Figure E: celerate Power vs. Time(Half Track)
Figure E shows the power used over time by the AEV using the
celerate code. A half circuit was completed in 34 seconds using
135.63J of energy. Sections of the graph with gradual slopes
resulted from the celerate command specifically. According to Table
6 in the appendix, the phase breakdown of this graph, to bring the
AEV to the desired power percentage of 25% required 5.59J and took
2 seconds. Energy usage then remained consistent as the AEV
travelled to the gate in another 9 seconds and used another 56.28J
Spikes in power, as seen at about 12 seconds, were caused by the
reversal of the motors in order to brake the AEV. After waiting at
the gate for 7 seconds, these patterns repeat as the AEV travels to
the R2 unit.
Figure F: motorSpeed Power vs. Time(Half Track)
Figure F shows the power used over time by the AEV using the
motorSpeed code. A half circuit was completed in 28 seconds using
119.34J of energy. Spikes or drops in the graph, like at beginning
and then at 2 seconds, were attributed to changes in speed using
the motorSpeed command or again from reversing the motor. According
to Table 7 in the appendix, the phase breakdown of this graph, the
first 2 seconds of this run used 23.28J of energy to bring the AEV
to a power percentage of 40%. The percentage was then dropped to
30% and the AEV only used another 19.78 joules to reach the gate
Again, instances where the speed or acceleration of the AEV is not
changing resulted in consistent sections of energy usage, as seen
in Figure 3.
In Performance Test 3, tests were done to determine what design
and coding elements could be changed to create a more efficient
AEV. Below are the results found from tests on a quarter track and
used to make these changes. The code structure that relied on
motorSpeed is again called the motorSpeed code and the structure
that used a large amount of power for a short period of time to
coast the AEV is called the burst code.
Table 3: Comparing motorSpeed to burst code
For the sake of time, tests to compare the two code structures
were limited to a quarter track and the Design Analysis Tool was
used to record how much energy was used by each. As stated in Table
3, the motorSpeed code travelled to the gate in 10 seconds and used
52.686J of energy. The burst code went the same distance in 7.42
seconds and used 40.558J.
Figure G: motorSpeed code Power vs. Time(Quarter Track)
The trends in Figure G were found to reflect those in Figure F,
as they both show data for the same code structure. Spikes or drops
in power were the result of changes in power percentages in
motorSpeed commands or the reversal of the motor. After the motors
were reversed to brake the AEV, the motors were cut and from 6.67
to 10 seconds the AEV is gliding to a stop. However, even though
this run is half the length of the run from Performance Test 2, the
time taken and the energy used are not half of what they during
Performance Test 2. This could be attributed to the additional 7
seconds the AEV has to wait at the gate and the extra distance it
has to travel through the gate area that added the approximately
15J to Performance Test 2’s data.
Figure H: burst code Power vs. Time(Quarter Track)
As seen in Figure H, the AEV used power for only about 2.5
seconds of the 7.42 seconds it took to reach the gate. The initial
spike of each non-zero section of the graph came from the “burst”
of power applied from a motorSpeed command. The motors were set to
a much higher percentage, for example 67% for the starting command,
and ran for periods of time no longer than 2 seconds. From
approximately 1.5 to 3.75 seconds, the motors were cut and the AEV
glided along the track without aid. When the group needed to stop
the AEV, the same braking strategy was used: run the motor in the
opposite direction. However, when that strategy was used with the
burst code, the AEV stopped moving almost immediately and did not
need to glide to the gate sensors.
During final testing, the group used Concept 1 with the burst
structure. Below are the results of that test.
Figure I: Final Test Power vs. Time
Figure I shows the power used over time during the final test.
Like Figure H above, spikes were caused by the “burst” of energy
supplied to the motor. Larger spikes come from motorSpeed commands
used to move the AEV and smaller spikes come from reversing the
motor and braking the AEV. To complete the circuit, the AEV used
180.149J of energy and took 48.961 seconds according to Table 8 in
the appendix.
Discussion
Concept 1 evolved from Design A, team member Sydney’s design.
The only change made to this design was the addition of right
trapezoids to two ends of the x-shape to act as wings. The
propellers were initially attached to the base but not enough room
was allowed for the propeller diameter, so the propellers were
moved to the wings. Having to use extra pieces to create room for
the propellers increases the cost of the Concept 1. On the other
hand, the new placement of the wings allowed for increased airflow
which improved its aerodynamics and efficiency as seen in Table 1.
These were two of the highest rated Scoring matrix categories (see
Table 2). The new components created a distribution of weight that
was even around the entire AEV. Additionally, Concept 1 resembles
an aircraft more than Design A. This was considered beneficial
because it helped Concept 1 adhere to the Star Wars theme
better.
With the same rectangular base, trapezoidal wings and wheel arm
as the current prototype, Concept 2 is based on the original group
design, Design E. The original design had a plastic covering made
of water bottles. This was done to enhance the aerodynamics of the
shape, but it was found that the added weight and cost undermined
the speed and efficiency and the bottle was removed. According to
Table 1, this reduction in weight allowed or increased speed and a
higher efficiency. The other change was to move the Arduino board
and the battery from the bottom of the base to the top. These
components were less likely to subject to environmental factors and
collisions in this new position. Also, repositioning the components
centered the weight about the wheel arm, improving traction and
stability. The only screening criteria that suffered was Adherence
to Theme as the bottle made Design E resemble an aircraft.
During scoring Concept 1 received a higher score than Concept 2
based on several criteria. The aerodynamic design of Concept 1
allowed for it’s higher score. Concept 2 had the same shape as the
provided reference design therefore it did not receive a better
score for aerodynamics. Both concepts had a better weight
distribution than the reference, which was deemed the most valued
criteria(Table 2).
Concepts 1 and 2 then went on to be tested during Performance
Test 1. This test allowed the team to study the physical behavior,
responsiveness to code, and their energy usage. The goal was to
ultimately choose a design to use for final testing. Both designs
were given the same arduino code that would take ideally take the
AEV to the gate and trip the first sensor, about a quarter of the
circuit. Concept 2 travelled for 5.63 seconds to the assigned 332
marks to trip the first sensor. Concept 1 travelled for 6.58
seconds but stopped just before 332 marks as it did not trip the
first sensor. It was understandable that the heavier model took
longer to complete the path with the same amount of power as more
work must be done to overcome inertia. Given this initial behavior,
it was believed that Concept 2 would actually be the better
performing design. However the team did further testing with the
design analysis tool in Matlab to calculate the energy used by each
concept.
After breaking down the test runs into phases, it was found that
at each phase Concept 2 used more energy than Concept 1, using a
total 67.46J whereas Concept 1 used 46.055J (see Tables 1 and 2).
Given that the concepts were set to travel the same distance, this
meant Concept 1 had a smaller power over distance ratio. These
results were unexpected since it was theorized that a lighter
design would use less energy. Looking back to the scoring matrix,
Concept 1 had been found to be the preferred design due to its
aerodynamic design and improved weight distribution. These factors
could then be attributed to energy efficiency over Concept 2. The
quantitative results from the Design Analysis Tool confirmed the
observations made with the screening and scoring matrices and
Concept 1 was chosen to move forward to Performance Test 2.
Using wind tunnel testing the team obtained data concerning a
3.0 inch diameter propeller, a 2.5 inch diameter propeller, a
pusher propeller configuration and a puller propeller
configuration. The team chose to use a 3.0 inch diameter propeller
because it provided a larger amount of thrust for every power
percentage used. These test also provided that the puller
configuration had a higher propulsion efficiency than the pusher.
The team decided to use the puller configuration on the way back to
the start with the R2 due to the added weight. Servo motors were
not added to the design therefore the propellers could not be
switched. On the way to pick up the R2 the design yielded the
pusher configuration. System Analysis 2 allowed the team to compare
the power used in each section of the code.
In performance test 2 Concept 1 was tested with two different
codes. These codes were written for half of the testing track to
compare energy efficiency. The first code tested used the
motorSpeed command to provide energy to the AEV. The second code
tested used the celerate command to provide energy to the AEV.
Initially the team thought using the celerate command would be more
energy efficient because it would not be using a large amount of
power constantly. The increase in power would be gradual using the
celerate command. After using the data analysis tool however, the
team found that the motorSpeed code was more energy efficient than
the celerate code as seen in Figure E and Figure F. The total
energy used for the motorSpeed code was 119.34J and the total
energy used for the celerate code was 135.63J. The gradual
acceleration was unnecessary because electric motors have less
internal friction due to less moving parts and therefore more
torque available to move the AEV quickly. The motorSpeed command
was also powerful enough to move the AEV where the celerate command
did not provide enough force to immediately start propel the
AEV.
In performance test 3, Concept 1 was tested comparing the
motorSpeed style code and the burst style code. The AEV was run on
a quarter track using both codes, and the energies from each run
were compared to see which code style the group wanted to move
forward with. The build of the AEV remained consistent for both
runs to reduce the amount of variability within testing. It was
thought that the burst code would be more efficient as most of its
time on the track would be spent gliding using momentum to reach
its destination. The team’s thoughts proved correct, as the burst
code ended up being more efficient using 40.558 joules for a
quarter track as opposed to motorSpeed’s 52.686 joules as shown in
Table 3 above. After, the team continued to finish the burst code
by basically mirroring the initial portion of bursting, gliding,
and reversing, to the other three sections of the track while
adjusting marks and speeds accordingly. The finishing of the code
occurred in the beginning of final testing, as the group had to
adjust to a new battery and perfect the final section of the
code.
During labs there were multiple instances where potential error
could have occurred. Inconsistency with the collection of the data,
interpretation of the data, moving room to room, inconsistent AEV
builds, and poor time management are just some of the potential
errors that could have occurred in these labs, especially during
the performance tests. As seen with the various performance test
graphs, not all tests were used with the same length of track, such
as using a half, quarter and even full track tests with different
labs running the codes. This could have skewed the data
interpretation, as differing codes may have performed better on a
separate section of track. A second error could have been the build
of the AEV not being consistent with each test. Due to time
constraints, it was sometimes forgotten to check and maintain build
consistency after each test run. However, a good amount of effort
was spent by the group to minimize this issue each test. Another
potential error includes lack of sufficient time managment. During
multiple labs, the group seemed not to finish what was wanted to be
accomplished This could have been remedied by creating sufficient
schedules for each lab while taking error and other minor setbacks
into account.
During final testing the team used Concept 1 from performance
test 1 and the burst code from performance test 3. To complete the
final code the team used the trial and error method to tweak and
adjust the code. The final code produced was consistent three out
of every four runs. This final run resulted after many issues. In
the process of creating a final code the marks were very
inconsistent and the battery died frequently thus effecting the
distance traveled by the vehicle. The final run used the least
amount of energy at 180.149 joules in 48.961 seconds (Figure I )
compared to the previous performance tests. The final code
performed almost exactly as the team had intended. Some of the
distances from gate to gate that the AEV travelled were a little
shorter than anticipated, this was due to efforts to minimize
energy usage along the track. The final test provided an energy
mass ratio of 638.83 leaving the team’s AEV at the second highest
ratio in the classThe final design cost $175.54. All the parts used
to assemble the AEV came from the kit. The only additional cost of
$0.32 came from the use of Star Wars stickers in order to adhere to
theme of the project. Admittedly, no efforts were made to reduce
the cost. The group felt that the extra investments made were not
extravagant and still allowed the vehicle to operate efficiently.
While having to add the trapezoid components to the body increased
the price, it ensured that the group would have a working AEV and
also allow for improved airflow. The eight stickers were $0.04 each
and were considered an inexpensive way to honor the theme. Despite
the extra cost the team still received the highest total score of
83.68 (Figure M).
Conclusions and Recommendations
Overall, these set of labs helped the team hypothesize,
construct, test, and program a successful Advanced Energy Vehicle.
The process was incremental, and the AEV was brought to fruition
through a series of tests and experiments to maximize efficiency
and minimize cost. Through the labs, it was found that the 3030
puller configuration for the propellers was most efficient for any
configuration of the AEV build. Therefore, it was used when
connected to R2D2 to maximize efficiency on the way back. It was
also found that moving the wings outward and centering them as seen
is Concept 1 also helped improve the efficiency. As for coding, the
group decided on a “burst” style code as it proved more efficient
than the motorSpeed and the celerate style codes. Furthermore,
based on the results of the various labs and classes, the group
chose Concept 1 as the final Advanced Energy Vehicle paired with
the burst style code to maximize efficiency. Ultimately, the group
ranked second highest in the class for Energy/mass ratio with
638.83 joules/kilogram, and highest in the class for total score
with a score of 83.68. This course has provided the group with
experience in working with others, solving problems using
collaboration, and tackling a large project by incrementally
solving and experimenting on smaller problems.
After working through multiple labs, there were several
instances where error had to be overcome. A few examples of such
error are as follows: moving room to room, using a faulty battery,
not consistently gathering data, and faulty reflective sensors.
Moving room to room caused the group to spend additional time to
construct two codes, ultimately wasting time that could have been
used furthering the code already written. This was addressed by
referring to previous codes to provide a foundation for each
iteration of code, rather than completely starting over in each
room. During performance tests, it was found the group did not
gather the same types of data from lab to lab. For example, not
getting all graphs as Power vs. Time or Power vs. Distance. To
resolve, the group made sure all subsequent data was consistent,
and made sense of the discrepancy to conjure a valid argument. A
small error that occurred later in the labs was realizing the
reflective sensors were faulty, and thus messing with the
performance of the code. This error was initially brought into
consideration by the lab instructor. After changing the sensors, it
was found the new ones increased consistency by a small margin, but
enough to improve overall performance. The final, and most abundant
error, was a faulty battery. During many, the performance would
suddenly diminish with no change to the code. To resolve this
error, the group made sure that a fully charged battery was used as
often as possible, and made the switches when necessary.
The group’s final design is arguably the best in the class, with
a few slight drawbacks. Whereas the design may have a few issues,
it is for the most part a consistent and successful build. What
makes this design so successful is the evenly distribution of
weight, a good balance of weight and power, and the level of
efficiency the AEV can hit. The vehicle features a decent amount of
parts but is all evenly distributed throughout the vehicle. It
provides the vehicle with a level of stability and sturdiness that
helps it perform its task with consistency. The AEV also features a
compact build which when compared to other designs, often helped it
maintain balance around the turns and when picking up the R2D2. All
of these advantages helped match it perfectly with the “burst”
style code, as the code relied on a decent amount of momentum to
run efficiently. The distribution of weight, sturdiness, and
compact build helped isolate the amount of momentum in one
direction, which resulted in a higher level of control over the
AEV. With all of this in play, it helped the AEV prove its spot as
one of the best by gaining the second highest energy/mass ratio and
the highest total score.
During the labs the group found many aspects that could be
improved upon. Overall, lab instruction was thorough and clear.
However, the group found that in various labs the actual amount of
time left to complete the lab was significantly shorter than needed
because of lengthy and somewhat unnecessary explanations of lab
instruction. A possible solution could be to provide quick online
video explanations for students to view and go over before actual
lab to provide more of a prior description to save in class time.
Another situation that could be improved was the whole wind tunnel
lab. Despite the technical malfunctions of the tunnels, it was
difficult to follow and fully understand the explanations of how
exactly the propellers functioned. A solution could be to create a
small handout with a good overview for what happens with each
propeller to help solidify and provide concise explanations for
every student; or the could have been split so half the class work
on propellers one day and half another lab session. This would help
with any confusion in future labs. The instructional staff could
also provide more of a “focus” for the lab by sharing expectations
for the lab report and any other subsequent document. Groups then
will have a more clear and concise goal as to what to work towards
throughout lab. Some small modifications that could be made would
be to provide a couple extra R2 units, as when once was broke, it
halted testing for several groups. The final, and most important
recommendation would be to provide a more consistent battery source
for the AEV’s. This may be fixed by either acquiring new batteries,
or simply finding new ways to keep them more consistent. The
problem was most groups’ AEV runs were actually misrun due to the
battery level, but were mistaken to be a problem with the actual
code. Fixing this would greatly improve consistency as well as
significantly quell the level of frustration felt by groups.
References
1. Dr. Whitfield, C., West, D., Allentstein, J. (2015, August
7). The Ohio State University Advanced Energy Vehicle Design
Project: Lab Manual. Retrieved from
https://eedcourses.engineering.osu.edu/sites/eedcourses.engineering.osu.edu/files/uploads/1182/AEVLab/AEVDocuments/LabManual/AEV_Lab_Manual_Rev_2015_08_07.pdf
Appendix
Figure J: Project Schedule
Provide the SolidWorks model of the final design and ensure that
the figure has the 3 primary orthographic views with overall
dimensions, estimated weight, estimated cost,
Table 4: Concept 1 Energy Phases
Phase
Arduino Code
Time(seconds)
Distance (meters)
Total Energy (Joules)
1
motorSpeed(4,35);
goFor(1);
1.02 sec
0.2976m
9.5224J
2
motorSpeed(4,23);
0.18 sec
0.1116m
1.2859J
3
goToAbsolutePosition(331);
4.321 sec
3.6084m
21.5746J
4
reverse(4);
motorSpeed(4,40);
0.30 sec
0.3224m
3.5038J
5
goFor(1);
1.02 sec
0.7688m
9.9900J
6
brake(4);
2.82 sec
0.62m
0.1780J
Total Energy
46.0547J
Table 5: Concept 2 Energy Phases
Phase
Arduino Code
Time(seconds)
Distance (meters)
Total Energy (Joules)
1
motorSpeed(4,35);
goFor(1);
1.02 sec
0.2976m
13.09J
2
motorSpeed(4,23);
0.18 sec
0.1116m
2.31J
3
goToAbsolutePosition(331);
4.321 sec
3.6084m
35.28J
4
reverse(4);
motorSpeed(4,40);
0.3 sec
0.3224m
4.90J
5
goFor(1);
1.02 sec
0.37688m
10.71J
6
brake(4);
2.82 sec
0.62m
1.17J
Total Energy
67.46J
Table 6: celerate Code Energy Phases
Table 7: motorSpeed Code Energy Phases
Table 8: Final Testing Phase Breakdown
Phase
Code block
Time(s)
Energy(J)
1
motorSpeed(4,67)
goFor(1)
brake(4)
goToAbsolutePosition(352)
0 - 4.5
30.7
2
reverse(4)
motorSpeed(4,34)
goFor(1)
brake(4)
goFor(7)
4.5 -13.5
10.3
3
reverse(4)
motorSpeed(4, 57)
goFor(2)
brake(4)
goToAbsolutePosition(764)
13.5 - 19.1
33.5
4
reverse(4)
motorSpeed(4,35)
goFor(1)
brake(4)
goFor(5)
19.1 - 25.5
11.3
5
motorSpeed(4, 58)
goFor(2)
brake(4)
goToAbsolutePosition(556)
25.5 - 28.5
32.33
6
reverse(4)
motorSpeed(4,30)
goFor(2.2)
brake(4)
goFor(7)
28.5 - 38.25
18.26
7
reverse(4)
motorSpeed(60)
goFor(2)
brake(4)
goToAbsolutePosition(27)
38.25 - 44.0
30.369
8
reverse(4)
motorSpeed(4,37)
goFor(1.5)
brake(4)
44 - 48.961
13.39
Figure K: Cost Sheet of Finalized Concept 1
Figure L: Orthographic Views of Concept 1
Figure M: AEV Final Testing Scoresheet
