Critical Design Report for AEV Project

Critical Design Report
Submitted to:
Inst. Richard C Busick
GTA
Jin Rong Yang
Created by:
Team P
Garrett Greco
Christine Li
Sara Stacy
Mike Zhang
Engineering 1182
The Ohio State University
Columbus, OH
20 April 2015

Executive Summary
The purpose of the AEV project was to introduce students to several aspects of project design and
allow them to explore the concepts of energy management and operational efficiency. The team had
to first complete several labs that gave them the skills necessary to build the AEV. Then, the team
continuously tested adjusted their AEV to improve efficiency while still completing the task stated in
the Mission Concept Review. The operational requirements of the AEV were to: start at the visitors
center, traverse to the entrance of the Jurassic Park, stop at the entrance gate and wait seven seconds
for it to open, navigate to the storage facility, pick up the caboose with the baby dinosaurs, traverse
back to the gate, wait seven more seconds, and return to the starting point. It was important for
students to be able to complete the operational objectives while innovating ways to increase the
efficiency of the vehicle because in the real world engineers have constraints to adhere to while
attempting to create a system with maximum efficiency.
The team used methods explored in lab to obtain their final AEV design and code. The first step was
for all team members to design a vehicle to be compared to both the other designs and a sample
design. Then, the team used concept screening and scoring to determine which vehicle they wanted
to continue testing. The most consideration was given to the vehicle design that demonstrated the
best comparable balance, weight, and efficiency. Once the team chose and built a vehicle, they used
that vehicle for the remainder of their testing. Various propellers and code configurations were then
tested for efficiency using calculations provided in lab. Once the team had an initial code, they
continuously made adjustments and re-tested the vehicle to achieve greater efficiency. However,
there were several setbacks during the final testing stages due to the wheel sensors and so the team
was unable to spend very much time improving the vehicle’s efficiency.
Based off of the the final AEV test, it was determined that the team did an adequate job adhering to
the operational objectives. The AEV completed the task stated in the mission concept review in under
two minutes and thirty seconds, which is all that was required of the vehicle. However, the vehicle
only had about average efficiency compared to the other vehicles tested in class. The major problem
that led to shortcomings in the vehicle’s efficiency was the wheel sensors. If the team had caught the
problem with the wheel sensors earlier in the testing stage, they most likely could have achieved a
higher efficiency score.
1

Table of Contents
Introduction………………………………………………….……………………...…….……………....……………..
.…..…3
Experimental
Methodology………………………………………………………………………………………………....3-5
Results…………………………………………….….………………...…………………...………….…………...…....
........5-9
Discussion…………………….………....………………...…………………….….………....………………...………
….…10-17
Conclusion &
Recommendations…………………………….………....…………...……….……………………..15
Appendix………………………………………………….………....………………………...…………………….…...
....16 -23
2

Introduction
The objective of this lab was to make modifications to the code and AEV to make it more efficient
from the previous labs in addition to the operational requirements. This included stopping at the gate
and waiting 7 seconds, gently picking up the caboose, stopping at the gate for 7 seconds on the
return, and returning the the start in under 2.5 minutes. The team was able to make a successful run
and accomplished all the goals on its second run after changing the battery and conducting a few
tests. This report documents the design changes the team made in order to increase efficiency and
finish a successful run.
Experimental Methodology
First the team brainstormed design concepts and scored the member’s designs based on their
balance, weight, maintenance, cost, efficiency, and size. The materials available to use include the
mandatory Arduino board with the motor controllers (seen in Figure 1 below), battery, two types of
arms, two wheels, and two sensors. There was 4 available motors that could be used and two
propeller for each of the two propeller designs. Various body shapes, T, X, and rectangular as well as
many possible wing and smaller rectangular attachments.
Figure 1: Arduino board and Propellers
4

After determining the best design, the team built and compared the design to a sample design. In the
initial stages of design, the team mainly familiarized itself with the Arduino commands and tested
various ways to get to reach the destination; this included learning and testing the difference between
the commands that use relative and absolute distances. The team also tested different ways to stop
the AEV before it reached the gate.
Next the team tested two different propeller shapes, one longer version and one shorter, square
version using the equipment shown below. This required a wind tunnel, speed controller, power
supply, thrust stand, and data acquisition system.
Figure 2: Wind Tunnel Equipment
The wind tunnel will help determine propulsion efficiency by measuring the power output to the
motor using a thrust stand. This data will be used in the future to calculate the propulsion efficiency
using the AEV analysis tool. The AEV analysis tool will allow the team to quickly create thrust vs.
voltage, power vs voltage, and power efficiency plots in the performance tests to determine the best
AEV and code to use.
5

Figure 3: Final design of the AEV
Later the efficiency was tested and the design and code was modified again during the performance
tests. In the first performance test, the team will build a second AEV concept; the new model, Figure 3
above, was made to compare with the previous design, Figure 4 below in the results section, using a
test code and then found the propulsion efficiencies of both designs. The new model was determined
to be the most efficient of the two and was used in the final test run.
In the second performance test, the team focused on making a code that will successfully complete a
run on the while meeting the criteria. The team will brainstorm ideas on what speeds and suitable
amounts of power to use in the code as well as how to stop the AEV reliably. The code was completed
using experience from previous labs and continuous testing and tweaking on the track. In the final
performance test, the team worked on improving efficiency and compared energy costs before and
after the alterations.
6

Results
Initially, the team developed a design from four brainstormed concepts and determined the best
design using both screening and scoring methods; this can be seen in Tables 1 and 2. The preliminary
design is shown below in Figure4. The main focus of this design was on balance. A T-shaped support
was chosen and place at the center of the AEV to balance the vehicle back to front; the base shape is a
cross shape, again promoting balance. Flat wings were placed on the sides of the AEV perpendicular
from the central location where the support arm was attached to the AEV. Motors were placed on the
wings at a distance to fit a 3-inch diameter propeller. The arduino was located at the back of the AEV
to provide separation from the front where the caboose would attach, avoiding the powerful magnet
at the front. The battery is located at the front of the AEV to balance the weight of the Arduino at the
back.
Figure 4: Preliminary Design for AEV
7

Table 1: Concept Screening for Initial Designs
Success Criteria Reference Design A Design B Design C Design D
Weight 0 - + 0 0
Balanced 0 0 - 0 +
Maintenance 0 - + 0 +
Cost 0 - + 0 0
Efficiency 0 + - 0 +
Size 0 - - - 0
Sum +'s 0 1 3 0 3
Sum 0's 6 1 0 5 3
Sum -'s 0 4 3 1 0
Net Score 0 -3 0 -1 3
Continue? Yes No Yes No Yes
Table 2: Concept Scoring of Best 2 Designs
A Reference Old Ref Design B Design D
Success
Criteria
Weight Rat ing Weighted Score Rating
Weighted Score
Rating
Weighted
Score
Weight 15% 3 0.45 4 0.6 3 0.45
Balanced 20% 3 0.6 2 0.2 4 0.8
Maintenance 10% 3 0.3 4 0.4 5 0.5
Cost 5% 3 0.15 5 0.25 3 0.15
Efficiency 40% 3 1.2 2 0.8 4 1.6
Size 10% 3 0.3 1 0.1 3 0.3
Total Score 3 2.35 3.8
Continue? No No Yes
8

Through further comparative testing and more screening and scoring, the team developed a new AEV
design. The new design, however, it not that dissimilar to the original design. For this design, the focus
again was to maximize the balance of the AEV to ensure stabilization on the track. However, with this
design there was also a focus on weight shedding. The cross shaped base was replaced by a simple
rectangular base. The T-shaped support arm is replaced by the L-shaped, which had to be used in
order to fit the battery on the top of the base. The wings are located at the back of the AEV and tilted
upwards. The Arduino is located at the bottom of the base and again placed at the back of the AEV to
ensure enough distance between it and the magnet holder at the front.
Figure 5 Final Design for AEV
The costs for Design A and Design B are nearly the same. The most expensive part in the AEV was the
Arduino, which is $100. The arduino is most vital part in the AEV since it records commands and codes
from the computer and transmits them to external sensors and motors. The motors, battery,
propellers, wheel sensors, and support arm compromise the other costly parts of the AEV. Because
both AEV designs contain the same amount of essential parts, they cost nearly the same. Both AEVs
have the same count of each essential part, and the discrepancies in estimated cost come from the
lack of base building material Design B uses.
Equipped with the knowledge and experience from previous labs, the team was able to prepare the
AEV for the final test runs. In the first test run, the vehicle did not complete the run. In previous tests
9

the AEV was running consistently, so the team expected the AEV to complete the run as it had been
previously. However, on the return trip the vehicle stopped short of the sensor so the gate was not
triggered. The team changed the battery and adjusted the code to the new battery before the second
run, and this fixed the problem.
Despite having completed the test run without having any points deducted, the team’s AEV was still
just barely in the bottom half of the class scores for efficiency (the test score sheet can be found in
the appendix) There are several factors that could have possibly contributed to the vehicle’s
performance. The team had several issues with the AEV’s wheel sensors in the weeks leading up to
the final test, and this kept them from being able to complete a final code until the day before testing.
Because the team took so long to finish coding the AEV, they were unable to continuously test it and
adjust the code to increase the efficiency. The vehicle was also one of the heavier AEVs, and it can be
noted that despite a few exceptions, the lighter AEVs performed better. The team however did place
very well compared to the other teams in terms of power to weight ratio. This is due to the reduced
weight and addition of a second motor.
The team specifically designed the code to reduce specific errors the teams noticed through the entire
semester while working with the AEV, especially with the loss of battery power and inconsistencies
between the two testing tracks. The code, in general, reduces the distance of unmarked coasting.
When coasting the AEV, the vehicle is more likely to be affected by battery loss and bumps in the
track. The code initially accelerates the AEV to a constant speed until a pre-determined distance is
reached. The AEV then coasts until another pre-determined distance is reached, then the AEV
reverses orientation of the motors and propels to a stop. This process was repeated for a total of four
sequences. There are some changes between the sequences. These include changes in the absolute
distance from the starting point, and changes in power and time of the reverse stop.
10

Discussion
Table 1: Concept Screening
Success Criteria Reference Design A Design B
Weight 0 + +
Balanced 0 0 -
Maintenance 0 - +
Cost 0 - -
Efficiency 0 + 0
Size 0 - +
Sum +'s 0 2 3
Sum 0's 6 2 3
Sum -'s 0 3 2
Net Score 0 -1 1
Continue? N/A No Yes
Table 2: Concept Scoring
A Reference Old Ref Design A Design B
Success
Criteria
Weight Rat ing
Weighted
Score
Rating
Weighted
Score
Rating
Weighted
Score
Weight 15% 3 0.45 2 0.3 2 0.3
Balanced 20% 3 0.6 4 0.8 4 0.8
Maintenance 10% 3 0.3 3 0.4 3 0.3
Cost 5% 3 0.15 2 0.1 4 0.2
Efficiency 40% 3 1.2 3 1.2 4 1.6
Size 10% 3 0.3 2 0.2 5 0.5
Total Score 3 3 3.7
Continue? N/A No Yes
The above tables break down the concept screening scores for the preliminary AEV and the final AEV.
Based off of the concept screening scores, the second only had negatives for cost and balance. The
preliminary design only scored better than the final design on balance due to the the T-Shape that
gave it more stability on the track. The concept scoring sheet shows that the second design performed
better in the efficiency and size categories. The second design was determined to have better
efficiency because of its smaller size and because testing showed the vehicle had a higher efficiency
score.
11

Figure 6 Propulsion Efficiency vs. Advance Ratio with an EP-3030 propeller
Figure 6 shows the relationship between propulsion efficiency and advanced ratio. With the
increment of advance ratio, the propulsion efficiency is declining. This supports the inference that a
decrease in power provided to the motor increases the efficiency of propulsion, and therefore
increases the efficiency of the AEV in terms of energy usage. This graph should be taken with
skepticism, as the same plot with sample data produced a much different result, where propulsion
efficiency increased with advance ratio.
12

Figure 7: Power Usage and Propulsion Efficiency of AEV over Final Run
Figure 7 above shows the power usage and propulsion efficiency of the AEV during the final test. The
AEV took little over 70 seconds to complete the entire track and all of the objectives that needed to
be completed. From the graph, four distinct rectangular boxes followed by four spike can be seen.
This is due to the programming of the AEV. The rectangular box of power used represents the
combined coding commands of acceleration and constant speed until a distance is reached. Once this
distance was reached, the AEV would break the motors, shown in the graph by no input of energy.
When the AEV would reach a designated distance, the motors would come back on, but be switched
to a reverse orientation and would propel backwards. This is the spike of energy usage which follows
the rectangular section. This process was, in essence, repeated four times: from start to gate, gate to
caboose, caboose to gate, and gate back to starting point. An increase in energy used can be observed
between the first two pairs of sections to the last two. This is due to the addition of the caboose,
which added more weight for the propellers to pull and therefore cause an increase in energy usage
for both the acceleration to constant speed and reverse stop. Throughout the graph of the efficiency
over time, the efficiency remains at a steady 12.25%, with occasional spikes during the coasting of the
AEV. This is most likely due to the AEV moving while having no power applied to the motors.
13

Table 3: Energy Used Based on Various Phases
Phase Code Time Energy
Used
1
reverse(4); //reverse all motors
celerate(4,0,25,2); //accelerate all motors from 0 to 25% power in 2
seconds
motorSpeed(4,25); //sets all motors at 25% power
goToAbsolutePosition(335); // move 335 marks
brake(4); // brake all motors
9.72 53.86
2
motorSpeed(4,33);
goFor(1); //continue for 1 seconds
brake(4); //brake all motors
goFor(7);//stop AEV for 7 seconds.
9.78 9.23
3
seconds
9.24 52.18
4
9.72 7.81
5
seconds
8.34 53.26
6
goFor(1.5); //continue for 1.5 seconds
goFor(7); //stop AEV for 7 seconds.
10.26 13.97
7
seconds
9.18 62.86
8
motorSpeed(4,35); //sets all motors at 40% power 6.42 21.12
14

Total Time and Energy Used 73.74 273.49
The following equations were used to calculate the results in Figure 5 and Table 3. Sample calculations
can be found in the appendix.
Time T =
TE
1000
Time (Seconds) = Time
measured/1000
(A1)
Current I = (
IE
1024) * V R * ( 1 Amp
0.185 V olts)
Current (Amps) = (EEPROM
Equivalent current (counts)/1024) *
Arduino Reference Voltage (counts)
* Amp/Volts conversion
(A2)
Voltage V = 1024
15 V* E Voltage (V) = 15 * EEPROM
equivalent voltage (counts) / 1024
(A3)
Power P = I * V Power Supplied (Watts, W) =
Voltage (V) * Current (A)
(A4)
Incremental
Energy
T )E = 2
(P −P )1 2
* ( 1 − T2
Incremental Energy = Average
power supplied from two points /
Difference in time
(A5)
15

Table 4 Code Breakdown for Lab 10
Phase Code Time (seconds) Energy
(J)
1
reverse(4); //reverse all the motors
celerate(4,0,25,2); //accelerate motors from 0 - 25% power in 2
sec
goToAbsolutePosition(335); // move (335 marks)
goToAbsolutePosition(479);// move 144 marks
11.64 45.86
2
reverse(4); //reverse all the motors
8.22 7.300
3
reverse(4); // reverse all motors
celerate(4,0,25,2); //accelerate all motors from 0 to 25% power
in 2 seconds
goToRelativePosition(355); // move 355 marks
10.80 41.48
4
motorSpeed(4,30); // set all motors at 30% power
goFor(1); // continue for 1 second
goFor(3); // continue 3 seconds
6.96 6.582
5
in 2 seconds
motorSpeed(4,25) ;//sets all motors at 25% power
13.14 49.00
6
motorSpeed(4,32); // set all motors at 32% power
8.22 7.800
16

7
in 2 seconds
goToRelativePosition(150);
15.30 60.92
8
motorSpeed(4,32); // set all motors at 32 % power
goFor(3); // continue for 3 seconds
6.96 7.605
Total Time and Energy Used 81.24 226.55
The tables indicates the final and preliminary codes for AEV. Comparing the two data sets and codes
of Lab 10 (Table 2 above) and final test, it is evident that the previous code in Lab 10 was more
efficient in terms of its energy usage, using 226.55 J compared to 273.49 J used by the AEV in final
test. Though Lab 10’s data was built with two separate codes, it can be assumed that this would have
no effect on the data and that difference in the charge in the battery is negligible between the two
runs. Qualitatively, however, the team observed a much more consistent and accurate run in final test
than in the two from Lab 10. The AEV in final test consistently would follow a absolute position
command to the same position for every test. This accuracy is something that is much more desired
by the team from the AEV rather than efficiency.
17

Conclusion and Recommendation
In the final test, the team tested the AEV with improved codes and brand new wheel sensors.
Compared to the test in lab 10, the AEV runs much smoother on the track. The AEV with new sensors
pauses at exact position where it needs to be stop, runs at a constant speed on the track and escorts
the caboose back to starting point safe and sound.
There are two potential errors in this lab. One error which could affect the efficiency and performance
of the AEV is inconsistency / error in the wheel sensors. The wheel sensors play a huge part in terms
of performance of the AEV. The majority of the phases of the AEV code utilized the wheel sensors,
whether it was running the motors at a constant speed until a distance was reached or waiting until a
distance is reached before reversing. If there are any inconsistency with the sensor, the AEV can not
perform these commands at the right time. This is especially true for the situation of this track in
particular, which requires accurate placement of the AEV to open the gate, attach to the caboose and
land in the welcome zone.
Another error that can potentially occur is the loss of power in the battery over time. When fine
tuning the code to ensure the AEV runs smoothly and meets all the objective, the battery would
continue to lose power. Because of this power loss, each change that was made to the AEV
throughout the tuning and coding would become less and less accurate and time goes on. The effect
of battery loss is most noticeably observed in the reverse stop. As the remaining battery power would
lower and lower, the constant speed that the AEV would reach would be less than previous tests, as
the speed is actually a result of the percentage of power in the battery. This would affect the stop, as
the AEV would not carry as much speed as it would normally and would stop too quickly or even go
backward due to the reverse stop.
In order to reduce the cost of the vehicle, the team’s main strategy was to use as few parts as
possible. The main issue with this was getting the battery and the Arduino to fit on the vehicle
without the Arduino touching any metal pieces. However, the team was able to fit all necessary parts
on the AEV after some trial and error. Compared to the original design, the final design had fewer
parts and a more compact frame. This gave the final design the lowest overall cost of all designs
tested by the team.
In the final run, it is recommended that the team makes sure the sensors are properly working,
ensuring that the battery is charged, and the wheel sensor is counting correctly. The wheel sensor
plays an essential part in the test run because the goToAbsolutePosition() commands depend on the
wheel sensors’ accuracy. If the wheel sensors are not functioning, the distance marks that AEV
collects are not accurate, which will cause the vehicle to run inconsistently. The team needs to also
pay attention to the battery because the battery power will be declined after AEV runs two test runs.
18

Appendix
Project schedule:
Member Tasks Role Start date Due date Percentage
completed
Garrett
Greco
Collect and process
data and charts
coder, graph
processor,
writer
3/12/2015 4/17/2015 100%
Christine
Li
Build second AEV
prototype, Complete
and edit design report.
AEV builder,
writer,
editor
3/12/2015 4/17/2015 100%
Mike
Zhang
Edit code for Arduino,
create concept
solidwork graph for
AEV, Notebook
coder,
SolidWork
grapher, writer
Notebook
Checker
3/12/2015 4/17/2015 100%
Sara
Stacy
Writing and edit lab
report and summary
AEV builder,
writer, recorder
3/12/2015 4/17/2015 100%
19

SolidWorks model
Estimated weight: 0.251 kg
Estimated cost: $153.4
20

Sample Calculations
Time
T=TE1000
T = 64821000
T = 6.482
Time (Seconds) = Time measured/1000 (A1)
Current
I = IE1024*VR*1
Amp0.185 Volts
I = (88 counts1024) *
2.46 * 1 Amp0.185
Volts
I = 1.1427 A
Current (Amps) = (EEPROM Equivalent current
(counts)/1024) * Arduino Reference Voltage
(counts) * Amp/Volts conversion
(A2)
Voltage
V = 15 * VE1024
V = 15 * 530
counts1024
V = 7.7637 V
Voltage (V) = 15 * EEPROM equivalent voltage
(counts) / 1024
(A3)
Power
P=IV
P = 7.7637 V * 1.1427 A
P = 8.8718 W
Power Supplied (Watts, W) = Voltage (V) *
Current (A)
(A4)
Incremental Energy
E=(P1-P2)2(T1-T2)
E = 8.8718 + 7.48862 *
(6.482 - 6.362)
E = 0.9816
Incremental Energy = Average power supplied
from two points / Difference in time
(A5)
22

Code
celerate(4,0,25,2); //accelerate all motors from 0 to 25% power in 2 seconds
motorSpeed(4,33);
23

goFor(1.5); //continue for 1.5 seconds
goFor(7); //stop AEV for 7 seconds.
24

Critical Design Report for AEV Project

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