C r i ti c a l D e s i g n R e v i e w...used to see how two different AEV designs perform on a test monorail. To complete the first performance test, the AEV had to start at the starting

Critical Design Review

Submitted to:

Inst. Richard Busick

GTA Amena Shermadou

Created by:

Team K

Lauren Klenk

Kami Russell

Brandon Griffith

Aaron Bell

Engineering 1182

The Ohio State University

Columbus, OH

20 April 2018

Abstract

An AEV was designed to function as a transport for citizens to travel from Linden to Easton

within Columbus, Ohio. Preliminary research was conducted to provide the basis for multiple

designs that were then critiqued with further advanced testing. Preliminary testing dealt with

motor power, sensors, and creating rough design ideas. Advanced research topics looked into the

propulsion efficiency of the design with propeller configurations as well as modeled how the

AEV would run in SolidWorks. Finally, performance testing compared the top two best designs

by undergoing different tests and code on the monorail.

During preliminary research, the motor took some time to get up to speed, which affected the

starting power. The sensors were found to be helpful in allowing the AEV to move an absolute

distance. From the advanced research conducted, it was found that the puller configuration of

the propeller was the most efficient. Due to this the final design had one puller configuration and

one pusher configuration to maximize the efficiency of the overall trip. SolidWorks showed how

each of the final designs were functional and could be built to test. It saved time by modeling

each design and running simulations rather than building and testing each individual design.

From performance testing team design 1 was found to be the most efficient and effective design

to function as a transport.

The second performance test maximized the efficiency of the code by running the same design

under two codes. This showed where energy could be saved or where more energy was needed,

especially for power braking. The final performance test encompassed everything from previous

tests and research. This created the final design.

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Table of Contents

Abstract 1

Table of Contents 2

Introduction 3

Experimental Methodology 4

Results 6

Discussion 12

Conclusions and Recommendations 15

References 17

Appendix 18

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Introduction

An in depth research analysis was performed to determine the most efficient design of an AEV

that would transport citizens from Linden to Eason for smart city Columbus. Preliminary

research was conducted to gain a better understanding of the components of an AEV and the

code. After completing preliminary research and developing an initial design to test, advanced

research was used to study specific topics that will help with designing the final product. The

advanced research topics solidworks simulation and wind tunnel propeller analysis were

conducted to help decide on specific design components for the AEV. Performance tests were

used to see how a system or design performs in different situations. Performance tests were also

used to see how two different AEV designs perform on a test monorail.

To complete the first performance test, the AEV had to start at the starting dock, go to the gate,

pause at the gate for seven seconds, then move through the gate. The purpose of this performance

test was to test two AEV designs in the same scenario to find out which design was more

efficient. To complete the second performance test, the AEV had to start at the starting dock, go

to the gate, pause at the gate for seven seconds, proceed through the gate, connect to the caboose,

pause for five seconds, then leave the loading zone. The purpose of the second performance test

was to test two AEV codes on the monorail to see which code performed more efficiently. For

the final performance test the AEV has to proceed through the gate, pick up the caboose, then

return to its starting position. The purpose of the final performance test is to see if the AEV is

capable of completing the mission statement and picking up the passengers and transporting

them to Easton from Linden.

In this report, the design process for the AEV will be discussed from preliminary research to

final testing. Following the introduction, the experimental methodology will be explained. Then,

the results of the research and development labs as well as performance testing will be presented.

Following the results section, the results for the lab will be discussed in more detail. The team

will then give recommendations and a conclusion. Finally, references will be given before the

appendix.

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Experimental Methodology

Preliminary research and development labs familiarized the team with the equipment and

concepts used throughout the AEV design process. More in depth steps are found in the Mission

Concept Review (MCR) And Deliverables in references. The electric motors were tested using

code written for the Arduino, shown in Figure 20 of the appendix. A sketchbook was set up in

Arduino to store all of the code written for the duration of the design process. Certain commands

were used to control the AEV’s movement. The reflectance sensors kept track of how many

times the AEV’s wheels spun. The sensors count the number of times the silver tape on the

wheel went by the sensor, and the distance the AEV travels could be found by multiplying the

number of times the wheel spins by the circumference of the wheel.

Figure 1: Attachment of reflectance sensors to AEV

Another motor test was conducted to compare the relation between the power produced by the

motors, the distance in which the AEV traveled, and the time frame in which it happened. The

test was run on the test track with code from the Arduino. The Design Analysis Tool plotted

these relations. This tool was used for the remainder of the project. Multiple designs were then

brainstormed by each team member. The designs were compared using a concept screening and

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concept scoring matrix. These rated each design on their specific design features and the highest

rating designs were kept.

Advanced research and development focused on propeller configuration and SolidWorks

modeling. Two different propeller configurations were tested using a wind tunnel, shown in

Figure 2.

Figure 2: Wind Tunnel set up

The wind tunnel was set to have a wind speed of 2.8 m/s. The power was incremented 5% each

trial and each trial recorded the percent power of the Arduino, current, RPM, and thrust scale

reading. A 3030 propeller was used and the test was run twice, once for each configuration:

puller and pusher. To change the configuration, the rotation of the blades was reversed by

placing it in reverse. The thrust calibration, power input and output, propulsion efficiency, and

advance ratio was then calculated for each trial and each configuration. The ratio of propeller

efficiency and advance ratio were graphed as well as power setting vs thrust calibration.

Each design was modeled within SolidWorks and a motion study was run. They were created in

an assembly and simulated using steps outlined on the SolidWorks website, “Motion Analysis”

as found in references. They were then built, and had code written to upload for performance

testing. The code was written in Arduino and was used to maximize the efficiency of the AEV.

The AEV was run through certain tests. It was programmed to run for a certain distance, stop

between two sensors to trigger the gate, wait for 7 seconds for the gate to open, then run through

the gate as shown in Figure 14 in the appendix. The AEV was to follow all safety rules while

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also not allowed to run into the gate or trigger an extra sensor. Then the second AEV design was

built and the same tests were performed, using the same code. The team observed each test and,

based off observations, were able to determine which AEV performed better and chose the best

of the two designs.

A second more in depth performance test was performed where the final design must go through

the gate, pick up the caboose, and travel back down the monorail. Two different codes were

used on the same design to determine the most efficient code, as seen in Figures 15 and 16. The

final performance test took the best design, and the best code and made a full round trip on the

monorail. The energy used and time taken was recorded.

Results

The preliminary research and development labs introduced the concepts and equipment as well

as allowed for the first steps to be taken in the design process. The electric motors were tested

and ran as expected. It was found that there was some resistance as the motor built up enough

speed to rotate the blades. This lab also showed that the AEV would not stop immediately, but

would need coasting room if relying solely on the motors. The reflectance sensor test proved

that the AEV could be programmed to go a certain distance, rather than relying on timing and

battery power. It also showed which direction was a positive forward or a negative backward, as

seen in Figure 11 in the appendix. Given a list of materials and a purpose statement, the team

was able to brainstorm ideas for an AEV which was eventually narrowed down. The motor was

further tested to find that the more power given, the farther the AEV would travel. When

braking, the power to the motors would be shut off for a coasting stop, or reversed in power for a

more immediate stop as shown in the figure below.

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Figure 3: Power vs. Distance motor test

Likewise, comparing the power outage over time travelled showed the resistance of the motors to

not immediately start running at the specified power setting. It also showed that nothing would

happen instantaneously.

Figure 4: Power vs. Time motor test

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Once the testing of individual components was finished, a concept screening and concept scoring

matrix was used to narrow down design ideas, as seen in Tables 3 and 4 in the appendix. The

team used observations from each lab to determine the weight each category should hold, and

filled in the corresponding tables. Both team designs were found to be the best out of the group

and were developed for further testing.

In the Advanced Research and Development labs, propeller configurations were studied and the

main AEV designs underwent motion studies. The propeller configurations consisted of a

“pusher” and a “puller”. With this setup, propulsion efficiency, power percentage, and the

calculated thrust for the two orientations were compared. The “pusher” configuration outputted a

positive value for thrust, while the “puller” configuration had a negative value for thrust. The

sign of the thrust was found to be less important than the magnitude and therefore it was

concluded that the best power setting for the motor was 60% as seen in Figure 5 below.

Figure 5: Thrust for a certain amount of power from A R&D lab

The advanced ratio showed how efficient the propeller was. The outlier points showed the closer

the ratio was to 1, the more efficient it was, as seen in Figure 6 below. However, to get the value

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closer to 1, the motor had to be going at a higher speed. Therefore, with the use of both

comparisons, the “puller” configuration was found to be the best propeller configuration to

produce the most efficient propulsion.

Figure 6: Propeller Efficiency vs. Advance Ratio for A R&D lab

The two final designs were assembled within SolidWorks for motion studies. The simulation

was more accurate with the addition of a monorail to simulate how the AEV would move. It was

found that all the components in each design worked well together and the team was able to

move forward in building the models. The motion simulation also allowed for design critique as

it was clear to see the best qualities from each design. SolidWorks presented a few problems.

The motion study did not account for air resistance or changed in height of the monorail. Thus,

further testing accounted for these factors. The advanced research and development labs created

two final designs. The first team design, as seen in Figure 7, was designed to optimize weight

distribution and stability. The second team design, as seen in Figure 8, was designed to optimize

aerodynamics and propulsion efficiency.

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Figure 7: AEV design 1 modeled in SolidWorks

Figure 8: AEV design 2, modeled in SolidWorks

The first performance test compared the two designs under the same circumstances to find which

performed better. Both AEV designs travelled on a monorail to a gate, stopped for 7 seconds,

and proceeded through the gate. Team design 1 moved faster at a lesser power than team design

2, making it more efficient. Team design 1 was also more stable and predictable even though

both designs had difficulty stopping at a desired point reliably. From these observed results, a

concept scoring and concept screening matrix was used to determine that team design 1 was

overall more efficient and a better design, as seen in Tables 1 and 2 in the Appendix.

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The second performance test focused on maximizing the efficiency of the code. Team design 1

was run under two different codes to determine which code was more efficient. The first code,

shown in Figure 15, allowed only 1 motor for the power braking while the second code, shown

in Figure 16, used both motors for power braking. This increased the power usage for the second

code, but also increased the accuracy, as shown in the sharp peaks in Figure 9 below. The energy

was found to be 110.754 J.

Figure 9: Energy Analysis for Second Performance Test

The final performance test used team design 1 and the second code. It ran for the entire length of

the track, starting and stopping at the gate each time and picking up the caboose. It used both

motors for power braking and limited power usage on the downhills. This was seen in the spikes

and valleys in Figure 10 below.

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Figure 10: Energy Analysis for Final Performance Test

The final energy used was 189.165 J and took 59 seconds. This is higher than performance test 2,

but only because the AEV had to travel twice the distance. By limiting power on the hills and

coasting, as seen when the power is 0, the energy did not double even though the distance

doubled. This was the final test of the project and all data was collected. The final cost of the

AEV came out to be $614,713.89. The final weight for team design 1 was 0.33 lbs and 0.30 lbs

for team design 2. The accuracy was 36 out of 40 as the AEV was stopped before it triggered the

second sensor on the return trip as well as it hit the caboose rather hard.

Discussion

All testing that was performed was helpful in determining what the final AEV design should be

and why it should be that design. For the start of testing, it was expected that the motors would

run, but it was not expected to take as much power as it did to move the AEV forward. This test

helped for future experiments as it was known to give the motor either more power in the

beginning or more time to start up before performing its tasks.

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The reflectance sensor test helped emphasize the importance of relying on absolute positions

rather than timing. Depending on the battery power, the AEV could travel different speeds each

time. With reflectance sensors, the AEV could accurately measure how far to go before stopping.

This was better in testing than in practice however. Even though the AEV was set to go an

absolute position, the power was cut off after moving that certain distance. Therefore, by the

time it had fully stopped, the AEV had travelled further than it should have.

Problems such as these required a test that compared the power setting to distance travelled as

well as how far it travelled over time. The AEV should be efficient, meaning it should travel far

in a short amount of time with limited power, but also be accurate and reliable. This test

quantitatively showed how the more power that is given, the more stopping distance is needed

for coasting, or the more power is needed for a power stop. These ideas were used in future

testing to account for the inaccuracy that the stopping distance was giving.

From these tests, possible AEV designs could be made. Each team member created a design that

was then compared using concept scoring and screening matrices, seen in Tables 3 and 4 in the

appendix. This enabled for a wider range of ideas that could be narrowed down to a few strong

ideas. The best design features from each sketch were taken and combined to form team designs.

It was also easier to break the design into different important categories to find which was the

most important to focus on. These designs were then narrowed down to two to be used for more

testing. These designs were not final, as they required more thorough testing to find any flaws.

The advanced research and development labs were used to specialize in specific parts of the

design. From the preliminary labs, it was found that efficiency was a key factor in a good AEV

design. Therefore, the propeller configuration lab was thought to enhance the effectiveness the

most. Two propeller configurations were explored and the efficiency was calculated. By

breaking down each propeller configuration, it was found that the puller configuration was

slightly better than the pusher configuration. The first team design was then tweaked to allow

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for 2 propellers, one in the pusher configuration and one in the puller configuration. The AEV

must travel in both directions and having two puller configurations would be effective for one

direction but not the second direction. Therefore, by having one of each configuration, the

efficiency for the entire trip was maximized by decreasing the efficiency of each direction

marginally.

The second advanced research topic was SolidWorks modeling. This was chosen to be useful in

comparing the many features of each AEV, as well as reducing time building multiple AEV’s.

The model was able to effectively show that the AEV designs that were chosen would work on

the track. It was also able to point out some of the flaws. Team design one had an evenly

distributed weight, a solid base, and could easily connect to the caboose. However, the motors

were not coplanar and the batter did not line up directly to the plate. Team design 2 was

aerodynamic but it had only one motor. It was also found to be unevenly distributed, the base

was not solid, and the battery does not fit snuggly. The motion studies were useful in

determining how the AEV would run on the track. However, it did not simulate any variances in

the monorail. It also did not account for variances in battery power or airflow. SolidWorks gave

an ideal AEV run and therefore more testing would be needed to determine how the AEV would

truely run.

The performance test was the first physical testing of the AEV. This allowed for observations,

rather than assumptions, on how the AEV would perform. Using the same code to compare the

two designs, as seen in Figure 14, team design 1 was stable on the track. However, it took some

trial and error to get it to stop exactly where it was supposed to, and varied for different days.

Team design 2 was less stable and less accurate. Possible errors included that the reflectance

sensors were not set up properly and were malfunctioning. This would affect the stopping

distance of the AEV and throw off an entire trial. Also, battery power percentages varied day by

day. While this was factored into the code, it was a major factor in throwing off the distance

each run. More testing was done to remedy this problem.

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After many tests and conceptual ideas, it was found that team design 1 was more efficient and

the overall best design as seen in Tables 1 and 2 as shown in the appendix. It was determined

that efficiency and stability were the most important categories as they would be the highest

points of failure. The propeller configuration allowed for a more efficient design power wise,

and the layout allowed for a more stable design. Likewise, having two motors instead of one

allowed for more power to be used in a single run. However, changes were continued to be

made to the final design within the code to make it more reliable for the future.

Now that the design was finalized, focus was spent on creating the most efficient code to reduce

energy usage for the second performance test. There was a fine line between efficiency and

accuracy. Increasing the power supply of the power break used up a lot of energy, but allowed

for a more accurate run. Therefore, the power supply was reduced in other areas of the track,

such as for downhills. The final performance test was able to take the best design, and the best

code and put it together for a complete performance.

The code was changed slightly from the second performance test and the final test. While the

power braking continued to use two motors, and effectively more power, energy was saved by

coasting downhill. This allowed for more energy to be spent on braking and improving accuracy

while still conserving energy overall. However, this did slow the AEV down as coasting relied

on the momentum of the AEV and it took longer to coast than to use a set power. Due to motor

failures and sensor malfunctions, only one final performance test was run. All data from the

final performance test was collected and analyzed for a final design. A few steps were taken to

reduce the overall AEV cost. The amount of energy was reduced by coasting downhill. Also, no

customized parts were used and the mass was relatively low as minimal parts were used to

construct the AEV.

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Conclusions and Recommendations

Each lab attributed to narrowing down the best AEV design to fulfill the purpose. The

preliminary research and development labs were helpful in understanding the equipment and the

main principles of the designs. The advanced research and development labs allowed for a more

in depth analysis of specific design attributes that were then used to create a more informed final

design. The first performance test was used as a concrete test to observe how the AEV would

perform, rather than simulating and theorizing how the AEV would work. The second

performance test improved the code and the final performance test put the best aspects of the

designs and code into one final run to achieve a final energy of 189.165 J, a final cost of

$614,713.89, and a final time of 59 seconds.

There were a few unexpected errors during the project. One error faced during the project was

that the reflectance sensors and Arduino broke multiple times causing for there to be less time to

test. There were many inconsistencies on the track as well with the multiple runs. The code was

changed to limit the inconsistency of the stopping distance by giving more power to the braking.

This allowed for less coasting time and a more accurate stopping distance. The monorail was

also inconsistent from room to room. Therefore, a certain code was only run on a certain track

and any progress made on one track did not necessarily relate to the other track. One possible

solution to this problem is to only test the AEV on one track, preventing complications with the

code or the efficiency of the AEV. Another error encountered while testing the AEV was

consistency. For example, during the test the AEV would sometimes overshoot or undershoot it’s

target. This problem could be solved by doing further research on the efficiency of the arduino

and the reflective sensors.

In conclusion, the proposed AEV design is the best because it is the most efficient and the least

costly compared to competitors and other teams within the company. Working as a team was

found to be beneficial for the project’s success. Completing this design project as a team was

found to be more efficient by dividing different tasks between team members. From these

teamworking skills, the team was able to come together and finish the final performance test with

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a overall accuracy source of 36 out of 40. The AEV was designed to achieve the overall goal of

transporting citizens from Linden to Easton. Some recommendations for this project in the future

would be to have more time for preliminary research or less preliminary research topics to

complete. Allowing for the focus on one design earlier on in the project. This would allow for

more time on the testing of one design instead of multiple designs. Given more time to test one

design, would allow for a more advanced and efficient AEV design.

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References

Advanced Research and Development and Performance Tests. (n.d.). The Ohio State University

Advanced Energy Vehicle Design Project.

Lab Manual Preliminary Research and Design. (n.d.). The Ohio State University Advanced

Energy Vehicle Design Project.

Mission Concept Review (MCR) And Deliverables. (n.d.). The Ohio State University Advanced

Energy Vehicle Design Project.

Motion Analysis. (n.d.). Retrieved February 23, 2018, from

http://www.solidworks.com/sw/products/simulation/motion-analysis.htm

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Appendix

Figure 1: Attachment of Reflective Sensors to AEV

Figure 2: Wind tunnel set up for propeller configuration lab

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Figure 3: Power vs. Distance P R&D lab

Figure 4: Power vs. Time motor test

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Figure 5: Propeller Efficiency vs. Advance Ratio for Puller configuration

Figure 6: Power Setting vs. Thrust for Puller configuration

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Figure 9: Energy Analysis for Second Performance Test

Figure 10: Final Performance Test Energy Analysis

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Total Count: 448; Position: 54; Direction: Forward Total Count: 448; Position: 54; Direction: Forward Total Count: 449; Position: 55; Direction: Forward Total Count: 449; Position: 55; Direction: Forward Total Count: 449; Position: 55; Direction: Forward Total Count: 450; Position: 54; Direction: Reverse Total Count: 450; Position: 54; Direction: Reverse Total Count: 450; Position: 54; Direction: Reverse Total Count: 451; Position: 53; Direction: Reverse Total Count: 451; Position: 53; Direction: Reverse Total Count: 452; Position: 52; Direction: Reverse

Figure 11: Data from reflectance sensor tests

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Figure 12: Team K AEV Design 1 Drawing with Bill of Materials

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Figure 13: SolidWorks drawing with bill of materials for design two.

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Table 1: Concept Screening Matrix

Success Criteria

Stability Minimal Blockage

Accessibility Efficiency Aerodynamic Safety Sum +’s

Sum 0’s

Sum -’s

Net Score

Continue?

Team Design 1

+ 0 + + 0 0 3 3 0 3 Yes

Team Design 2

- + 0 - 0 - 1 2 3 -2 No

Table 2: Concept Scoring Matrix

1 - bad

5 - good

Team Design 1

Team Design 2

Success Criteria

Weight

Rating Weighted Score

Rating Weighted Score

Stability 20% 4 .8 1 .2

Minimal Blockage

10% 3 .3 4 .4

Accessibility 15% 4 .6 4 .6

Efficiency 30% 3 .9 2 .6

Aerodynamic 10% 2 .2 2 .2

Safety 15% 3 .45 1 .15

Total Score 3.25 2.15

Continue? Develop No

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Table 3: Concept Screening Matrix for Preliminary R&D

Success Criteria

Reference Design A (Kami)

Design B (Lauren)

Design C (Brandon)

Design D (Aaron)

Team Design 1

Team Design 2

Stability 0 + 0 0 + + +

Minimal Blockage

0 - + + + + 0

Accessibility 0 0 + + + + 0

Efficiency 0 + - + 0 + +

Aerodynamic 0 - 0 - 0 + +

Safety 0 + - - 0 + +

Sum +’s 0 3 2 3 3 6 4

Sum 0’s 5 1 2 1 3 0 2

Sum -’s 0 2 2 2 0 0 0

Net Score 0 1 0 1 3 6 4

Continue? Combine Revise Revise Revise Revise Yes Yes

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Table 4: Concept Scoring Matrix for Preliminary R&D labs

1 - bad

5 - good

motorSpeed(4,30); //motor speed for all motors at 30% power

reverse(1); //reverse motor 1

motorSpeed(1,30); //motor one set to 30% power

goToAbsolutePosition(218); //previous commands go until sensors mark out 218 marks

brake(4); //brake all motors

motorSpeed(1,45); //Set motor one to 45% power

reverse(2); //reverse motor two

motorSpeed(2,45); //Set motor 2 to 45% power

goFor(3); //previous command goes for 3 secs

brake(4); //All motors brake

goFor(6); //Previous command goes for 6 secs



Figure 14: Arduino Code for performance test 1 (Model 1).

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motorSpeed(4,30); //Set all motors to 30% power

reverse(1); //reverse motor one


goToAbsolutePosition(210); //Previous code runs until 210 marks have been reached



reverse(2); //reverse motor two



brake(4); //Brake all motors



reverse(2); //Reverse motor two

motorSpeed(2,30); //Set motor two to 30% power





reverse(1); //Reverse motor one




Figure 15: First Arduino Code for Performance Test Two.

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goToAbsolutePosition(210); //Previous command goes until 210 marks are achieved


reverse(4); //Reverse all motors








Figure 16: Second code for performance test two

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Figure 17: Efficiency vs. Advanced ratio of Pusher propeller configuration

Figure 18: Thrust vs. percent power for pusher propeller configuration

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goToAbsolutePosition(214); //Previous command goes until 214 marks are achieved




goFor(1); //Previous command goes for 1 sec


goFor(8); //Command goes for 8 secs


motorSpeed(4,35); //Set all moros to 35% power



goFor(2); //Command goes for two secs



goFor(1); //Command goes for 1 sec




goToRelativePosition(-198); //Command goes until 198 marks




goFor(3.2); //Command goes for 3.2 secs


goFor(5.5); //Command goes for 5.5 secs


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goFor(1); //Command goes for 1 sec


Figure 19: Final Performance Test Code

//accelerate motor 1 from 0 to 15% power in 5 seconds

celerate(1,0,15,2.5);

//motor 1 at 15% power for 1 second

motorSpeed(1,15); goFor(1);

//brake motor 1

brake(1);

//accelerate motor 2 from 0 to 27% power in 4 seconds

celerate(2,0,27,4);

//motor 1 goes for 2.7 seconds at 27% power

motorSpeed(1,27); goFor(2.7);

//motor 2 decelerates from 27 to 15% power for 2 seconds

celerate(2,27,15,1);

//decelerate motor 2 from 27% to 15% power in 1 second

brake(2);

//brake motor 2

reverse(2);

//reverse motor 2

celerate(4,0,31,2);

//accelerate all motors from 0% to 31% power in 2 seconds


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//all motors at 35% power for 1 second

brake(2); goFor(3);

//brake motor 2 for 3 seconds

brake(4); goFor(1);

//brake all motors for 1 second

reverse(1);

//reverse motor 1

celerate(1,0,19,2);

//accelerate motor 1 from 0% to 19% power in 2 seconds

motorSpeed(2,35); motorSpeed(1,19); goFor(2);

//motor 2 at 35% power and motor 1 at 19% power for 2 seconds


//all motors at 19% power for 2 seconds

celerate(4,19,0,3);

//decelerate all motors from 19% to 0% power in 3 seconds

Figure 20: Code for motor tests for Preliminary Research and Development

Figure 21: Final performance test score sheet.

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Appendix B

Schedule

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Documents

C r i ti c a l D e s i g n R e v i e w...used to see how two different AEV designs perform on a test monorail. To complete the first performance test, the AEV had to start at the starting