Progress Report #5

Progress Report: Lab 05

Instr. Busick, GTA Chang

Group D: Blake Harriman, Kyle Kottyan, Kyle Pellikan, Joe Sudar

=====================================================================================

Week 4

Backward-Looking Summary

Situation

The items investigated during last lab included: various implementations of the Arduino code, doing test

runs on the track with the different implementations of the code, and examining data collected from the

test runs, and adjusting the final design of the AEV. These items are important because they are the

basis of the final decisions the team needs to make for the AEV design and the code. The test runs were

the most important aspect of the lab because the team can use the observations and data collected

from the test runs to finalize a design that best fits our goals and to adjust the Arduino code to obtain

the most efficient runs on the track.

Data Analysis & Discussion

The data displayed in Figure 1 below details the power usage of the AEV over the course of one test run.

The different phases as labeled each correspond to the different commands that were uploaded to the

Arduino microprocessor. Table 1 describes the different commands used in the test scenario, the time

for each phase, and how much energy each command used.

Figure 1: Power (watts) vs. Time (seconds) during the Arduino test run designated by phase

Table 1: Total Energy Usage by Command of Phases 1-5

Phase Arduino Code Time (seconds) Total Energy (Joules)

1 motorSpeed(4,30) 0.18 0.2666

2 goToRelativePosition(130) 4.62 5171.477169

3 reverse(4) 0.3 422.3269642

4 motorSpeed(4,10)

goFor(1) 0.84 530.5344525

5 brake(4) 2.88 0

Total Energy Used: 6124.605186

The team analysis of this data provides insight on what should be improved with the different

commands uploaded to the Arduino. The change the team immediately recognized was the power

supplied to the Arduino at the beginning and end of the run. The transition from Phase 1 to 2 could be

more quickly reached if the team supplied a higher input of power initially to allow the vehicle to coast

for a short period before requiring more power input. Regarding the “braking phase” or Phase 4, the

power supplied to the reversed propellers should be increased to act as an opposing force to the

forward motion. Making these changes, the team hopes to reduce the energy usage while more

precisely controlling the actions of the vehicle along the track. By continuing to test different coding

scenarios and collecting the data from it, the team will be able to further refine the code based on the

power usage and speed.

Figure 2 below is a MATLAB plot of the energy usage (measured in Joules) versus the distance that was

traveled during the duration of the test run. The data shows that the vehicle gradually used more energy

as the distance traveled increased. When the vehicle reached the braking phase (Phase 4 as seen in

Figure 1), the energy used dramatically dropped. The slight curve in the data can be attributed to the

increase in speed the vehicle experienced during the initial acceleration.

Figure 2: Total energy usage vs. distance traveled

Figure 3 below is a plot of the total energy usage (measured in Joules) versus the elapsed time of the

run. As time passed, the vehicle was supplied with constantly increasing power from the interval of 1 to

5 seconds while executing the command. The remainder of the roughly 9 second interval shows the

cessation of energy supply followed by a small input for the braking phase, which eventually ceases.

Figure 3: Energy usage vs. Time Elapsed

Week 5

Forward-Looking Summary

Situation

For the next lab, the team must prepare for adjusting the AEV design. The test runs the team will have

during next lab will provide an opportunity to observe any parts of the design that may be causing

inefficiencies when the AEV travels along the rail. After collecting the data, the lead designers will get

together and propose changes to the design. After they have come to a consensus, the rest of the team

will get together to determine what changes need to be made to the design. After coming to a

consensus on the changes to the design the coders can then adjust the code accordingly and more test

runs can be made to test the new design and the new code the team has come up with. After multiple

designs and code implementations have been made, the team can then create a concept scoring matrix

to compare the designs and code implementations to each other. This will allow the team to rate each

design and determine the design with the greatest potential for success. Determining which design to

continue with allows for changes to be made to improve overall efficiency of the final AEV.

The main concepts the team needs to focus on for the remainder of the semester include: the Arduino

code, the design of the AEV, visualizing the AEV in SOLIDWORKS, and keeping the website updated.

These responsibilities are important because they are the basis of the whole project and are the most

important aspects to look at when determining the best method to use when writing code for the AEV.

The team will need to do test runs with each small adjustment to the code to make sure that it works

correctly for the design and, if need be, the team needs to make small adjustments to the design and

placement of the components to make the AEV more efficient while on the track. This is where

visualizing the AEV in SOLIDWORKS comes in. Creating the AEV in the program will make it easy for the

team to view all the small aspects of the AEV and making small adjustments to it will make it easier to

do because the changes can be implemented in SOLIDWORKS before an actual adjustment is made.

Keeping the website regularly updated will help the team keep track of the progress that has been made

throughout the semester. The team can use this information to make decisions concerning the Arduino

code, the AEV design, and other related features of the project.

Individual Responsibilities

Blake Harriman:

Blake has the responsibility of coding the Arduino and making decisions on design adjustments for the

AEV. After each test run Blake will adjust the code based on the observations made and data collected

from the run. Blake will also make various implementations for the test runs to find the most efficient

method of completing the tasks given to the AEV. Also, after the test runs, observations made specific to

the effects the design of the AEV had on the run will determine whether changes need to be made to

the design of the AEV and what kind of changes they would be. Changing the code and the design of the

AEV go hand and hand with each other and it will be Blake’s responsibility to oversee this aspect of the

group responsibilities.

Kyle Kottyan:

Kyle K. has been tasked with leading the creation and refinement of the code used to communicate

actions to the Arduino microcontroller and to collect data using MATLAB commands and tools. He, along

with Blake, will adjust and expand the current set of code that has been created in the project thus far.

The goal for them is to work with the hardware to optimize the power usage to achieve the lowest

power usage possible. The organization of the data and code structures will allow Kyle to understand

the fundamental basis of the project to collaborate with the team to achieve a successful product.

Kyle Pellikan:

Kyle P. has the responsibility of taking care of the website to keep track of the progress reports in an

organized and neat fashion. Creating the website will help chronicle the weeks that went into designing,

creating, and testing the AEV. Once a week, Kyle will update the website with new information, detailing

what was accomplished during the week. Kyle is also responsible for the design of the AEV itself. If he

notices anything based on the data collected from the progress reports, he will inform the team to see if

they can make any adjustments to the AEV to improve it. It will be easier to look back on all the progress

reports and the data collected if it is all located in an easy to access website.

Joe Sudar:

Joe’s responsibility is creating a three-dimensional representation of the AEV design is a key element to

the AEV Design Project. Using SOLIDWORKS to model the AEV will allow for small design adjustments to

be made before altering the physical design. This will be crucial when optimizing the AEV’s

aerodynamics comes into play. Without a proper 3D visual, there will be no way compare different

designs to choose which will be the most favorable design. The SOLIDWORKS model also allows for the

creation of a part to increase the efficiency of overall design. This rapid prototyping ensures that many

different designs can be created and applied to the overall design and have its pros and cons assessed

for potential future changes.

 

Weekly Goals

– Prepare for oral presentation by discussing the overall progress of the

project

– Update the u.osu.edu website to include all major documents and

accomplishments made thus far in the semester

– Make design and code adjustments based on the data collected from

test runs to minimize power usage and improve the aerodynamics of

the vehicle

– Improve accuracy of movements while moving on the rails

Appendix

Team Meeting Notes

Meeting 07: February 15, 2017, 7:00 PM, Hitchcock Hall 224 (Open Lab)

Team Members: In Attendance: Job/Responsibility:

Blake Harriman X Data Collector

Kyle Kottyan X Coding

Kyle Pellikan X Meeting Notes

Joe Sudar X Builder/Note Taker

Summary – Ran the AEV on the track to collect data on power usage at different

phases in the run.

– Resolved issues that were encountered while analyzing and calculating

data in MATLAB.

– Delegated permanent jobs for the remainder of the AEV project.

Program Scripts

Arduino Data Collection Scenario Program

//Inner-track testing, about 14 ft. from start to area before gate

//Power setting: 30% power

//Set both motors to 30% power

reverse(4);

motorSpeed(4,30);

//Convert 14 ft. to marks (0.4875 in/mark)

goToRelativePosition(130);

//Reverse motors and run at 10% speed to slow the AEV and then cease motor

//operation

reverse(4);

motorSpeed(4,10);

goFor(1);

brake(4);

MATLAB Data Calculation & Collection Program

clc, clear

%==========================================================================

% Name: Group D: Blake Harriman, Kyle Kottyan, Kyle Pellikan, Joe Sudar

% @author: Kyle Kottyan

% Date: February 16, 2017

% Class: Instr. Busick, GTA Chang – Thurs. 2:20 PM lab

%

% Program Title: Performance Analysis

%

% Program Description: This program extracts the data from the Arduino to

% give information about the power usage of the vehicle during the

% different stages of its movement. The Arduino data is then used to make

% calculations for the quantities described below.

%

%==========================================================================

%% Initial Import & Calculations

load(‘performance_analysis_1′);

% Time calculation (seconds)

time = (te)./1000;

% Current calculation (amps)

current = (ie./1024).*2.46.*(1/0.185);

% Voltage calculation (volts)

voltage = (15.*ve)/1024;

% Distance calculation from marks (meters)

distance = 0.0124.*marks;

% Position calculation (meters)

s = 0.0124.*pos;

% Calculated power usage from voltage and current data sets

power = voltage.*current;

% Total energy sum initialized

% Loop will find the incremental energy and will add the calculated value

% for incremental power to give the overall energy used.

totalEnergy = 0;

for i = 2:149

incremEnergy = ((power(i) + power(i-1))/2)*(time(i)*time(i-1));

totalEnergy = totalEnergy + incremEnergy;

end

% Plots figure with different phases of energy usage

figure(1)

plot(time,power,’LineWidth’,2)

xlabel(‘Time (seconds)’)

ylabel(‘Power (watts)’)

title(‘AEV Power Usage vs. Time’)

grid on

box on

figure(2)

plot(distance,power,’LineWidth’,2)

xlabel(‘Distance (meters)’)

ylabel(‘Power (watts)’)

title(‘AEV Power Usage vs. Distance Traveled’)

grid on

box on

%% Energy Usage Per Phase

% Using Riemann summation methods to determine the amount of power used per

% phase of test run

%==========================================================================

%% Phase 1 Power Usage

xR = 0.18;

xL = 0.0;

iL = knnsearch(time,xL);

iR = knnsearch(time,xR);

P1 = power(iL:iR);

t1 = time(iL:iR);

energyP1 = 0;

% Incremental and Total Energy for Phase 1

for i = 2:size(P1)

incremEnergy = ((P1(i) + P1(i-1))/2)*(t1(i)*t1(i-1));

energyP1 = energyP1 + incremEnergy;

end

%% Phase 2 Power Usage

xR = 4.80;

xL = 0.18;

iL = knnsearch(time,xL);

iR = knnsearch(time,xR);

P2 = power(iL:iR);

t2 = time(iL:iR);

energyP2 = 0;

% Incremental and Total Energy for Phase 1

for i = 2:size(P2)

incremEnergy = ((P2(i) + P2(i-1))/2)*(t2(i)*t2(i-1));

energyP2 = energyP2 + incremEnergy;

end

%% Phase 3 Power Usage

xR = 5.10;

xL = 4.80;

iL = knnsearch(time,xL);

iR = knnsearch(time,xR);

P3 = power(iL:iR);

t3 = time(iL:iR);

energyP3 = 0;

% Incremental and Total Energy for Phase 2

for i = 2:size(P3)

incremEnergy = ((P3(i) + P3(i-1))/2)*(t3(i)*t3(i-1));

energyP3 = energyP3 + incremEnergy;

end

%% Phase 4 Power Usage

xR = 5.94;

xL = 5.10;

iL = knnsearch(time,xL);

iR = knnsearch(time,xR);

P4 = power(iL:iR);

t4 = time(iL:iR);

energyP4 = 0;

% Incremental and Total Energy for Phase 3

for i = 2:size(P4)

incremEnergy = ((P4(i) + P4(i-1))/2)*(t4(i)*t4(i-1));

energyP4 = energyP4 + incremEnergy;

end

%% Phase 5 Power Usage

xR = 8.82;

xL = 5.94;

iL = knnsearch(time,xL);

iR = knnsearch(time,xR);

P5 = power(iL:iR);

t5 = time(iL:iR);

energyP5 = 0;

%Incremental and Total Energy for Phase 1

for i = 2:size(P5)

incremEnergy = ((P5(i) + P5(i-1))/2)*(t5(i)*t5(i-1));

energyP5 = energyP5 + incremEnergy;

end

%% Plot of Total Energy vs. Time and Distance

energyUsage = time .* power;

% Time vs. Energy Usage

figure(1)

plot(time, energyUsage, ‘LineWidth’,2)

title(‘Total Energy Used vs. Time’)

xlabel(‘Time (seconds)’)

ylabel(‘Energy (Joules)’)

grid on

box on

% Distance Traveled vs. Energy Usage

figure(2)

plot(distance, energyUsage, ‘LineWidth’, 2)

title(‘Total Energy Used vs. Distance Traveled’)

xlabel(‘Distance Traveled (meters)’)

ylabel(‘Energy (Joules)’)

grid on

box on

Sample Calculations

Kyle Kottyan – Group D

Time (ms)

Current

(ADC counts)

Voltage

(ADC counts)

Marks

(cumulative wheel counts)

Marks

(position wheel counts)

1020 81 557 6 6

Time:

� = �#

1000 = 1020

1000 = 1.020 ������� (��)

where:

t = time (seconds)

tE = EEPROM time (milliseconds)

Current:

� = �#

1024 ∗ �7 ∗ 1 ���

0.185 ����� = 81

1024 ∗ 2.46 ∗ 1

0.185 = 1.05 ���� (��)

where:

I = current (amps)

IE = EEPROM current (ADC counts)

VR = 2.46 volts (Arduino reference voltage)

Voltage:

� = 15 ∗ �#

1024 = 15 ∗ 557

1024 = 8.16 ����� (��)

where:

V = voltage (volts)

VE = EEPROM voltage (ADC counts)

Supplied Power:

� = � ∗ � = 8.16 ����� ∗ 1.05 ���� = 8.57 ����� (��)

where:

P = power (watts)

V = voltage (volts)

I = current (amps)

Distance:

� = 0.0124 ∗ ����� = 0.0124 ∗ 6 = 0.0744 ������ (��)

where:

d = distance (meters)

marks = wheel counts accumulated by reflectance sensors

Position:

� = 0.0124 ∗ ��� = 0.0124 ∗ 6 = 0.0744 ������ (��)

where:

s = AEV position (meters from starting point)

pos = wheel counts recorded by reflectance sensors

Incremental Energy:

�F = �F + �FHI

2 ∗ �FHI − �F = 8.69 + 8.79

2 ∗ 1.08 − 1.02 = 0.52 ������

where:

Ej = incremental energy (at index j) (Joules)

Pj = power (watts)

tj = time (seconds)

Blake Harriman – Group D

Time (ms)

Current

(ADC counts)

Voltage

(ADC counts)

Marks

(cumulative wheel counts)

Marks

(position wheel counts)

120 78 558 0 0

Time:

� = �#

1000 = 120

1000 = 0.120 ������� (��)

where:

t = time (seconds)

tE = EEPROM time (milliseconds)

Current:

� = �#

1024 ∗ �7 ∗ 1 ���

0.185 ����� = 78

1024 ∗ 2.46 ∗ 1

0.185 = 1.01 ���� (��)

where:

I = current (amps)

IE = EEPROM current (ADC counts)

VR = 2.46 volts (Arduino reference voltage)

Voltage:

� = 15 ∗ �#

1024 = 15 ∗ 558

1024 = 8.17 ����� (��)

where:

V = voltage (volts)

VE = EEPROM voltage (ADC counts)

Supplied Power:

� = � ∗ � = 8.17 ����� ∗ 1.01 ���� = 8.25 ����� (��)

where:

P = power (watts)

V = voltage (volts)

I = current (amps)

Distance:

� = 0.0124 ∗ ����� = 0.0124 ∗ 0 = 0 ������ (��)

where:

d = distance (meters)

marks = wheel counts accumulated by reflectance sensors

Position:

� = 0.0124 ∗ ��� = 0.0124 ∗ 0 = 0 ������ (��)

where:

s = AEV position (meters from starting point)

pos = wheel counts recorded by reflectance sensors

Incremental Energy:

�F = �F + �FHI

2 ∗ �FHI − �F = 8.25 + 8.8

2 ∗ 0.24 − 0.18 = 0.51 ������

where:

Ej = incremental energy (at index j) (Joules)

Pj = power (watts)

Tj = time (seconds)

Joe Sudar – Group D

Time (ms)

Current

(ADC counts)

Voltage

(ADC counts)

Marks

(cumulative wheel counts)

Marks

(position wheel counts)

180 78 558 0 0

Time:

� = �#

1000 = 180

1000 = 0.180 ������� (��)

where:

t = time (seconds)

tE = EEPROM time (milliseconds)

Current:

� = �#

1024 ∗ �7 ∗ 1 ���

0.185 ����� = 78

1024 ∗ 2.46 ∗ 1

0.185 = 1.01 ���� (��)

where:

I = current (amps)

IE = EEPROM current (ADC counts)

VR = 2.46 volts (Arduino reference voltage)

Voltage:

� = 15 ∗ �#

1024 = 15 ∗ 558

1024 = 8.17 ����� (��)

where:

V = voltage (volts)

VE = EEPROM voltage (ADC counts)

Supplied Power:

� = � ∗ � = 8.17 ����� ∗ 1.01 ���� = 8.25����� (��)

where:

P = power (watts)

V = voltage (volts)

I = current (amps)

Distance:

� = 0.0124 ∗ ����� = 0.0124 ∗ 0 = 0 ������ (��)

where:

d = distance (meters)

marks = wheel counts accumulated by reflectance sensors

Position:

� = 0.0124 ∗ ��� = 0.0124 ∗ 0 = 0 ��

��� (��)

where:

s = AEV position (meters from starting point)

pos = wheel counts recorded by reflectance sensors

Incremental Energy:

�F = �F + �FHI

2 ∗ �FHI − �F = 8.28 + 8.81

2 ∗ 0.24 − 0.18 = 0.51������

where:

Ej = incremental energy (at index j) (Joules)

Pn = power (watts)

tn = time (seconds)

Kyle Pellikan – Group D

Time (ms)

Current

(ADC counts)

Voltage

(ADC counts)

Marks

(cumulative wheel counts)

Marks

(position wheel counts)

720 79 558 3 3

Time:

� = �#

1000 = 720

1000 = 0.72 ������� (��)

where:

t = time (seconds)

tE = EEPROM time (milliseconds)

Current:

� = �#

1024 ∗ �7 ∗ 1 ���

0.185 ����� = 79

1024 ∗ 2.46 ∗ 1

0.185 = 1.02 ���� (��)

where:

I = current (amps)

IE = EEPROM current (ADC counts)

VR = 2.46 volts (Arduino reference voltage)

Voltage:

� = 15 ∗ �#

1024 = 15 ∗ 558

1024 = 8.17 ����� (��)

where:

V = voltage (volts)

VE = EEPROM voltage (ADC counts)

Supplied Power:

� = � ∗ � = 8.174 ����� ∗ 1.025 ���� = 8.38 ����� (��)

where:

P = power (watts)

V = voltage (volts)

I = current (amps)

Distance:

� = 0.0124 ∗ ����� = 0.0124 ∗ 3 = 0.037 ������ (��)

where:

d = distance (meters)

marks = wheel counts accumulated by reflectance sensors

Position:

� = 0.0124 ∗ ��� = 0.0124 ∗ 3 = 0.037 ������ (��)

where:

s = AEV position (meters from starting point)

pos = wheel counts recorded by reflectance sensors

Incremental Energy:

�F = �F + �FHI

2 ∗ �FHI − �F = 8.378 + 8.386

2 ∗ . 780 − .720 = 0.50 ������

where:

Ej = incremental energy (at index j) (Joules)

Pj = power (watts)

tj = time (seconds)

Progress Report #4

Team D Progress Report: Week 4

Instr. Dick Busick, GTA Chris Chang

Blake Harriman, Kyle Kottyan, Kyle Pellikan, Joe Sudar

Backward-Looking Summary: Week 3

Situation

The items investigated this lab included: learning techniques for creative design thinking, learn the obstacles that come with creativity, familiarize with AEV kit components, learn basic orthographic drawing, and brainstorm ideas for AEV concept sketch. These items are important because they are the foundation of the AEV. The goal of this lab was to establish a basic concept, or idea, of what the AEV should look like. The team had to decide which main parts they were to use, as well as create a design that was aerodynamic and efficient. The team also need to factor in the design into their code, because changing the design will affect how the AEV will run with the code.

Design Analysis

Blake Harriman:

The motivation behind Blake’s design is balance in exchange for slightly more power usage.  The AEV tilts to the side when put on the track so there needs to be some form of counterbalance to prevent the AEV from falling off the track as it is moving around.  Blake’s solution to this is putting the battery to the side of the AEV.  The arduino will be put on the underside of the base to add more weight to further balance the AEV while it is on the track.  After examining both of the mounts, Blake’s design works best with the T-Shape over the L-Shape.  Wings are located at the back of the T-Shape design to provide further balance to the AEV with the propellers on the side.  Potential problems that Blake’s design has are heavy power consumption and falling off the track if the counterbalance is not properly implemented.  However, the heavy design should allow for the AEV to travel steadily along the track as long as all factors are taken into account when the code is being written.

Kyle Kottyan:

The motivation behind this design was a focus on a light and narrow build to minimize the use of power and maximize the aerodynamics of the vehicle running on the track. This design made use of the T-shaped rail mount to balance the weight of the vehicle to the center. The wing placement in the design is to reduce the possibility of interfering parts in front of the motors. The Arduino microcontroller was placed on top of the vehicle in case the vehicle derails at any point. Considering the addition of a custom part, this design suggests creating a domed front casing to further improve the vehicle’s aerodynamics.

Kyle Pelikan:

The motivation behind this design was that of a fighter jet or plane. The arduino is on the bottom to make room for the battery and the rail mount on the top, as well as the magnet that will be on the front of the AEV. However, with this design most of the weight is on the left side of the AEV causing it to tilt in that direction. This design is also quite large making it a bit heavier and less aerodynamic. In order to counteract this, counterweights would need to be implemented on the other side, not only adding to the cost but as well as increasing the weight of the overall AEV. The propellers are underneath the wings, which are placed on the opposite ends of the T-Shape. A 3D-printed part will also be added to the front in order to make the AEV more aerodynamic, again at the sacrifice of more weight. Overall, the whole problem with this design is that the AEV itself is heavy, draining more power in order to get it moving and wasting more energy.

Joe Sudar:

The motivation behind this design was the similar to a formula one car. The frame of the design was long and began skinny in the front and widened towards the back. The placement of the rail mount in the middle of the base created an off balance lean to the left. The battery placement made up for this, adding weight to the right side to correct the issue.  To keep weight as central as possible, the arduino board was placed on the underside of the AEV design. The wings added weight and width to the design, having an overall negative effect on its efficiency. The overall design was rather large and very heavy. This design was not very economic in its utilization of space. Because of its large shape and unused space, the design used more power than necessary, wasting energy resulting in a relatively low efficiency.

Group Analysis of Final Design:

After taking into consideration all of the pros and cons of each individual design the team has come up with the final design shown in the figure below.

The AEV will use the small rectangular base with the L-Shape mount.  The arduino will be located at the front of the base with the mount located at the back.  Wings will also be placed at the back of base with the propellers attached underneath the wings.  The battery will be attached directly below the mount and, finally, the magnet will be located directly at the front.  The AEV still tilts slightly while it is on the track so, to counterbalance the tilt, the arduino is put to the side of the base.  This version of the counterbalance is more effective than other methods in the individual designs because the position of the counterbalance does not need to be as drastic allowing for more reliable balance.  The weight of the final design is still relatively high which means more power will be needed to move it along, but the team has decided that balance should be prioritized slightly more than efficiency to make sure that the AEV has a smooth journey along the track.

Takeaways:

  • The main consideration for the final design was weight and size. Considering this focus, the custom part to be 3D-printed will focus on improving the aerodynamics of the vehicle.
  • The culmination of the team’s individual designs resulted in an overall better design by considering all of the components involved in each.

Forward-Looking Summary: Week 4

Situation

The purpose of Lab 04 is to investigate the tools that will be used to calculate values for voltage, current, runtime, and reflectance sensor wheel counts. This data will be used to optimize the power usage and the time taken for the vehicle to complete the project’s main task. For Lab 04, the team will need to collaboratively create a test-run scenario to collect preliminary data to then import into MATLAB for interpretation. Plotting the data will provide information about which phases of the test run consume the most energy. The code for the overall project will be constantly updated to reflect testing data.

The second part of Lab 04 is focused on the design analysis tool which will be used to automate data collection and performance analysis. In doing so, the team will be able to collect data in a time-efficient manner resulting in less work and more opportunities to make refinements to the design of the vehicle.

Weekly Schedule

Sunday, February 5, 2017 (1:00 PM)

Houston House 2nd Floor Lobby, Duration: 1-2 hours

Responsibilities:

  • Blake H: Backward looking section Progress Report Week 3 and 4
  • Kyle K: Data Analysis, calculations, plots based on lab 3 data, final revisions of week 2 Report
  • Kyle P: Forward looking sections Progress Report week 3 and 4
  • Joe S: Schedule creation, AEV Design, Orthographic AEV Design

Wednesday, February 8, 2017 (7:00 PM)

Houston House 2nd Floor Lobby, Duration: 1-2 hours

Responsibilities:

  • Blake H: Edit and Finalize Progress Reports Week 3 and 4
  • Kyle K: Upload orthographic AEV drawings finalize Reports
  • Kyle P: Edit and Finalize Progress Reports 3 and 4
  • Joe S: Meeting Notes, Edit Reports, create forward looking schedule

Weekly Goals

  • Complete Progress Reports 3 and 4 (by Thursday, February 9, 2017)
  • Come to final design conclusion (by Thursday, February 9, 2017) through cross-comparing each teammates individual designs
  • Begin formulating Arduino code to perform basic tasks
  • Test Arduino on track

Appendix

Advanced Energy Vehicle Team Concept Designs

Figure 1: Kyle Pellikan’s AEV Design (Design 1)

Estimated Mass: approximately 270 g

Bill of Materials for Design 1 (Kyle P.)
Arduino $100
Motors $19.98
Propellers $0.90
T-shape $2.00
T-arm $3.00
Wheels $15.00
Battery Supports $1.00
Servo Motor $5.95
Count Sensor and Connector $4.00
Total: $151.83

——————————————————————————————————————————————-

Figure 2: Joe Sudar’s AEV Design (Design 2)

Estimated Mass: Approximately 290 g

Bill of Materials for Design 2 (Joe S.)
Arduino $100
Motors $19.98
Propellers $0.90
T-shape $2.00
T-arm $3.00
Wheels $15.00
Battery Supports $1.00
Servo Motor $5.95
Count Sensor and Connector $4.00
Total: $151.83

—————————————————————————————————————————-

Figure 3: Blake Harriman’s AEV Design (Design 3)

Estimated Mass: approximately 300 g

Bill of Materials for Design 3 (Blake H.)
Arduino $100
Motors $19.98
Propellers $0.90
T-shape $2.00
T-arm $3.00
Wheels $15.00
Battery Supports $1.00
Servo Motor $5.95
Count Sensor and Connector $4.00
Total: $151.83

—————————————————————————————————————————-

Figure 4: Kyle Kottyan’s AEV Design (Design 4)

Estimated Mass: approximately 260 g

Bill of Materials for Design 4 (Kyle K.)
Arduino $100
Motors $19.98
Propellers $0.90
T-shape $2.00
T-arm $3.00
Wheels $15.00
Battery Supports $1.00
Servo Motor $5.95
Count Sensor and Connector $4.00
Total: $151.83


Figure 5: Group D’s Final AEV Design

Estimated Weight: 260 g

Bill of Materials for Design 5 (Final Team Design)
Arduino $100
Motors $19.98
Propellers $0.90
T-shape $2.00
T-arm $3.00
Wheels $15.00
Battery Supports $1.00
Servo Motor $5.95
Count Sensor and Connector $4.00
Total: $151.83

Progress Report #3

Team D Progress Report: Week 3

Instr. Dick Busick, GTA Chris Chang

Blake Harriman, Kyle Kottyan, Kyle Pellikan, Joe Sudar

Week 3: Backward Looking Summary

Situation

The items investigated in this lab meeting included reflectance sensor readings and the efficiency of different propeller configurations in a wind tunnel. The reflectance sensors will be used to manage the movement of the AEV along the rails since the reflectance sensors are used to record position in relation to the start and stop points or in relation to a previous point. The reflectance sensors change their readings per the changes from either a reflective to non-reflective surface or vice versa. Using the diameter of the mounted wheel for the vehicle, the team can make more precise measurements as to where the vehicle should go and will go when passed different Arduino commands.

The second portion of the lab was dedicated to taking various relevant measurements of different propeller types and configurations in a wind tunnel. This setup was to simulate the movement of the AEV along the tracks. The data collected from the wind tunnel was to be used to calculate other values such as the propeller efficiency and the advance ratio. These values will be used to determine the most power efficient setting for the vehicle that will move the AEV in a time-efficient manner. The team must determine whether the optimal power setting will even move the AEV, and if so, will it be fast enough to complete the task in the allotted time.

Results & Analysis

Figure 1 below is a plot of the propulsion efficiency versus the advance ratio for all four different configurations of the propellers. This graph is used to determine the most efficient propeller configuration and power setting that will produce the most thrust with the least amount of energy. The tabular data for the 3030-push system can be found in the Appendix in Table 1 and Table 2.

Figure 1: Propulsion Efficiency vs. Advance Ratio for 3030 and 2510 Push and Pull Configurations

Figure 2 is a plot of the thrust scale reading versus the power setting of the Arduino for all propeller configurations used in the wind tunnel testing. For each power setting used in the lab, there was a corresponding thrust value depending on the type of propeller system, push or pull. The push systems demonstrated an increasing trend while the pull systems gradually decreased in thrust as the power setting increased.

Figure 2: Thrust vs. Arduino Power Setting for 3030 and 2510, Push and Pull Configurations

The team learned in the lab that certain functions can be used to tell the AEV how far along to go on the track. This means that the team will have direct control over how far and how long the AEV runs on the track. However, because the AEV cannot stop instantly after the code tells it to stop running, the team must take that into consideration when using said functions to move the AEV. The team has decided to use a 2510 push system at 30% max power for the propellers so the coders must take this into consideration as well when they are writing code. Going forward and backwards have different factors attached to them (power, brake time, etc.) so the coders must split the code into two sections of code. The first section will consist of the code that operates the AEV when moving forward and the second section will consist of the code that operates when the AEV is moving backwards. Each section must be coded differently to adjust for the different factors that the different motions have attached to them.

Errors that occurred during the lab included running out of lab time and the collected data from the wind tunnels did not match the expected data the team got beforehand. The skewed data that was collected from the wind tunnels was problematic because it suggested that the efficiency of the motors the team was using was highly improbable. Running out of time was especially stifling because the team was not able to test the sensors with actual code running instead of having to manually test it.  This means that the team was not able to adequately test the propellers to observe their efficiency while on the track. Therefore, the team must do more testing to determine which propeller is best suited for the AEV design.

Takeaways:

  • Functions to control how far along the track the AEV goes
  • Propeller efficiency
  • Push 2510 configuration at 30% max power to minimize power usage
  • AEV has a rectangle base with L-Shaped mount, wings on the back, and the arduino in the front

Week 4: Forward Looking Summary

Situation

For the next lab, the team must brainstorm and design an AEV concept sketch. They must communicate their ideas clearly, as each individual member will design an AEV concept sketch and show their designs to the rest of the group. Afterwards, the group will get together, and finalize a concept sketch in orthographic view, keeping the design considerations in mind. The team members must describe the main features of their design as well as how it will affect the efficiency of the AEV. They must also consider the weight of the AEV as well as the price of the materials that they will be using for it.

Weekly Schedule

Sunday January 29, 2017 (1:00 PM)

Houston House 2nd Floor Lobby, Duration: 1-2 hours

Responsibilities:

  • Blake H. – Look ahead, understand lab 3 and what is required
  • Kyle K. – Calculations based on collected data
  • Kyle P. – AEV Initial Design
  • Joe S. – Meeting notes and weekly schedule format

Wednesday February 1, 2017 (7:00 PM)

Houston House 2nd Floor Lobby, Duration: 1-2 hours

Responsibilities:

  • Blake H. – Revise Progress Report Week 2
  • Kyle K. – Graph calculated data
  • Kyle P. – Revise Progress Report Week 2
  • Joe S. –  Forward Looking Schedule

Weekly Goals

  • Analyze data and create realistic plots (by Thursday, Feb. 8)
  • Revise Progress Report Week 2 (by Thursday, Feb. 8)
  • Finalize design decision along with measurements of mass

Appendix

Team Meeting Notes

Meeting 03: January 26, 2017, 2:20 PM, 2nd Floor Houston House
Team Members: In Attendance: Job/Responsibility:
Blake Harriman X AEV lab build
Kyle Kottyan X Group D representative to collect wind tunnel data
Kyle Pellikan X AEV lab build
Joe Sudar X AEV lab build


This meeting was during lab time to complete the steps of Lab 02a and 02b. The first portion of the lab consisted of mounting and testing the operation of the reflectance sensors. The second portion involved taking wind tunnel data to help aid with determining the most efficient blade and configuration system.

Goals for next meeting:

  • Have data saved into spreadsheet
  • Calculate missing values and plot the appropriate graphs

Summary:

  • Assembled the AEV with reflectance sensors to ensure proper functioning
  • Took wind tunnel data at the wind tunnel
  • Design for vehicle explained further

Notes:

  • Base of the AEV is the T-Shape
    • Battery located on the underside of back
    • Triangle shaped wings on sides of back
    • Motors/Blades go on underside of wings
    • T-Shaped Rail Mount
    • Arduino underneath base in the middle
    • Battery on top of back towards the side for counterbalance
  • AEV tilts while on track so counterbalance is needed
  • Built most of the AEV
  • Reflection sensors are accurate (forwards and backwards are correct)
Meeting 04: February 4, 2017, 6:00 PM, 2nd Floor Houston House
Team Members: In Attendance: Job/Responsibility:
Blake Harriman X Timekeeper, 2nd lab report work (backward looking), 3rd lab report work
Kyle Kottyan X Wind tunnel calculations, report revision for resubmission
Kyle Pellikan X 2nd report work (forward looking)
Joe Sudar X Notetaker, schedule creator, work for 2nd and 3rd reports


The purpose of this meeting was to discuss the plan to complete a revision for the first progress report and a schedule for completing the next two that are due on Thursday, February 9, 2017.

Goals for next meeting:

  • Fix data in spreadsheet with TA information
  • Revise the first progress report
  • Create individual orthographic sketches for AEV design
  • Complete the next two reports by Thursday, Feb. 9
  • Give portfolio access to all team members and begin updating with progress reports and meeting notes

Summary:

  • Completed calculations from lab data taken during Lab 02b
  • Revised the first Progress Report to fit comments made by TA
  • Completed Weekly Schedule and Goals

Notes:

  • Data looks wrong, plan to contact TA about problem (resolved)
  • Progress Report 2 needs revised and updated with proper/required information

Wind Tunnel Data Tables

Table 1: Wind Tunnel Testing Data for 3030-Push System

Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.01 0 186.4 0
0.1 1856 185.6 10
0.22 2994 185.8 15
0.31 4191 186.1 20
0.4 5209 187 25
0.49 6347 188 30
0.58 7544 189.4 35
0.51 9760 193.5 40
0.67 9760 193.7 45
0.75 10958 195.2 50
0.79 12035 196.1 55
0.83 13413 199.3 60


Table 1 contains the raw data taken from the 3030-Push System tunnel that was tested during lab time. It contains the recorded current, RPM, thrust scale reading, and Arduino power setting. The data was used to calculate the unknown values detailed in Table 2 below.

Table 2: Wind Tunnel Data Analysis for 3030-Push System

Thrust Calibration (g) RPM Power Input (watts) Power Output (horsepower) Power Output (watts)
5.5896 0 0 0.000205894 0.153535133
5.9184 1856 0.074 0.000218005 0.162566611
5.8362 2994 0.2442 0.000214978 0.160308742
5.7129 4191 0.4588 0.000210436 0.156921937
5.343 5209 0.74 0.00019681 0.146761524
4.932 6347 1.0878 0.000181671 0.135472176
4.3566 7544 1.5022 0.000160476 0.119667089
2.6715 9760 1.5096 9.84052E-05 0.073380762
2.5893 9760 2.2311 9.53774E-05 0.071122892
1.9728 10958 2.775 7.26685E-05 0.05418887
1.6029 12035 3.2153 5.90431E-05 0.044028457
0.2877 13413 3.6852 1.05975E-05 0.007902544
Thrust Calibration (Newtons) Propulsion Efficiency (%) Propeller Advance Ratio
0.054833976 0 #DIV/0!
0.058059504 219.6846097 1.187890307
0.057253122 65.64649533 0.736380898
0.056043549 34.20268901 0.526061658
0.05241483 19.83263838 0.42325291
0.04838292 12.45377606 0.347364804
0.042738246 7.966122274 0.292248729
0.026207415 4.860940779 0.225893894
0.025401033 3.187794917 0.225893894
0.019353168 1.952752086 0.201197701
0.015724449 1.36934212 0.183192722
0.002822337 0.21444002 0.164372207


Table 2 contains calculations for the thrust calibration (in grams and Newtons), power input, power output (in horsepower and watts), propulsion efficiency and propeller advance ratio. Data from Table 1 was used to calculate the values in Table 2. The data for Tables 1 and 2 are summarized in Figure 1 and 2, representing the data for the 3030-Push configuration.

Arduino Code

 //@author Kyle Kottyan

 //External Sensors Outside Track

 //Run all motors at 25% power for 2 seconds.

 motorSpeed(4,25);

 goFor(2);

 //Run all motors at 20% power and travel 16 ft. from start

 motorSpeed(4,20);

 goToAbsolutePosition(394);

 //Reverse all motors

 reverse(4);

 //Run all motors at 30% power for 1.5 seconds

 motorSpeed(4,30);

 goFor(1.5);

 //Brake all motors

 brake(4);

 //External Sensors Inside Track

 //All motors at 25% power for 2 seconds

 motorSpeed(4,25);

 goFor(2);

 //Run all motors at 20% power and travel 13.5 ft. from start

 motorSpeed(4,20);

 goToAbsolutePosition(333);

 //Reverse all motors

 reverse(4);

 //Brake all motors

 brake(4);

Progress Report 3: Individual Component

Group D – Kyle Kottyan

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.4 5209 187 25


Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)


T
c=|0.411*(187-200)|=5.343 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)


P
in=7.4*0.4*(25100)=0.74 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration


P
out=Tc*v=((5.3431000)*9.8)*2.8=0.15 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)


J=
2.8(520960)*0.0762=0.4233

Propulsion Efficiency

=(0.150.74)*100=20.27%

——————————————————————————————————————————————-

Group D – Blake Harriman

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.67 9760 193.7 45


Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)


T
c=|0.411*(193.7-200)|=2.589 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)


P
in=7.4*0.67*(45100)=2.2311 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration


P
out=Tc*v=((2.5891000)*9.8)*2.8=0.071 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)


J=
2.8(976060)*0.0762=0.227

Propulsion Efficiency

=(0.0712.2311)*100=3.18%

————————————————————————————————————-

Group D – Joe Sudar

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.49 6347 188 30


Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)


T
c=|0.411*(188-200)|=4.932 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)


P
in=7.4*0.49*(30100)=1.0878 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration


P
out=Tc*v=((4.9321000)*9.8)*2.8=0.14 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)


J=
2.8(634760)*0.0762=0.35

Propulsion Efficiency

=(0.141.0878)*100=12.87%

——————————————————————————————————————————————-

Group D – Kyle Pellikan

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.22 2994 185.9 15


Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)


T
c=|0.411*(185.9-200)|=4.932 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)


P
in=7.4*0.22*(15100)=.244 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration


P
out=Tc*v=((5.749 1000)*9.8)*2.8=0.157 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)


J=
2.8(299460)*0.0762=0.7364

Propulsion Efficiency

=(0.157.244)*100=64.3%

Progress Report #2

Team D Progress Report: Week 3

Instr. Dick Busick, GTA Chris Chang

Blake Harriman, Kyle Kottyan, Kyle Pellikan, Joe Sudar

Week 3: Backward Looking Summary

Situation

The items investigated in this lab meeting included reflectance sensor readings and the efficiency of different propeller configurations in a wind tunnel. The reflectance sensors will be used to manage the movement of the AEV along the rails since the reflectance sensors are used to record position in relation to the start and stop points or in relation to a previous point. The reflectance sensors change their readings per the changes from either a reflective to non-reflective surface or vice versa. Using the diameter of the mounted wheel for the vehicle, the team can make more precise measurements as to where the vehicle should go and will go when passed different Arduino commands.

The second portion of the lab was dedicated to taking various relevant measurements of different propeller types and configurations in a wind tunnel. This setup was to simulate the movement of the AEV along the tracks. The data collected from the wind tunnel was to be used to calculate other values such as the propeller efficiency and the advance ratio. These values will be used to determine the most power efficient setting for the vehicle that will move the AEV in a time-efficient manner. The team must determine whether the optimal power setting will even move the AEV, and if so, will it be fast enough to complete the task in the allotted time.

Results & Analysis

Figure 1 below is a plot of the propulsion efficiency versus the advance ratio for all four different configurations of the propellers. This graph is used to determine the most efficient propeller configuration and power setting that will produce the most thrust with the least amount of energy. The tabular data for the 3030-push system can be found in the Appendix in Table 1 and Table 2.

Figure 1: Propulsion Efficiency vs. Advance Ratio for 3030 and 2510 Push and Pull Configurations

Figure 2 is a plot of the thrust scale reading versus the power setting of the Arduino for all propeller configurations used in the wind tunnel testing. For each power setting used in the lab, there was a corresponding thrust value depending on the type of propeller system, push or pull. The push systems demonstrated an increasing trend while the pull systems gradually decreased in thrust as the power setting increased.

Figure 2: Thrust vs. Arduino Power Setting for 3030 and 2510, Push and Pull Configurations

The team learned in the lab that certain functions can be used to tell the AEV how far along to go on the track. This means that the team will have direct control over how far and how long the AEV runs on the track. However, because the AEV cannot stop instantly after the code tells it to stop running, the team must take that into consideration when using said functions to move the AEV. The team has decided to use a 2510 push system at 30% max power for the propellers so the coders must take this into consideration as well when they are writing code. Going forward and backwards have different factors attached to them (power, brake time, etc.) so the coders must split the code into two sections of code. The first section will consist of the code that operates the AEV when moving forward and the second section will consist of the code that operates when the AEV is moving backwards. Each section must be coded differently to adjust for the different factors that the different motions have attached to them.

Errors that occurred during the lab included running out of lab time and the collected data from the wind tunnels did not match the expected data the team got beforehand. The skewed data that was collected from the wind tunnels was problematic because it suggested that the efficiency of the motors the team was using was highly improbable. Running out of time was especially stifling because the team was not able to test the sensors with actual code running instead of having to manually test it.  This means that the team was not able to adequately test the propellers to observe their efficiency while on the track. Therefore, the team must do more testing to determine which propeller is best suited for the AEV design.

Takeaways:

  • Functions to control how far along the track the AEV goes
  • Propeller efficiency
  • Push 2510 configuration at 30% max power to minimize power usage
  • AEV has a rectangle base with L-Shaped mount, wings on the back, and the arduino in the front

Week 4: Forward Looking Summary

Situation

For the next lab, the team must brainstorm and design an AEV concept sketch. They must communicate their ideas clearly, as each individual member will design an AEV concept sketch and show their designs to the rest of the group. Afterwards, the group will get together, and finalize a concept sketch in orthographic view, keeping the design considerations in mind. The team members must describe the main features of their design as well as how it will affect the efficiency of the AEV. They must also consider the weight of the AEV as well as the price of the materials that they will be using for it.

Weekly Schedule

Sunday January 29, 2017 (1:00 PM)

Houston House 2nd Floor Lobby, Duration: 1-2 hours

Responsibilities:

  • Blake H. – Look ahead, understand lab 3 and what is required
  • Kyle K. – Calculations based on collected data
  • Kyle P. – AEV Initial Design
  • Joe S. – Meeting notes and weekly schedule format

Wednesday February 1, 2017 (7:00 PM)

Houston House 2nd Floor Lobby, Duration: 1-2 hours

Responsibilities:

  • Blake H. – Revise Progress Report Week 2
  • Kyle K. – Graph calculated data
  • Kyle P. – Revise Progress Report Week 2
  • Joe S. –  Forward Looking Schedule

Weekly Goals

  • Analyze data and create realistic plots (by Thursday, Feb. 8)
  • Revise Progress Report Week 2 (by Thursday, Feb. 8)
  • Finalize design decision along with measurements of mass

Appendix

Team Meeting Notes

Meeting 03: January 26, 2017, 2:20 PM, 2nd Floor Houston House
Team Members: In Attendance: Job/Responsibility:
Blake Harriman X AEV lab build
Kyle Kottyan X Group D representative to collect wind tunnel data
Kyle Pellikan X AEV lab build
Joe Sudar X AEV lab build


This meeting was during lab time to complete the steps of Lab 02a and 02b. The first portion of the lab consisted of mounting and testing the operation of the reflectance sensors. The second portion involved taking wind tunnel data to help aid with determining the most efficient blade and configuration system.

Goals for next meeting:

  • Have data saved into spreadsheet
  • Calculate missing values and plot the appropriate graphs

Summary:

  • Assembled the AEV with reflectance sensors to ensure proper functioning
  • Took wind tunnel data at the wind tunnel
  • Design for vehicle explained further

Notes:

  • Base of the AEV is the T-Shape
    • Battery located on the underside of back
    • Triangle shaped wings on sides of back
    • Motors/Blades go on underside of wings
    • T-Shaped Rail Mount
    • Arduino underneath base in the middle
    • Battery on top of back towards the side for counterbalance
  • AEV tilts while on track so counterbalance is needed
  • Built most of the AEV
  • Reflection sensors are accurate (forwards and backwards are correct)
Meeting 04: February 4, 2017, 6:00 PM, 2nd Floor Houston House
Team Members: In Attendance: Job/Responsibility:
Blake Harriman X Timekeeper, 2nd lab report work (backward looking), 3rd lab report work
Kyle Kottyan X Wind tunnel calculations, report revision for resubmission
Kyle Pellikan X 2nd report work (forward looking)
Joe Sudar X Notetaker, schedule creator, work for 2nd and 3rd reports


The purpose of this meeting was to discuss the plan to complete a revision for the first progress report and a schedule for completing the next two that are due on Thursday, February 9, 2017.

Goals for next meeting:

  • Fix data in spreadsheet with TA information
  • Revise the first progress report
  • Create individual orthographic sketches for AEV design
  • Complete the next two reports by Thursday, Feb. 9
  • Give portfolio access to all team members and begin updating with progress reports and meeting notes

Summary:

  • Completed calculations from lab data taken during Lab 02b
  • Revised the first Progress Report to fit comments made by TA
  • Completed Weekly Schedule and Goals

Notes:

  • Data looks wrong, plan to contact TA about problem (resolved)
  • Progress Report 2 needs revised and updated with proper/required information

Wind Tunnel Data Tables

Table 1: Wind Tunnel Testing Data for 3030-Push System

Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.01 0 186.4 0
0.1 1856 185.6 10
0.22 2994 185.8 15
0.31 4191 186.1 20
0.4 5209 187 25
0.49 6347 188 30
0.58 7544 189.4 35
0.51 9760 193.5 40
0.67 9760 193.7 45
0.75 10958 195.2 50
0.79 12035 196.1 55
0.83 13413 199.3 60


Table 1 contains the raw data taken from the 3030-Push System tunnel that was tested during lab time. It contains the recorded current, RPM, thrust scale reading, and Arduino power setting. The data was used to calculate the unknown values detailed in Table 2 below.

Table 2: Wind Tunnel Data Analysis for 3030-Push System

Thrust Calibration (g) RPM Power Input (watts) Power Output (horsepower) Power Output (watts)
5.5896 0 0 0.000205894 0.153535133
5.9184 1856 0.074 0.000218005 0.162566611
5.8362 2994 0.2442 0.000214978 0.160308742
5.7129 4191 0.4588 0.000210436 0.156921937
5.343 5209 0.74 0.00019681 0.146761524
4.932 6347 1.0878 0.000181671 0.135472176
4.3566 7544 1.5022 0.000160476 0.119667089
2.6715 9760 1.5096 9.84052E-05 0.073380762
2.5893 9760 2.2311 9.53774E-05 0.071122892
1.9728 10958 2.775 7.26685E-05 0.05418887
1.6029 12035 3.2153 5.90431E-05 0.044028457
0.2877 13413 3.6852 1.05975E-05 0.007902544
Thrust Calibration (Newtons) Propulsion Efficiency (%) Propeller Advance Ratio
0.054833976 0 #DIV/0!
0.058059504 219.6846097 1.187890307
0.057253122 65.64649533 0.736380898
0.056043549 34.20268901 0.526061658
0.05241483 19.83263838 0.42325291
0.04838292 12.45377606 0.347364804
0.042738246 7.966122274 0.292248729
0.026207415 4.860940779 0.225893894
0.025401033 3.187794917 0.225893894
0.019353168 1.952752086 0.201197701
0.015724449 1.36934212 0.183192722
0.002822337 0.21444002 0.164372207


Table 2 contains calculations for the thrust calibration (in grams and Newtons), power input, power output (in horsepower and watts), propulsion efficiency and propeller advance ratio. Data from Table 1 was used to calculate the values in Table 2. The data for Tables 1 and 2 are summarized in Figure 1 and 2, representing the data for the 3030-Push configuration.

Arduino Code

 //@author Kyle Kottyan

 //External Sensors Outside Track

 //Run all motors at 25% power for 2 seconds.

 motorSpeed(4,25);

 goFor(2);

 //Run all motors at 20% power and travel 16 ft. from start

 motorSpeed(4,20);

 goToAbsolutePosition(394);

 //Reverse all motors

 reverse(4);

 //Run all motors at 30% power for 1.5 seconds

 motorSpeed(4,30);

 goFor(1.5);

 //Brake all motors

 brake(4);

 //External Sensors Inside Track

 //All motors at 25% power for 2 seconds

 motorSpeed(4,25);

 goFor(2);

 //Run all motors at 20% power and travel 13.5 ft. from start

 motorSpeed(4,20);

 goToAbsolutePosition(333);

 //Reverse all motors

 reverse(4);

 //Brake all motors

 brake(4);

Progress Report 3: Individual Component

Group D – Kyle Kottyan

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.4 5209 187 25

Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)

Tc=|0.411*(187-200)|=5.343 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)

Pin=7.4*0.4*(25100)=0.74 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration

Pout=Tc*v=((5.3431000)*9.8)*2.8=0.15 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)

J=2.8(520960)*0.0762=0.4233

Propulsion Efficiency

=(0.150.74)*100=20.27%

——————————————————————————————————————————————-

Group D – Blake Harriman

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.67 9760 193.7 45

Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)

Tc=|0.411*(193.7-200)|=2.589 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)

Pin=7.4*0.67*(45100)=2.2311 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration

Pout=Tc*v=((2.5891000)*9.8)*2.8=0.071 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)

J=2.8(976060)*0.0762=0.227

Propulsion Efficiency

=(0.0712.2311)*100=3.18%

————————————————————————————————————-

Group D – Joe Sudar

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.49 6347 188 30

Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)

Tc=|0.411*(188-200)|=4.932 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)

Pin=7.4*0.49*(30100)=1.0878 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration

Pout=Tc*v=((4.9321000)*9.8)*2.8=0.14 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)

J=2.8(634760)*0.0762=0.35

Propulsion Efficiency

=(0.141.0878)*100=12.87%

——————————————————————————————————————————————-

Group D – Kyle Pellikan

Sample Calculations

Propeller Diameter (m) Wind Tunnel Velocity (m/s) Power Supply (Volts) Current (amps) RPM Thrust Scale Reading (g) Arduino Power Setting (%)
0.0762 2.8 7.4 0.22 2994 185.9 15

Calibrated Thrust

Tc = Calibrated thrust (g)

T = Thrust scale reading (g)

T0 = Thrust scale reading at 0% power (g)

Tc=|0.411*(185.9-200)|=4.932 grams

Power Input

Pin=V*I*(P%100) V = Power supply voltage (volts)

I = current (amps)

P%= Arduino power setting (%)

Pin=7.4*0.22*(15100)=.244 volts

Power Output

Pout=Tc*v Tc = Calibrated thrust (Newtons)

v = Wind tunnel velocity (m/s)

 

Tc-Newtons=Tc-grams(/1000)*9.8 Tc – Thrust calibration

Pout=Tc*v=((5.749 1000)*9.8)*2.8=0.157 volts

Advance Ratio

v = Wind tunnel velocity (m/s)

D = diameter of propeller (m)

J=2.8(299460)*0.0762=0.7364

Propulsion Efficiency

=(0.157.244)*100=64.3%

Progress Report #1

Team D Progress Report: Week 2

Instr. Dick Busick, GTA Chris Chang

Group D: Blake Harriman, Kyle Kottyan, Kyle Pellikan, Joe Sudar

Backward Looking Summary: Week 2

Situation

The items investigated during lab two included: coding Arduino, testing the motors, testing the blades, and testing the efficiency of the battery.  The importance of coding the Arduino was to expose the group to how some of the commands work when setting up how the AEV will be moving and stopping when on the rails.  The goal of testing the motors, blades, and battery was to see how fast the blades and motors responded to the implemented code to see how important and precise the timing needs to be for maximum efficiency while the AEV is on the rails.  During the lab, half of the team worked on coding the Arduino while the other half worked on planning the layout of the AEV.

The coders implemented the code in a way that allowed the team to see how fast the blades would speed up to the desired power, and how long it would take them to stop completely when braking. After running and editing the code multiple times, the team observed that the motors have some input and output lag to them. This is most likely attributed to the time that it takes for the Arduino to properly process the code and turn those instructions into actual actions. This means that timing is important for the code for the AEV to start and stop as quickly and as efficiently as possible.  Other than the input and output lag, the motors seemed to be efficient in terms of power which means the blades should run smoothly while the AEV is on the rails.

The team came up with a design using the T-shape template as the base as our initial draft.  The battery will go on the underside of the back of the T-shape while the Arduino will be located near the front of the base.  Wings will go on the sides of the base near the back and will hold the motors and blades on the sides of them.   With this setup, the team believes that the equally spread weight in addition to the location of the wings, motors, and blades, will allow the AEV to run efficiently, while, at the same time, having minimal time to start up and to slow down.  The team plans to 3D print a part out to add to the front of the AEV to make it more Aerodynamic without adding a significant amount of weight to it.

Problems during the lab were caused by coding errors and improper code input into the Arduino.  The first few implementations of the Arduino code failed completely because of how the code was input meaning the motors did not work and the blades did not respond at all.  After making sure errors did not exist in the code, the team input the code into the Arduino again and waited slightly longer after uploading the code to the Arduino.  For the first few trials, errors occurred that included: only half of the code running, the motors starting and stopping abruptly rather than over the desired time they were supposed to start and stop, and the code running in the incorrect order.  Editing to the code solved some of these problems, but by the end of the lab, there were still a few problems that the team had not solved.  Further editing and research into coding the Arduino will allow the team to overcome these issues in the future.

The first lab of the AEV project was focused on learning the fundamentals of the hardware and software that would be used in the lab.  The second lab focuses on collecting, observing and interpreting data from the vehicle. The second lab involves working with the reflectance sensors that will enable more precise movement of the AEV along the metal rails. The reflectance sensors will be installed on the vehicle and connected to the Arduino so that readings may be taken from it. The overall purpose of the lab is to become familiar with the way that the AEV reacts during its movement.

Takeaways:

  • Arduino code uploaded to the microcontroller in order to execute actions.
  • The AEV will be precisely controlled through the proper and clever organization of the provided Arduino functions.
  • Space on the vehicle must be seriously considered to create a sturdy, light and power-efficient product.

Forward Looking Summary: Week 3

Situation

The second portion of Lab 2 explores the energy consumption and thrust production of the AEV. The team is required to determine the efficiency of the AEV propulsion system by subjecting different propeller configurations to wind tunnel testing. The wind tunnel is used to simulate the motion of the AEV by supplying the voltage equivalent of the lithium battery that will be used to power the vehicle. The combination of the thrust produced by the propellers as well as the energy supplied from the battery will enable the movement of the AEV along the metal tracks. One goal of the project is to create a system that uses the least amount of power so determining the optimal motor speed for the Arduino and the greatest thrust-producing blade setup is crucial to produce the most efficient movement of the AEV. Collecting this data will require the team to do a further analysis to determine whether the optimal motor speed percentage will effectively and efficiently move the vehicle.

For the second lab, the team decided to divide the next lab’s tasks amongst the team. For the first part of the lab, Joe, Kyle P., and Blake are to put together the AEV and rig the reflectance sensors for testing. Kyle K. will act as a representative for the team to collect wind tunnel data on different propeller sizes and systems (push or pull). After collecting data and ensuring that the reflectance sensors are oriented properly, code will be written to test the Arduino on the inner and outer tracks.

Upon completion of Lab 2 and looking forward to Lab 3, there are multiple tasks that the group plans to accomplish. First and foremost, the group wishes to create a rough 3-dimensional representation of the AEV using the program SOLIDWORKS. After completing the 3-D representation the physical AEV will be assembled. Outside of the AEV work, a rough code will be written to begin to troubleshoot the AEV project. The documentation of the project will be consistently updated as different goals are completed. An overall weekly goal is to complete and edit progress reports that follow the rubric and answer all guided questions.

Weekly Schedule

Sunday, January 22, 2017 (1:00 PM)

Houston House 2nd Floor Lobby, Duration: 1 – 2 hours

Responsibilities:

  • Blake H. – Backward looking plan for Progress Report 2, record meeting minutes notes and discussion
  • Kyle P. – Establish weekly schedule and goals, organize progress report format, revise report
  • Kyle K. – Code Arduino scenario and test it using propellers, forward looking plan for Progress Report 2
  • Joe S. – Revise report

Wednesday, January 25, 2015 (7:00 PM)

Houston House 2nd Floor Lobby, Duration: 1 – 2 hours

Responsibilities:

  • Blake H. – Revise report
  • Kyle P. – Revise report
  • Kyle K. – Revise report
  • Joe S. – Revise report

Weekly Goals

  • Assemble, format and revise Progress Report 2
  • Discuss design of AEV considering weight and aerodynamics

Appendix

Meeting Minutes

Meeting 01: January 19, 2017, 2:20 PM, 208 Hitchcock Hall
Team Members: In Attendance: Job/Responsibility:
Blake Harriman X Coder/Scenario Tester
Kyle Kottyan X Coder/Scenario Tester
Kyle Pellikan X Established online portfolio (u.osu.edu/aevtabled/)
Joe Sudar X Notetaker

This meeting was focused on preparing and organizing the team’s materials, online portfolio, and the software to be used to code the vehicle to run on the ceiling rails. To ensure that the software was properly installed on the lab computer, Kyle K. and Blake H. translated the pseudocode from scenario one in the lab manual into the corresponding functions given to us during the lab.

Goals for next meeting:

  • Brainstorm design for team’s vehicle

Summary:

  • Project Google Drive folder created for the team’s documents
  • Online project portfolio created for the team
  • Arduino software installed on lab computers with proper settings
  • Motors tested using translated test scenario pseudocode

Notes:

  • Base(T)
    • Battery located on back
    • Arduino placement?
    • Wings on back w/ motors
    • Rail mount(T)
Meeting 02: January 25, 2017, 7:00 PM, 2nd Floor Houston House
Team Members: In Attendance: Job/Responsibility:
Blake Harriman X Backward looking report section, notetaker
Kyle Kottyan X Forward looking section, coder
Kyle Pellikan X Schedule creation, report editor
Joe Sudar X Goal creation, report editor

This meeting scheduled to complete the contents for the first progress report. At this meeting, we additionally created a schedule to organize future meetings.

Goals for next meeting:

  • Finalize AEV design to begin building

Summary:

  • Discussed the contents of the first progress report to be submitted
  • Previewed the next lab and discussed the uses of the data collected
  • Created goals for Lab 02 and Lab 03

Notes:

  • Revised progress report 1
  • Created schedule for meetings (Sundays, Wednesdays)
  • Discussed Lab 02 and 03

Arduino Code

Scenario 1:

 //Accelerate motor 1 from 0 to 15% power in 2.5 seconds

 celerate(1,0,15,2.5);

 //Run motor 1 at 15% max power for 1 second

 motorSpeed(1,15);

 goFor(1);

 //Brake motor 1

 brake(1);

 //Accelerate motor 2 from 0 to 27% max power in 4 seconds and go for 2.7 seconds

 celerate(2,0,27,4);

 goFor(2.7);

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

 celerate(2,27,15,1);

 //Brake and reverse motor 2

 brake(2);

 reverse(2);

 //Accelerate both motors from 0 to 31% max power in 2 seconds

 celerate(4,0,31,2);

 //Run both motors at 35% max power for 1 second

 motorSpeed(4,35);

 goFor(1);

 //Brake motor 2

 brake(2);

 //Run motor 1 at 35% max power for 3 seconds

 motorSpeed(1,35);

 goFor(3);

 //Brake both motors for 1 second

 brake(4);

 goFor(1);

 //Reverse motor 1

 reverse(1);

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

 celerate(1,0,19,2);

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

 motorSpeed(2,35);

 motorSpeed(1,19);

 goFor(2);

 //Set both motors to speed 19% of max speed for 2 seconds

 motorSpeed(4,19);

 goFor(2);

 //Decelerate both motors from 19 to 0% in 3 seconds

 celerate(4,19,0,3);

 //Brake both motors

 brake(4);

AEV Designs

AEV Designs Created By Each Member

 

Final Design Created By Team