Aerodynamics in vehicle design

AUTONOMOUS CONTROL SYSTEM WITH DRAG MEASUREMENT
CAPABILITY FOR A WIND TUNNEL MODEL VEHICLE
Shadeed Mahmud
Bachelor of Engineering
Electronic Engineering Major
Department of Electronic Engineering
Macquarie University
April 4, 2016
Supervisor: Dr. Sammy Diasinos

ACKNOWLEDGMENTS
I would like to acknowledge and sincerely thank Dr. Sammy Diasinos for his
support, advice and enthusiasm during my thesis at Macquarie University.

STATEMENT OF CANDIDATE
I, Shadeed Mahmud, declare that this report, submitted as part of the requirement for the award of Bachelor of Engineering in the Department of Electronic
Engineering, Macquarie University, is entirely my own work unless otherwise referenced or acknowledged. This document has not been submitted for qualification
or assessment an any academic institution.
Student’s Name: Shadeed Mahmud
Student’s Signature: Shadeed
Date: 1 April 2016

ABSTRACT
Aerodynamics in vehicle design are a crucial aspect of the design as the design
will lead to performance. Often these aerodynamic testing is done in scale models
in a wind tunnel over a rolling road to simulate moving ground. As the ground
under the model car will be moving and the wind speed will be varying the
model vehicle will tend to not remain in its initial position. Therefore structures
such as stints and struts are used to hold the model vehicle in place and a force
transducer attached to the struts measures the aerodynamic drag that the vehicle
is experiencing. However in reality, in normal mode of operation the vehicle
will not have these structures around it therefore why should we have such an
arrangement during testing? To make the testing more realistic and understand
the complete aerodynamics of the model vehicle, an alternative method to hold
the vehicle in place is proposed in this project. This project is about developing
an autonomous control system within the model vehicle that will enable the model
vehicle to hold its position with a high degree of accuracy and also measure the
drag that the model vehicle is experiencing. This way we eliminate any additional
structures around the vehicle that can affect the aerodynamics of the vehicle. This
document is the progress report for the project.

Contents
Acknowledgments iii
Abstract vii
Table of Contents ix
List of Figures xi
List of Tables xiii
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Project Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Literature Review 5
2.1 An Autonomous Control System . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Self-Driving Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Mathematics 9
4 Sensors 11
4.1 Sensor Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.2 Sensor Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5 Implementing Steering Control 17
6 Conclusions 21
Bibliography 23
ix

List of Figures
1.1 Project Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Project Timeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 How self-driving car see the road [2] . . . . . . . . . . . . . . . . . . . . . . 6
3.1 Radius of Curvature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1 Arrangement of LDRs and Laser Dot . . . . . . . . . . . . . . . . . . . . . 12
4.2 Initial Position of LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.3 Incremented Position of LDR . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.4 LDR values corresponding to their position increment . . . . . . . . . . . . 14
4.5 Ultrasonic Sensor Test Arrangement . . . . . . . . . . . . . . . . . . . . . . 14
4.6 Percentage Error in Measurement Using Ultrasound . . . . . . . . . . . . . 15
4.7 Measured Distance vs Actual Distance . . . . . . . . . . . . . . . . . . . . 15
5.1 The Sensor Setup Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5.2 The model vehicle maintaining its position on the running ground . . . . . 18
5.3 The Feedback Computer Interface . . . . . . . . . . . . . . . . . . . . . . . 19
xi

List of Tables
xiii

Chapter 1
Introduction
Wind tunnel testing to understand the aerodynamics of a vehicle has long been a part of
the automotive industry. If a vehicle is to move in space, it will have to move through air
and therefore it will be subjected to air resistance or drag force. Having a body that is
aerodynamic, such that the air resistance faced by the vehicle is reduced to a minimum
where needed and applied where needed, can drastically improve the performance of a
vehicle. In order to understand the aerodynamics of a vehicle, often model vehicle are
subjected to wind tunnel testing. The challenge has always been there to make these
testings as realistic as possible. A rolling road under the vehicle simulates a moving ground
thus contributing towards making the test more realistic. However having a rolling ground
under the model vehicle will cause the vehicle to lose its position when subjected to the
strong wind forces. Therefore structures such as stints and struts are used to hold the
vehicle in position and measure the drag the vehicle is experiencing [3]. The aim of this
project is to come up with an alternative method using which the vehicle can maintain its
position independent of stints or struts thus making the test more realistic. Therefore an
autonomous control system running a mathematical algorithm that can control the vehicle
and maintain position and measure the drag is suggested as a solution. This document is
a progress report of the project.
1.1 Background
The development of such a control system has been attempted by a student at Macquarie
University in NSW Australia. While a lot of preliminary research and work has been
done by the student the mathematics behind the control system needed to be developed
further. A control system was developed which was not fully autonomous and was not
performing within the acceptable tolerance region. There was room for improvement in
this project.
1
2 Chapter 1. Introduction
1.2 Project Overview
This section discusses the overview of the project including the particular project specifications and a timeline of completion of expected stages of the project.
Project Specification
The overall aim of this project is to build an autonomous position maintaining Control
System capable of measuring drag for a wind tunnel model vehicle. The model vehicle
needs to be at the center of the moving ground inside the wind tunnel. There is a
tolerance range of +-5mm making a total of 10mm or 1cm length of total variance that
can be present. At the beginning the vehicle needs to steer itself and maintain position
at a speed of 5m/s. This is illustrated in figure 1.1 below.
Figure 1.1: Project Specifications
The project is broken down in several steps in order to establish a systematic approach
to the design process. The steps are given below.
• Develop algorithm to produce a radius of curvature that the vehicle will turn by
from input parameters of displacement and angle.
• List sensors that can be used to measure displacement and angle.
1.2 Project Overview 3
• Implement steering control such that the vehicle aligns itself when pointing towards
the central target line.
• Implement steering control such that the vehicle aligns itself when pointing away
from the central target line.
• Implement throttle control.
• Implement total system to operate at slow speed (5m/s).
• Implement drag calculation for the model vehicle.
• Implement total system to operate at high speed.
Project Timeline
The project timeline is best illustrated by the aid of a gantt chart. This is given below in
figure 1.2.
4 Chapter 1. Introduction
Figure 1.2: Project Timeline
Chapter 2
Literature Review
2.1 An Autonomous Control System
The focus of this project is the autonomous control system that will allow for the model
vehicle to control itself on the running belt inside the wind tunnel and also allow us to
measure the drag the vehicle is experiencing. The control system will be implemented in
a model car, thus making the car self-driven. This does not mean that we are trying to
build our own self driven car. The focus is the control system, a control system that will
be able to control the vehicle with a high degree of accuracy and measure the drag the
vehicle is experiencing. This gives rise to the question that why do we need to have such
a control system? This is the first step in the design process.
In the automobile industry, especially in motor sports such as F1, aerodynamics of
vehicles is important down to the point where meticulous precision in aerodynamic design
will result in the car being faster by crucial seconds thus making a difference in the final
result of a race or so. In 1972, Colin Chapman showed the way ahead for Formula 1. The
legendary designer and team boss equipped his Lotus 72 with revolutionary aerodynamics. This resulted in Emerson Fittipaldi in winning the World Championship for Lotus.
According to Steven de Groote from F1 Technical, aerodynamics are the most important
factor in the design of a Formula One car [1]. Now, when determining the aerodynamics
of a vehicle, wind tunnel testing for these vehicles is essential.
Wind tunnel testing is done to understand the air flow around the vehicle and hence
determine the aerodynamic of the vehicle. In understating the aerodynamics of a vehicle
the drag that the vehicle experiences due to high speed air flow is a key quantity. Currently
the drag is measured in the following steps. The vehicle is placed on a moving ground
inside a wind tunnel and as the air flows over the vehicle, the drag force causes the vehicle
to move back. Here, structures such as stints and struts are used to hold the vehicle in
position and the force that is applied to hold the vehicle in position is measured and used
to calculate the drag.
While this is a method that has been widely used, we would like to propose an alternative way of doing it. We believe that having any other body attached to the body
of the vehicle will alter the air flow around the vehicle and hence the aerodynamics will
5
6 Chapter 2. Literature Review
be affected. Therefore we would like to develop a control system that will autonomously
control the vehicles position on the moving ground inside the wind tunnel and in doing
so measure the power going to the drive system and calculate the drag the vehicle is
experiencing.
This control system will run an algorithm that will take two parameters as its input.
The first being a horizontal displacement from the target line and the second being an
angle of deviation from the central line. The algorithm will be capable of using these
two parameters to calculate a single radius of curvature and the vehicle will follow that
curvature path and align itself with the central line. We need sensors that will enable us
to sense and input the displacement and angle in real time. Since our control system will
be implemented on a model vehicle therefore we looked at self-driving cars and the set of
sensors such cars use to obtain autonomous driving.
2.2 Self-Driving Car
Self-driven car has been an interesting phenomenon in the past years and technological
giants and automobile giants such as Google and Mercedes respectively and certain other
companies have managed to establish such cars [2]. Since our control system will essentially result in a self-driven car therefore we look at existing technologies and how these
can be used to develop our control system.
Figure 2.1: How self-driving car see the road [2]
2.2 Self-Driving Car 7
From figure 2.1 it can be seen that these car use the following list of sensors.
• Camera
• Radar
• Lidar
A self-driving car uses these sensors along with Global Positioning System (GPS) to
sense its surroundings and plan its path. The GPS is used to navigate from an initial
location to a final destination and the other set of on-board sensors are used to localize
the vehicle with its surroundings. These sensor data are then fed back to a central
processing unit where the data is analysed and instructions are outputted to the vehicle
for autonomous driving. Autonomous manoeuvres such as obstacle detection, obstacle
avoidance, maintaining position and adjusting speed are amongst the common tasks that
a self-driving vehicle would do. Considering the manoeuvres that a self driving vehicle
would do we began to develop a comprehensive list of sensors that we could use to input
data to our autonomous control system. This is given in the next chapter.
8 Chapter 2. Literature Review
Chapter 3
Mathematics
The mathematics behind the control system algorithm is briefly discussed here. When the
vehicle is out of alignment and needs to get back in alignment it will follow a path similar
to an arc of a circle. In order of determine the curvature of that arc it is essential that
we determine the radius of curvature of an imaginary circle that will result in the vehicle
following the arced path and return to its target line. This is illustrated in figure 3.1
below.
Figure 3.1: Radius of Curvature
The inputs to the control system algorithm are two parameters
1. The horizontal displacement of the vehicle from the central line.
9
10 Chapter 3. Mathematics
2. The angle of deviation from the central line.
Taking these inputs and applying the rules from circle theorem geometry and simple
trigonometry we can work out the coordinates of the center of the circle and using that
we can work out the radius of curvature.
The x-coordinate of the center of the circle is given by:

x = h tan( 2 )

(3.1)

180 – θ
Therefore the radius is given by:
radius = x
tan(θ) + h (3.2)
Combining these two equations we can have a single equation that takes the displacement and angle as its input and returns the required radius of curvature. This equation
is given below.

radius = tan(θ) + h

(3.3)

h tan(1802-θ)
where h is the displacement and θistheangle:
Chapter 4
Sensors
Sensors are a key component of this project. We are trying to make this control system
such that it maintains the position of the model vehicle with an accuracy of +-5mm.
Therefore sensors that can provide us with such accuracy are very essential. As discussed
above the inputs to our autonomous control system algorithm that will determine the
radius of curvature that will bring the vehicle back to the central position are a horizontal
displacement and an angle of deviation. So it is necessary that we find sensors that
can measure these quantities. In order to measure the horizontal displacement of the
vehicle, the approach that Nicholas Todesco had taken was to measure the distance of
the vehicle from a side wall. The sensors that he had used were IR proximity sensors.
While measuring proximity is a good way to go about this challenge but the accuracy
of the sensors were an issue. Therefore a research was conducted to find the best suited
sensors for our case. The choice of sensors was bounded by certain attributes. The main
factor was the accuracy of the sensor. In addition to this, the project has a timeline and a
budget, these were constraints that were also incorporated in the choice of sensors. Before
we head into the sensor selection list there are some additional quantities that need to be
measured. Quantities such as voltage and current drawn from the battery by the motor
are required to calculate the drag that the model vehicle is experiencing.
4.1 Sensor Selection
The sensor selection is best described in the form of a table. When researching sensors
the following attributes were considered.
• Range of sensing capability
• Accuracy of sensing
• Frequency of sensing
• Cost of sensor
11
12 Chapter 4. Sensors
• Lead time
In correspondence to these desired attributes a table was constructed and this table
is given at the end of this document titled Table of Sensors. From that table the desired sensors and other required components were chosen and a purchase order form was
completed and authorised by my supervisor for purchase. The purchase order has been
lodged and currently awaiting delivery. This purchase order is also included at the end of
this document titled purchase order form.
4.2 Sensor Test
Once we decided that we are going to use a combination of ultrasound sensors and LDR
array, we needed to test the accuracy of these sensors.
LDR Array and Laser Dot Combination
In testing the LDR, I made a linear translator out of the available equipment in the
laboratory. A set of LDRs were attached side by side and a red laser dot was shone
on one of the LDRs. The linear translator was used to translate the LDR arrangement
until the laser was shining onto the second LDR. The change in output due to change
in resistance of the LDRs upon shining the laser light was recorded corresponding to the
linear translation of the LDRs and a graph was plotted to identify the maximum distance
of translation that can occur before the sensors outputs completely change their values.
The arrangement of the equipment is shown in figure 4.1, 4.2 and 4.3 below.
Figure 4.1: Arrangement of LDRs and Laser Dot
The graph that was obtained is shown in figure 4.4 below and it is seen that as we
increment the position of the LDRs, one sensor value decreases and the other increases
and at an increment of 3mm the value of the second sensor becomes greater that the first
sensor. Hence the difference curve changes quadrant on the plot.
4.2 Sensor Test 13
Figure 4.2: Initial Position of LDR
Figure 4.3: Incremented Position of LDR
Ultrasonic Sensor
The ultrasonic sensor was also tested to determine the accuracy of the sensor. The
distance to an object from the sensor was measured using both the ultrasonic sensor and
a ruler to get the real distance. The distance of the object was varied and recorded. For
each distance value a hundred distance reading samples were taken and the average was
calculated. Then a graph was plotted to understand the difference in measured values
and actual values. The equipment arrangement is shown in figure 4.5 below.
Two graphs were plotted and are shown in figure 4.6 and 4.7 below. It was seen
that the percentage error between the actual values and the measured values fluctuated
between 0 and 0.14
From both the graphs it can be seen that the distance the sensor has an optimum
performance range when it is measuring distance greater than 200mm. This is actually
suitable for our purposes.
14 Chapter 4. Sensors
Figure 4.4: LDR values corresponding to their position increment
Figure 4.5: Ultrasonic Sensor Test Arrangement
4.2 Sensor Test 15
Figure 4.6: Percentage Error in Measurement Using Ultrasound
Figure 4.7: Measured Distance vs Actual Distance
16 Chapter 4. Sensors
Chapter 5
Implementing Steering Control
This chapter talks about the implementation of steering control. The plan was to measure the distance of the vehicle from a side wall and maintain that distance steering
autonomously. Using ultrasonic sensors this can be achieved but this is not good enough
in terms of our tolerance range. Therefore the idea of using an LDR array in combination
a laser dot is introduced. This setup is illustrated with the aid of figure 5.1 below.
Figure 5.1: The Sensor Setup Plan
We would have an array of LDRs and have the laser pointing to the center of the
array. Whenever there is a change in the vehicles’ position the laser will not point to the
center of the array but on the side or corner. Depending on which side or corner of the
LDR array the laser is pointing at the vehicle can be steered so that the laser dot always
point to the center of the array. The LDR array and laser combination was tested and it
was found that a translated movement of 3mm can be detected easily. This is good for us
17
18 Chapter 5. Implementing Steering Control
as we are trying to reach a tolerance range of +-5mm. The size of the LDR array will be
dependant on the accuracy of the ultrasonic sensors. The more accurate the ultrasonic
sensor smaller the array and vice versa.
Keeping this in mind an attempt to implementing the steering control was made just
using the ultrasonic sensor. The outcome of the attempt was very positive. The vehicle
managed to maintain its position in the center of the running ground. The system was not
fully autonomous as the calibration needed to be done manually but once the calibration
was done and auto pilot mode was enabled the control system was able to maintain the
vehicles’ position autonomously. In order to test the autonomous steering of the control
system, whilst the vehicle was in the center of the running ground, I intentionally nudged
the vehicle in both right and left directions to throw it of its alignment and the vehicle was
able to steer back to the center of the running ground. Figures 5.2 and 5.3 below show
the arrangement of the setup and an early version of the computer interface respectively.
The computer interface will be modified later.
Figure 5.2: The model vehicle maintaining its position on the running ground
To illustrate the auto steering I have included the link to a video showing the vehicle
moving back to its target position once thrown of it.
Link: https://youtu.be/nqMSSwhozzg
Even though the vehicle was moving back to the target line, from visual inspection it
can be seen that at times the vehicle is swaying by more than 5mm. So it was not within
our tolerance range but it was very close to it. Therefore I am optimistic that using the
LDR array and laser dot combination we will be able to achieve the level of precision and
tolerance we are trying to achieve. These part have been ordered and waiting for delivery.
19
Figure 5.3: The Feedback Computer Interface
20 Chapter 5. Implementing Steering Control
Chapter 6
Conclusions
In conclusion, this is a progress report that indicates the progress that I have made halfway
into the allocated time for this project. In summary so far the progress that I have made
includes the development of an algorithm that takes two parameters, the displacement and
deviation angle as inputs and outputs a single radius of curvature that steers the vehicle
in position. Next I researched sensors that can realise the inputs needed for my control
system algorithm and conducted tests to determine the accuracy of the sensors. Once
satisfied with the sensor list a purchase order was issued and currently awaiting delivery.
Further I have started to implement the steering control mechanism and program and had
a trial. So far the vehicle is steering autonomously and maintaining position as mention
above. However it is still not within our acceptable tolerance. So further work needs to
be done in terms of achieving the steering tolerance, measuring drag and throttle control
within tolerance.
21

23
24 Chapter 6. Conclusions
Bibliography
[1] S. D. Groote, The Importance of Aerodynamics,” F1 Technical, 2006.
[2] A. Sage, Where’s the lane? Self-driving car confused by shabby U.S. roadways,” Los
Angeles, 2016.
[3] N. Todesco, Control System For A Self-Driving Wid Tunnel Model,” 2014.
25
Table of sensors
Ultrasonic Sensors Laser Sensors Current Sensor
Proximity Sensors Accelerometers Voltage Sensor

Name	Range	Accuracy	Frequency	Cost	Lead time	Source
Parallax Ultrasonic Distance Sensor	2cm – 3m	11 – 12 %	5kHz	Approxima tely AU$40.53 + AU$10.99 (shipping)	9 days – 26 days	ebay http://www.ebay.com.au/itm/Paral lax-PING-Ultrasonic-Sensor- /111915452926?hash=item1a0eae 49fe:g:qGkAAOSwWTRWzgOw
HC-SR04 Ultrasonic Sensor	2cm – 400cm	3mm (0.075% – 15%)	40Hz	AU$1.90	3 days – 9 days	ebay http://www.ebay.com.au/itm/Ultra sonic-Sensor-Module-HC-SR04- Distance-Measuring-Sensor-for arduino-GO- /371496302868?hash=item567ee7 b914:g:2jUAAOSwAKxWWSN1
AU$5.90		ebay http://www.ebay.com.au/itm/Ultra sonic-Module-HC-SR04- Distance-Measuring-Transducer Sensor-For-Arduino-AVR- /121307918976?hash=item1c3e83 f280:g:Er0AAOxy039TN2Rz
IR Proximity sensors	10cm – 80cm	20-40%	25Hz	AU$13.20 AU$4.80 + AU$1.99 (shipping)	22 days – 35 days	ebay http://www.ebay.com.au/itm/GP2 Y0A21YK0F-Y0A21-10-80cm Infrared-IR-distance-sensor Arduino-with-wires- /331522630938?hash=item4d304 9e51a:g:ZrEAAOSwBahVIHiP
ebay http://www.ebay.com.au/itm/For Sharp-1X-GP2Y0A21YK0F-IR Analog-Sensor-Distance-10CM- 80CM-Cable-for-Arduino- /121843335964?hash=item1c5e6d c31c:g:PBYAAOSw1S9Wc9aB
AU$29.95+ AU$7 AU$29.95+ AU$9	2 – 5 working days 2 – 3 working days	Jaycar http://www.jaycar.com.au/Kits%2 C-Science-%26- Learning/Science-Lab Equipment/Specialty Equipment/Linker-Infrared Distance-Sensor-for Arduino/p/XC4585

Parallax Laser Range finder	15cm – 122cm	3% avg 5% max	1Hz	USD 99 + (shipping) Refer to figure 1 below	10 days – 3 months Refer to figure 1 below	RobotShop http://www.robotshop.com/en/par allax-15-122cm-laser rangefinder.html
Adafruit 9- DOF Absolute Orientation IMU Fusion BNO055 Breakout Sensor Module	60 mg	100Hz	AU$46.51 + AU$34.36 (shipping)	9 days – 19 days	ebay http://www.ebay.com.au/itm/Adaf ruit-9-DOF-Absolute-Orientation IMU-Fusion-BNO055-Breakout Sensor-Module- /181739369372?hash=item2a508 2b39c:g:u8EAAOSw6BtVTUbf
Adafruit 9- DOF Accel/Mag/ Gyro+Temp Breakout Board – LSM9DS0 (ADA: 2021)	±60mg	100kHz – 400kHz	AU$41.95 + AU$7.95		ebay http://www.ebay.com.au/itm/Adaf ruit-9-DOF-Accel-Mag-Gyro Temp-Breakout-Board LSM9DS0-ADA-2021- /131696505346?hash=item1ea9b9 3202:g:DgsAAOSwL7VWkL0I
DC Voltage Detector & Sensor Module For Arduino ADC / Great for Battery Monitor	DC0- 25V	Max resolution = 0.00489V	X	AU$5.75	9 days – 10 days	ebay http://www.ebay.com.au/itm/DC Voltage-Detector-Sensor-Module For-Arduino-ADC-Great-for Battery-Monitor- /291479021665?hash=item43dd8 0e861:g:k4cAAOSwZQxW4tU7
DC Voltage Sensor Module Voltage Detector Divider for Arduino DG S	DC0- 25V	1%	X	AU$2.42	3 weeks – 5 weeks	ebay http://www.ebay.com.au/itm/DC Voltage-Sensor-Module-Voltage Detector-Divider-for-Arduino DG S/191736124965?_trksid=p20476 75.c100009.m1982&_trkparms=a id%3D222007%26algo%3DSIC. MBE%26ao%3D1%26asc%3D20 140117125611%26meid%3Ddd7 d67f12a744706a3bfc5a74b1032b 2%26pid%3D100009%26rk%3D 3%26rkt%3D10%26sd%3D25192 0051911

1 Pc 5A Range Current Sensor Module ACS712 for Arduino	0-5A	Output error = 1.5%	X	AU$1.88 + AU$0.26	3 weeks – 5 weeks	ebay http://www.ebay.com.au/itm/1- Pc-5A-Range-Current-Sensor Module-ACS712-for Arduino/321829220524?_trksid= p2047675.c100009.m1982&_trkp arms=aid%3D222007%26algo%3 DSIC.MBE%26ao%3D1%26asc %3D20140117125611%26meid% 3Ddd7d67f12a744706a3bfc5a74b 1032b2%26pid%3D100009%26rk %3D4%26rkt%3D10%26sd%3D2 51920051911
30A Range Current Sensor Module ACS712 ACS712EL C-30A Chip Module (Arduino)	DC0- 30A	1.5%	X	AU$7.87		ebay http://www.ebay.com.au/itm/30A Range-Current-Sensor-Module ACS712-ACS712ELC-30A-Chip Module-Arduino- /181840213940?hash=item2a568 577b4:g:b8YAAOSwLVZV1XZT
Arduino Voltage And Current Sensor Consume Voltage Load Detection Module Hot	DC3- 25V DC0- 3A	Voltage resolution = 0.00489V Current reading accuracy = 2%	X	AU$2.25	3 weeks – 5 weeks	ebay http://www.ebay.com.au/itm/Ardu ino-Voltage-And-Current-Sensor Consume-Voltage-Load Detection-Module Hot/251920051911?_trksid=p204 7675.c100005.m1851&_trkparms =aid%3D222007%26algo%3DSI C.MBE%26ao%3D1%26asc%3D 20140106155344%26meid%3D6 d1cb87087054ec48e047e9958d7f 1b2%26pid%3D100005%26rk%3 D1%26rkt%3D4%26sd%3D1416 25778140

Purchase Order Form

Item No.	Item Description	Price per unit	Number of Units	Total
1	Light dependant resistors	AUD 1.06	25	AUD 26.5
2	2N2222 NPN Transistor	AUD 0.40	25	AUD 10
3	Adafruit 9-DOF Absolute Orientation IMU Fusion BNO055 Breakout Sensor Module	AUD 46.51	2	AUD 93.02 + 34.36 (shipping)
4	30A Range Current Sensor Module ACS712 ACS712ELC- 30A Chip Module (Arduino)	AUD7.87	2	AUD15.74
5	DC Voltage Detector & Sensor Module For Arduino ADC / Great for Battery Monitor	AUD5.75	2	AUD11.50
6	HiTEC HS-422 Servo	AUD 20	1	AUD 20 + 8 (shipping)
7	4051 Multiplexer	AUD 0.724	3	AUD7.24 (10 pack – min order quantity = 10)
8	1050mAh 2 Cell LiPo batteries	USD 15.95	2	USD 31.90 + 21.05 (shipping)
Total:	AUD226.36 + USD52.95 = AUD296.05 (approx)

Student Name:	________________________ Shadeed Mahmud
Student Number:	________________________ 42799627
Project Title:	________________________ Autonomous Control System for a Self-driving Wind Tunnel Model Vehicle
Supervisor Name:	________________________ Sammy Diasinos

Supervisor Signature: Date:

________________________ ________________________ 24/03/16

When completed please submit this form to the Laboratory Manager together with supporting quotation
information and supplier details. Without this information the purchase order cannot be processed.
Please note that if the total budget exceeds $300, the approval of the Head of Department is required to
authorise further purchases.

This document lacks the signature of the supervisor as we have been maintaining digital meeting
records in google calendar. The digital records of the meetings followed by the calendar itself are
attached below for reference.
Consultation Meetings Attendance Form (e-version)
Comments
Discuss Project specifications and
aims
Discuss sensors and testing methods
Discuss sensors and equipment
budget
Sort out room allocation issue due to
laser operation in F9C111 lab
SHADEED MAHMUD Mar 2016 (Eastern Time – Melbourne, Sydney)
2 8 2 9 1 2 3 4 5
6 7 8 9 1 0 1 1 1 2
1 3 1 4 1 5 1 6 1 7 1 8 1 9
2 0 2 1 2 2 2 3 2 4 2 5 2 6
2 7 2 8 2 9 3 0 3 1 1 2
1 2 p m – Weekly
m e e t i n
g for
thesis
1 1 a m – Weekly
m e e t i n
g for
thesis
1 1 a m – Weekly
m e e t i n
g for
thesis
1 1 a m – Weekly
m e e t i n
g for
thesis
Sun M o n T u e W e d Thu Fri S a t