Digest care 2015 vol. 3 no.1

Cetakan Pertama / First Printing, Oktober 2015
Hakcipta / Copyright  Politeknik Ungku Omar
Hakcipta terpelihara.
Tidak dibenarkan mengeluarkan ulang dalam apa jua bentuk dan dengan apa jua cara
sama ada elektronik, fotokopi, mekanikal, mahupun lain-lain, mana-mana bahagian,
ilustrasi atau kandungan Diges Ilmiah CARe ini sebelum mendapat keizinan bertulis
daripada Politeknik Ungku Omar.
All rights reserved.
No part of this publication may be reproduced or transmitted in any form or by any
means, whether by electronic, mechanical, photocopying, or other, any part, or
illustrations of this Digest, without prior permission from Polytechnic Ungku Omar.
Diterbitkan di Malaysia oleh / Published in Malaysia by
Politeknik Ungku Omar, Ipoh, Perak
www.puo.edu.my
ISSN 2289-5388

i
SIDANG REDAKSI
PENAUNG
MEJAR (K) DATO’ HJ. MD. NOR BIN YUSOF
PENGARAH POLITEKNIK UNGKU OMAR (PUO)
PENASIHAT
MEJAR (K) ZAIRI BIN OSMAN
KETUA JABATAN KEJURUTERAAN MEKANIKAL (JKM), PUO
KETUA EDITOR
DR. CHOONG CHEE GUAN
PEGAWAI PENYELARAS CARe (CENTRE of AIR-CONDITIONING AND REFRIGERATION), PUO
EDITOR
DR. SAW CHUN LIN
KETUA ELEMEN KOLABORASI CARe, PUO
EN. LIM SEE MENG
KETUA ELEMEN PENERBITAN CARe, PUO
EN. DIDI ASMARA BIN SALIM
PENOLONG KETUA ELEMEN PENERBITAN CARe, PUO

ii
SENARAI KANDUNGAN
SIDANG REDAKSI i
SENARAI KANDUNGAN ii
KATA-KATA ALUAN PENGARAH iii
KATA-KATA ALUAN KETUA JABATAN KEJURUTERAAN MEKANIKAL iv
EXPERIMENTAL DESIGN OF MAGNETIC GENERATOR USING NEODYMIUM MAGNETS:
ROTATIONAL MOTION INFLUENCES AGAINST POWER GAIN USING DIRECT POWER SUPPLY
1-6
LIM SEE MENG
MOHD NAZRI MOHD SABRI
CHOONG CHEE GUAN
SAW CHUN LIN
THERMAL MODELLING of SOLAR INTEGRATED COLLECTOR WATER HEATING SYSTEM
7-16
SAW CHUN LIN
MUHAMMAD REDZUAN CHE NOORDIN
DIDI ASMARA SALIM
CHOONG CHEE GUAN
ROTATIONAL MOTION INFLUENCES AGAINST POWER GAIN
17-24
ROHIMI YUSOF
MOHAMAD AZHAR SAIDIN
CHOONG CHEE GUAN
SAW CHUN LIN
EFFECT OF BIOFUEL ON THE EMISSION OF FOUR STROKE MOTORCYCLE ENGINE COMBUSTION
25-32
AHMAD NAWIR ABD RANI
NORSIHAN MOKHTAR
SAW CHUN LIN
CHOONG CHEE GUAN

iii
PERFORMANCE OF MAGNETIC GENERATOR INFLUENCED BY LOAD VARIATIONS
33-39
DZULKHAZMI LUTPI
MOHD AZLAN JAMALUDDIN
CHOONG CHEE GUAN
SAW CHUN LIN
FATIGUE DAMAGE ASSESSMENT USING STOCHASTIC PROCESS BY INCORPORATING SAMPLING
DATA WITH THE PROBABILISTIC MODEL
40-51
SALVINDER SINGH KARAM SINGH
MOHAMAD AZHAR SAIDIN
CHOONG CHEE GUAN
SAW CHUN LIN

iv
KATA ALUAN TUAN PENGARAH
Assalamua’laikum w.b.t dan salam sejahtera.
Syukur saya ke hadrat Allah S.W.T kerana limpah kurniaNya kita dengan jayanya
dapat menerbitkan Diges Ilmiah CARe Volume 3 No. 1 / Oktober 2015 Politeknik
Ungku Omar. Sekalung tahniah diucapkan kepada barisan Ahli Jawatan Kuasa
Penerbitan yang bertungkus lumus bekerjasama dan memberi komitmen yang
padu sehingga terhasilnya Diges Ilmiah CARe.
Penulisan ilmiah merupakan salah satu cabang penting dalam memastikan
percambahan ilmu pengetahuan. Manakala pembudayaan ilmu pula merupakan pra-syarat penting ke arah
pembinaan sahsiah yang unggul, pembangunan bangsa yang kuat serta pembinaan tamadun yang tinggi.
Dengan demikian, Politeknik Ungku Omar (PUO) juga tidak ketinggalan dalam membudayakan ilmu
melalui penulisan ilmiah ini dan seterusnya menghasilkan Diges Ilmiah ini demi perkongsiaan ilmu yang
akhirnya akan menjurus kepada percambahan ilmu pengetahuan.
Diges Ilmiah CARe ini merupakan satu platform yang membolehkan warga PUO berkongsi ilmu
pengetahuan yang mana ia memuatkan pelbagai kajian yang telah dijalankan dalam bidang pengajaran dan
pembelajaran kejuruteraan untuk dimanfaatkan bersama oleh semua termasuk warga luar politeknik.
Usaha murni ini adalah bertujuan untuk menyampaikan maklumat sejajar dengan proses penghijrahan atau
transformasi pendidikan tinggi hari ini serta mewujudkan para pendidik yang mempunyai sikap
membudayakan bidang penyelidikan, perkongsiaan maklumat dan mewujudkan inovasi baru untuk panduan
umum.
Kajian dan maklumat yang diterbitkan ini diharap dapat dimanfaatkan ke arah melahirkan satu tamadun
yang berilmu, berinovasi dan mempunyai minda kelas pertama. Semoga usaha penerbitan ini dapat
diteruskan demi menjadikan PUO sebagai institusi rujukan ilmu yang terbilang setanding dengan institusi
pengajian tinggi yang lain. Akhir kata saya, merakamkan setinggi-tinggi penghargaan dan terima kasih
kepada semua yang terlibat dalam penghasilan Diges Ilmiah CARe ini.
Sekian, terima kasih.
MEJAR (K) DATO’ HJ. MD. NOR BIN YUSOF
Pengarah
Politeknik Ungku Omar

v
KATA ALUAN
KETUA JABATAN KEJ. MEKANIKAL
Assalamua’laikum w.b.t dan salam sejahtera.
Saya ingin memanjatkan kesyukuran ke hadrat Allah S.W.T kerana dengan
keizinanNya kita berhasil menerbitkan Diges Ilmiah CARe Volume 3 No. 1 /
Oktober 2015 Politeknik Ungku Omar. Tahniah saya ucapkan kepada Ahli
Jawatankuasa Unit Penerbitan yang bekerja keras merealisasikan penerbitan Diges
Ilmiah CARe.
Diges Ilmiah CARe ini merupakan satu wadah bagi warga PUO berkongsi ilmu
pengetahuan yang melibatkan pelbagai kajian yang telah dijalankan dalam
pelbagai bidang pengajaran dan pembelajaran kejuruteraan untuk dimanfaatkan bersama oleh semua warga
politeknik.
Pelbagai maklumat dapat disalurkan sejajar dengan proses penghijrahan atau transformasi pendidikan tinggi
hari ini bagi mewujudkan warga pendidik yang berdaya saing dan mempunyai sikap membudayakan ilmu
pengetahuan. Secara tidak langsung, ianya dapat mendekatkan warga pendidik dengan pelajar khususnya
dan masyarakat amnya.
Komuniti setempat sejujurnya amat menghormati warga pendidik kerana golongan ini dianggap pemangkin
kemajuan dan menyuburkan nilai-nilai murni di dalam kehidupan seharian. Maka dengan adanya platform
sebegini, ia dapat memberi nilai tambah kepada warga pendidik.
Kajian dan maklumat yang diterbitkan ini diharap dapat dimanfaatkan ke arah melahirkan satu tamadun
yang berilmu, berinovasi dan mempunyai minda kelas pertama. Akhir kata saya selaku Ketua Jabatan ingin
merakamkan setinggi-tinggi penghargaan dan terima kasih kepada semua yang terlibat dalam penghasilan
Diges Ilmiah CARe ini.
Sekian, terima kasih.
MEJAR (K) ZAIRI BIN OSMAN
Ketua Jabatan
Jabatan Kejuruteraan Mekanikal
Politeknik Ungku Omar

DIGES ILMIAH CARe VOL. 3 NO. 1 OKTOBER 2015
1
EXPERIMENTAL DESIGN OF MAGNETIC GENERATOR USING
NEODYMIUM MAGNETS: ROTATIONAL MOTION INFLUENCES AGAINST
POWER GAIN USING DIRECT POWER SUPPLY
Lim See Meng,
Mohd Nazri Mohd Sabri,
Choong Chee Guan,
Saw Chun Lin
Abstract
An experimental design of Magnetic generator by using neodymium magnet is designed to
instructor as an additional teaching method to the student in order to understand the
properties and principle of permanent magnet influence on the electrical power.
Experiment is conducted on the design in order to analyse and to determine the rate of
electrical power produce over time by using a constant power supply. The experiment also
determined the relationship of the rotational motion with the power produce over the time
by using a constant power supply.
Keywords: Magnetic generator, neodymium magnets, rotational motion, power gain
Introduction
Electricity is an important component in today's human life. There is a wide range of
usage, for example lighting up house electrical appliances and also without electricity
humans will be living like how the Stone Age did. Unfortunately, electricity costs have
been steadily escalating and the cause of this is the reliance of power producers on crude
oil to keep the generators going. Energy resources on the planet earth is also running out,
besides that pollution's and natural disasters are increasing due to the over usage of earths
energy.
Hence, this research has proposed an idea to produce free energy electricity through
magnetism [4]. A green motor produces no pollution as it runs. Magnet powered motors
will not need gasoline and does not get hot. Through this innovation life will become safer
and affordable. Magnetic generator supplier can be used in any houses to reduce this
burden. If this project is brought under the spotlight to be developed in future, it can be
used in a larger scale and can be used to power up electrical appliances more efficiently.
Furthermore, automatically this step to develop this technology will help towards bringing
a greener world.

2
Figure 1: Magnetic Generator with coupling shaft as power transmission design with 12V
DC Inverter
Using magnets as a source of energy is considerably cheaper than making use of other
alternative renewable sources of energy such as the sun and wind [3]. This energy source
is not exactly a new idea however the methods for producing magnetic motor free energy
at an affordable price has only emerged recently. Magnetic motor energy has emerged as
one of the top energy sources for the following reasons, as it is consistent and enduring.
Magnetic motor energy can also provide power for a large range of appliances throughout
the home for years on end without interruption. Another benefit of magnetic motor energy
is that a minimum of unveiled the world first commercial machine that can power a house
from a permanent, clean, green and virtually free energy source. Another enormous
benefit of magnetic motor energy is that it is not locked to any specific location. Solar
generators and wind generators are reliant on the sun and wind to some extent respectively
and if conditions are not optimal, they will not be able to produce the required or even any
amount of energy. In the case of magnetic motor free energy, however, there are no
limitations in terms of the location in which energy can be produced. These generators are
completely stand-alone and can be employed in almost any situation that can be imagined.
LED/Bulb
Driven
Magnet
Switch
12V DC
Inverter Neodymium Magnet
Dynamo Motor
Multimeter
LED Switch
Driver Magnet

3
Literature Review
The theory for the project is by using magnetic effect to produce electricity. Electric
generator is a device that converts mechanical energy to electrical energy. A generator
forces electric current to flow through an external circuit. The source of mechanical
energy may be a reciprocating or turbine steam engine, water falling through a turbine or
waterwheel, an internal combustion engine, a wind turbine, a hand crank, compressed air,
or any other source of mechanical energy an electric generator is a device that converts
mechanical energy to electrical energy. A generator forces electric current to flow through
an external circuit. For example in this project the use of neodymium magnets is used to
rotate the generator.
A neodymium magnet, the most widely used type of rare-earth magnet, is a permanent
magnet made from an alloy of neodymium, iron and boron to form the Nd2Fe14B
tetragonal crystalline structure. Developed in 1982 by General Motors and Sumitomo
Special Metals, neodymium magnets are the strongest type of permanent magnet
commercially available [2]. They have replaced other types of magnet in the many
applications in modern products that require strong permanent magnets, such as motors in
cordless tools, hard disk drives and magnetic fasteners.
One of the main theories that support this project is the Faraday's Law of Induction.
Faraday's law of induction is a basic law of electromagnetism predicting how a magnetic
field will interact with an electric circuit to produce an electromotive force (EMF) a
phenomenon called electromagnetic induction. It is the fundamental operating principle of
transformers, inductors, and many types of electrical motors, generators and solenoids [1].
Analysis on Magnetic Generator
Results and Calculation
Power (watt, W) = Potential difference (volt, V) x current (amp, A)
P = VI
Potential Differences, V = Current (Amp, A) x Resistance, (ohm)
V= IR

4
by substituting;
Power, P = VI
P = (IR) I
P = I2
R (Watt)
Table 1: Experimental Results
Time(s) Output
Current,
mA
Output
Voltage,
V
Power
gain, W
RPM
30 0.40 2.5 1.00X10-3
600
60 0.37 2 0.74X10-3
598
90 0.34 1.8 0.61X10-3
594
120 0.31 1.5 0.47X10-3
558
150 0.30 1.4 0.42X10-3
494
180 0.28 1.2 0.34X10-3
458
Results and Discussions
a. Time against current graph plot
Graph 1: Time against Current
Based on the graph shown above, the amount of current produced is 0.3mA for 90
seconds. It started to decrease from 0.3mA to mA when it reaches 120 seconds. This was
due to the driven motor was getting hot. When the motor starts to heat up, the rotation of
the driven motor started to decrease from 600 rpm to 594 rpm. The reduction causes the
amount of current produced decreased. But, after 120 seconds the amount of current
produce started to be constant again until 180 seconds. Thus, it can be deduced that as
time increasing the amount of current produce is constant and the rotation per minute is
also constant.

5
b. Time against power graph plot
Graph 2: Time against Power
The graph shows that, the power gains produced by the magnetic generator design were
constant. The reading of power gains is 0.56m Watt when the magnetic generator design
running for 30 seconds until 90 seconds. After that the power was decreasing from 0.56m
Watt to 0.53m Watt, the power gains then constant again for about 60 sec until reaching
180 seconds. Thus, it can be deduced that as time increasing the amount of power gains
are constant.
c. Relationship of power gains and rotations per minute
Graph 3: Relationship of power and rotations per minute
Based on the graph shown above, the speed of rotation per minutes was at constant against
power gain by magnetic generator design. When the magnetic generator was running for
about 30 seconds until 90 seconds, the reading of power gains was 0.56m Watt and the
speed of the rotation per minutes during that time was 600 rpm. After that, on 90 seconds
to 120 seconds the power gains decreases from 0.56m Watt to 0.53m Watt. During that
time, the speed of rotation per minutes decreased from 600 rpm to 594 rpm. And on 90
seconds to 180 seconds, the graph was showing a constant reading of power gains.

6
Reading shown 0.53m Watt and the rotation motion was 594 rpm. Thus, it can be deduced
that as time increasing the amount of power gain is constant and the rotations per minute
is also constant.
Conclusion
From this experimental study, it can be concluded, by using 12 Volts DC inverter power
supply, the electrical power produce was constant as time increasing. The power source
used influences the rotations per minute of the driven magnets. If the power supply is
constant, the rotations per minute are also constant. It can be deduced that, as power input
increases, the rotations per minute produce of driven magnets increase.
As a power supply is constant, the value of the power gain and the current will also be
constant. It can conclude that, the higher the power input the higher the power gain and
current will be produced during experimental.
References
[1] James Shipman, Jerry Wilson, Aaron Todd, James Shipman, Jerry Wilson, Aaron
Todd. 2010. Introduction to Physical Science, Revised Edition. 12th
Ed. United States:
Brooks/ Cole Cencage Learning.
[2] K.H.J Buschow, F.R. de Boer. 2003. Physics of Magnetism and Magnetic Materials.
1st
Ed. New York: Springer Science & Business Media.
[3] Satya Prakash. 2007. Physics Vol. 1 and 2, Revised Edition. 3rd
Ed. India: V.K.
Enterprises.
[4] Raymond Davidson a/l David Silva, Clement Emang Yusup Ngau, Mohamad Munzir
Mohd Khalil Khasah, Putra Nur Fitri Nordin. 2015. Magnetic Generator Supplier
(MGS). Diploma in Mechanical Engineering. Ipoh, Perak: Polytechnic Ungku Omar.

7
THERMAL MODELLING of SOLAR INTEGRATED COLLECTOR WATER
HEATING SYSTEM
Saw Chun Lin,
Muhammad Redzuan Che Noordin,
Didi Asmara Salim,
Choong Chee Guan
Abstract
Solar collectors are used to absorb sunlight and converted it into heat energy to produce
hot water. In current situation, solar water heater uses electricity to heat water in the night
time. Many phase changes materials recommended for solar application to reduce the
dependency of electric heater such as paraffin wax, calcium chloride hexahydrate, calcium
nitrate tetrahydrate and other materials but not all are enhancing the performance of solar
collector. In this research, the objective is to develop 3D simulation model and simulate
the heat transfer and the fluid dynamics in system and solve using computational fluids
dynamic (CFD) technique. The simulation model developed is used to validate the
experimental results. In the first stage, 3D model is modelled and developed in ANSYS
version 15.0. Two cases are compared that are without PCM cases. At the second stage,
3D simulation integrating with energy model, turbulent model and radiation model are
included. The results shows that the performance of the The temperature results from
simulation of hot water for the modeling without PCM (air) range from 52.9o
C (325.9K)
to 63.3o
C (336.3K). The percentage of error between experiment result and simulation
result for the modeling without PCM is not above than 9.2% for hot water temperature
produced.
Keywords: Solar collector, CFD, 3D modelling, 3D simulation, hot water
1.0 Introduction
A solar intergrated collector (ICS) water heating system is simply a combination of
collection and storage in a single unit. Its shappe is not very complex, making it easier and
cheaper for manufacturing. The elimination of a separate vertical storage tank and the
collector from the conventional solar heater makes it cost effective. However, it has a
relatively low efficiency [1,2]. Though the basic idea on solar energy storage has not
changed, many interesting solar collectors have been proposed and tested. Some examples
of these are water-filled oil barrels as solar collectors [3], a solar collectoir with a sand-
mix concrete absorber with burried-in ground [4], an air collector including rock particles
as the absorber [5], and a metallic box solar collector [6]. De Biejer [7], described the
development of a novel ICS system that incorporates two cylindrical tubes, an outer
absorbing tube coated with selective surface and an inner storage tube. Transparent

8
insulation material (TIM) represents a special class of thermal insulation in which
transparent cellular array is immersed in an air layer. TIM is transparent in nature and
reduces unwanted heat losses. The performance characteristics of solar transparent
honeycomb-insulated passive hot water system using both water and ground as
collector/storage was investigated [4,8]. A comparative study of TIM-insulated cuboids
ICS system was carried out by Reddy and Kaushika [9].
The numerical methods most commonly used in the literature were based on the enthalphy
method and the effective heat capacity method. The heat exchanger enhancement analysis
in a finned PCM storage with the heat exchanger was investigated both numerically and
experimentally [10-13]. In this paper, The experimental measurement of Solar Water
Heater is validated by computational fluids dynamics simulation on Without PCM case
from 1.00PM to 5.00 PM that mainly discuss on the temperature of the system.
2.0 Simulation for the case without PCM with experimental measurement
The ANSYS software enables to predict with confidence that their products will thrive in
the real world. The ANSYS software helps ensure product integrity and drive business
success through innovation. This software also used to simulating foresees how product
designs will behave and how manufacturing processes will operate in real-world
environments .After the construction of three dimension modeling in the half scale
completed, we change from the half scale modeling to the real prototype scale. In ANSYS
FLUENT interface, the half scale model is rescaled back same as actual prototype.
3.0 Governing equations for water enclosure
The equations describe momentum and energy transfers in free convection originate from
conversation principles. The inertia and viscous forces remain important,as does energy
transfer by convection and diffusion. The governing equations for this model can be
written as
Continuity equation:-
+ + =0
(1)
Navier-stokes equation X-momentum:-
+ ( ) (2)

9
Energy equation:-
= ) (3)
The net head flux and ambient temperature are given as boundary conditions for top
surface, whereas the bottom and side surfaces are adiabatic conditions. The properties of
insulations materials as shown in Table 1.
Table 1 Properties of insulation material [14]
Material Density (kg/m3
) Thermal
conductivity
(W/mK)
Maximum
temperature (˚C)
Felt wool 136 – 168 0.039 -
Fiberglass wool 12 – 80 0.0032 – 0.040 450
Cork board 144 0.042 -
Mineral rockwool 32 – 176 0.032 – 0.035 650
3.1 Governing Equations for PCM enclosure : Melting and solidification Model
The governing equations for transient analysis of melting of the phase-change
material include the the Navier-Stokes (momentum) equations, the continuity equation
and the energy equation. Boussinesq approximation is used to model the bouyancy forces.
The equations are given in tensor notation as :
Continuity equation :
⃗=0 (4)
Momentum equation :
⃗⃗
+ ⃗⃗⃗⃗ ⃗⃗⃗⃗=- ⃗⃗⃗⃗+ ⃗ ) (5)
Energy equation :
( ⃗. = (6)
For the solid PCM and the eclosure, the continuity and momentum equations can be
ignored because there is no convection effect on the materials. The energy equation is
given as:-
c ( )= ) (7)
The subscript s denotes the solid PCM or the enclosure. The energy balance for the solid-
liquid interface in the melting process is expresses as:-
| - | = (8)

10
Where S is the solid-liquid phase-change interface ; n is the normal of the solid-liquid
interface and L the latent heat of PCM fusion. In the solidification process, the subscripts
l and s are interchanged and the latent heat of fusion L is replaced with-L in Equation (8).
4.0 Numerical solution
The numerical modeling and analysis og the solar intergrated collector storage
system has been carried out using the commercial computational fluids dynamics (CFD)
software fluent. The geometry modeling and mesh were generated in GAMBIT. An
enthalphy porosity technique is used in FLUENT for modeling the solidification/melting
process. In this technique, the melt interface was not tracked explicity. Instead, a quantity
called the liquid fraction, which iundicates the fraction of the cell in the domain. The
liquid fraction has been computed based on the enthalphy balance. The meshy region is a
region in which the liquid fraction lies between o and 1. The meshy zone was modeled as
pseudo porous medium in which porosity decreases from 1 to 0 as the material solidifies.
When the material has fully solidified in the cell,porosity becomes zero and, hence, the
velocities drop to zero. Some assumptions were made in the numerical calculations : the
heat conductivity and density of the phase-change material and the enclosures are constant
; the values for the PCM were chosen as average of the solid and liquid material
properties. The problem was solved in two-dimensional domain. The heat transfer in the z
direction and the convection heat transfer coefficient in the liquid PCM during the
solidification process have been neglected.
5.0 Experimental procedures
5.1 Simulation Model
3D model of solar water heater collector with dimension of 50cm x 50 cm is drawn
in AUTODESK INVENTOR before being export to ANSYS FLUENT for simulation.
The 3D as shown in Figure 1.

11
Figure 1 : Assembly of all part with isometric view
6.0 Result and Discussions
Table 2 shows the result of 10o
C inclination solar collector without PCM simulation by
using ANSYS FLUENT software. As can be seen from the result, the average solar
radiation is decreasing per one hour time interval after solar noon. This is due to the
temperature changes at each one hour time intervals as it is approaching late evening.
Each interval, the surrounding temperature will drop from peak hour which is 1:00PM
until 5:00PM. Due to this, since the amount of solar radiation produced reduced, the
ambient temperature also reduced respectively.
As the average solar radiation reduced at each interval, the average air temperature is also
reduced. At 1:00 PM, the result from the simulation shows that the average air
temperature inside PCM storage is 73.7o
C while at 5:00 PM the average air temperature is
44.6o
C. The average air temperature is decreased as the average solar radiation is
decreased.
The result also shows that the average collector plate temperature also decreased at each
interval. This is due to the ambient temperature dropping at each interval. From the table,
it shows that, the temperature changes of average collector plate temperature from 1:00
PM to 2:00 PM is difference as compare to others intervals at 2:00 PM to 3:00 PM and
4:00 PM to 5:00 PM. The temperature changes of average collector plate temperature is
slightly smaller which is 5.4o
C while the others intervals are at 9.8o
C. The others intervals

12
are constantly decreased at 1:00 PM to 2:00 PM, is actually the peak hour of the radiation
produced by the sun (solar noon). During this peak hours, the ambient temperature are
dropping lesser as compare to other time which is 3.00 PM to 5.00 PM. Thus, it can be
deduce that, the average solar radiation is directly proportional to average collector plate
temperature
During peak hour which is at 1:00PM to 2:00PM, the average hot water temperature is
increasing from 61.5o
C to 63.3o
C as obtained from simulation. After 2:00 PM till 5:00 PM
the average hot water temperature are decreased as average solar radiation are decreased.
This may be due to the temperature surrounding at peak hour (1:00 PM to 2:00 PM) are
decreasing slightly lesser as compare to other hour (3:00 PM to 5:00 PM).
Table 2: Result of 10o
inclination solar collector without PCM
Figure 2 shows the relation between average solar radiation, average collector plate
temperature and average hot water temperature. As can be seen, the solar radiation is
decreased from 752.0W/m2
to 330.3W/m2
at 1.00 PM to 5.00 PM. Hence, the average
collector plate also decreasing. When solar radiation decreased, the average collector plate
also decreased. The solar radiation is directly proportional to average collector plate.
Average solar radiation and average collector plate at 1:00 PM is the highest as compared
to others intervals because it is within peak hour/solar noon, which is 752.0W/m2
and

13
84.5o
C. Meanwhile, the value of average hot water at 1.00 PM is 61.5o
C and increased to
63.3o
C at 2.00 PM. The others intervals, the average solar radiation, average collector
plate temperature and average hot water temperature are decreased.
Figure 2 : Relations between average solar radiation, average collector plate
temperature and average outlet temperature
5.2 Temperature Contour of Solar Water Heater Collector
Figure 3 : Temperature Contour a) 1.00 PM b) 2.00 PM
84.5
79.1
69.3
59.5
49.7
61.5 63.3
59.6
55.8
52.9
752
665.5
576
451.4
330.3
0
100
200
300
400
500
600
700
800
40
50
60
70
80
90
100
0 1 2 3 4 5 6
Temperature(°C)
Hours (PM)
Average Collector Plate
Temperature (°C)
Average Hot Water
Temperature (°C)
Average Solar Radiation
(W/m²)
Averagesolarradiation
(W/m2)
a)
b)

14
Figure 4 : Temperature Contour a) 3.00 PM b) 4.00 PM
Figure 5 : Temperature Contour at 5.00 PM

15
Figure 3 to 5 show that the isometric view (all part) without PCM. It shows the relations
between solar radiation and the average glazing temperature for the second glazing. As
can be seen from the above Figure 3 ,the highest average solar radiation is at 1.00 PM
(solar noon) as compared to other’s interval which is 752.0 W/m2
and the average top
glazing 2 temperature for is 80.4o
C. While, at the others interval time, the solar radiation
decreased from 665.5 W/m2
to 330.3 W/m2
from 2.00 PM until 5.00 PM as it is
approching night. Since the amount of solar radiation produced is reduced, the average top
glazing temperature also reduced respectively. The average of top glazing 1 temperature at
2.00 PM is 80.4 o
C, 3.00 PM is 73.1 o
C,4.00 PM is at 50.5 o
C and at 5.00 PM is 40.2 o
C. It
can be deduce that, when the amount of solar radiation also reduced, the average top
glazing temperature reduced.
5.3 Hot Water Temperature Contours
Figure 6: Temperature contours a)1.00 PM b) 2.00 PM c) 3.00 PM
a) Without PCM (1PM) b) Without PCM (2PM)
c) Without PCM (3PM)
M

16
Figure 6 shows the hot water temperature contours at 1.00 PM without PCM case (Air)
that is 61.5o
C, while at 2.00 PM is 53.0o
C and 3.00 PM is 59.6o
C.
8.0 Conclusions
Simulation model has been successfully developed. The hot water temperature results
from the simulation without PCM (air) range from 52.9o
C to 63.3o
C from 1.00PM to
5.00PM of solar radiation harvested. The percentage of error between experiment result
and simulation result for the modeling without PCM is not more than 9.2% for hot water
temperature produced.
References
[1] Bar-Cohen, A. 1978. Thermal Optimization of Compact Solar Water Heaters. Sol,
Energy, 20 (2), pp. 143-196.
[2] Shimdt, Ch., Geotzberger,A., and Shmidt, J. 1988. Test Results and Evaluation of
Integrated Collector Storage Systems With Transparent Insulation. Sol. Energy,
41(5), pp. 487-494
[3] Reis, A., Albuquerque, P., Almedia, F., Duarte, J., Martins, J., and Pereira, R. 1982.
Water Heating by Means of Solar Energy Collecting Barrels. Solar Collector
storage, Alternative Energy Sources IV, Vol. 1. Veziro, T. N, ed. Ann Arbour
Science, Ann Arbor, MI, pp. 101-111.
[4] Reddy, K. S., Avanti, P., and Kaushika, N. D. 1999. Finite Time Thermal Analysis of
Ground Integrated-Storage Solar Water Heater With Transparent Insulation Cover.
International. Journal of Energy Res., 23, pp, 925-420.
[5] Hamdan, M. A. 1998. Investigation of an Inexpnsive solar Collector storage System,
Energy Convers. Manage., 39 (5-6), pp. 415-420.
[6] Audi, M. S. 1992. Experimental Study of Solar Space Heating Model Using
Jordanian Rocks for Storage. Energy Convers. Manage., 33 (9), pp. 883-842.
[7] De Beijer, H. A. 1998. Product Development in Solar Water Heating. Proc. of 5th
World Renewable Energy Congress, Pergamon Press, Florence, Italy, pp. 201-204.
[8] Geotzberger, A., and Rommel, M. 1987. Prospects for Intergrated Storage Collector
System in Europe. Sol. Energy, 39, pp. 211-219.
[9] Reddy, K. S., and Kaushika, N. D. 1999. Comparative Study of TIM Cover System
for Intrgrated-Collector-Storage Water Heaters. Sol. Energy Mater. Sol. Cells, 58,
pp. 431-446.
[10] Bonacina, C., Comini, G., Fasano, A., and Primicerio, M. 1973. Numerical Solution
of Phase-Change Problems. Int. J. Heat Mass Transfer, 16, pp. 1825-1832.
[11] Alexiades, V., and Solomon, A. D. 1993. Mathematical Modelling of Melting and
Freezing Processe. Hemisphere Pub., Washington.
[12] Stritih, U., and Novak, P. 2000. Heat Transfer Enhancement at Phase Change
Processes. Proc. of 8th International Confrence on Thermal Energy Storage, Vol. 1,
Terrastock Pub., Stuggart, pp. 333-338.
[13] Ahmet , K., Ozmerzi, A., and Bilgin, S. 2002. Thermal Performance of a Water-
Phase Change Material Solar Collector. Renewable Energy 26(3), pp. 391-399.
[14] Marian Jacobs Fisk, H. C. and William Anderson. 1982. Introduction to Solar
Technology. Addison–Wesley Publishing Company, Inc, pp. 68–70.

17
NEODYMIUM MAGNETS: ROTATIONAL MOTION INFLUENCES AGAINST
POWER GAIN
Rohimi Yusof,
Mohamad Azhar Saidin,
Choong Chee Guan,
Saw Chun Lin
Abstract
An experimental design for the application of magnetic generator using Neodymium
magnet is produced. The design can be used as one of the additional teaching aids in order
to understand the properties and principle of permanent magnet influences on the
electrical power. Experiment is conducted on the design in order to analyse and to
determine the rate of electrical power produce over time. The experiment can also
determine the relationship of rotational motion with the electrical power produce as the
time is linearly increasing.
Keywords: Magnetic generator, neodymium magnets, rotational motion, power gain
Introduction
Magnetic Generator is one of the free energy converters that were design to produce a free
energy. Producing a ways to obtain a free energy is one of the steps to counter the
exhausting energy problem. As we all know, the fuel that was use nowadays currently
depleting and exhausting. This because, petroleum is a non-renewable energy sources and
in order to extract fuel, we need a large amount of fossil fuel. Unfortunately, fossil fuel
takes thousands of years to generate. Due to that, petroleum is considered as non-
renewable energy. Therefore, magnetic generator was design and invented to counter the
issues. The generator will produce energy by the application of magnetism. Analysis need
to be produce in order to increase its efficiency, performance and usage toward creating a
free energy. By the ends of the days, a generator able to be produce and could acts as
other energy sources despite the petroleum, the non- renewable energy.

18
Figure 1: Magnetic Generator with shaft as power transmission design
The magnetic generator is design based on the cooling fan as shown in Figure 1. The
cooling fan serves as the motor to run the generator. The neodymium magnet is attached at
certain location or point on the motor. The dynamo serves as the alternator. It will
generate electrical energy and power then channel them onto the provided electrical
appliances. The provided appliances are bulb or LED light. There are two generators
installed on the board. The first alternator will be connected with pulley and belting whiles
the other one is connected with shaft. Each of these power transmitters can be attached
and detached easily. The voltmeter is also installed on the board. It will measure the
amount of voltage generated when the magnetic generator is running.
Figure 2: The position of the Magnetic Generator components
LED/Bulb
Driven
Magnet
Switch
12V Battery
Neodymium Magnet
Dynamo Motor
Multimeter
LED Switch
Driver Magnet

19
The location of the components for magnetic generator that were installed on the board is
shown in Figure 2. Both power transmissions were detached.
Literature Review
Concept of Electromagnetism
Work is done when charges change its positions. The charge will change its position along
the electric field lines. The work is done either involving the increasing of kinetic energy
or storing the potential energy. The kinetic energy is denotes as positive work while the
potential energy denotes as the negative work [4].
Figure 3: Permanent magnet motor with linear magnets and fields
Figure 3 shows the function of a self-energized permanent magnet motor. The magnetic
motor will produce a rotary power by applying this concept. A North Pole neodymium
magnet is attached as part of the rotor [2]. This is done in order to examine the magnetic
force field situation in a 90° sector A-A about the stator position. It is assume that, upon
the shaft, the rotor is mounted in a convenient fashion, attached with a flywheel in order to
release and store the energy [4].
The flywheel through the shaft macro work needs to be furnished. This is because; macro
work is needed in order to drive the rotor during the first half of its travel through sector
A-A. It is different during the second half of the sector. The rotor on the shaft
accomplished the work. The work is back through the shaft and energy is stored in the
flywheel. When the flywheel is rotate, the system produces a rotational motion. Rotational
motion is measured by using radians, revolution or degree. Radians can be defined with
the angle for the arc length is equal to radius of the circle.
Ohm law

20
In 1826 a German scientist George Simon Ohm took a series of observations of current
and voltage by applying potential difference (V) across various metallic conductors and
expressed them in the form of a law called Ohm’s law. Accordingly, if the physical
conditions (e.g., temperature, pressure etc.) of a metallic conductor remain unchanged,
then the current produced in a conductor is directly proportional to the potential difference
applied across its two ends [3].
Figure 4: Voltage against Current
Where R is a constant of proportionality and is called the electrical resistance of the
conductor. Equation (1) is called Ohm’s law. If graph is plotted between applied potential
differences (V) and current (I) produced for a given conductor at constant temperature, the
graph is in the form of a straight line (Fig.4). Ohm’s law holds for metallic conductors [3].
Electrical Resistance
The resistance of a conductor is defined as the ratio of potential difference applied and
current flowing through the conductor, i.e. [3].
Electrical Energy
An electric current is the flow of charge, which transfer electrical energy from a battery or
power supply to component in a circuit [1]. The rate of flow is measured in ampere (A)
[1].
The components transform some of this electrical energy into other forms of, e.g. a
resistor transform electrical energy into heat energy [1]. The rate at which energy is
transformed in a device is called power [1]. This can be calculated using the formula;
Power (watt,W) = Potential difference (volt,V) x current (amp, A)

21
Transforming Energy
As the charge passes through a device, energy is transformed [1]. The amount of energy
transformed by every coulomb of charge depends on the size of the potential difference
[1]. The greater the potential difference, the more energy transformed by every coulomb
of change [1].
Results and Calculation
Power (watt, W) = Potential difference (volt, V) x current (amp, A)
P = VI
Potential Differences, V = Current (Amp, A) x Resistance, (ohm)
V= IR
by substituting;
Power, P = VI
P = (IR) I
P = I2
R (Watt)
Time(s) Output
Current,
mA
Output
Voltage,
V
Power gain,
W
RPM
30 0.40 2.5 1.00X10-3
600
60 0.37 2 0.74X10-3
598
90 0.34 1.8 0.61X10-3
594
120 0.31 1.5 0.47X10-3
558
150 0.30 1.4 0.42X10-3
494
180 0.28 1.2 0.34X10-3
458

22
a. Time against current graph plot
Graph 1: Time against Current
Based on the graph shown above, the amount of current produce by the magnetic
generator design is decreasing as the time increasing. The current is decreasing linearly for
90 second. After it reaches 120 seconds, it starts to deplete less as compare to 90 second
before. This may due to ununiformed rotation of the driven magnet that keeps decreasing.
This also may be due to the condition of the power source, as the power source is also
depleting. Ideally, the current will linearly decreasing as time increasing. Thus, it can be
deduce that, the amount of current produce is inversely proportional to time taken.
b. Time against power graph plot
Graph 2: Time against Power

23
As we can see for the graph, it shows the amount of power produce by the magnetic
generator design is decreasing as the time increasing. Supposedly, the power gain should
linearly decrease due to the time increases. This may due to ununiformed rotation of the
driven magnet that keeps decreasing. This also may be due to the condition of the power
source, as the power source is also depleting. Thus, the power gain is inversely
proportional to the time taken.
c. Relationship of power gains and rotations per minute
Graph 3: Relationship of power and rotations per minute
As shown on the graph, the rotation of the driven magnet is decreasing. It shows that, the
lesser the rotations per minute of the driven magnet, the lesser the power gains. The graph
theoretically should be decreasing linearly due to the decreasing of power gains and
rotation gains as the time taken is increasing. In this experiment, the graph is not linearly
decreasing. This may due to ununiformed rotation of the driven magnet that keeps
decreasing. This also may be due to the condition of the power source, as the power
source is also depleting. Thus, it is deduce that, the power gain is directly proportional to
the rotation of the driven magnet.
Conclusion
The rate of power gains is affected by the time for the magnetic generator to produce
current. As the current produce is increasing the amount of power is also increasing within
the time interval until the magnetic generator reaches its maximum limit of power

24
produced. The power gain is also influence by the number of rotation of the fan. As the
number of rotation increases, the current produce, power gains are also increasing. But the
decrement of the current and rotations per minute are influence by the power source that
was used which is 12V battery. As time increases the battery power decreases. Due to this
nature, it influences the rotations per minute and the current produce by magnetic
generator design in this experiment. Thus, it can be concluded based on this experiment
kinetic motion affected the changes in electrical power
References
Ed. United States:
1st
[3] Satya Prakash. 2007. Physics Vol. 1 and 2, Revised Edition. 3rd
Ed. India: V.K.
Enterprises.
[4] Raymond Davidson a/l David Silva, Clement Emang Yusup Ngau, Mohamad Munzir
Mohd Khalil Khasah, Putra Nur Fitri Nordin. 2015. Magnetic Generator Supplier
(MGS). Diploma in Mechanical Engineering. Ipoh, Perak: Polytechnic Ungku Omar.

25
EFFECT OF BIOFUEL ON THE EMISSION OF FOUR STROKE
MOTORCYCLE ENGINE COMBUSTION
Ahmad Nawir Abd Rani,
Norsihan Mokhtar,
Saw Chun Lin,
Choong Chee Guan
Abstract
Increased interests on alternatives fuels has been observed in the past few years as a result
of increasing energy demand and forecasted depletion of fossil resources. This paper
investigates the influence of using unleaded gasoline–bioethanol blends on SI engine
exhaust emission. YAMAHA, Legends 110cc was used for conducting this study due to
selection criteria of four stroke, one cylinder SI engine type. Exhaust emission and
exhaust temperature investigation was conducted using unleaded gasoline–ethanol
blended biofuel with eight different percentages of mixture that are 5%, 10%, 15%, 20%,
25%, 30%, 35% and 40% of bioethanol. At variable engine speed ranging from 1000 rpm
to 2500 rpm the emission of carbon monoxide (CO) and carbon dioxide (CO2) are
measured. The results showed that blending unleaded gasoline with ethanol increases the
emission of carbon monoxide (CO), while it decreases the temperature, carbon dioxide
(CO2). It is found that the 15 and 20 vol. % bioethanol in the blended fuel gave the best
results for all measured parameters at all engine speeds.
Keywords: Bioethanol, SI Engine, RPM, AFR, Carbon Monoxide (CO) and Carbon
Dioxide (CO2).
Introduction
Bio-ethanol is by far the most widely used bio-fuel for transportation. Worldwide
production of ethanol from biomass is one way to reduce both consumptions of crude oil
and environmental pollution. Bio-fuel burns to produce carbon dioxide and water in
complete combustion and possesses high octane fuel contains, subsequently has replaced
lead in petrol that harm to environment [1]. By using bio-ethanol blended gasoline fuel
for automobiles can significantly reduce dependency on petroleum and improved the
exhaust greenhouse gas emission. Oil palm trunk shows potential as raw materials from
the waste of feedstock from oil palm industry can be largely utilized in Malaysia to be
extracted to produce bioethanol to be blended with petrol [2]. In this meantime, some
other potential alternative for the future are waste vegetable oil [6] and Non-edible crude
vegetable oil [5]. In this paper, engine with carburettor motocycles Yamaha Lagenda

26
110cc is tested the emission of CO and CO2 using 8 different percentages of petrol RON
95-bio-ethanol mixtures as well as exhaust temperature.
Experimental Procedures
Figure 1 shows SV-2Q full automatic exhaust gas analyzer equipped with TEN INNOVA
500 device. In accordance with non light disguising infrared absorption method, computer
analysis directly measure the thickness of HC, CO and CO2 from the exhaust gas of
vehicles. The analyzer introduces advanced foreign technology. It is composed of
complete imported machinery cores, boasts the advantages of accuracy measurement 
5%, high endurance and speed. The device an be used for auto manufacturing factories,
vehicles inspection stations and garages.
Figure 1: TEN INNOVA Device
Figure 2 below shows the data collection processes and analysis where motocycle engine
Yamaha Lagenda 110cc is used as experimental test rig.

27
Figure 2: Data Measurement Methodology
Two tests have been performed that is exhaust emission and temperature of the engine.
RON95 and RON95-bioethanol blended at 5%, 10%, 15%, 20%, 25%, 30%, 35% and
40% are used for both tests. On exhaust emission measurement, the engine is put free
running at 1000rpm. While, on the exhaust temperature, a thermocouple device is used to
measure the temperature data before and after 6.6 Km at free running 1000 rpm. Figure 3
shows the engine test rig whereas Table 1 shows the specification of Yamaha Lagenda
110cc engine.

28
Figure 3: Motorcycle Yahama Lagenda 110cc test rig.
Table 1: Motorcycle Yamaha Lagenda 110cc Specification
Motorcycle
Size
Length (mm) 1910
Width (mm) 675
Height (mm) 1040
Gross Weight (kg) 97
Transmission 4 Speeds- Chain Drive
Test Engine
Engine Type Air Cooled, 4 Stroke, 2 Valves and Single
Cylinder SOHC
Displacment (cm3) 110.3
Bore x Stroke (mm x
mm)
51 x 54
Compression Ratio 9.3:1
Maximum Power (W) 6.6kW@8000 rpm
Maximum Torque (Nm) 9.0Nm@5000 rpm
Idling Speed 10800  100 rpm
The average of 3 set of data have been calculated for CO and CO2 emission as shown in
Table 2 and Table 3. On the running rpm of 1000, the motorcycle engine 110cc produced
CO range from 0.93% to 0.23% while, CO2 emission range from 3.33% to 4.27%. The
results show that the CO emission increases whereas CO2 emission deceases respectively
with percentage of biofuel that is from 5% until 40%.

29
Table 2: CO Emission Rate
PERCENTAGE 5% 10% 15% 20% 25% 30% 35% 40%
CO
0.10 0.08 0.10 0.08 0.09 0.13 0.13 0.20
0.09 0.09 0.09 0.06 0.09 0.13 0.15 0.18
0.10 0.10 0.09 0.07 0.08 0.13 0.14 0.15
AVERAGE 0.09 0.09 0.11 0.10 0.13 0.17 0.19 0.23
Table 3: CO2 Emission Rate
PERCENTAGE 5% 10% 15% 20% 25% 30% 35% 40%
CO2
4.10 4.20 4.00 4.30 4.60 3.70 3.68 3.51
4.40 4.80 4.60 4.40 4.60 3.60 3.57 3.16
4.30 4.90 4.50 4.90 4.50 3.50 3.49 3.33
AVERAGE 4.27 4.63 4.37 4.53 4.57 3.60 3.58 3.33
The average data of CO and CO2 are plotted in Figure 4 and Figure 5. By using the
petrol RON95, the emission of CO is 3.98% and CO2 is 3.42%, the reduction of
percentage emission is analysed when using the biofuel. Hamada et al., (2012) tested
Yamaha FZ150i engine on petrol RON95 and obtained similar emissions that are 4.41%-
7.41% CO and 3.84% - 8.31% CO2 at engine running speed of 1800 rpm to 3200 rpm [3].
The comparison of the results show that higher CO emission means incomplete
combustion and bad mixture of biofuel since motorcycle use traditional carburettor to mix
biofuel and air that cannot adjust optimum engine combustion condition. Lee et al.,
(2004) mentioned that needed controlling system to the engine using carburettor to
optimum the engine combustion [4].

30
Figure 4: CO Emission Average Rate
Figure 5: CO2 Emission Average Rate
Temperature Effect
The engine is tested at distance 6.6km long from Simpang Pulai Toll to Ipoh Selatan Toll
at the speed 100km/hour. On the Figure 6, the temperature is measured at free running.
The results show the temperature of exhaust range from 76.2o
C to 77.0o
C. Only blended
fuel of 5% to 35% is used for the test since 40% blended fuel cause engine knocking when
running at long distance.

31
Figure 6: Exhaust Temperature at Free Running
Figure 7: Exhaust Temperature after 6.6 Km distance
After 6.6 Km distance, the temperature of engine exhaust is measured at free running
condition. The effect of blended fuel resulted the temperature of the exhaust reduces
accordingly to the percentages of blended mixture as shown in Figure 7. The minimum
temperature percentage of reduction is 14.1% while the maximum percentage reduction is
39.1%.

32
Conclusion
The reduction of 35.38% CO2 and 97.7% CO emission are obtained when petrol RON95
is replace with RON95-bioethanol blended biofuel. The reduction in the exhaust
temperature also shows that biofuel can be used as alternative fuel to help to reduce global
warming from the lower exhaust temperature.
References
[1] Cathcart, G., Houston, R., and Ahern, S. 2004. The potential of gasoline direct
injection for small displacement 4-stroke motorcycle applications. SAE Technical
Paper.
[2] Hamada, K. I., and Rahman, M. M. 2014. An Experimental Study For Performance
And Emissions Of A Small Four-Stroke Si Engine For Modern Motorcycle,
International Journal of Automotive and Mechanical Engineering (IJAME), Universiti
Malaysia Pahang, Vol. 10, pp. 1852-1865.
[3] Hamada K. I., Rahman MM, Aziz ARA. 2012. Influence Of Engine Speed And
Mixture Strength On Instantaneous Heat Transfer For Direct Injection Hydrogen
Fuelled Engine. Energy Education Science and Technology Part A: Energy Science
and Research. Vol. 30, pp.153-72.
[4] Lee, F. S., Tseng, S. C., Tsen, C. C., Wang, J. C., 2004. Fuel Injection Motorcycle
Engine Model Development. IEEE International Conference on Networking, Sensing
and Control. pp. 1259-64.
[5] Pramanik K. 2003. Properties And Use Of Jatropha Curcas Oil And Diesel Fuel
Blends In Compression Ignition Engine. Renewable Energy. Vol.28, pp. 239-48.
[6] Yu C, Bari S, Ameen A. 2002. A Comparison Of Combustion Characteristics Of
Waste Cooking Oil With Diesel As Fuel In A Direct Injection Diesel Engine.
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile
Engineering. Vol. 216, pp. 237-43.

33
NEODYMIUM MAGNETS: PERFORMANCE OF MAGNETIC GENERATOR
INFLUENCED BY LOAD VARIATIONS
Dzulkhazmi Lutpi,
Mohd Azlan Jamaluddin,
Choong Chee Guan,
Saw Chun Lin
Abstract
An experimental design is produced for the application of magnetic using neodymium
magnet. By using the magnetic generator experimental design, test and experiment are
conducted. The performance of the magnetic generator experiment is conducted. Loads
influence is chosen to determine the performance of the magnetic generator design. By
varying the load applied on the design, rotations per minute of the design and the power
generated can be gained. As a result, the amount of load of the magnetic generator design
can carry can be known. By knowing the amount of load that the magnetic generator
could carry, weight of the alternator can be chosen. Thus, the optimum power generated
can be achieved through the experiment conducted.
Keywords: Magnetic generator, load, rotational motion, load variation
Introduction
With the current increase of usage of electricity appliances machines in residential areas,
the demand for an alternative and cheaper way to produce electricity is high. Because of
this, many researchers have done researches and experiments to produce clean energy
which is free to reduce the usage of non - renewable energy. Free magnetic energy is one
of the renewable energy because of the high potential of magnetism to be used as a main
source of energy and to replace non-renewable energy [8]. This step also saves our planet
and prepares our future generation for a better future. This concept of free or renewable -
energy thus led the project to focus on developing alternative method to generate
electricity so that it can be used to power household appliances using free energy. This
project is conducted by using magnetic electric generator to produce electricity from
strong neodymium magnets. When magnets of the same pole are brought together, they
intend to repel [3]. Making use of this repelling effect, it produces a rotation movement in
the rotor with an output to a generator which produces electricity. The current produced is
then brought to a circuit board where electrical devices are connected to it. The usage of

34
mechanical energy to produce electricity as implemented on this project, making a high
possibility that the prototype of this project can be commercialized.
Figure 1: Magnetic Generator with shaft as power transmission design
The magnetic generator is design based on the cooling fan as shown in Figure 1. The
cooling fan serves as the motor to run the generator. The neodymium magnet is attached at
certain location or point on the motor. The dynamo serves as the alternator. It will
generate electrical energy and power then channel them onto the provided electrical
appliances. The provided appliances are bulb or LED light. There are two generators
installed on the board. The first alternator will be connected with pulley and belting whiles
the other one is connected with shaft. Each of these power transmitters can be attached
and detached easily. The voltmeter is also installed on the board. It will measure the
amount of voltage generated when the magnetic generator is running.
Literature Review
Magnetic Flux
The amount of magnetic field or the magnetic induction passes through a surface (the area
through which the flux passes), are known as the magnetic flux [6, 7]. The magnetic flux
can be called as the flow of energy which is the magnetic energy around the magnet. As
can be seen from the diagram below, the flow of energy from the North Pole to the South
Pole is the magnetic flux (see Figure 2).

35
Figure 2: The magnetic field and the magnetic flux of a bar magnet
(1)
(2)
Force
Force can be defined with mass time’s acceleration. In engineering it is usual to consider
acceleration to the gravity (g) because the acceleration due to gravity as constant and the
weight force directed perpendicular to the surface of the earth for most earthbound objects
[4-5].
Force = mass x gravity, N (3)
Angular Velocity
Rotational velocity can be known by using right-hand rule. Rotation velocity is also
known as angular velocity. It is denoted as , which stand for omega. The direction will
be pointed to the axis of rotation. The formula is radians per seconds [2, 5].
(4)
Torque
Torque was created by the rotational motion. The Torque can be defined with the force are
at distance (r) times force (F) [1, 4].
T = force x distance, Nm (5)

36
Power
Power can be defined in rotational shaft as the work performed per unit of time, since the
shaft angular velocity is:-
(6)
Thus, the unit of power is Watt.
(7)
Measuring Method and Steps
Plug in the Neodymium Magnet onto the driver motor. Then, switch ON or run the
magnetic generator until it is in constant motion without load. Wait a few second until the
rotation of the driven magnet is steady. After that, take the reading of the rotations per
minute using tachometer. Record the reading from the tachometer. Switch OFF the
machine. Then, apply the load given toward the end tip of the shaft. Run the machine
again. Repeat step 2 until 8 by applying other amount of loads. Tabulate all data recorded.
The electromagnetic force is fixed.
Diameter of shaft = 0.002 m
Weight of the load holder = 5 grams
Weight of one load = 5 grams
Applied
Load, N
Rotations
per minute,
rpm
Angular
Velocity, Torque, Nm
Power,
Watt
0.04905 600 62.2219 4.905 x 10-5
0.0031
0.9810 594 62.2035 98.1 x 10-5
0.0610
1.9620 400 41.8879 196.2 x 10-5
0.0822
1.9430 397 41.5737 294.3 x 10-5
0.1224
3.9240 298 31.2065 392.4 x 10-5
0.1225
4.9050 0 0 450.5 x 10-5
0
*The magnetic force is fixed and the amount of driven magnet is constant which is single.

37
Based on the data obtain, it can be deduced that the applied load influence the rotations
per minute of the wheel. As the loads increases the rotation of the wheel decreases. At
point where load is at 4.905 N the rotation stopped. This is due to the driven magnet
cannot carry that load. At that point, the torque produce by the fan is higher than the
previous point which is at 1.9430 N. The force acts in order to rotate the shaft needs to be
higher. To get higher rotation, force need to higher. In order to get higher force less
weight is compulsory.
Graph 1: Applied loads against rotation per minute
Based on the result obtained on Table 1, it can be deduced that, the load that had been
applied on the shaft will influence the rate of the torque. As the load is increase, the output
power will be decrease. The load had been applied on the shaft will influence the rate of
the rotation of the driven magnet. By measuring the rotations per minute produce, power
gains are calculated at each variation of load that has been applied. When load applied is
at 3.924 N, the maximum power gains is 0.1225 W. After the design reaching it maximum
power, the power then started to decrease as the load start to increase. Thus, from that
point, it is deduce that, as the load is increase, the output power will decreased. Graph is
plotted to show the result.

38
Graph 2: Applied loads against power generated
Recommendation
Choosing the right weight of alternator can optimize the performance of the design itself
as well as the output power the magnetic generator design can generate. It can be deduce
that, the load influences the selection of the alternator. The suggested load is 3.9240 N. the
alternator with equal weight of suggested load can optimize the performance of the
magnetic generator.
Conclusion
As a conclusion the amount of load influences greatly in designing the magnetic generator
design. This is because; by knowing the limitations of the design can carry, higher
rotations per minute, RPM can be gained. By doing this experiment, the suitable load can
be determined. By knowing the amount of load that the design can carry, the amount of
weight for the alternator that can be used can be determined and suggested for the
magnetic generator design.
References
[1] Nicholas, J. Giordano. Just the Fact 101: Textbook Key Facts. Cram 101, Textbook
Reviews.
[2] Nicholas, J. Giordano. College Physics: Reasoning and Relationships. 2nd
Ed. Boston,
USA: Brooks/Cole Cencage Learning.
[3] Serway, Raymond. A. 2012. Principle of Physics, Volume 2. 5th
Ed. Boston, USA:
Cengage Learning.
[4] Larry, D. Kirkpatrick. 2010. Physics: A conceptual world view. 7th
Ed. Canada:
Brooks/Cole Cencage Learning.

39
Ed. United States:
[6] Md. Abdul Salam. 2014. Electromagnetic Field Theories for Engineering. 1st
Ed.
Springer Science & Business Media.
[7] Shobhna Sharma. 1999. Physics. 1st
Ed. India: Krishna Prakashan Media (P) Ltd.
1st

40
FATIGUE DAMAGE ASSESSMENT USING STOCHASTIC PROCESS BY
INCORPORATING SAMPLING DATA WITH THE PROBABILISTIC MODEL
Salvinder Singh Karam Singh
Mohamad Azhar Saidin
Choong Chee Guan
Saw Chun Lin
Abstract
This paper presents the stochastic process that incorporates the sampling data to assess the
fatigue damage for the case study of an automobile componen. Since the service load of
the component is random, the characterization for assessing fatigue damage is used to
determine the fatigue damage. The objective of this study is to assess the fatigue damage
by computationally generating synthetic random loading signals using the stochastic
process by incorporating the maximum and minimum loads of the sampling data. The
proposed methodology for fatigue damage assessment is reviewed by transforming the
time series sequence to the cycle by cycle definition with consideration of the local
maxima and minima values. Likewise, the fatigue damage assessment through the strain-
life (ε-N) approach using various mean stress factor was model to determine the fatigue
damage behaviour of the material. The results indicate that the proposed model provided
a good statistical verification when comparing against the ductile cast iron A536 when
compared with the practical applications. Hence, the proposed model provides an efficient
and accurate stochastic model for the fatigue damage assessment for the automobile
component.
Keywords: Fatigue damage, cycle counting, stochastic, mean stress.
1. Introduction
Studies associated with fatigue damage and risk assessment of automobile components
involve a very large number of uncertainties. Therefore, fatigue damage assessment of
mechanical automobile components is a constant key issue that deals with the durability
and the structural integrity of the components. The fatigue damage for the mechanical
components is unavoidable even though it is designed to last a lifetime with a signiﬁcant
safety limit [1-2]. Moreover, the fatigue failure of the mechanical component is due to the
service loading experienced by the component and it is usually categorized as a non-
deterministic issue [3]. One of the major concerns of fatigue failure in the automotive
industry is the damage of the component where the fatigue failure occurring on
component will lead towards a severe failure towards the engine block and its other
connecting subcomponents as shown by [4-9] in their reviews. Hence, the fatigue failure

41
of the component is caused by high cycle and low stress of bending and torsion loads that
occurs during its life cycle.
Fatigue damage assessment is a primary mode in assessing the life cycle of the automobile
component under variable amplitude loading. However, to model the fatigue damage
analysis, an understanding of the physics and mechanism of the fatigue failure during its
life cycle is important. Since the fatigue failure of the component is considered to be
stochastic in nature considering the randomness of the material properties and the loading
effect. Hence, the technique using the local maxima and minima for predicting the fatigue
damage for components under dynamic loading was proposed by Rychlik et al. [10] is
now widely used in the automotive industry for fatigue damage assessment[11]. However,
Therefore, when modelling the fatigue damage, the mean stress correction models through
the ε-N curves should be done individually [12]. The selection of an appropriate mean
stress correction model should be based on the statistical methods will provide better
understanding for damage assessment for the component [13]. Likewise, the mean stress is
important in assessing the fatigue life of a component. In the case of the component, the
rotating bending and axial loading are based on the multi-axial cyclic load along with the
effects of the mean stress.
This aim of this paper is to assess fatigue damage of the component by computationally
generating synthetic random loading signals using the stochastic process by considering
the mean stress effects. Even though extensive studies have been carried out to assess
fatigue damage through experimental loading signal analysis, it is still considered to be
lengthy and costly. Hence, the stochastic process for fatigue damage assessment proposed
in this paper branches out from this tradition with the consideration of a stochastic
approach for life-cycle assessment. This proposed approach has immediate advantages of
bridging the gap between the deterministic and non-deterministic methods in assessing the
fatigue life cycle assessment in terms of the fatigue damage as presented in the strain-life
(ε-N) curve.
2. Methodology
2.1 Stochastic for fatigue damage assessment
The problem addressed in the present paper is to assess the fatigue damage of the
component based on the probabilistic occurrence of operational stresses during the life-
cycle of the component with good computational efficiency and high accuracy for the

42
practical application. The development of the schematic algorithm for damage assessment
for the component was based on the lifetime warranty of the component. In particular, the
considered operational stress conditions are associated with fatigue damage and have a
significant impact on the life-cycle of the component. Hence, the state condition
associated with its operational stress is modelled using the stochastic chain to synthetically
generate the random loads (Step 1). For the case of fatigue damage assessment of the
component based on the probabilistic occurrence of operational stresses, a simple
stochastic chain model scheme is shown in Figure 1. The two considered states are
bending (B), and torsion (R) under the case of normal operational conditions where it
occurs under the recurrent condition until fatigue damage occurs. These actions aim at
addressing the fatigue damage assessment issues without consideration of maintenance.
Maintenance is neglected because failure of the component is rarely being done unless
there are misalignment issues on the piston which acts on the engine block which leads to
the replacement of the component.
The component is always subjected to environmental stressors, fatigue, material aging,
extreme loads, and other agents that have a detrimental effect on its reliability during its
life cycle. Hence, an increase in the probability of failure Pf(t2) > Pf(t1) is generated
as t2 > t1 over the given loading sequence (Step 2). The build-up of the transition matrices
is based on the probabilistic occurrence of operational stresses with consideration that the
failure due to torsion is considered to be less than 10% of the bending stress. Hence, from
the transition matrices, the random loads signal is calculated using the well-established
cycle counting method from where the fatigue damage assessment is determined using the
mean stress technique (Step 4) to model the strain-life (ε-N) of the component. Therefore,
the for damage assessment using stochastic approach to incorporate automotive sampling
data into probabilistic model is as follows:-
Figure 1: Schematic algorithm development for damage analysis.
Step 4Step 3Step 2Step 1
Characterization of
failure modes.
Probability failure
criterion.
Pf (t2) > Pf (t1)
Cyclic counting
technique.
Damage
analysis.

43
 Step 1: Model the failure modes that occur during its life cycle with an
understanding when the component starts its service life at time t1, it has a certain
probability of failure Pf (t1).
 Step 2: Use the probabilities of failure state condition at the previous step and
simulate the probability criterion in multiple samples through the time transition Pf
(t2) > Pf (t1).
 Step 4: The random loads signal is calculated using the well-established cycle
counting method obtained from the transition matrices.
 Step 5: Assess the fatigue damage of the component using the mean stress
technique and statistically validate for an accurate model.
2.2 Probabilistic model
The stochastic can be mathematically modelled to predicts the condition of the future
state, with the understanding that the current state condition is independent of the past
state condition which is defined as
(1)
for t ≥ 0 where i,j are nonnegative integers.
Extensive experimental analysis [4-9] provided the basic understanding to model the
physics and mechanism of failure for the component during its life cycle over a given
period of time can be modelled as
(2)
where ,n+1
The failure state condition for the component caused from the operating stress of
bending and torsion is modelled through the transitional probabilistic matrix based on its
warranty period as shown
(3)
where PBB = probability of bending only, PTT = probability of torsion only, PBT =
probability of bending and torsion and vice versa, n = time.

44
The loading vector matrix L, is used to analyse the loading condition each time the
chain visits the individual state condition. It is common practice to compute the
probability of failure of the component through its life cycle, where the proposed approach
provides a finer discretization in time for the practical applications of the component.
(4)
The introduction of the probability vector, µ is to avoid confusion over the failure
state condition that occurs on the component, hence, the stochastic model can be
expressed as a generalized term,
(5)
Hence, from the random load generated from E(t), the preceding generalized term is
associated through probability density function f(x) for failure of the component during its
life cycle
(6)
2.3 Fatigue damage model
The fatigue life predictions for the component subjected to random loads, is best obtain
using the cycle counting method, where this method pairs the local minima and maxima to
equivalent load cycles. The cycle counting technique is obtained by reaching the same
level of the local minima and maxima with a small downward or upward excursion by
moving forward or backward for each cycle with minimum less than i and maximum
greater than j will provide a rise of the interval as shown in Figure 2.

45
30 40 50 60 70 80 90 100 110
0.5
1
1.5
2
2.5
3
3.5
4
4.5
x 10
-5
Time
Strain
tn
Time
ma
-ma
+ma
Ci
RFC
t1
Rainflow Counting
t2
Figure 2: Cycle counting technique from the loading on the component.
The accuracy of the stochastic model in damage assessment for the ductile cast iron
A536 is validated using the mean stress correction factor for the monotonic and cyclic
properties of the material. The obtained data is compared against the numerically
generated random loads based on the damage accumulation rule using the total strain
approach as defined,
(8)
(9)
where K and n are the material properties.
The strain based fatigue analysis, the mean stress models of Coffin Manson,
Morrow and by Smith, Watson and Topper is applied to quantify the assess the fatigue
damage behaviour under low cycle fatigue of mean stresses as
(10)
(11)
(12)
where b = fatigue strength exponent; c = fatigue ductility exponent; σ’f = fatigue strength
coefficient; ε’f = fatigue ductility coefficient; Nf = cycle life.

46
3. Results and Discussion
3.1 Stochastic simulation analysis
The probabilistic occurrence of operational stresses had been synthetically generated the
random load generated from E(t) is compared and statistically validated to determine its
accuracy. The error estimation method is adopted to compare the sampling data against
the computationally generated data from the proposed stochastic simulation analysis. It is
observed that 1% - 8% of divergence is observed between the sampling data and the
proposed stochastic simulation analysis during the modelling sequence of maximum and
minimum load. In order to show the difference between the minimum and maximum
loads, Figure 3 and 4 plots the 10% error estimation for stochastic simulation analysis and
sampling data. This shows the 10% error estimation with lower and upper bounds of 90%
intervals to ensure that the data obtained are within the proposed maximum and minimum
region. The divergence is considered to be minimal because the prediction of data for
fatigue life assessment is usually at 10% of the life with a confidence level.
The main purpose of generating this sequence of numerically random data is due to the
limited availability of sampling data for the component life cycle assessment. The main
advantages of this proposed stochastic chain model is its high accuracy in generating
numerically random data though the failure probability criterion as it represents the
identical the working condition of the component. This will provide a helping hand in
determining the fatigue damage assessment predictions more accurately in comparison
towards the lengthy and costly experimental analysis as indicated. Hence, the stochastic
Chain simulation analysis displayed its properties of generating new yet near similar
sequence of numerically random data through the maximum and minimum load
conditions which is near similar to field data.

47
Figure 3: Comparison of stochastic simulation analysis and sampling data for minimum
loads.
Figure 4: Comparison of stochastic simulation analysis and sampling data for maximum
loads.
3.2 Damage assessment
The damage assessment for the component is obtained with a combination of Equation 10-
12 to model the damage accumulation through the cycle counting technique based on the
rotation per minute is illustrated in Figure 5. The damage histogram provides does
essential as it provides an influential understand for fatigue behaviour of the material
1000 2000 3000 4000 5000 6000 7000
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
RPM
Load(MPa)
Sampling data
Markov Chain analysis data
Upper confidence level
Lower confidence level
1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
RPM
Load(MPa)
Sampling data
Markov Chain analysis data
Upper confidence level
Lower confidence level

48
under random loading sequence. It is observed that the fatigue damage for the component
is highest during the rotation velocity of 2500 rpm. During this period, the component is
considered to be optimal and is it’s considered to be at its normal operating condition.
Figure 5: Fatigue damage at rotational velocity of 2500 rpm.
From the cycle counting technique, the random load stress signals is converted to strain
signal to model the fatigue damage of the component under low cycle fatigue as shown in
Figure 6 and 7 respectively. The main reason for modelling fatigue in terms of low cycle
is that complexity of the component in terms of geometry. Besides that, the presence of
notches along the component that indicates there are influences of localized plasticity
during fatigue failure. On the other hand, when testing a material based on the testing
specimen, it can take up to several years to complete the load cycles, hence the use of low
cycle fatigue justifies attempt to model the fatigue damage of the component which
consist of failure in the plasticity region [2] based on its material properties.
The component is subjected to the cyclic load that tends to exhibit fatigue damage
behaviour that is modelled using the various mean stress models and this is shown as an
increasing linear damage as illustrated in in Figure 6 and 7. The damage assessment from
the stochastic analysis is compared against experimental fatigue data for the given ductile
cast iron Grade A536. The accuracy of the proposed stochastic model is performed with a
90% confidence level. It is observed that as the strain increases the damage would also

49
increase because of the presence of the oil seal on the journal of the component that
indicates that the oil seal will act as a notch resulting in cyclic plastic deformation.
Even there are minimal differences between the mean stress models, the SWT model is
the most suitable model to be used in fatigue damage assessment because the SWT models
fatigue damage when the maximum tensile stress becomes a positive maximum tensile
stress. Hence, this is to ensure that with good predictions subsequently offer successful
designs of component and therefore, any unwanted damage can be avoided.
Figure 6: Stress based assessment;(a) Damage assessment; (b) Predicted fatigue stress life
curve (S-N).
Figure 7: Strain based assessment (a) Damage assessment; (b) Predicted fatigue
strain life (ε-N) curve.

50
From the graphical interpretations, it is observed that the elastic-plastic strain is dominant
where the mean stress life are relatively low and the behaviour of the fatigue is in the
long-life regime due to the cyclic loading occurring on the component when dealing with
low cycle fatigue damage of the component. This is observed through the mechanical
properties of the material such as the yield and ultimate tensile strength values which is
related to function of the automobile component which is to withstand service loads of
high cycle with minimal fatigue damage. It is known that the failure of this component
will lead towards a catastrophic failure, hence this stress-strain-damage important
information regarding the effects of damage-strain during the loading sequence which
could be used to optimize the design of the component in order to understand the fatigue
life cycle assessment of the component.
4. Conclusion
In this paper, the assessment for the fatigue damage for the automobile component based
on stochastic approach to incorporate automotive sampling data into probabilistic model.
The failure probability criterion is developed through the understanding of the
mechanisms and physics of failure the component and is modelled using the stochastic
state condition. The stochastic model is used to model the fatigue damage based on the
operational stress conditions of the component through the cycle counting technique. The
accuracy of the stochastic model is reported to be accurate with its capability to replicate
actual sampling data when compared towards the actual loading sequence obtained from
the automobile industry. This approximation indicates there is minimal divergence of the
proposed model in modelling the sequence of maximum and minimum load for fatigue
damage assessment.
In this study, the damage assessment is calculated from the cycle counting technique is
compared against the experimental analysis data that as mentioned earlier for the ductile
cast iron Grade A536 with the consideration of the mean stress correction models.
Therefore, we propose the use of the Smith, Watson and Topper (SWT) mean stress
correction method to model fatigue damage assessment of the component for this study.
This is because the SWT was selected to model fatigue damage when the maximum
tensile stress becomes a positive maximum tensile stress. The SWT has been successfully
applied to grey cast iron as this is the equivalent material used for the component. The

51
SWT is suitable in describing the fatigue damage of the automotive components though
the mean stress model under low cycle fatigue damage.
Acknowledgement
The authors would like to thank the Sector of Higher Education Malaysia, Ministry of
Education Malaysia for funding this study and also the national automobile industry for
providing the necessary information pertaining to the success of this study.
References
[1] Sander M, Müller T and Lebahn J. 2014. Influence of mean stress and variable
amplitude loading on the fatigue behaviour of a high-strength steel in VHCF regime.
Int J Fatigue 2014; 62: 10-20.
[2] Bisping JR, Peterweth B, Bleicher C, Wagener R and Melz T. 2014. Fatigue life
assessment for large components based on cycle counting counted local strains
using damage domain. Int J Fatigue.
(Article in press:- http://dx.doi.org/10.1016/j.ijfatigue.2014.05.008).
[3] Benasciutti D and Tovo R. 2010. On fatigue cycle distribution in non-stationary
switching loadings with Markov Chain structure. Prob. Eng. Mech; 25: 406-418.
[4] Jung DH, Kim HK, Y. S. Pyoun, A. Gafurov, G. C. Choi and J. M. Ahn, Reliability
prediction of the fatigue life of a component. J Mech Sci Technol 2009;23: 1071-
1074.
[5] Alfares MA, Falah AH and Elkholy AH. 2007. Failure analysis of a vehicle engine
component. J Fail Anal Prev 7(1): 12-17.
[6] Fonte M, Li B, Reis L and Freitas M. 2013. Component failure analysis of a motor
vehicle. Eng Fail Anal 35: 147-152.
[7] Chen X, Yu X, Hu R and Li J. 2014. Statistical distribution of component fatigue:
Experiment and modelling. Eng Fail Anal 4: 210-220.
[8] Silva FS. 2003. Analysis of a vehicle component failure. Eng Fail Anal 10(5): 606-
616.
[9] Bahumik SK, Rangaraju R, Venkataswamy MA, Baskaran TA and Parameswara
MA. 2003. Fatigue fracture of component of an aircraft engine. Eng Fail Anal 9(3):
255-263.
[10] Rychlik I. 1987. A new definition of cycle counting method. Int J Fatigue 9(2): 119-
121.
[11] Johannesson P. 2002. On cycle counting cycles and the distribution of the number of
interval crossings by a Markov chain. Prob Eng Mech 17: 123-130.
[12] Choi KS and Pan J. 2009. Simulations of stress distributions in component sections
under fillet rolling and bending fatigue tests. Int J Fatigue 31(3): 544-557.
[13] Bocchini P, Saydam D and Franggopol DM. 2013. Efficient, accurate and simple
Markov Chain Model for the life cycle analysis of bridge groups. Struct Saf 40: 51-
64.

Digest care 2015 vol. 3 no.1

Recommended

Recommended

More Related Content

Similar to Digest care 2015 vol. 3 no.1

Similar to Digest care 2015 vol. 3 no.1 (20)

More from CARE JKM

More from CARE JKM (6)

Recently uploaded

Recently uploaded (20)

Digest care 2015 vol. 3 no.1