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MEC351
REFRIGERATION AND AIR CONDITONING
MINI PROJECT TITLE
COMPUTATIONAL FLUID DYNAMICS (CFD)
NO. NAME GROUP UiTM ID NO.
1 MOHD AIMRAN BIN SUAID EM1106A2 2010984189
2 HAZIMAN BIN ZAKARIA EM1106A2 2010190923
3 FIRDAUS BIN KAMARUZAMAN EM1106A2 2010123471
4 KAMAR RAZY BIN KAMAR HISHAM EM1106A2 2010705121
LECTURER NAME: PM MUHAMMAD ABD RAZAK
DATE PERFORMED: 23RD SEPTEMBER 2013
DATE SUBMITTED: 30TH SEPTEMBER 2013
2013

Acknowledgment
Firstly, grateful to Allah almighty with overflow and His grace we have completed this
mini project under subject MEC 351, Air Conditioning and Refrigeration. Without health and
facility given by Allah surely we cannot do the project successfully. We also wish to present
as high appreciation and thanks to our lecturers, Prof. Madya Muhammad bin Razak and Encik
Mohammad Hisyam for all the knowledge and guidelines that have been given. This mini
project is one of our caused work that need to be learn in MEC351. This project is focusing on
how the flow of air distribution from an air conditioning system in a room. In order to make
the project succeed, we have been started by discussing, surveying, analysis and lastly
designing using computerized fluid dynamic by using Solid work software.
OBJECTIVE
 One of our objectives is to conduct preliminary study on air distribution pattern using
Computational Fluid Dynamics (CFD) in an air condition space. From this objective,
we can polish our skills in SolidWorks by using Flow Simulation which we learned in
class.
 Our next objective is to study the temperature distribution patterns, flow trajectory and
any others related entities to air conditioning.
 Our third objective is to determine the efficiency of the diffuser by comparing the actual
and theoretical air quantity (CFM).

INRODUCTION
In order to provide favourite air quality, not only the velocity and temperature
distribution, but also their transient profile are need to be known in a ventilated room. A
reasonable ventilating system with good dynamic characteristic may establish favourite
temperature profile and take away contaminate released in the room rapidly. Such transient
property is also significant for the design and performance of the indoor climate control system.
The analysis of the transient behaviour of a building may be carried out through two approaches
via experimental investigation and computer simulation.[1] In principle, direct measurement
give the most realistic information concerning indoor airflow and pollutant transport, such as
the distribution of air velocity, temperature, and relative humidity and contaminate
concentration. Because the measurable normally must be made at many location and take a lot
of time. A complete measurement may require many months of work. Moreover, to obtain
conclusive result, the supply air airflow and temperature from the heating ventilating and air
conditioning (HVAC) system and temperature of the building or room enclosure should be
maintained unchanged during the experiment.[2] This is especially difficult to achieve because
the outdoor air conditions change over time and the temperature of the building enclosure and
air flow and/or temperature from the HVAC system will also change accordingly.
Alternatively, the heat and pollutant transport can be determined computationally by
solving a set of conservation equations describing the flow, energy and contaminate in the
system. Due to the limitation of the experimental approach and the increase in performance
and affordability of high speed computers, the numerical solution of this conservation of this
conversation equation provide a practical option for determine the airflow, heat and pollutant
distribution in building. The best method is by using computational fluid dynamics (CFD)
technique.[3]
We setup our case to concern the design of a displacement ventilation system in a
lecture room at the UITM PP campus to determine is the room is considered to achieve an
acceptable level of the thermal comfort and indoor air quality. The thermal comfort is
considered to be related to air velocity, air temperature, relative humidity, mean radiant
temperature, turbulence intensity and activity level (ISO 1990, Fanger et al 1989). After few
discussion we decided to take Bilik Kuliah Mekanikal 2.18 as our study case due to its
geometric and uses condition. The room is operated for 12n hour condition and the area of the
space is 1080 square feet whit 9.8 feet of height to ceiling. The relative humidity of the room

is 55% of the relative humidity and the outside air is 60% which bring out 5% difference of
relative humidity between inside and outside air. The class is normally fit with maximum of
30 student and air change is been set up for 1.0 air change per hour.
Before going through the simulation section firstly we need to estimate the cooling load
and velocities of the room diffuser. Some important factor need to be consider to estimate the
cooling load is orientation of building, used spaced, dimension of space, ceiling height, Colum
and beam, construction material, surrounding conditions, windows ,door, people, and lighting.
The solar heat gain thought ordinary glass is been taken to its fixed value which normally the
peak time is on September and March. To get solar heat gain for glass the equation bellow is
used
𝑐𝑜𝑙𝑙𝑖𝑛𝑔 𝑙𝑜𝑎𝑑 𝑓𝑜𝑟 𝑔𝑙𝑎𝑠𝑠 (
𝐵𝑡𝑢
𝐻𝑟
) = (𝑝𝑒𝑎𝑘 𝑠𝑜𝑙𝑎𝑟 ℎ𝑒𝑎𝑡 𝑔𝑎𝑖𝑛) × 𝑤𝑖𝑛𝑑𝑜𝑤 𝑎𝑟𝑒𝑎, 𝐹𝑡2
×
𝑠𝑡𝑜𝑟𝑎𝑔𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑠ℎ𝑎𝑑𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 .
Overall shade factor is taken 0.56 because the class is using light colour inside venetian blind.
The weight of the wall (𝑙𝑏/𝐹𝑡2
) is around 16 to 36 for the4
1
2
” brick wall with 5/8” plaster.
Thus we can get the transmission coefficient for the 0.48 Btu/hr sq.ft for 4 ½” brick wall with
cement plaster on both side for the external wall with 7 ½ mph of wind flow and 0.4 for the
internal wall and 0.25 for floor tile on 4” to 6” concrete floor with suspended board ceiling.
From all the data we can define heat gain through wall and roof by using this equation
ℎ𝑒𝑎𝑡 𝑔𝑎𝑖𝑛 𝑡ℎ𝑟𝑜𝑢𝑔ℎ𝑡 𝑤𝑎𝑙𝑙 𝑎𝑛𝑑 𝑟𝑜𝑜𝑓 = 𝑎𝑟𝑒𝑎(𝑓𝑡2) ×
𝑒𝑞𝑢𝑎𝑣𝑎𝑙𝑒𝑛𝑡 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡(℉) × 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 (𝑈)
totally transmission heat gain through all glass is determined by another equation which
infiltration cannot be accurately assessed easily and is usually not computed but allowed for by
taking a factor of safety of 10% in the load calculation for both room sensible and latent heat
total.
ℎ𝑒𝑎𝑡 𝑔𝑎𝑖𝑛 𝑡ℎ𝑟𝑜𝑢𝑔ℎ𝑡 𝑎𝑙𝑙 𝑔𝑙𝑎𝑠𝑠
= 𝑎𝑟𝑒𝑎(𝑓𝑡2) × 𝑈 𝑓𝑎𝑐𝑡𝑜𝑟
× (𝑜𝑢𝑡𝑑𝑜𝑜𝑟 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 − 𝑖𝑛𝑑𝑜𝑜𝑟 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒) − 5℉
The internal heat gain from the people can be divided into sensible heat gain and latent
heat gain which effect the activities of the people in the room. Standard heat gain from the
people who make seated and very light work activities at 75℉ is 240 Btu/hr for sensible heat
and 160 Btu/hr for latent heat.

Heat gain for the fluorescent lighting is determined by ℎ𝑒𝑎𝑡 𝑔𝑎𝑖𝑛 =
𝑡𝑜𝑡𝑎𝑙 𝑙𝑖𝑔ℎ𝑡 𝑤𝑎𝑡𝑡𝑠 × 1.25 × 3.4. Thus the room sensible heat (RSH) can be calculated by total
up all the solar heat gain, transmission heat gain and internal heat of room sensible heat and
factor of safety of 10% is added.
Total effective room sensible heat (ERSH) finally can be calculated by sum out the total
room sensible heat with outside air that can be obtained usingℎ𝑒𝑎𝑡 𝑔𝑎𝑖𝑛 =
𝑣𝑒𝑛𝑡𝑎𝑙𝑎𝑡𝑖𝑜𝑛, 𝑐𝑓𝑚 × 𝑑𝑒𝑠𝑖𝑔𝑛 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒, ℉ × 𝑏𝑦 − 𝑝𝑎𝑠𝑠 𝑓𝑎𝑐𝑡𝑜𝑟, 𝐵𝐹. The
bypass factor is a characteristic of the cooling coils used and units design. It represent the
portion of air that is considered to pass through the cooling coils without being cooled.
𝑡ℎ𝑒 𝐵𝐹 = 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑎𝑖𝑟
𝑡𝑖𝑚𝑒 𝑓𝑜𝑟 𝑎𝑖𝑟 𝑡𝑜 𝑐𝑜𝑛𝑡𝑎𝑐𝑡 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑜𝑓 𝑐𝑜𝑖𝑙
𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑐𝑜𝑖𝑙 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 (𝑟𝑜𝑤𝑠 𝑜𝑓 𝑐𝑜𝑖𝑙,𝑠𝑝𝑎𝑐𝑖𝑛𝑔 𝑜𝑓 𝑐𝑜𝑖𝑙 𝑡𝑢𝑏𝑒𝑠)
.for
package unit is taken 0.3 and 0.1 for chilled water or central DX system. These should be
compared with the final equipment bypass factor .there should be a different of 8% or more tan
the heat estimate for outside air should be recalculated. The effective room sensible heat is
totalled. The room latent heat from the ventilation outside air is obtained from
𝑜𝑢𝑡𝑠𝑖𝑑𝑒 𝑎𝑖𝑟 𝑙𝑎𝑡𝑒𝑟𝑛 ℎ𝑒𝑎𝑡 = 𝑣𝑒𝑛𝑡𝑖𝑙𝑎𝑡𝑖𝑜𝑛, 𝑐𝑓𝑚 × 𝑑𝑒𝑠𝑖𝑔𝑛 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 ℎ𝑢𝑚𝑖𝑑𝑖𝑡𝑦, (
𝑔𝑟
𝑙𝑏
) × 0.68 ×
𝐵𝐹 the effective room total heat is then obtain by ERTH = ERSH + ERLH. Thus the all the
data is been calculated and the grand total heat (GTH) is been calculated and been recorded in
table below.
Cooling load estimation table
Sheet no : 1.0 date : 21th September 2013
Estimated by : Kamar Razy job no : none
Space used for: Educational Facilities Equipment operation : 12 hrs/day
Size: 27 ft x 40 ft = 1080 sq ft = 10584 cu ft
Psychometric analysis
Condition db wb % RH Gr/lb
Outside air (OA) 92 80 60 136
Room (RM) 75 64 55 72
Different 17 16 5 64
Ventilation cfm
30 people x 5 Cfm/person = 150
1080 Sq ft x 0.4 Cfm/sqft = 432
1 a/hr x 176 Vol/60 = 176

Item Description Area (sq
ft)
x BTU x U BTU/hr Total
Solar heat gain
wall E 289 x 16 x 0.48 2222
glass E 103 x 167 x 0.28 x 0.56 2688 4910
Transmission heat gain
All glass 118 x 1.13 x 17 2273
wall 828 x 0.40 x 12 -
5
3973
door 78 x 0.35 x 16 -
1
435
Ceiling 1080 x 0.25 x 16 -
1
4320
floor 1080 x 0.22 x 16 -
1
3802 14803
Internal heat
Item No.
People 30 x 240 7200
Light 1080 4 x 1.25 x 3.4 5400 12600
Room sensible heat sub total 32313
Safety factor (10%) 3231
Total room sensible heat (RSH) 35544
Outside air
cfm BF ℉
Outside air 430 x 0.2 x 1.09 x 17 1594
Total effective room sensible heat (ERSH) 37138
Room latent heat
People 30 x 160 4800
Safety factor (10%) 280
Room latent heat (RLH) 5280
cfm Gr/lb BF
Outside air 432 x 64 x 0.2 x 0.68 3760
Effective room latent heat (ERLH) 14120
Effective room latent heat (ERTH)
Outside air
heat
cfm ℉
Sensible 432 x 17 x (1-0.2
BF)
x 1.09 6404
latent 232 x 64
gr/lb
(1-0.2
BF)
x 0.68 15040 21444
Grand total heat (GTH) 35564

THEORY
Computerize Fluid Dynamic (CFD)
CFD is useful in a wide variety of applications and here we note a few example to give
a rough understanding of its use in industry. First we must understanding principle of fluid
flows encountered in everyday life include meteorological phenomena (rain, wind, hurricanes,
floods, and fires), environmental hazards (air pollution, transport of contaminants), heating,
ventilation and air conditioning of buildings, cars etc. , combustion in automobile engines and
other propulsion systems, interaction of various objects with the surrounding air/water,
complex flows in furnaces, heat exchangers, chemical reactors etc., processes in human body
(blood flow, breathing, drinking. ) and so on and so forth.[4]
The simulations shown below have been performed using the FLUENT software. There
a lot of CFD software that can be used to doing the simulation such as Open FOAM, Open
Flower ,FLASH ,HYDRA ,FLUENT ,Solid work and many more. Computational Fluid
Dynamics (CFD) provides a qualitative (and sometimes even quantitative) prediction of fluid
flows by means of mathematical modelling (partial differential equations), numerical methods
(discretization and solution techniques),software tools (solvers, pre- and post-processing
utilities),CFD enables scientists and engineers to perform ‘numerical experiments’ (i.e.
computer simulations) in a ‘virtual flow laboratory. CFD can be used to simulate the flow over
a vehicle. For instance, it can be used to study the interaction of propellers or rotors with the
aircraft fuselage. The following figure shows the prediction of the pressure field induced by
the interaction of the rotor with a helicopter fuselage in forward flight.
Rotors and propellers can be represented with
models of varying complexity. The temperature
distribution obtained from a CFD analysis of a mixing
manifold is shown below. This mixing manifold is part
of the passenger cabin ventilation system on the
Boeing 767. The CFD analysis showed the
effectiveness of a simpler manifold design without the need for field testing. Bio-medical
engineering is a rapidly growing field and uses CFD to study
the circulatory and respiratory systems.[5]

The following figure shows pressure contours and a cutaway view that reveals velocity vectors
in a blood pump that assumes the role of heart in open-heart
surgery. [6]
Flow and heat transfer in industrial processes (boilers, heat
exchangers, combustion equipment, pumps, blowers, piping, etc.),
aerodynamics of ground vehicles, aircraft, missiles, film coating,
thermoforming in material processing applications, flow and heat transfer in propulsion and
power generation systems, ventilation, heating, and cooling flows in buildings, chemical
vapour deposition (CVD) for integrated circuit manufacturing and heat transfer for electronics
packaging applications.[7] CFD is attractive to industry since it is more cost-effective than
physical testing. However, one must note that complex flow simulations are challenging and
errorprone and it takes a lot of engineering expertise to obtain validated solutions.
There a few advantage using the CFD which the first reason is relatively low cost. By
using CFD physical experiments and tests can be run on essential engineering and data for
design can be expensive thus by using CFD the cost is reduce. Thus CFD simulations are
relatively inexpensive, and costs are likely to decrease as computers become more powerful.
The second consideration is about speed, CFD simulations can be executed in a short
period of time. Besides that it’s also have a quick turnaround means engineering data can be
introduced early in the design process without having to calculate all the data. Besides that we
also can simulate real conditions compare using traditional method. The other benefit is many
flow and heat transfer processes cannot be (easily) tested, CFD provides the ability to
theoretically simulate any physical condition, ability to simulate ideal conditions, CFD allows
great control over the physical process, and provides the ability to isolate specific phenomena
for study, constant heat flux, or constant temperature boundaries. No but not less CFD can
obtain comprehensive information because experiments only permit data to be extracted at a
limited number of locations in the system (e.g. pressure and temperature probes, heat flux
gauges, LDV, etc.).[8]
Besides that CFD allows the analyst to examine a large number of locations in the
region of interest, and yields a comprehensive set of flow parameters for examination.
Besides its all benefit CFD also have its own limitations. CFD need a physical model
to configure the calculation. CFD solutions rely upon physical models of real world processes
(e.g. turbulence, compressibility, chemistry, multiphase flow, etc.) and the CFD solutions can

only be as accurate as the physical models on which they are based. Beside that we also may
experience numerical errors from solving equations on a computer invariably introduces
numerical errors for example round-off error: due to finite word size available on the computer.
Round-off errors will always exist (though they can be small in most cases).[9] The other
example is truncation error: due to approximations in the numerical models. Truncation errors
will go to zero as the grid is refined. Mesh refinement is one way to deal with truncation error.
The last one is came from the boundary conditions, as with physical models, the
accuracy of the CFD solution is only as good as the initial/boundary conditions provided to the
numerical model. For example: flow in a duct with sudden expansion. If flow is supplied to
domain by a pipe, you should use a fully-developed profile for velocity rather than assume
uniform conditions.

PROCEDURE
1. A suitable room within the campus which is BKM 2.18 was selected as our Mini Project
model room.
2. The scale of the room was measured and specifications of construction was identified
in order to calculate the cooling load estimation.
3. Data from specifications of construction was key in into cooling load estimation table
to find the total Effective Room Sensible Heat (ERSH) of the room.
4. From the total ERSH, the required air quantity (CFM) for the room was determined.
5. The total CFM from a diffuser was identified as theoretical data.
6. Next, the actual model room was designed by using Solid Work Programme.
7. All values were converted to standard unit in Solid Work Programme.
8. The simulation data is keep in and fully description is shown below
9. The simulation is run and the data is obtained (detail drawing, temperature distribution
and flow trajectories is obtained.
10. The data is analyses and the recommended is suggested

5. System units is defined and mostly SI
unit is selected.
6. In the analysis page we set up heat group
from the simulation tree (heat conduction,
radiation and time interval).
DETAIL PROCEDURE
.
1. A room (BKM2.18) was selected
as our mini project model.
2. The dimension of the room was
measured.
3. The room scale is 1:1 with the dimension
measured with 30 persons occupied.
4. Set up the flow stimulation wizard.

7. The environment pressure has been
set up at the room grilled at 101325Pa.
8. The heat source from the people is rated at
713W which equivalent to 12000 Btu/hr of
sensible and latent heat.
9. The heat transfer from east window is
rated at 788W = 2688 Btu/hr is
determined.
10. Solid material from all part of building
is determined and the data is recorded.
11. After all the required data is been
added in including heat transfer, material,
solar radiation and surface boundary the
simulation ready to be run.
12. In the simulation the data is obtain which the
temperature flow and transfer, pressure and air
velocity is recorded for 10 minutes time interval
and the flow trajectories also obtained.

SIMULATION RESULT
Detailed drawing of BKM 2.18
Dimension 27 ft x 40 ft x 9.8 ft
window facing east 5.8 ft x 2.6 ft (3 units)
Window facing west 9.8 ft x 1.7 ft (1 unit)
Wood door at west 5.8 ft x 6.7 ft (2 units)
Diffuser 2 ft x 2 ft (4 units)
Grilled 2 ft x 2ft ( 2 units)
Fluorescent lamp 4 ft x 1 ft ( 20 units)

Temperature distribution analysis
Time interval Analysis Result
Initial -The initial temperature
inside the room is set up
around 31℃
- human heat source is set up
around 36℃ with sensible
and latent heat value
- wall temperature is around
32℃ with different heat
transmission and radiation
value
1 second - cold air from the diffuser
start to blow down to room
- the air velocity of one
diffuser is rated at 4 m/s
3 second - the cold air reach the floor
of the room
-the cold temperature
distribution is became bigger
from the four channel of the
diffuser
- the velocity of the four
diffuser is maintain around 4
m/s

9 second - at this rate, cold
distribution is sparred mostly
to fulfill the room
- cold temperature is move
upward to heat surface
- the average temperature is
room is drop to 2℃ make the
surounding temperature is
around 28℃
30 second - almost all the part of the
room have reduced in
temperature
-the human body also seen
the drop of temperature
- wall of the room also
reduced in 1℃ 𝑡𝑜 2℃
- surrounding temperature is
around 25℃
80 second - the surround temperature is
drop to around 23℃
- the wall and human body is
also reduced for about 1℃
-the inlet velocity is
maintained around 4 m/s

190 second -the room temperature
reduce to 21℃
- the wall is heat is reduced
same act happen to human
- the distributing is
constantly distributed cooled
air
- the ceiling heat reducing is
slower that other wall due
heat from the light
460second -The room temperature
remain constant
-Its seem the heat is reach
its limit transfer rate
-the human and wall
temperature also remain
constant
Final ( 600 second) -the heat surrounding is
remain constant for 400
second
-the flow rate for each
diffuser is remain at rate 4
m/s
-the human temperature is
drop for about 1℃ to 2℃
same thing happen to wall

The flow trajectories is a traces the motion of a single point, often called a parcel, in the flow.
Trajectories are useful for tracking atmospheric contaminants, such as smoke plumes, and as
constituents to Lagrangian simulations, such as contour advection or semi-Lagrangian
schemes. Thus in our flow trajectories we observes on the flow of diffuser and its distribution
to the room and grilled suction flow. The flow is smooth with the acceptable pressure around
101400Pa to the ground and the turbulent wave is nice.
Cut plot and surface plot

INPUT DATA
Initial Mesh Settings
Automatic initial mesh: On
Result resolution level: 1
Advanced narrow channel refinement: Off
Refinement in solid region: Off
Geometry Resolution
Evaluation of minimum gap size: Automatic
Evaluation of minimum wall thickness: Automatic
Computational Domain
Size
X min -5.411 m
X max 6.408 m
Y min -1.816 m
Y max 1.296 m
Z min -4.225 m
Z max 4.052 m
Boundary Conditions
2D plane flow None
At X min Default
At X max Default
At Y min Default
At Y max Default
At Z min Default
At Z max Default
Physical Features
Heat conduction in solids: On
Heat conduction in solids only: Off
Radiation: On
Time dependent: On
Gravitational effects: On
Flow type: Turbulent only
High Mach number flow: Off
Default roughness: 0 micrometer
Gravitational Settings
X component 0 m/s^2
Y component -9.81 m/s^2
Z component 0 m/s^2

Radiation
Default wall radiative surface: Blackbody wall
Radiation model: Ray Tracing
Default outer wall radiative surface: Blackbody wall
Environment radiation
Environment temperature 20.05 °C
Spectrum Blackbody
Solar Radiation
Location Kuala Lumpur
Date 09/23
Time 12:00:00
Zenith direction Y axis of Global coordinate system
Angle measured from North X axis of Global coordinate system
Angle 0 rad
Cloudiness 0
Default outer wall condition: Adiabatic wall
Initial Conditions
Thermodynamic parameters Static Pressure: 101325.00 Pa
Temperature: 30.05 °C
Velocity parameters Velocity vector
Velocity in X direction: 0 m/s
Velocity in Y direction: 0 m/s
Velocity in Z direction: 0 m/s
Solid parameters Default material: Silicon
Initial solid temperature: 30.05 °C
Radiation Transparency: Opaque
Concentrations Substance fraction by mass
Refrigerant R-134a
0.5000
Air
0.5000
Material Settings
Fluids
Air
Refrigerant R-134a
Solids
Silicon
Glass

Solid Materials
Glass Solid Material 1
Components Window-2@Assem2
Window-3@Assem2
Window-1@Assem2
Long Window-1@Assem2
Solid substance Glass
Radiation Transparency Opaque
Boundary Conditions
Inlet Velocity 1
Type Inlet Velocity
Faces Face<15>@LID4-1
Face<16>@LID3-1
Face<13>@LID6-1
Face<14>@LID5-1
Coordinate system Global coordinate system
Reference axis X
Flow parameters Flow vectors direction: Normal to face
Velocity normal to face: 4.000 m/s
Fully developed flow: Yes
Thermodynamic parameters Approximate pressure: 101325.00 Pa
Refrigerant R-134a
0.5000
Air
0.5000
Environment Pressure 1
Type Environment Pressure
Faces Face<17>@LID1-1
Face<18>@LID2-1
Reference axis X
Thermodynamic parameters Environment pressure: 101325.00 Pa
Refrigerant R-134a
0.5000
Air
0.5000

Heat Volume Sources
VS Temperature 1
Source type Temperature
Temperature 32.05 °C
Components ORANG ORANG-13@Assem2
ORANG ORANG-1@Assem2
Reference axis X
Heat Surface Sources
SS Heat Generation Rate 1
Type Heat generation rate
Faces Face<1>@Window-2
Face<1>@Window-3
Face<1>@Window-1
Reference axis X
Toggle On

Heat generation rate 788.000 W
Faces Face<1>@Door-1
Face<1>@Door-2
Reference axis X
Toggle On
Faces Face<1>@Part1 test-1
Coordinate system Face Coordinate System
Reference axis X
Toggle On
Face<3>@Part1 test-1
Face<1>@Part1 test-1
Reference axis X
Toggle On
Faces Face<1>@Long Window-1
Reference axis X
Toggle On
Reference axis X
Toggle On
Reference axis X

Toggle On
Calculation Control Options
Finish Conditions
Finish conditions If one is satisfied
Maximum physical time 600.000 s
Solver Refinement
Refinement: Disabled
Results Saving
Save before refinement On
Advanced Control Options
Flow Freezing
Flow freezing strategy Disabled
Manual time step (Freezing): Off
Manual time step: Off
View factor resolution level: 3

DISCUSSION
The simulation is completely been analyse and the problem is been identified to be
discussed to find the solution or proper method that will be discussed in the recommendation
chapter.
From the result that we get there are no a large different between the theoretical value
and experimental value thus we can conclude the air velocity or the CFM supply for one
diffuser in the room is effective due to its high efficiency of the CFM supply. From the
theoretical data one outlet supply 380 CFM per diffuser and the experimental data one diffuser
supply about 400 CFM thus the efficacy of the real room diffuser is 95%. There are few way
that can be done to increases the efficiency of CFM supply to the room but in this case the data
number seam good and acceptable thus there no need room for improvement.
We also having some difficulties to get the real data from simulation program due to
lack of equipment and time. The simulation program need a super and high efficiency computer
to run so we need to eliminate the unimportant part that exist in the room such as table, chair
and computer apparatus that not provide latent and sensible heat. We also reduce the fine of
our model to reduce the mesh of the simulation program to avoid from the program suddenly
stop working due to large calculation that need to be done. This factor maybe will affect our
simulation data and increase the margin of data error. Besides that problem also came from
the right selection of material to be added in the simulation due to lack of experience to this
simulation program. We having difficulties to select the right material to be used in the
simulation due to lots of material standard in the simulation program library. Thus we only
chooses the similar or the most suitable data that we think is right.
The data input and calculation is also the factor of error in this simulation data due to
lack of experience and careless in calculation of cooling load. This is been seen in our
calculation of our cooling load where there a some mistake in cooling load estimation but the
error is not to large so the error can be neglected because of the safety of factor that has been
take when estimation been made. There also mistake when we keep in the data for the initial
temperature for human due to our lack of knowledge so we set up the initial temperature of
human is 32℃ instead of the real temperature 37℃ but that data is not important. The main
point is to see whether the people inside the room get the total comfort or not, thus from our
simulation result the human body reduce its temperature in the range of 1℃ to 3℃ so the
comfort level is acceptable.

We also have problem to set a good air distribution pattern. We only mange to make a
diffuser to blow the air straight downward and air spur from the bottom floor to upper until its
fill out the room. We try to make a diffuser to spread from top before its reach the bottom of
the floor for better air distribution but it’s failed. We also try to make the diffuser to flow spiral
but the result is not satisfied because the air hit the wall of the room first before go to centre of
the room thus it cerate excessive throw near wall thus will probably lead to bouncing and
creating draft
Spiral flow distribution which create excessive throw

Recommendation
Form the problem and data we get from the simulation there are few recommendation
that can be made regarding to the data get and the procedure of the simulation process.
To increase the efficiency of the diffuser the calculation need to be done correctly and
the knowledge of the computerized fluid dynamic need to be improved especially for the first
time user to get a better result and understanding of the simulation and data produced. The
selection of the material need to be choose correctly and the modelling need to be more detail
for better result but the suitable hardware need to be considered before taking a complex design.
From the simulation the inlet velocity of the diffuser and grill need to be determined
correctly to get the acceptable air distribution which avoid excessive draft, exercise room air
temperature and exercise fluctuation. When this problem happen, we need to consider the input
for boundary condition that we set up earlier and the fan outlet which can give a large change
to air distribution in a room.
The best air distribution is a swirl type diffuser due to its distribution pattern but went
using this type of diffuser the inlet velocity and the radian of air distribution need to be
considered and calculated correctly to avoid exercise draft and loss of heat transfer in certain
part.

CONCLUSION
Our Mini Project is beginning with selecting venue, measuring, collecting data and
analysing in order to perform flow simulation by using Computational Fluid Dynamics (CFD)
package available in SolidWorks. From the flow simulation we find that the theoretical result
and experimental result is almost the same.
In case of Mini Project result, we had determine the experimental value of air quantity
(CFM) supply in one diffuser is 400CFM with is 5% different compare to theoretical value.
From this result, we were discovered some possible causes of error that occur during the Mini
Project such as lack of equipment and time, mistake data input, lack of experience and careless
in calculation of cooling load and so on.
From the flow simulation, we can study the temperature distribution patterns, flow
trajectory and other related entities to air conditioning. We are also able to conduct preliminary
study on air distribution pattern by using CFD in air condition space. This project give us an
advantages for us as it polish our skill and knowledge in computer program by using CFD in
order to perform flow simulation.
As the conclusion, our main objective of this Mini Project which is to conduct a
preliminary study on air distribution pattern in air conditioned space using CFD is achieved.

REFERENCES
1,2,3 Applied Computational Fluid Dynamics, André Bakker (2006),© Fluent Inc.
4,9 An Introduction to Computational Fluid Dynamics, Fluid Flow Handbook
,Nasser Ashgriz & Javad Mostaghimi, Department of Mechanical & Industrial
Eng., University of Toronto, Toronto, Ontario
5,6 http://www.cham.co.uk/phoenics/d_polis/d_info/cfdcan.htm
8 Patankar, Suhas V. (1980). Numerical Heat Transfer and Fluid FLow.
Hemisphere Publishing Corporation. ISBN 0891165223.
7
http://www.engineering.com/DesignSoftware/DesignSoftwareArticles/ArticleI
D/6341

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