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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 the41

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 𝑎𝑖𝑟𝑡𝑖𝑚𝑒 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

FLOW TRAJECTORIES

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

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Face<3>@Part1 test-1

Face<1>@Part1 test-1


Reference axis X

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Faces Face<1>@Long Window-1


Reference axis X

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Reference axis X

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Reference axis X

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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

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