A CFD MODEL DEVELOPMENT AND PERFORMANCE ...umpir.ump.edu.my/id/eprint/27974/1/A CFD model...peti sejuk. Model CFD untuk peti sejuk terpilih telah dibangunkan dalam perisian Ansys.

A CFD MODEL DEVELOPMENT AND

PERFORMANCE INVESTIGATION OF FROST FREE

REFRIGERATOR WITH NANOPARTICLES

SUSPENDED IN THE LUBRICANT

MUHAMMAD EHTESHAMUL HAQUE

DOCTOR OF PHILOSOPHY

UNIVERSITI MALAYSIA PAHANG

iii

SUPERVISOR’S DECLARATION

I hereby declare that I have checked this thesis and in my opinion, this thesis is adequate

in terms of scope and quality for the award of the degree of Doctor of Philosophy in

Mechanical Engineering.

__________________________________

Supervisor’s Signature

Full Name : PROF. DATO’ DR. ROSLI ABU BAKAR

Position : PROFESSOR

Date :

iv

STUDENT’S DECLARATION

I hereby declare that the work in this thesis is based on my original work except for

quotations and citation which have been duly acknowledged. I also declare that it has

not been previously or concurrently submitted for any other degree at Universiti

Malaysia Pahang or any other institutions.

________________________________

Author’s Signature

Full Name : MUHAMMAD EHTESHAMUL HAQUE

ID Number : PMM12007

Date :

i

A CFD MODEL DEVELOPMENT AND PERFORMANCE INVESTIGATION OF

FROST FREE REFRIGERATOR WITH NANOPARTICLES SUSPENDED IN THE

LUBRICANT

MUHAMMAD EHTESHAMUL HAQUE

Thesis submitted in fulfillment of the requirements for the award of the degree of

Doctor of Philosophy

Faculty of Mechanical Engineering

UNIVERSITI MALAYSIA PAHANG

MARCH 2019

ii

DEDICATION

“This thesis is dedicated to my wife Zareen, and kids Maheen, Rafay, and Zayan”

For their immense and endless love; support and encouragement throughout the study

iii

ACKNOWLEDGMENTS

In the name of Allah the most merciful, the most beneficent. All praise to Allah, the

Creator and Sustainer of the entire universe. Peace and blessings of Allah to his Last

messenger Prophet Muhammad صلى الله عليه وسلم. Thanks to Allah Almighty who granted me the

strength and ability to achieve this task.

I wish to express my sincere gratitude to my supervisor, Professor Dato’ Dr. Hj. Rosli

Bin Abu Bakar for his invaluable guidance, suggestion, and support throughout my

research. I would also like to thank my co-supervisor, Dr. Gan Leong Ming for his

timely support and assistance.

I am thankful to the Faculty of Mechanical Engineering and all staff. I would also like

to express my sincere gratitude to post grade students, faculty and staff of thermal

laboratory lab and HVAC lab in the mechanical faculty for their help in facilitation

during my experimental work. Special thanks to Billy Anak Anak for helping me in

making the test rig. I would also like to acknowledge the help and assistance provided

by Mr. Yousof Taib, and for providing me the refrigerator, tools, instrumentations, and

material for the project.

This research was financially supported by Universiti Malaysia Pahang under the grant

number GRS 140311. I wish to acknowledge the financial assistance provided by NED

University of Engineering and Technology and Higher Education Commission (HEC)

of Pakistan.

Finally, I would like to thank my wife and children for their patience and support during

my research project.

Muhammad Ehteshamul Haque

iv

ABSTRACT

In a refrigerator, airflow and temperature distribution along with the properties of the

lubricating oil defines its efficiency and performance. The purpose of this work is to

develop a numerical model to predict the airflow and temperature inside the refrigerated

space and use nanoparticles in the compressor lubricating oil to improve the efficiency

of the refrigerating system. The present research focuses to improve the performance

and efficiency of the refrigerator through the analysis by CFD and nanotechnology. The

objective is to develop a CFD model for airflow and temperature distribution inside the

refrigerator and validated it with experimental results. The model is then used for

parametric study to modify the inside geometry of the refrigerator to improve better

airflow and temperature distribution. To improve the cyclic efficiency of the refrigerator

Nano-particles are added into the compressor lubricant to improve the lubricity of

Polyol ester oil (POE), thereby improving the performance of the refrigerator. In this

research work, CFD model has been developed for a domestic no-frost refrigerator. The

conservation equations of energy mass and momentum are solved by using Finite

Volume Method (FVM) in an environment of three-dimensional unstructured mesh.

Experiments were conducted on a no-frost domestic refrigerator to compare and

validate the results of the CFD model. Nano particles when added to the lubricating oil

is called Nano-lubricant. In the present study, three nanoparticles namely Al2O3, TiO2

and SiO2 have been added to the lubricant oil of a domestic refrigerator and experiments

have been performed to determine the enhancement in the performance of the

refrigerator. A CFD model for the selected refrigerator has been developed in Ansys

software. This CFD model has been validated by experimental results. A comparison of

CFD model and experimental results of surface temperature in freezer and refrigerator

compartment are within the acceptable range of 5% difference. In the freezer

compartment the difference in temperature on a vertical line at the center of freezer as

predict by the CFD model and experiment is less than one percent. Similarly, the

temperature difference, as measured by experiment and predicted by the CFD model, on

a central vertical line inside the refrigerator compartment, is less than three percent. The

result of the parametric study by using the developed CFD model showed improvement

in the temperature distribution inside the refrigerator compartment. Through this

research work, it is established that CFD can be used successfully to model the airflow

and temperature distribution inside the refrigerator. The results of experiments with

nanoparticles suspended in the lubricant oil of the compressor showed better

performance of the refrigerator as compared to pure Polyol Easter (POE) oil system.

The energy consumption of 0.05% SiO2 nanolubricant is 9.4% less than pure POE oil

system. Similarly, the energy consumption of compressor with 0.1% TiO2 nanoparticles

is 6.84% lower than the pure POE oil system. COP of the refrigerator increased by 29%

when 0.1% SiO2 nanoparticle was added to the compressor lubricant. Therefore, the

addition of nanoparticles in the refrigerator system has very good potential to improve

the energy consumption and COP of the unit.

v

ABSTRAK

Dalam peti sejuk, aliran udara dan pengagihan suhu bersama-sama dengan sifat-sifat

minyak pelincir mentakrifkan kecekapan dan prestasinya. Tujuan kerja ini adalah untuk

membangunkan model berangka untuk meramalkan aliran udara dan suhu di dalam

ruang yang didinginkan dan menggunakan nanopartikel dalam minyak pelincir

pemampat untuk meningkatkan kecekapan sistem penyejukan. Penyelidikan kini

memberi tumpuan untuk meningkatkan prestasi dan kecekapan peti sejuk melalui

analisis oleh CFD dan nanoteknologi. Dalam kajian ini, model CFD telah dibangunkan

untuk peti sejuk beku domestik. Persamaan pemuliharaan jisim dan momentum energi

diselesaikan dengan menggunakan Kaedah Volum Hingga dalam persekitaran mesh tak

berstruktur tiga dimensi. Eksperimen dilakukan di peti sejuk domestik tanpa beku untuk

membandingkan dan mengesahkan keputusan model CFD. Nano zarah apabila

ditambah ke minyak pelincir dipanggil nanolubricant. Dalam kajian ini, tiga

nanopartikel iaitu Al2O3, TiO2 dan SiO2 telah ditambah kepada minyak pelincir kulkas

domestik dan eksperimen telah dilakukan untuk menentukan peningkatan dalam prestasi

peti sejuk. Model CFD untuk peti sejuk terpilih telah dibangunkan dalam perisian

Ansys. Model CFD ini telah disahkan oleh keputusan percubaan. Perbandingan model

CFD dan hasil eksperimen suhu permukaan dalam peti sejuk dan ruang peti sejuk

berada dalam julat yang boleh diterima. Dalam petak penyejuk beku perbezaan suhu

pada garis menegak di pusat beku seperti yang diramalkan oleh model CFD dan

eksperimen adalah kurang daripada satu peratus. Begitu juga, perbezaan suhu, seperti

yang diukur oleh percubaan dan diramalkan oleh model CFD, pada garis menegak pusat

di dalam petak peti sejuk, adalah kurang daripada tiga peratus. Hasil kajian parametrik

dengan menggunakan model CFD yang maju menunjukkan peningkatan dalam

pengedaran suhu di dalam ruang peti sejuk. Melalui kerja penyelidikan ini, didapati

CFD dapat digunakan dengan baik untuk memodelkan aliran udara dan pengedaran

suhu di dalam peti sejuk. Keputusan eksperimen dengan nanopartikel yang digantung di

minyak pelincir pemampat menunjukkan prestasi yang lebih baik dari peti sejuk

berbanding dengan sistem minyak Tulen Eolol (POE) tulen. Penggunaan tenaga 0.05%

SiO2 nanolubricant adalah 9.4% kurang daripada sistem minyak POE tulen. Begitu

juga, penggunaan tenaga pemampat dengan 0.1% TiO2 nanopartikel adalah 6.84% lebih

rendah daripada sistem minyak POE tulen. COP peti sejuk meningkat sebanyak 29%

apabila 0.1% SiO2 nanoparticle ditambah kepada pelincir pemampat. Oleh itu,

penambahan nanopartikel dalam sistem peti sejuk mempunyai potensi yang sangat baik

untuk meningkatkan penggunaan tenaga dan COP unit.

vi

TABLE OF CONTENTS

DECLARATION

TITLE PAGE

DEDICATION ii

ACKNOWLEDGMENTS iii

ABSTRACT iv

ABSTRAK v

TABLE OF CONTENTS vi

LIST OF TABLES xi

LIST OF FIGURES xiii

LIST OF SYMBOLS xvii

LIST OF ABBREVIATIONS

xix

CHAPTER 1 INTRODUCTION 1

1.1 BACKGROUND 1

1.2 Vapor Compression Refrigeration Cycle 2

1.2.1 Refrigerants 5

1.3 Nanofluid 6

1.3.1 Nanofluid Preparation 6

1.3.2 Thermal Conductivity of Nanofluids 7

1.3.3 Viscosity of Nanofluid 7

1.3.4 Pressure Drop Caused By Nanoparticles 8

1.3.5 Coefficient Of Friction and Wear Rate of Nanofluids 8

1.4 CFD study of refrigerator 8

1.5 Problem statement 9

1.6 Research Objective 10

1.7 Statement of Novelty and Contribution 10

1.8 Scope of study 11

CHAPTER 2 LITERATURE REVIEW 12

vii

2.1 Introduction 12

2.2 CFD modeling of refrigerator 13

2.2.1 Steps involved in CFD 13

2.2.1.1 Pre-Processing 13

2.2.1.2 Solving 14

2.2.1.3 Post-Processing 14

2.3 Mathematical models 18

2.3.1 Laminar Flow 18

2.3.2 Turbulent Flow 19

2.3.2.1 Direct Numerical Simulation (DNS) 19

2.3.2.2 Reynolds Averaged Navier-Stokes Model (RANS) 19

2.3.2.3The k-ε model 20

2.3.2.4 Large Eddy Simulation 21

2.4 Numerical Techniques 21

2.4.1 Discretization Procedure 21

2.4.2 Spatial Discretization of Conservation Equations 23

2.4.3 Central Differencing Scheme 24

2.4.4 First-Order Upwind Scheme 25

2.4.5 Second-Order Upwind Scheme 25

2.4.6 QUICK Scheme 26

2.4.7 Equations’ temporal discretization 26

2.4.8 Pressure-Velocity Coupling Algorithms 28

2.4.9 SIMPLE Algorithm 28

2.4.10 SIMPLEC Algorithm 33

2.4.11 Solution methods of Algebraic Equations 34

2.5 CFD And experimental studies of refrigerators and refrigerating

systems

34

2.5.1 Static Refrigerator 35

2.5.2 Ventilated Refrigerator 43

2.6 Nanofluid application in refrigerating systems 50

2.6.1 Preparation of Nanofluids 51

2.6.2 One-Step Method 52

2.6.3 Two-Step Method 52

viii

2.6.4 Thermophysical Properties of Nanofluids 53

2.6.5 Thermal Conductivity (k) 54

2.6.6 Viscosity (µ) 55

2.7 Effect of nanofluid on performance of refrigerating systems 58

2.7.1 Heat Transfer Enhancement 59

2.7.2 Pool Boiling and Flow Boiling 59

2.7.3 Viscosity and pressure drop 62

2.7.4 Solubility of Refrigerant into Lubricating Oil 63

2.7.5 Friction Coefficient and Wear Rate 64

2.7.6 Effect of Surfactants 64

2.7.7 COP Enhancement 65

CHAPTER 3 METHODOLOGY 67

3.1 Numerical simulation of refrigerator 67

3.1.1 Importance of Numerical Simulation 67

3.1.2 Physical Model for Simulation 69

3.1.3 Geometry for the CFD Domain Development 72

3.1.4 Mesh Generation and Grid Independence 74

3.1.5 Solution Methods and Solution Controls 78

3.1.6 Convergence Criteria and Solution Monitoring 80

3.1.7 Solution Control and Under-Relaxation Factors 81

3.2 Assumptions and Boundary Conditions 82

3.2.1 Governing Equations of CFD 83

3.2.2 Continuity Equation 83

3.2.3 Momentum Equation 83

3.2.4 Energy Equation 84

3.3 Boundary Conditions 84

3.4 Experimental Setup and Procedure 94

3.4.1 Experimental Apparatus 94

3.4.2 Pressure Measurement 96

3.4.3 Temperature Measurement 98

3.4.4 Velocity Measurement 103

ix

3.4.5 Energy and Power Measurement 103

3.4.6 Experimental Procedure for CFD model validation 104

3.5 Application of Nanoparticles 105

3.5.1 Preparation of Nanolubricants 106

3.5.2 Thermal Conductivity 109

3.5.3 Viscosity 109

3.5.4 System Evacuation 110

3.5.5 System Charging 112

3.5.6 Data Collection and Analysis 112

3.5.6.1 Coefficient of Performance (COP) 112

3.5.6.2 Performance Test 113

3.6 Summary 113

CHAPTER 4 RESULTS AND DISCUSSION 115

4.1 Introduction 115

4.2 Temperature and Velocity Simulations 115

4.2.1 Airflow Simulation in the Refrigerator and Freezer

Compartments

115

4.2.2 Temperature Simulation in Refrigerator and Freezer

Compartments

124

4.3 Validation of numerical model with experimental Results 131

4.3.1 Comparison of Temperature in Freezer Compartment 132

4.3.2 Comparison of Temperature in Refrigerator Compartment 133

4.3.3 Study of Nusselt Number 134

4.4 Parametric Study 138

4.4.1 Comparing temperature on a vertical line for parametric study 140

4.5 Performance of the refrigerator with nanoparticles based lubricants 142

4.5.1 Thermophysical Properties of Nanoparticles based Lubricants 142

4.5.1.1 Thermal Conductivity 143

4.5.1.2 Viscosity 145

4.5.2 POE Oil and Nanolubricant with 0.05% Nanoparticles 146

4.5.3 POE Oil and Nanolubricant with 0.1% Nanoparticles 151

x

4.5.4 Results of Power and Energy Consumption 155

4.5.5 Results of Pressure Measurement 157

4.5.6 Coefficient of Performance of the Refrigerator with POE Oil

and Nanolubricant

159

4.6 Summary 162

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 165

5.1 Recommendations for future work 168

REFERENCES 170

LIST OF PUBLICATIONS 188

APPENDICES 190

xi

LIST OF TABLES

Table 2.1 CFD application for refrigerator 16

Table 2.2 CFD simulations of refrigerators 49

Table 2.3 Models for thermal conductivity of nanofluids 55

Table 3.1 Specification of the refrigerator 69

Table 3.2 Parameters setting for mesh sizing 77

Table 3.3 Parameter settings for inflation layers 77

Table 3.4 Mesh Statistics and Mesh Metric for Skewness and Aspact Ratio 77

Table 3.5 Basic settings for CFD simulation 79

Table 3.6 Solution Method Settings 80

Table 3.7 Residual Monitors Settings 81

Table 3.8 Under Relaxation Factors 81

Table 3.9 Walls of refrigerator and freezer used for calculations 86

Table 3.10 Physical and thermal properties of wall materials 87

Table 3.11 Inside and outside temperatures of air and surfaces 87

Table 3.12 Thermal resistances of walls and overall heat transfer coefficient 93

Table 3.13 List of Instruments used and their specifications 104

Table 3.14 Thermophysical properties of Nanoparticles 107

Table 3.15 Test conditions for energy consumption procedures 114

Table 4.1 Comparison of numerical and experimental temperatures at

selected points on a vertical line in freezer

133

Table 4.2 Comparison of numerical and experimental temperatures at

selected points on a vertical line in refrigerator

134

Table 4.3 Empirical calculation for Nusselt number variation along the

height of the refrigerator

137

Table 4.4 The design parameter values 139

Table 4.5 Temperature values at three points of all parametric study 142

Table 4.6 Comparison of temperature on different surfaces inside the

freezer compartment with 0.05% by volume of nanoparticles

added to POE oil

148

Table 4.8 Comparison of temperatures on different surfaces inside the

freezer compartment with 0.1% by volume of nanoparticles

added to POE oil

152

xii

Table 4.9 Comparison of temperatures on different surfaces inside the

refrigerator with 0.1% by volume of nanoparticles added to POE

oil

154

Table 4.10 Energy consumption of the systems with Lubricant and

nanolubricants

156

Table 4.11 Experimental data of system parameters 160

Table 4.12 COP calculation and analysis for all systems 160

Table 4.13 Comparison table for performance improvement of refrigeration

systems

163

xiii

LIST OF FIGURES

Figure 1.1 Schematic diagram of vapor compression refrigeration system 3

Figure 1.2 T-s and p-h chart of Vapor compression cycle 4

Figure 2.1 Cell P and its neighbor cells 24

Figure 2.2 Face e and its interpolated value 25

Figure 2.3 Face e and its interpolated value (QUICK Scheme) 26

Figure 2.4 One dimensional grid point cluster 29

Figure 2.5 Wavy or zigzag velocity or pressure field 30

Figure 3.1 Flow chart for CFD simulation 68

Figure 3.2 Refrigerator used for the experiment 70

Figure 3.3 Air flow and heat transfer in refrigerator 71

Figure 3.4 A detail view of the simulated refrigerator 72

Figure 3.5 Inside detail view of the simulated refrigerator 73

Figure 3.6 Fluid Domain (a) Full domain (b) half domain 74

Figure 3.7 Mesh generation for the refrigerator 76

Figure 3.8 Temperature profile for Mesh A, B, and C along a vertical line 78

Figure 3.9 Designation of each wall 85

Figure 3.10 Schematic diagram of refrigerator walls 86

Figure 3.11 Thermal resistance network for walls of freezer compartment 88

Figure 3.12 Thermal resistance network for walls of refrigerator compartment 88

Figure 3.13 (a) Freezer back wall (b) Air box (c) Ducts for air inlet and outlet

from the refrigerator section (d) Air distributor for refrigerator

compartment

95

Figure 3.14 Evaporator and Defrost heater 96

Figure 3.15 Pressure Transducers 97

Figure 3.16 Pressure Meters 97

Figure 3.17 (a) Lutron temperature data logger (b) Pico temperature data

logger

99

Figure 3.18 Schematic diagram of inside and outside locations for

thermocouples to record temperatures

99

Figure 3.19 Inside surfaces at which temperature readings were recorded 100

Figure 3.20 Thermocouple settings in (a) freezer (b) refrigerator sections 101

xiv

Figure 3.21 Thermocouple settings in refrigerator with shelf 102

Figure 3.22 Sealing the refrigerator to prevent air leakage 102

Figure 3.23. Velocity measurement with handheld anemometer 103

Figure 3.24 (a) Power Analyzer (b) Clamp meter 104

Figure 3.25 Flow diagram for experiment 106

Figure 3.26 FeSEM image of (a) TiO2 and (b) SiO2 nanoparticles 107

Figure 3.27 Electronic balance 108

Figure 3.28 (a) Stirring hotplate (b) Ultrasonic homogenizer 108

Figure 3.29 Thermal conductivity and specific heat measuring apparatus 109

Figure 3.30 Brookfield LVDV 11 Viscometer 110

Figure 3.31 Robinair model 15601 vacuum pump 111

Figure 3.32 Schematic sketch for charging and evacuation 111

Figure 4.1 (a) Velocity contour on symmetry plane (b) Velocity vector on

symmetry plane

117

Figure 4.2 (a) Velocity contour (b) Velocity vector, on XZ plane in freezer

compartment at Y=1.25 m

118

Figure 4.3 (a) Velocity contour (b) Velocity vector, on XZ plane in freezer

compartment at Y=0.7 m

118

Figure 4.4 Velocity in freezer along a vertical line (x=0.23, z=0.425) 119

Figure 4.5 Velocity in refrigerator along a vertical line (x=0.23,z=0.425) 120

Figure 4.6 Velocity variations in freezer at the line(y = 1.1, 1.2, 1.3 m, z =

0.425 m)

121

Figure 4.7 Velocity variations in freezer at the line (y = 1.2 m, x = 0.23 m) 122

Figure 4.8 Velocity variations in refrigerator at the line(y = 0.7m, z= 0.425

m)

123

Figure 4.9 Velocity variations in refrigerator at the line (y = 0.7 m, x = 0.23

m)

123

Figure 4.10 (a) Velocity streamlines (b) Velocity volume rendering 124

Figure 4.11 Temperature contour on Z=0.54 m plane 125

Figure 4.12 Temperature contour on Y=1.152 m plane 126

Figure 4.13 Temperature contour on Y=0.45 m plane

127

Figure 4.14 Temperature variations in freezer at the line (x = 0.23 m, z =

0.425 m)

128

xv

Figure 4.15 Temperature variations in refrigerator at the line (x = 0.23 m, z =

0.425 m)

128

Figure 4.16 Temperature variations in freezer at line (y =1.152 m, z=0.425 m) 129

Figure 4.17 Temperature variations in freezer at the line (x = 0.23 m, y =

1.152 m)

130

Figure 4.18 Temperature variations in refrigerator at the line (y = 0.51 m, z =

0.425 m)

130

Figure 4.19 Temperature variations in refrigerator at the line (x = 0.23 m, y =

0.51 m)

131

Figure 4.20 Comparison of numerical and experimental temperature results at

symmetry plane of freezer (x=0.23 and z=0.425)

132

Figure 4.21 Comparison of numerical and experimental results at symmetry

plane of refrigerator (x=0.23 and z=0.425)

134

Figure 4.22 Points and lines used for Nusselt number calculation 135

Figure 4.23 Nusselt number variation along the length of the refrigerator 137

Figure 4.24 L-Shaped air distributor 138

Figure 4.25 Dimensions of L-shaped air distributor 139

Figure 4.26 Refrigerators with original and modified air distributors 140

Figure 4.27 Location of vertical line (dash line) in the refrigerator 141

Figure 4.28 Temperature variations along a vertical line at the center for

refrigerator

142

Figure 4.29 Al2O3 nanoparticles suspended in POE oil 144

Figure 4.30 TiO2 nanoparticles suspended in POE oil 144

Figure 4.31 SiO2 nanoparticles suspended in POE oil 145

Figure 4.32 Room temperature 146

Figure 4.33 Temperature variations on freezer floor and ceiling 147

Figure 4.34 Temperature variations on shelf of the freezer compartment 148

Figure 4.35 Temperature variations on two shelves of refrigerator

compartment

149

Figure 4.36 Temperature variations on vegetable shelf and back wall of

refrigerator compartment

150

Figure 4.37 Temperature variations on floor and ceiling of the freezer

compartment

151

xvi

Figure 4.38 Temperature variations on shelf and back wall of the freezer

compartment

152

Figure 4.39 Temperature variations on two shelves of refrigerator

compartment for 0.1% nanoparticles by volume added to POE oil

153

Figure 4.40 Temperature variations on vegetable shelf and back wall of

refrigerator compartment with the addition of 0.1% nanoparticles

by volume into POE oil

154

Figure 4.41 Comparison of power consumption over a period of one hour 155

Figure 4.42 Comparison of average power consumption for lubricant and

nanolubricants

156

Figure 4.43 Compressor discharge pressure for POE oil and POE oil mix with

0.05% nanoparticles

158

Figure 4.44 Compressor discharge pressure for POE oil and POE oil mix with

0.1% nanoparticles

158

Figure 4.45 Compressor suction pressure for POE oil and POE oil mix with

0.05% nanoparticles

159

Figure 4.46 Compressor suction pressure for POE oil and POE oil mix with

0.1% nanoparticles

159

Figure 4.47 Comparison of COP values 161

xvii

LIST OF SYMBOLS

g Acceleration due to gravity (m/s2)

h Specific enthalpy (kJ/kg K)

k turbulent kinetic energy

Lref Characteristic length of geometry. (m)

Ma Mach number

Nu Nusselt Number

p Pressure (Pa)

Q̇̇cond Conductive heat transfer rate (kW)

Q̇̇conv Convective heat transfer rate (kW)

QH Heat rejected from Condenser (kJ)

qH Heat rejected from condenser per unit mass (kJ/kg)

QL Heat absorbed in the evaporator (kJ)

qL Heat absorbed in evaporator per unit mass (kJ/kg)

Q̇̇rad Radiation heat transfer rate (kW)

R-11 Trichlorofluoromethane

R113 Trichlorotrifluoroethane.

R134a Refrigerant Tetrafluoroethane

R-744 Refrigerant grade Carbon Dioxide

Ra Rayleigh Number

RABS Thermal resistance of ABS plastic against heat conduction, (kW-1)

Rcond Thermal resistance of a layer against heat conduction, (kW-1)

RCV,i Thermal resistance by convection between the internal wall surfaces

and inside air (kW-1)

RCV,O Thermal resistance by convection between the outside air and

external wall surfaces (kW-1 )

RPU Thermal resistance of polyurethane foam against conduction, (kW-1)

RRAD,i Thermal resistance between the internal wall surfaces of the

refrigerator-freezer, (kW-1)

RRAD,O Thermal resistance between each external wall surface and the

neighbor surfaces, (kW-1)

RST Thermal resistance of steel against heat conduction, (kW-1)

Rtotal Total thermal resistance (m2 kW-1)

xviii

re, rp, and rw Displacement vectors

s Specific entropy

S_∅ the source term for the general property

SiO2 Silicon dioxide

T Temperature

T∞ Ambient temperature (K)

Tb Bulk air temperature

TiO2 Titanium dioxide

TNS Absolute temperature of the neighboring surface (K)

Ts Surface temperature (K)

u Velocity in x-direction

v Velocity in y-direction

w Velocity in z-direction

wc Work done by compressor per unit mass

α Thermal diffusivity (m2/s)

β Coefficient of volume expansion (K-1)

ε turbulent kinetic energy dissipation rate

μ and μt Viscosity and turbulent viscosity

ν Kinematic viscosity of air (m2/s)

ρ Density (kg/m3)

σ_k Constant

σ_ε Constant

ϕ For a general fluid property such as temperature or pressure

Cμ, Cε1, Cε2 Coefficients in approximated turbulent transport equations

xix

LIST OF ABBREVIATIONS

ABS Acrylonitrile Butadiene Styrene

AC Alternating Current

AHAM American Household Appliance Manufacturers

ANN Artificial Neural Network

ANSI American National Standards Institute

CFC Chlorofluorocarbons

CFCS CFCs

CFD Computational Fluid Dynamics

CNT Carbon Nano Tube

COP Coefficient of performance

CPU Central Processing Unit

CS Convection Scheme

CTAB Cetyltrimethyl Ammonium Bromide

DNS Direct Numerical Simulation

DO Discrete Ordinates

DOE Department of Energy

DTAB Dodecyltrimethylammonium Bromide

EFD Experimental Fluid Dynamics

EG Ethylene Glycol

EES Engineering Equation Solver

FDM Finite Difference Method

FEM Finite Element Method

FVM Finite Volume Method

GA Genetic Algorithm

GAMBIT Geometry and Model Building Intelligent Toolbox

GWP Global warming potential

GRS Graduate Research Scholarship

HC Hydrocarbon

HCFC Hydrochlorofluorocarbon

HCTAB Hexadecyltrimethylammonium bromide

HEC Higher Education Commission

http://www.sigmaaldrich.com/catalog/product/sigma/h6269

xx

HFC Hydro fluorocarbon

HVAC Heating Ventilation and Air-conditioning

IPCC Intergovernmental Panel on Climate Change

ISO International Organization For Standardization

JIS Japanese Industrial Standards

KWH Kilowatt Huor

LBM Lattice Boltzmann method

LES Large-Eddy Simulation

MD Molecular dynamic

MNRO mineral based refrigeration oil

MO Mineral oil

NS Not Specified

ODP Ozone depletion potential

Pe Peclet number

PVP Polyvinyl Pyrrolidone

PISO Pressure-Implicit Split-Operator

PIV particle image velocimetry

POD Proper Orthogonal decomposition

POE Polyol ester oil

PRESTO PREssure STaggering Options

PU Polyurethane

PVD Physical vapor deposition

QUICK Quadratic Upwind Interpolation for Convective Kinetics

RNG Renormalization group

RANS Reynolds Averaged Navier-Stokes

SAE Society Of Automotive Engineers

SDBS Sodium Dodecyl Benzene Sulphonate

SDS Sodium Dodecyl Sulfate

SIMPLE Semi-Implicit Method for Pressure Linked Equation

SIMPLEC SIMPLE-Consistent

ST Steel

UNEP United Nations Environment Program

USA The United States of America

170

REFERENCES

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