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Sub-cooled pool boiling enhancement with nanofluids

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Sub-cooled pool boiling enhancement with nanofluids
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English
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Rice, Elliott Charles
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University of South Florida
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Comsol
Heat Flux
Heat Transfer Coefficient
Nanoparticles
Phase Change
Dissertations, Academic -- Mechanical Engineering Nanotechnology -- Masters -- USF   ( lcsh )
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bibliography   ( marcgt )
non-fiction   ( marcgt )

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Summary:
ABSTRACT: Phase-change heat transfer is an important process used in many engineering thermal designs. Boiling is an important phase change phenomena as it is a common heat transfer process in many thermal systems. Phase change processes are critical to thermodynamic cycles as most closed loop systems have an evaporator, in which the phase change process occurs. There are many applications/processes in which engineers employ the advantages of boiling heat transfer, as they seek to improve heat transfer performance. Recent research efforts have experimentally shown that nanofluids can have significantly better heat transfer properties than those of the pure base fluids, such as water. The objective of this study is to improve the boiling curve of de-ionized water by adding aluminum oxide nanoparticles in 0.1%, 0.2%, 0.3% and 0.4% wt concentrations in a sub-cooled pool boiling apparatus. Enhancement to the boiling curve can be quantified in two ways: (i) the similar heat fluxes of de-ionized water at smaller excess temperature, indicating similar quantity of heat removal at lower temperatures and (ii) greater heat fluxes than de-ionized water at similar excess temperatures indicating better heat transfer at similar excess temperatures. In the same fashion, the secondary objective is to increase the convective heat transfer coefficient due to boiling by adding different concentrations of aluminum oxide nanoparticles.
Thesis:
Thesis (M.S.M.E.)--University of South Florida, 2011.
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Includes bibliographical references.
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by Elliott Charles Rice.
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Title from PDF of title page.
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Document formatted into pages; contains 141 pages.

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Sub-Cooled Pool Boiling Enhancement with Nanofluids by Elliott Charles Rice A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering Department of Mechanical Engineering College of Engineering University of South Florida Major Professor: Frank Pyrtle III, Ph.D. Muhammad M. Rahman, Ph.D. Craig Lusk, Ph.D. Date of Approval: March 24, 2011 Keywords: heat flux, heat transfer coeffici ent, nanoparticles, phase change, comsol Copyright 2011, Elliott Charles Rice

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Dedication I would like to dedicate this thesis to my sister, Audrey. Thanks for always believing in me and showing me what it m eans to strive for academic excellence. Although a master’s thesis cannot compare to a dissertation, I hope th at while finishing your Ph.D. that this thesis somehow inspires you to complete your goal. I have no doubt that you will be successful in your studies. Love your little brother, or “monster” or “mickey dee’s” or “numb-nut” or whatever other names you feel li ke calling me today.

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Acknowledgements First and foremost I would like to thank the God for His mercy and grace. This thesis is proof of Your everlasti ng Word, “Share you plans with the LORD and you will succeed.” Proverbs 16:3 Contemporary English Version. I would like to thank my parents Ella a nd Jerome Rice for always supporting their favorite “professional” student. Thank you for all your help and encouragement over the years. I would like to thank my research advi sor Dr. Frank Pyrtle III for allowing me to join his research group and gui ding me along the way to comple tion of this thesis. Also, thank you for allowing me work with you during the Research Experience for Undergraduates program. I would also like to th ank Dr. Rahman and Dr. Lusk for being a part of my committee and supporting me dur ing my time at USF. Thank you, Dr. Hess for allowing me to use your profilometer. Thank you times 10 to Bernard Batson for everything. Also a very special thank you to NSF Bridge to the Doctorate Florida Georgia Louis Stokes Alliance for Minority Participation (FGLSA MP) project award HRD #0217675. Thank you, Anca Mirsu-Paun, P h.D. for listening to me. Thank you, Catherine Burton for all your he lp. I would like to thank my lab-mates. Thank you Ardit Agastra, for motivating me to finish and remaining calm at all times. Thank you John Shelton, for taking me under your wing, wr iting a million LabView programs for me, staying up late with me in the lab to ensure I finish and for saving my butt all of the time. Thank you Christian Martinez, for having the sa me “always be the best” drive as I do and helping me analyze my results. Well Christ ian, it looks like we are both finally 21!

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i Table of Contents List of Tables iii List of Figures iv List of Symbols vii Abstract viii Chapter 1Introduction 1 1.1 Background and Motivation 1 1.2 Regimes of Boiling Heat Transfer 5 1.3 Emergence of Nanofluids 7 1.4 Objectives of the Current Study 8 Chapter 2Literature Review 10 2.1 Nanofluids 10 2.1.1 Addition of Surface Agents to Nanofluids 11 2.1.2 Effects of pH on Nanofluids 12 2.2 Nanoparticles 13 2.2.1 Aluminum Oxide 14 2.2.2 Copper II Oxide 14 2.2.3 Silica Oxide 14 2.2.4 Titanium Dioxide 15 2.2.5 Zinc Oxide 15 2.2.6 Carbon Nanotubes 15 2.3 Nanofluid Heat Transfer 16 2.3.1 Thermal Conductivity Enhancement with Nanofluids 16 2.3.2 Critical Heat Flux Management with Nanofluids 20 2.3.3 Pool Boiling Characteristics with Nanofluids 23 2.3.4 Industry Related Applications of Nanofluids 25 Chapter 3Experimental Setup and Procedure 29 3.1 Nanofluid Preparation 29 3.2 Boiling Apparatus 30 3.2.1 Copper Sleeve 31 3.2.2 Cartridge Heater 32 3.2.3 Stainless Steel Plate 33 3.2.4 Copper Hat 34 3.2.5 Glass Cylinder 35 3.2.6 Thermocouple Wire 35

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ii 3.3 Data Acquisition System 36 3.4 Boiling Surface Preparation 37 3.5 Surface Roughness Measurement 38 3.6 COMSOL Model 38 3.7 Thermocouple Calibration Procedure 41 3.8 Experimental Procedure 41 Chapter 4Results and Discussion 43 4.1 Heat Transfer Calculations 43 4.2 Uncertainty Analysis 52 4.3 Surface Roughness Measurement Results 55 4.4 Experimental Heat Flux Results 57 4.5 Experimental Heat Transfer Coefficient Results 70 Chapter 5Conclusions and Recommendations 77 5.1 Conclusions 77 5.2 Recommendations 78 References 80 Appendices 83 Appendix A: Heat Flux, Heat Tran sfer Coefficient Calculations 84 Appendix B: Heat Flux Uncertain ty Analysis Calculations 104 Appendix C: Heat Transfer Coefficien t Uncertainty Analysis Calculations 114 Appendix D: Surface Roughness Images 119 Appendix E: COMSOL Thermal Resistance Data 123 Appendix F: Heat Flux Curves for All Data Points 125 Appendix G: Heat Transfer Coeffici ent Curves for All Data Points 128

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iii List of Tables Table 1: Properties of Aluminum Oxide Nanoparticles 29 Table 2: COMSOL Calculated Thermal Resistances 49 Table 3: Surface Roughness Data 56 Table A: De-Ionized Wate r Data Calculations 84 Table B: 0.1% wt Nanofluid Data Calculations 88 Table C: 0.2% wt Nanofluid Data Calculations 92 Table D: 0.3% wt Nanofluid Data Calculations 96 Table E: 0.4% wt Nanofluid Data Calculations 100 Table F: De-Ionized Water Heat Flux Error Bar Calculations 104 Table G: 0.1% wt Nanofluid Heat Flux Error Bar Calculations 106 Table H: 0.2% wt Nanofluid Heat Flux Error Bar Calculations 108 Table I: 0.3% wt Nanofluid Heat Flux Error Bar Calculations 110 Table J: 0.4% wt Nanofluid Heat Flux Error Bar Calculations 112 Table K: De-Ionized Water Heat Transfer Coefficient Error Bar Calculations 114 Table L: 0.1% wt Nanofluid Heat Transfer Coefficient Error Bar Calculations 115 Table M: 0.2% wt Nanofluid Heat Transfer Coefficient Error Bar Calculations 116 Table N: 0.3% wt Nanofluid Heat Transfer Coefficient Error Bar Calculations 117 Table O: 0.4% wt Nanofluid Heat Transfer Coefficient Error Bar Calculations 118 Table P: Thermal Resistance Data from COMSOL 123

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iv List of Figures Figure 1: Boiling Heat Transfer Curve 5 Figure 2: Boiling Apparatus 31 Figure 3: Copper Sleeve 32 Figure 4: Cartridge Heater a nd Variable Autotransformer 33 Figure 5: Stainless Steel Plate 34 Figure 6: Copper Hat 35 Figure 7: LabVIEW Front Panel Program 36 Figure 8: LabVIEW Block Diagram 37 Figure 9: Path of Heat Flow 39 Figure 10: Boiling Apparatus Temperature Profile 40 Figure 11: Water Temperature Profile 40 Figure 12: COMSOL Copper Hat Boundary Conditions 45 Figure 13: Boundary Temperature Prof ile at Bottom of Copper Hat 45 Figure 14: Boundary Temperature Profile of Side View of Copper Hat 46 Figure 15: Boundary Temperature Pr ofile at Top of Copper Hat 46 Figure 16: Heat Flux Path through Copper Hat 47 Figure 17: COMSOL Calculated Thermal Resistances 48 Figure 18: Thermal Circuit Schematic 50 Figure 19: Thermal Circuit 50 Figure 20: Heat Flux Curve of De-Ionized Water vs 0.1% wt Nanofluid 58

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v Figure 21: Heat Flux Curve of De-Ionized Water vs 0.2% wt Nanofluid 60 Figure 22: Heat Flux Curve of De-Ionized Water vs 0.3% wt Nanofluid 61 Figure 23: Heat Flux Curve of De-Ionized Water vs 0.4% wt Nanofluid 63 Figure 24: Heat Flux Curve 0.1% wt vs 0.2% wt Nanofluid 65 Figure 25: Heat Flux Curve 0.2% wt vs 0.3% wt Nanofluid 66 Figure 26: Heat Flux Curve 0.2% wt vs 0.4% wt Nanofluid 67 Figure 27: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.1% wt Nanofluid 70 Figure 28: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.2% wt Nanofluid 71 Figure 29: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.3% wt Nanofluid 72 Figure 30: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.4% wt Nanofluid 73 Figure 31: Heat Transfer Coefficient Curve 0.1% wt vs 0.2% wt Nanofluid 74 Figure 32: Heat Transfer Coefficient Curve 0.2% wt vs 0.3% wt Nanofluid 75 Figure 33: Heat Transfer Coefficient Curve 0.2% wt vs 0.4% wt Nanofluid 76 Figure A: Copper Hat after 0.1% wt Nanofluid Experiment 119 Figure B: Copper Hat after 0.2% wt Nanofluid Experiment 120 Figure C: Copper Hat after 0.3% wt Nanofluid Experiment 121 Figure D: Copper Hat after 0.4% wt Nanofluid Experiment 122 Figure E: Heat Flux De-Ionized Water All Data Points 125 Figure F: Heat Flux 0.1% wt Nanofluid All Data Points 125 Figure G: Heat Flux 0.2% wt Nanofluid All Data Points 126 Figure H: Heat Flux 0.3% wt Nanofluid All Data Points 126

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vi Figure I: Heat Flux 04% wt Nanofluid All Data Points 127 Figure J: Heat Transfer Coefficient De-Ionized Water All Data Points 128 Figure K: Heat Transfer Coefficient 0.1% wt Nanofluid All Data Points 128 Figure L: Heat Transfer Coefficient 0.2% wt Nanofluid All Data Points 129 Figure M: Heat Transfer Coefficient 0.3% wt Nanofluid All Data Points 129 Figure N: Heat Transfer Coefficient 0.4% wt Nanofluid All Data Points 130

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vii List of Symbols Symbol Meaning Units q heat transfer rate W q” heat flux W/m2 k thermal conductivity W/m K A cross sectional area normal to heat flux m2 T temperature oC or Kelvin T temperature at convective boundary oC or Kelvin Tsat saturation temperature oC or Kelvin TA..B temperature difference between points A and B oC or Kelvin Te excess temperature oC or Kelvin Rth thermal resistance oC/W LA..B distance between points A and B m h convective heat transfer coefficient W/m2 K D diameter m diameter of copper hat m U uncertainty associated with measurement varies

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viii Abstract Phase-change heat transfer is an im portant process used in many engineering thermal designs. Boiling is an important phase change phenomena as it is a common heat transfer process in many thermal systems. Phase change processes are critical to thermodynamic cycles as most closed loop sy stems have an evaporator, in which the phase change process occurs. There are ma ny applications/processes in which engineers employ the advantages of boiling heat transfer as they seek to improve heat transfer performance. Recent research efforts have experimentally shown that nanofluids can have significantly better heat transfer propert ies than those of the pure base fluids, such as water. The objective of this study is to improve the boiling curve of de-ionized water by adding aluminum oxide nanoparticles in 0.1%, 0.2%, 0.3% and 0.4% wt concentrations in a sub-cooled pool boiling appa ratus. Enhancement to the boiling curve can be quantified in two ways: (i) the similar heat fluxes of de -ionized water at smaller excess temperature, indicating similar quantity of heat removal at lower temperatures a nd (ii) greater heat fluxes than de-ionized water at similar excess temperatures indicating better heat transfer at similar excess temperatures. In the same fashion, the secondary objective is to increase the convective heat transfer coefficient due to boiling by adding diffe rent concentrations of aluminum oxide nanoparticles.

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1 Chapter 1 Introduction 1.1 Background and Motivation Phase change heat transfer is a very e ffective process of removing thermal energy from a body. Processes that involve condensation and ev aporation are extensively investigated phase change heat transfer pr ocesses. When a flui d in a gaseous state temperature falls below the saturation temperat ure, which itself is pressure dependent, the fluid condenses and returns to the liquid stat e. Inversely, in an evaporative process a fluid, in the liquid phase, is raised to a temperature above its saturation temperature and changes to the vapor phase. Evaporation oc curs at the solid-liquid interface whereas a phase change that is driven by heat transfer from the soli d surface to the liquid interface is termed boiling. This physical pheno menon can be explained by Newton’s Law of Cooling e sat s sT h T T h q (1) where "sqis the heat flux (W/m2), h is the heat transfer coefficient (W/m2 K) and T is the temperature (K). Te is also known as the excess temperature. Boiling is classified as a convective h eat transfer process since fluid motion occurs and consequently is a driving factor for heat transfer. However, boiling is unique as compared to other convective heat transf er processes because a phase change occurs

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2 during the process. The phase change allows heat to be transferred to and from the surface without significantly affecting the fluid temperature, which can lead to large heat transfer rates that correspond to small temperature differences. The latter also leads to large heat transfer coefficien ts as compared to typical si ngle phase convection processes. Partially due to large heat transfer coefficients, which allow for greater heat transfer, boiling is a highly de sirable heat transfer process to engineers. For example, boiling is critical to thermodynamic systems. In a power cycle, the working fluid is usually heated, until phase change occurs a nd the resulting vapor is used to drive a turbine or cylinder. In refrigeration cycles, evaporators absorb the heat until a phase change, due to boiling, occurs. The result ing vapor, flows into the condenser, and condenses back into the working fl uid and the process begins again. Boiling also plays a key role in the thermal management industry. Thermal management devices are critical to furthe r development in the electronics industry, particularly microelectronics. As technol ogy continues to increase, faster and smaller devices are being manufactured. These smaller devices produce significantly higher heat fluxes, are required to operate for longer peri ods in hazardous ther mal environments, and are more sensitive to temperature in general. In order to increase operating temperatures, reduce burnout, and increase product life cycle it is essential that thermal management devices evolve and become more efficient. Boiling heat transfer is already used in the thermal management industry in heat sinks, thro ugh heat pipes, to e ffectively cool central processing units (CPUs) and graphical proce ssing units (GPUs). Heat pipes work by taking advantage of phase change heat transfer. Inside the heat pipe there, is a working fluid, usually water or ammonia but sometime s mercury for high temperature operations

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3 or liquid helium for low temperature heat tran sfer, which partially fi lls the pipe in liquid form. When the heat pipe absorbs the thermal energy dissipated from th e device that it is cooling, all of the energy is used to boil th e fluid, thus initiating a phase change from liquid to a vapor. The device is protected from burnout as the majority of the thermal energy released was used by the evaporation process which in turn provided a small temperature increase in the device. In the manufacturing industry, engineer s also take advantage of boiling heat transfer when it comes to metallurgy, in the form of spray cooling. Spray cooling is a heat transfer technique in which liquid fl uid impinges, usually from a high pressure nozzle, and wets a surface. The wetted surf ace is cooled by the dropl ets of fluid as they absorb heat from the surface. In twophase spray cooling, the kind in which a metallurgist would use, the wetted surface is at a temperature above the saturation temperature of the surface a nd the impinging droplets boil off the surface. Two phase heat transfer is the most desirable form of spray cooling because of the amount of heat removed from the surface, whic h is indicative to the effectiv eness of boiling heat transfer. Boiling heat transfer is a very comp lex process; successf ul characterization depends upon numerous parameters such as latent heat, nucle ation sites, bubble formation, growth, size and detachment, buoyanc y driven fluid forces, vapor formation, dynamics of liquid-bubble intera ctions, density variation betw een phases, fluid velocities, apparatus orientation, surface roughness and in some cases gravitational fields. Boiling heat transfer is also dependent on ther mo-physical properties such as thermal conductivity and surface tension. Boiling can be classified by different modes: subcooled and saturated. During sub-cooled boiling the fluid temperature is below the

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4 saturation temperature and bubbles formed at the heated surf ace can condense back into the fluid while during saturated boiling the flui d temperature is greater than the saturation temperature. In this mode bubbles formed at the heated surface are propelled through the fluid by buoyancy forces and, if a free surface is present, are free to escape to the environment. Extensive research has been performe d to reveal and understand the underlining mechanisms of boiling heat transfer, particul arly in the area of pool boiling. Pool boiling occurs when a heated surface is inserted into a large, relative to the size of the heated surface, body of quiescent liquid, in which th e motion of the fluid surrounding the surface is primarily driven by bubble formation and cu rrents due to natural convection. If the bulk temperature of the liquid is below the sa turation temperature of the fluid then it is termed sub-cooled pool boiling, hence the abilit y of the bubbles to condense back into the fluid. When the bulk temperatur e of the fluid is maintained at its saturation level, the process is considered saturated pool bo iling. Shiro Nukiyama was the first to experimentally reveal different regimes of pool boiling in the 1930s [18]. Nukiyama gradually heated nichrome, nickel, iron and platinum wires, submerged and orientated horizontally, in saturated water at standard at mospheric pressure, to experimentally verify the maximum values of heat transfer for water in pool boiling. The results of the experiments for the maximum value of heat transfer turned out to be higher than previously believed at the time. Nukiyama’s plot of the heat flux versus the excess temperature formed the basis of the boiling heat transfer curves used in the current study.

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5 1.2 Regimes of Boiling Heat Transfer The standard boiling heat transfer curve c onsists of four basic regimes: (i) free convection boiling, (ii) nucleat e boiling, (iii) boiling transition and (iv) film boiling. Each regime has unique characteristics that identify it. Figure 1 shows a plot of the standard boiling heat transfer curve [19]. Figure 1: Boiling Heat Transfer Curve In the free convection boiling regime, as shown from the origin to point A in Figure 1, heat is transferred from the surf ace by natural convection bubble formation at

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6 the surface is yet to occur and consequently fl uid motion is due to convection forces. The second regime, nucleate boiling (shown from point A to C), is when bubble formation first begins to occur. Bubbles begin to form and detach from the surface slowly at first, but then increase rapidly over time as more nucleation sites become active. This bubble detachment causes better mixing of the flui d than natural convec tion alone and heat transfer from the surface and surrounding fluid is increased. Heat transfer is enhanced until point C, considered the maximum heat flux or more commonly the critical heat flux, where the third regime, boiling transition begi ns. Transition boiling (shown from point C to D) is when the nucleation sites become so numerous that bubble formation and detachment begin to form a vapor surface around the surface, making it difficult for the liquid to wet the surface. Due to the vapor form ed at the surface, the majority of the heat is forced to conduct through vapor, lowering th e heat flux to a minimum at point D. Point D is often referred to as the Leidenfr ost point. The final regime, film boiling, occurs when the surface is completely covered by vapor and heat transfer is dominated by conduction and radiation. Consequently, the heat flux will begin to increase with an increase in the excess temper ature from this point forward. This is known as the boiling crisis because the heat flux will now continue to increase without a decrease as long as the excess temperature increases. It can be di fficult to control the surface temperature. Point E is often referred to as the burnout point, however point E and C both represent the same heat flux, and as the boiling heat transf er curve illustrates to reach point E, the Leidenfrost point must be reached, which exists to due to poor heat transfer performance. The latter exhibits a waste of energy to achie ve the same heat flux, therefore for practical

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7 heat transfer applications the de termination of point C, the crit ical heat flux, is the desired heat flux. 1.3 Emergence of Nanofluids In the thermal sciences and engineering, significant amounts of research has been done studying various fluids. Particularly in heat transfer engin eering, a plethora of research has been done examining the nature and performance of various fluids. As technology continues to advance, it is heat transfer that con tinues to play an increasingly important role in that advancement. In the electronic industry, particularly microelectronics, heat transfer is very importa nt. Therefore the heat transfer applications of fluids are important. It has been s hown that convective co oling solutions using gaseous fluids, such as air, can be more th an adequate for devices such as a desktop computer. However, for more advanced devi ces such as computer servers, engines or advanced thermodynamic systems such as power plant operation, cooling systems involving liquid fluids are desi rable. To meet the perfor mance demands, engineers began to focus on different fluids and ways to e nhance the performance of such fluids. One such way is a mix of two different phases of matter. Mixing phases of matter, lead to the idea to enhance fluids by adding particles of solids to a liquid creating a new fluid. In theory, this new fluid is to be the best of both worlds, by providing some of the performance benefits using solids while ma intaining the ability to use fluids in apparatuses such as heat exchangers. In prac tice, this hybrid did provide an increase in performance as expected. However, clogging, sedimentation, and clumping of particles were some of the problems that prevented this idea from successful integration into heat transfer applications. Thus this idea was abandoned by many engineers and researchers.

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8 It was not until the late 1990s did this idea resurface. In 1995 Choi coined the term “nanofluid” while trying to develop a new e ngineering fluid [3]. Choi’s nanofluid contained nano-sized particles dispersed in a liquid. Through experimentation, it was demonstrated that the nanofluid had remarkab ly better heat transfer properties than the original fluid did. Previous problems with th is type of mixture were overcome by the use of nano-sized particles. The first liquid-sol id mixtures contained particles in the microscale and although small, particles of that size are difficult to keep in suspension. At the nano scale the particles are small enough to stay in suspension, and under the right conditions they can stay in suspension for an indefinite period. Permanent suspension has several advantages such as preventing aggregation and clogging. Explanation of the enhancement is still debated, but the scient ific community is in accord on one thing: nanoparticles have been shown, experimentall y, to greatly enhance the heat transfer properties of the original fluids by a ve ry small addition of nanoparticles. 1.4 Objectives of the Current Study It is clear that nanofluids can have better heat transfer properties than traditional fluids. The increases in thermal conductivit y, critical heat flu x, and heat transfer coefficients are not to be ignored but instead quantified and sought after to further enhancement. The purpose of the current study is to determine the effectiveness of alumina nanofluids in cooling a copper surface in a sub-cooled pool boiling experiment. The effectiveness of the alumina nanofluids ar e compared to de-ion ized water. More specifically the objectives of th e current study are as follows.

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9 1) Improve the boiling curve of de-ioni zed water by adding aluminum oxide nanoparticles in various concentrations in a sub-cooled pool boiling apparatus. 2) Increase the convective heat transfer coefficient due to boiling by adding aluminum oxide particles in various concentrations in a sub-cooled pool boiling apparatus. The primary and secondary objectives can bot h be observed with the same methodology. If the applied heat flux of nanofluids vers us the excess temperature were plotted, and compared to de-ionized water, then an im provement in the boiling curve can be observed in several ways. One way to see enhancement is when similar heat fluxes at smaller excess temperature are observed, which indi cates that comparable and/or the same quantity of heat is being removed at lower te mperatures. Graphically this is represented by shifting the boiling heat transf er curve horizontally to the left. Another way to see enhancement is when greater heat fluxes are produced at similar and/or the same excess temperatures. The latter indica tes better heat transfer at similar excess temperatures which is graphically indicated by shifting th e boiling heat transfer curve vertically upwards. Enhancement in the convective heat transfer coefficient can be seen in the same way as the heat fluxes.

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10 Chapter 2 Literature Review 2.1 Nanofluids There are many different nanofluids and th ey are usually prepared depending on the need of the researcher. Basic preparat ion techniques are as fo llows: add a desired amount of nanoparticles on a mass or volume basi s in relation to the total mass or volume of the fluid. Nanoparticles are usually disp ersed into the fluid by a process known as ultrasonication for a situationally dependent amount of time. Ultrasonication is a process in which the creation of reciprocating high a nd low pressure waves are created in a liquid, causing small bubbles to form and burst. The latter is the basic pr inciple of cavitation and the resulting fluid movement causes str ong hydrodynamic shear forces which in turn can be used to thoroughly mix reactants. Although most nanofluids contain nanopart icles dispersed by ultrasonication, keeping the particles in suspensi on for extended periods of time is still a challenge. The most widely used techniques to prevent sedimentation are by adding active surface agents and controlling the pH of the nanofluid. So me suspension techniques change the surface properties of the nanopartic les and reduce the tendency of the nanoparticles to conglomerate into clusters, which prevents uniform dispersion, wh ich can have adverse effects on heat transfer.

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11 2.1.1 Addition of Surface Agents to Nanofluids The additions of surface agents, which ar e also referred to as surfactants, are selected depending on the proper ties of the base fluid and na noparticles themselves. Y. Xuan et al. used oleic acid in addition to salt as surfactants to help with dispersion and suspension of copper nanoparticles in tran sformer oil and water. S.M.S Murshed et al. used oleic acid and cationic surfactant hexadecrltrimethlammonium bromide (CTAB) to keep Titanium Dioxide (TiO2) nanoparticles in water based nanofluids. Y.J. Hwang et al. used sodium dodecyl sulfate (SDS) fo r water based MWCNT nanofluids. Jin Huang et al. investigated the eff ect of using sodium dodecylbenzene sulfonate (SDBS) as a surfactant in aluminum oxide-water and copper-water nanofluids. Jin Huang et al. noticed that ultrasonication can have adverse effects on nanofluids after extended periods of time. The rese archers prepared nanofluids in 150 ml beakers with 0.1% weight fractions of both aluminum oxi de and copper nanoparticles. SDBS was added to the nanofluids and then the same na nofluid was prepared without SDBS. Both nanofluids were sonicated for an hour at a frequency of 40 KHz After sonication the average particle size was measured. Nanofluid s without SDBS had an average particle size of 5560 nm while the nanofluids containing SDBS had an average particle size of 130 nm indicating better dispersion with surfactants. X-Q Wang et al. states that although adding surfacta nts is intended as a method to suppress particle clusters from forming, su rfactants can affect the heat transfer performance of nanofluids suggesting that ex cessive use of surfactants can deteriorate heat transfer performance.

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12 2.1.2 Effect of pH on Nanofluids The pH of the solution has been show n to affect the suspension time, so controlling the pH of the solution can be important. K.B. Anoop et al. performed research with 45 nm and 150 nm aluminum oxide nanoparticle s creating nanofluids with weight concentrations of 1%, 2%, 4%, and 6%. The nanofluids had pH values of 6.5, 6, 5.5 and 5 respectively. K.B. Anoop et al. set aside 2.5 l of each concentration and noticed the nanoparticles stayed in suspension for seve ral weeks. The rationa le for the extended period of suspension is knowledge of the iso-el ectric point (IEP). The IEP corresponds to the point of zero zeta potential (ZZP). The zeta potential is the measurement of the stability of a colloidal system, a system in wh ich matter in one of three phases, is finely dispersed in matter in a different phase, such as a nanofluid. At the ZZP the net charge between particles are at a maximum, wherei n the attraction between particles is great enough to overcome the hydrodynamic forces surrounding the particle, causing the particles to conglomerate. K.B. Anoop et al. kept the nanofluid away from the ZZP, preventing the particles from clumping together. Jin Huang et al. further investigated the effect of pH on nanofluids by observing the pH effects on nanofluids consisting of al uminum oxide and coppe r nanoparticles with water as the base fluid. The re sults of that research show th at nanofluids can be kept in suspension for extended periods of time, and the pH corresponds to the absorbency and zeta-potential point, depending on the nanopartic le concentration. Ji n Huang’s research shows that aluminum oxide and copper nanopart icles both fall out of suspension rapidly when in water with a pH less than 2. Furthe r investigation with adjusting the pH showed that the pH and absorbency and zeta potential to be directly related, such as for each

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13 increase in pH, the zeta potential increases also. The optimum pH value for aluminum oxide nanoparticles in de-ionized water was found to be 7.5-8.9 and any pH value greater than or equal to 7.6 for copper nanoparticles. 2.2 Nanoparticles The most common types of nanoparticles used in research are alumina oxide (Al2O3), copper II oxide (CuO), silica oxide (SiO2), titanium dioxide (TiO2), and zinc oxide (ZrO2). Pure metallic nanoparticles such as gold, silver, iron, platinum, and copper are also being used in research. A fe w researchers have even begun to use carbon nanotubes in nanofluid researc h. However, the majority of nanoparticles are oxides; consequently they are primarily used in water based nanofluids. Non-oxides nanoparticles are used in water based na nofluids. For example, pure metallic nanoparticles have been used in aqueous so lutions but difficulties associated with keeping the particles in suspension for exte nded periods of time, limit their usage. Instead, pure metallic nanoparticles are predominantly used in oils, like engine oil, or alcohols such as ethanol. Nanoparticles are usually made by a s ynthesis technique, whereas a metal precursor, in the bulk, is heated to produce a vapor. Reactive gases are added to the newly formed vapor to create a new molecula r structure. Next the vapor-reactive gas vapor is cooled at a controlled rate, causing nanoparticles to condense out of the process. Also nanoparticles are sometimes created from a more traditional ch emical process in which an appropriate chemical solution is comb ined with reactants. Once reactants start the chemical reaction, nanoparticles precipitate out of the solution.

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14 2.2.1 Aluminum Oxide Aluminum oxide (Al2O3), or alumina is the most widely used nanoparticle since its thermal properties are well documented [ 5, 6, 8, 11, 14, 17]. Alumina can be acquired in sizes as low as 40 nm Al2O3 comes in various shapes but are usually spherical, the thermal conductivity approximately 30 W/m*K depending on how pure the aluminum is. This particle is often used to increase the thermal conductivity of th e base fluid. Alumina is often used in de-ionized water, ethylene glycol, and oil. 2.2.2 Copper II Oxide Copper II Oxide (CuO) is another highly us ed nanoparticle for nanofluids [1, 3, 7, 11, 15]. The sizes and shapes depend on the manufacturer but some have been reported as small as 14 nm The thermal conductivity of c opper II oxide is approximately 20 W/m*K Copper II oxide is often used in boiling tests and to increase the thermal conductivity of the base flui d. Nanofluids have been created with CuO nanoparticles added to de-ionized water, oil, ethylene glycol. 2.2.3 Silica Oxide Silica oxide or silicon dioxide is anot her well used nanoparticle in nanofluid research [8]. Thermal conductivity is around 1.4 W/m*K and sizes as small as 22 nm have been achieved. Silica oxide is often used in boiling tests to examine the critical heat flux. Silica oxide nanoparticles have been a dded to oil, de-ionized water, and ethylene glycol to create nanofluids.

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15 2.2.4 Titanium Dioxide Titanium dioxide or titania is often used in boiling tests to study how it affects the critical heat flux [5, 8]. Sizes have been reported as low as 85 nanometers. The particles are usually spherically shaped. Titania nanopa rticles have been adde d to ethylene glycol and de-ionized water to create nanofluids. 2.2.5 Zinc Oxide Zinc oxide or zirconium oxi de is another nanoparticle and is being one of the newest particles being studied [10, 14]. Mo st research is done using boiling tests to examine the CHF. Some manufacturers have reached sizes as small as 20 nm The thermal conductivity is approximately 2 W/m*K Zinc oxide nanoparticles have been used with base fluids of de-ionized wate r and ethylene glycol to create nanofluids. 2.2.6 Carbon Nanotubes Carbon nanotubes are the strongest material s on earth, but in addition to their great strength they also have significant heat transfer properties. Th eoretical calculations list the thermal conductivity of carbon nanotubes 6600 W/m*K at room temperature [23]. J Hone et al. states that in the when carbon nanotubes are aligned that thermal conductivity is gr eater than 200 W/m*K at room temperature. Carbon nanotubes come in two varieties: single walled carbon na notubes (SWNT) and double walled carbon nanotubes (DWNT). SWNT have been manufactured as large as 1.25 nm in diameter [22]. Carbon nanotubes have been added to de-ionized water, ethyle ne glycol, oil, and decene.

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16 2.3 Nanofluid Heat Transfer Research has shown that nanofluids can have greater heat transf er properties than traditional fluids. Understanding and quantifyi ng the superior properties is important, as such, much research is being done, however th e majority of nanofluid research can be categorized into thermal c onductivity enhancement, critical heat flux management, and pool boiling characterization. In addition to traditional heat transfer research some researchers are conducting nanofluid experiment s which readily lend themselves to more traditional industry applications. 2.3.1 Thermal Conductivity Enhancement with Nanofluids The thermal conductivity of a material is a unique transport property, defined as the proportionality constant, k [W/m*K], in Fourier’s Law. The thermal conductivity is a heat transfer material property, which is mo st commonly used to quantify heat transfer effectiveness. Generally the higher the th ermal conductivity, greater quantities of heat can be transferred at a faster rate through a material as compared to a material of lesser thermal conductivity. It has been shown through experimentat ion that nanofluids have significantly increased thermal conductivity compared to th eir base fluids. Equally important are analytical models to predict the enhancemen t caused by nanoparticles addition. One such model, used by many researchers, is Maxw ell’s theoretical model for predicting the effective thermal conductivity of suspensions with spherical particles [1]. Maxwell’s model for predicting the effective thermal c onductivity of liquid-soli d fluid suggests that as particle volume fraction increases so t oo does the effective thermal conductivity [1].

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17 Hamilton and Crosser modified Maxwell’s theo retical correlations to account for particle shape [1]. H.U. Kang et al. took the previ ous model and used it to predict the thermal conductivity of silica oxide using water as the base fluid and compared the results to the experimental thermal conductivity values ac quired from using the transient hot wire method [1]. H.U. Kang et al results show that Hamilton and Crosser’s model is indeed capable of predicting the thermal conductivity of nanoparticles. This point is significant in understanding thermal conductivity of nanofluids. Hamilton and Crosser’s model was developed using Maxwell’s model for effec tive thermal conductivity as the base model from which their work is based upon. Sin ce Maxwell’s model was not developed with nanofluids in mind, yet was sufficient enough develop nanofluid correlations, suggests thermal conductivity enhancement from micr o sized particles gives good insight to enhancement from using nanoparticles. It also suggests that Maxwell’s model is developed enough to use as a starting point to develop future nanofluid thermal conductivity models. Yimin Xuan and Wilfried Roetzel conducte d research to investigate nanofluid thermal conductivity. Yimin Xuan and Wilfri ed Roetzel reported that Hamilton and Crosser’s model has been shown to satisfa ctorily predict the th ermal conductivity of nanofluids whose ratio of conduc tivity of the solid/liquid phase s is larger than 100. They believe that nanofluids behave like a single fluid for the most part but particle shape and sizes are not to be ignored and that model’s such as H.U. Kang et al. which incorporate these factors, are imperative to the unders tanding of thermal conduc tivity enhancement of nanofluids [4].

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18 Another philosophy used to predict the ther mal conductivity of nanofluids is based off of effective medium theory a nd the concept of fractal dimension for nanoparticle clusters while payi ng attention to nanoparticle cl usters size and the particle size. B-Xuan et al. developed a fractal model that can predict the effective thermal conductive of a nanofluid. Eff ective medium theory has two models that can predict the thermal conductivity of nanofluids, the Maxwell-Grant correlations (MG) and Bruggeman model. For low particle concentr ations both correlations provide the same results when compared to experimental re sults. However, fo r high volume fraction concentrations the Bruggeman model is the mo st accurate of the two. Therefore B-Xuan et al [3] used the latter to predict the effective thermal conduc tivity of nanoparticle clusters and MG to predict the thermal conduc tivity of the nanopartic le suspensions. By defining the necessary fractal indexes and with fractal theory, B-Xuan developed a fractal model by combining the MG and Bruggeman models. The thermal conductivity of copper II oxide nanofluids at mass concen trations of 0.02%, 0.04% and 0.06% were found experimentally and compared to those pr edicted from B-Xuan’s fractal model. BXuan [3] model proved to be accurate but notes that when exceeding 0.5% mass concentrations deposition be gins to occur. Nanopart icle deposition is beyond the assumptions used to develop the fractal model, hence it is no longer able to predict the thermal conductivity. It is relevant to mention that na nofluids may have fluctuating thermal conductivity when used in certain apparatuses du e to interactions with the device itself. For example, Dongsheng Wen discovered th at when using nanofluids containing nanoparticles with a bulk thermal conductivity of 50 W/m*K in a microchannel that

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19 nanoparticles sometimes cluster at the walls or migrate in general. Therefore the local thermal conductivity of the fluid changes dr astically with this increased non uniform particle distribution [12]. As the non-uniform particle distribution in creases the constant thermal conductivity assumption becomes invalid [12]. It is very noteworthy to point out that the general method of creating nanofluids, the addition of nanoparticles to a specified base fluid, is not the only way to create nanofluids with high thermal conductivity. Min-Shen Liu et al tried to increase the thermal conductivity of a base fluid with copper nanoparticles with the chemical reduction method [15]. Min-Shen Liu et al added copper acetate to de-ionized water and mixed it slowly and uniformly while using hydrazine as a reduci ng agent [15]. Copper nanoparticles precipitated out of the soluti on. Volume concentrations produced, were below 0.2%. The chemical reduction caused the so lution to turn from light brown to dark brown [15]. Three basic types of nanoparticle s were produced; (i) spherical (ii) square shapes and (iii) needle asso rted shapes. Min-Shen Liu et al. uses the ratio of the thermal conductivity of the nanofluid to the thermal conductivity of th e base fluid to compare the various concentrations [15]. Also, spheri cal shape nanoparticles appear to cause the highest increases in thermal conductivity. Th e particles ranged in diameters from 50-100 nm and were produced in volume fraction conc entrations of 0.05% to 0.02%. Min-Shen Liu et al. research demonstrated that the chem ical reduction method can be used to produce a nanofluid that can effectively increase the ther mal conductivity of the based fluid [15].

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20 2.3.2 Critical Heat Flux Man agement with Nanofluids Another highly researched topic is how nanofluids affect the critical heat flux (CHF). The CHF is the heat flux associated with the point in a heat transfer process whereas the heat transfer reaches a maximu m effectiveness. That is, the maximum amount of heat is being removed from the heat transfer surface. Any future attempts to remove more heat are futile. Physically, for many convective processes such as spray cooling and pool boiling, the heated surface is at a temperature so great that all the fluid near the heated surface evaporates. This vapor forms a blanket between the heated surface and surrounding fluid, causing heat to conduct through vapor before reaching a liquid fluid. Vapors inherently are inferior conductors of th ermal energy as compared to liquids. Having to dissipate heat through the va por blanket first, signi ficantly lowers the effectiveness of heat transfer. Thus knowledge of this phenomenon is important. In fact a higher CHF is often very de sirable and nanofluids have be en shown to affect the CHF in many experiments. H Kim et al. performed comprehensive inve stigations to understand the phenomenon behind the enhanced CHF observed from the use of nanofluids. H Kim et al. looked at pool boiling of titania and alumina with diameters of 85 nm and 47 nm respectively. Previous experi ments showed that that even small volume concentrations significantly increased the cri tical heat flux, so the experiment used concentrations ranging from 0.0001% to 1%. In the ba th heaters were powered by NiCr 2 mm diameter wire and then by Ti 25 mm diameter wire. Each heater showed the same basic trend, significant increases in the CHF at small na nofluid concentrations when compared to pure water. Large increases in the CHF were observed in nanofluid with concentrations

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21 up to 0.01%. Concentrations > 0.01% were char acterized by smaller jumps in the CHF as nanofluid concentrations approach 1%. It is worth noting that the alumina fluid outperformed the titania fluid showing greater increases in the critical heat flux at the same concentrations. After the boiling te sts surface conditions of the heaters were examined with a scanning electron microscope (SEM). The SEM showed that as particle concentrations increased so did the layer of nanoparticles depos ited on the surface which suggests that the main reason for the enhanced CHF is indeed the layer of nanoparticles formed from boiling [5]. Then a pool boiling experiment measuring the CHF using a nanoparticle coated heater in pure water was compared to a na noparticle coated heater in a nanofluid. The results of the pool boiling expe riment show that the nanoparticle coated heater produced higher heat fluxe s in nanofluids than the nano particle coated heater in pure water. It is important to point out that the nanoparticle coated heater used in the pure water pool boiling test produced higher he at fluxes than the smooth heater did in a pure water pool boiling test. This result supports H Kim et al belief that the deposition of nanoparticles is the main contributor to the enhancement of the CHF. Those results give some insight on CHF behavior at lo wer concentrations. Particle deposition increased with particle concentrations whic h in turned increased the CHF. At higher concentrations a more dynamic layer of nanopa rticles coated the he ater revealing why no more enhancement to the heat flux occurred after a certain concentration. In Cheol Bang et al investigated the critical he at fluxes in pool boiling with alumina nanofluids using smooth heaters orie nted in both the horizontal and vertical directions [6]. Results from that research s how that regardless of the orientation of the heaters the CHF is increased by the addition of nanoparticles. Still, more dramatic

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22 enhancement occurs in horizontal orientati on with a 32% increase in the CHF compared to a 13% increase in the vertical orientation [6]. In Cheol Bang et al. suggests that the CHF enhancement is influenced by factors such as geometry, different nanoparticles, the surface roughness of the heaters, as well as the size of the nanoparticles [6]. Zhen-Hua Liu et al. investigated copper II oxid e nanofluids and how the CHF would be affected using satu rated and sub-cooled water as the base fluid with jet impingement on the heater surface. The result s of that investigation demonstrated that using both saturated and sub-cooled water as a base fluid pool boiling experiments produced higher CHF than pure water. Als o, the CHF enhancement gradually increases with particle concentration [7]. The CHF stopped increasing when the particle concentration exceeds 1 wt% (weight percenta ge), with the maximum increase of the CHF of 25% compared to pure water [7]. During jet boiling a sorp tion layer builds and continues to grow until a certain value at which point the CHF stops increasing. Hyungdae Kim et al. performed research and compared the CHF of various nanofluids and examined how surface we t-ability, surface roughness, and maximum capillary wicking height of the nanoparticle coated surface affect the CHF [8]. The nanoparticles used were TiO2, Al2O3 and SiO2 at 85 nm 47 nm and 90 nm respectively. The experiment measured the critical heat fluxes and compared them to the values predicted by Zuber’s correlati ons and pure water. The results were within 85% of the values predicted by Zuber’s correlations and show that volume concentrations up to 0.01 % cause increase in the critical heat flux by up to 170% but at 0.1 % only SiO2 showed improvement [8]. When nanofluid concentr ations > 1% were used no improvement in the CHF over pure water was generated. Hyungdae Kim et al. reported that surface wet-

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23 ability, surface roughness and capillary wicking hei ght all affect the CHF. Still, they do not account for the unusual CHF enhancement, but instead nanoparticle concentration is the largest factor involved in CHF enhancement [8]. 2.3.3 Pool Boiling Characteristics with Nanofluids Pool boiling is a process when a heated surface is submerged in a bath of fluid and the volume of the heated surface is much smaller than the volum e of fluid, the heat surface is submerged in. The heated surf ace is heated to a temperature above the saturation temperature of the fluid and heat tr ansfer occurs at the solid-liquid interface causing the liquid at the surface to form vapor lowering its density, causing the vapor to rise further from the surface. As vapor tr avels through the fluid, the surrounding fluid replaces the vapor at the heat ed surface. When nanofluids are used as the working fluids in pool boiling experiments they have been known to significantly alter the pool boiling curve and the boiling heat transfer coefficient. S.K. Das et al investigated pool boiling character istics of alumina nanofluids to understand how they would behave in a conve ctive cooling situation, using smooth and rough heaters (caused by nanoparticle depositio n) at volume fracti ons concentrations ranging from 1% to 4%. The ratio of the thermal conductivity of the nanofluid to the base fluid was calculated and shown to increas e with temperature a nd was highest at 1% volume fraction and lowest at 4% volume fraction. Also, the surface roughness of the heaters was altered from experimentation, in cluding the pre-coated heater. As boiling continued more nanoparticle deposition o ccurred. The nanoparticle deposition also caused the boiling point to increase with temp erature, deteriorating the boiling properties

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24 of the base fluid by lowering the heat transfer coefficient. However, it is noted that careful attention must be paid to the local heat fluxes as the pool boiling leaves the surfaces at a higher temperature than the ba se fluid, which could be undesirable [9]. Cheol Bang et al. also researched the boiling heat transfer performance in pool boiling of alumina nanofluids using smooth heat ers oriented in both the horizontal and vertical directions [6]. The pool boiling heat transfer coefficients of the nanofluids are compared to that of pure water and the result s show that the nanofluid coefficients were actually worse than that of pur e water [6]. As particle concentration increased the boiling heat transfer coefficient decreased shif ting the boiling curve to the right [6]. Manoj Chopkar et al. is currently researching zinc oxide nanofluids in boiling test and comparing the results to pure water and nanofluid using surfact ants, which increase the boiling heat transfer, while looking at continued use of the same surface [10]. 0.005%, 0.01%, 0.02%, 0.5%, 0.07%, and 0.15% zinc oxide volume particle concentrations with 1% surfact ant and average of three runs show results show that a decrease in boiling performance the more you run the experiment without cleaning the heater surface, degrading even to that point whereas boili ng performance is below pure water [10]. Surfactants combined with nanof luids deteriorate the boiling heat transfer coefficient much more significantly than nanofluids without surf actants added [10]. Again, nanoparticle deposition appears to be th e main cause of the deterioration of the boiling heat transfer coefficient [10].

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25 2.3.4 Industry Related Applications of Nanofluids Nanofluids are beginning to be researched in a variety of ways which can be readily related to industry applications. On e such application has been done comparing the performance of nanofluids against engine oil. S.-C. Tzeng [11] used two distinct nanofluids in his research. One nanofluid cons isted of copper II oxide particles, the other contained aluminum oxide particle nanofluid s. Both nanofluids were created using automatic transmission oil as the base fluid. Automatic transmission oil with antifoam added, an additive used to prevent unwanted air from mixing with the oil, was compared to nanofluids in a 4-wheel drive transmission sy stem. The engine has a four blade rotary system in which improvements in heat tran sfer could increase e ngine life and overall performance of the automobile. For this se t up 40 grams of nanoparticles are added to 840 grams of automatic transmission fluid which was added to the oil cavity of the rotary blade coupling. Data collection was done for temperature placing sensors at 24 unique points on the rotary blades for measurement in both the axial and radi al directions. The engine runs continuously for 60 minutes wh ile a data recorder collects the data. Afterwards a temperature dist ribution is plotted showing the oil’s performance over a period of time. The blades spin at 400, 800, 1200, and then 1600 RPMs during this 60 minute interval for each fluid. Tzeng’s results are astounding, as they show that CuO performs the best with the lowest temperat ures, therefore it transfers heat the most efficiently, regardless of the blade speed. Al2O3 comes in second while antifoam is the worst additive for heat transfer. In fact, antif oam is the worst as it degrades the quality of the oil at higher speeds. Both nanofluid s outperform pure automatic transmission fluid.

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26 Another industry related use of nanoflu ids is in the quenching industry. K. Narayan et al. performed comparative analysis of water and nanofluids in steel quenching tests [13]. The nanofluid, or nanoquenchant, consisted of Al2O3 particles no greater than 50 nm with water as the base fluid [13]. Th e parameter analyzed was surface wettability. Using FTA software and an image record er, operating at 60 frames per second, the wetting behavior was examined. A furnace wa s used to heat the test specimen. The furnace was of tubular design and oriented vert ically at both ends. Thermocouples were used to measure the furnace temperature. The test specimen was heated to 850 OC and quenched in 1500 mL of coolant [13]. Usi ng lumped capacitance analysis, the heat transfer coefficient was calculated and re sults show that the boiling heat transfer coefficient is lower for the nanoquenchant than pure water. However the spreading of the nanofluid on the substrate continued for well over 1000 ms while it stopped at 200 ms for water. K. Narayan et al. suggests that results indicate th at for industry applications there is a need for nanofluids various quench sever ity. Quench severity, is a term used by metallurgists to describe the cooling rates of various quench-ants. K. Narayan et al. suggest that nanofluids with lo w cooling severity would be id eal for thin sections of high quench sensitivity materials, while nanofluids with high cooling severity would be ideal for thick sections of low quench sensitivity materials [13]. C. Choi et al investigated the use of nanofluids as a coolant by performing tests with three different nanofluids acting as c oolants in an electri cal transformer [14]. Spherical shaped Al2O3, AIN, and rod shaped Al2O3 with sizes of 13 nm 50 nm 2 nm 20-200 nm respectively were added to transforme r oil [14]. Ethylene alcohol and oleic acid were added to stabil ize the nanofluids as an additive to prevent sedimentation. With

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27 volume fractions up to 4%, all three nanofluid s outperformed pure transformer oil, with greater thermal conductivity, c onvective heat transfer co efficients and convection properties [14]. One interesting resultant that came from C. Choi et al. research is that the spherical shaped Al2O3 oil based thermal conductivity was nearly double that of Al2O3 with water as the base fluid, showing e nhancement greater than 20% at 4% volume fraction [14]. AIN nanofluid showed an increase in thermal conductivity of 8% and improvement in the heat transfer coefficient by 20%. It is important to mention that nanoparticles concentration could not be in creased much higher. Higher nanoparticle concentrations would require th e amount of additives used to increases, which in return increases the fluid viscosity causing chemi cal instability. Therefore, the particle concentration cannot be in creased without end [14]. D.P. Kulkarni et al. investigated nanofluids in a diesel engine of electrical generators to improve perfor mance [17]. D.P. Kulkarni et al. added 45 nm alumina nanoparticles to a 50-50 ethylene glycol-water mixture and comp ared that mixture to 2%, 4%, and 6% alumina nanofluids. D.P Kulkarni et al measured the specific heat of the nanofluid using the correlations presented by Buongiorno [17]. Results show that as the particle concentration increases, the specific heat of the nanofluid s decreases, implying that for higher particle c oncentrations, less heat input is required to increase the temperature of the nanofluid [17]. If the time required to heat reduces, and if the nanofluids are used as jacket water, the engine will heat up faster and may result in less emission to the environment, since higher concentration of pollutants are emitted during the engine warm-up [17]. After, replaci ng the jacket water with an alumina based nanofluid in the diesel engine; it was observe d that as particle c oncentration increased,

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28 the diesel engine cogeneration efficiency decr eased [17]. The efficiency decrease may be attributed to a decrease in the specific heat associated with an increase in particle concentration [17]. However, the heat exch anger used in the system saw increases in efficiency with increasing part icle concentrations, which could be beneficial if that excess heat is channeled away from the generator and used to heat buildings [17]. D.P. Kulkarni et al. suggests that future research shoul d focus on measuring the thermophysical properties of different nanofluids as a functi on of temperature and concentration as the results could lead to heat exchangers designed specifically fo r nanofluids [17].

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29 Chapter 3 Experimental Setup and Procedure 3.1 Nanofluid Preparation In this investigation Al2O3 nanoparticles were chosen because of their well documented thermal properties, ease of disper sion in de-ionized water, and wide spread use in the research communit y. Aluminum Oxide nanoparticles were added to de-ionized water on a mass basis with concentrations of 0.1%, 0.2%, 0.3% and 0.4% wt. The nanoparticles were manufactured by the Al fa Aesar Corporation. The manufacturer provided specifications are: Table 1: Properties of Alu minum Oxide Nanoparticles. Avg. Particle Size Purity Formula Weight Boiling Point Melting Point Specific Surface Area Refractive Index 45 nm 99.5% 101.96 2980o 2045o 36 m2/g 1.768 The feed water into the resear ch facility was de-ionized using a water filtration system. The water filtration system used was th e Barnstead E-pure , manufactured by the Barnstead International Corporation a nd the model number is D4641 120 VAC. The average resistivity of the de-ionized wate r was 18.0 megohm-cm. The mass of the deionized water was weighed on a digital scale. The digital scale was manufactured by the Ohaus Corporation and the model number is Adventurer Pro AV8108. At this point,

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30 nanoparticles were added to the de-ionized wa ter and then sonicated for a minimum of 12 hours using a Tabletop Ultrasonic Cleaner. Th e sonicator used was manufactured by the Fisher Scientific Corporati on and the model number is FS-140H. During sonication, the temperature of the nanofluid is increased causing some evaporation. To avert the nanoparticle concentration from changing, a lid was placed on the beakers used to sonicate the nanofluid, therefor e any changes to the mass concentration was considered to be negligible. 3.2 Boiling Apparatus The device used to conduct the experiment for this investigation is considered to be a sub-cooled pool boiling apparatus. It c onsists of a cartridge heater inserted into a copper sleeve. The copper sleeve is connect ed to the boiling speci men, a copper hat, via thermal paste. The copper hat rests on top of a stainless steel plate. An open glass cylinder is fixated on top of the stainless stee l plate, de-ionized water and nanofluids are stored in the cylinder. A lid was placed on top of the glass cy linder to prevent the majority of the fluid from evaporating. The lid has 2 shafts that allo w vapor to escape. A rubber hose is connected to each shaft, a funne l is connected to one end of the hose and the other hose rests in the f unnel, catching vapor which conde nses back into the fluid. However, some vapor can still escape, the am ount of which was considered insignificant. The boiling apparatus rests on top of 3 stands, each supporting the st ainless plate. The copper sleeve is inverted belo w the stainless plate by a retention clamp to a stand. The height can be adjusted by s liding the heater up the stand a nd fixed at a desired location with a lock nut. The set up is shown in Figure 2.

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31 Figure 2: Boiling Apparatus. 3.2.1 Copper Sleeve The copper sleeve is made from a single piece of tellurium copper. Tellurium copper was chosen in this investigation due to its high thermal conductivity (401 W/mK ) and ease of machinability. The copper sleeve is 4 1/3” long, with di fferent diameters at each end. At the bottom end the diameter is 1” and concentrically machined there is a hole drilled to depth of 2 2/ 3” with a diameter of 13 mm The top end of the copper sleeve has a diameter of 12 mm and is 1 7/32” long. Three 1 mm holes are drilled at depth of 6mm into the upper portion of the copper sleeve for thermocouples. The first thermocouple hole is located 3 mm from the top surface, the second is located 13 mm from the top surface and is rotated 120o from the first thermocouple, while the last

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32 thermocouple hole is located 23 mm from the top surface and rotated 240o from the first thermocouple. The copper sleeve is shown in Figure 3. Figure 3: Copper Sleeve. 3.2.2 Cartridge Heater The heater used in this investigation was a cylindrical cartridge heater manufactured by the OMEGA Corporation a nd the model number is CIR-30301/120V. The copper heater is rated for maximum wattage of 750 W and for 120 volts AC The dimensions of the heater were measured using a digital caliper. The diameter was determined to be 12.6 mm and the length was determined to be 79 mm Before insertion into the copper sleeve a thin layer of OmegaTherm thermal paste was applied to the body of the heater. The cartridge heater was connected to a variable autotransformer. The variable autotransformer was manuf actured by the Staco Energy Products Corporation and the model number is 3PN1010B The variable autotransformer has an input voltage of 120 volts, maximum output of 140 volts and a constant current load of 10 A maximum. The cartridge heater and variab le autotransformer are shown in Figure 4. 4 1/3’’ 2 2/3’’ 1 7/32’’ Thermocouple Holes 1’’ 13 mm 12 mm

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33 Figure 4: Cartridge Heater and Variable Autotransformer 3.2.3 Stainless Steel Plate Stainless steel was chosen as a surfac e to balance the boiling apparatus on the stands but primarily for its low thermal conductivity (17.7 W/m*K ) and thermal expansion properties, compared to copper. The coefficient of thermal expansion of stainless steel is 17 *10-6 in/in/oC and the coefficient of thermal expansion of copper is 17.6 *10-6 in/in/oC The idea is that over extended periods of time the copper hat would expand at a faster rate than the stainless steel plate, creating a tighter seal as temperatures increases. The outer diameter of the stainless steel plate is 6’’ and the inner diameter is 12 mm The plate has a thickness of 2 mm and is shown in Figure 5. 79 mm 12.6 mm

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34 Figure 5: Stainless Steel Plate 3.2.4 Copper Hat In the same fashion as the copper sleeve the copper hat is made from a single piece of tellurium copper, due to its high thermal conductivity and machinability. The copper hat is has an outer diameter of 20 mm and inner diameter of 12 mm The inner sleeve has a thickness of 2 mm while the outer sleeve has a thickness of 1 mm The copper hat rests inside the stai nless steel plate and was sealed to the plate by applying an adhesive to the outer sleeve. The adhesive used in this investigation was Silicone II made by GE. Silicone II is not water soluble and considered to be permanently flexible. The adhesive provi ded a water tight seal, which c ould be easily removed when needed. During experimentation the sealan t had no noticeable a dverse effects on the nanofluids. 6” 12 mm

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35 Figure 6: Copper Hat. 3.2.5 Glass Cylinder During this investigation an open ende d glass cylinder was used to hold the nanofluids. Glass was chosen primarily fo r visual observation and for its low thermal conductivity of 1.4 W/m*K The glass cylinder has a diameter of 4”, height of 5 3/4”, and thickness of 2.5 mm Silicone II was used to adhere the glass cylinder to the stainless steel plate. 3.2.6 Thermocouple Wire The thermocouple wire used in this inve stigation was type K and manufactured by Omega Engineering Inc. The part number is GG-K-30-SLE and has a nominal size of 0.037’’ x 0.050’’. The AWG number is 30, the conductor is insulated in glass wrap and the overall insulation is glass braid. The temp erature range of the thermocouples is from -200 C to 1350 C while the insulation is rated up to 482 C Before each experiment each thermocouple was cleaned with ArcticClean 1 Thermal Material Remover and 20 mm 12 mm 2 mm 1mm

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36 ArcticClean 2 Thermal Surface Purifier, then Arctic Silver 5 was applied to the thermocouples before insertion into the copper sleeve. 3.3 Data Acquisition System The research facility was equipped w ith a computer and data acquisition equipment made by National Instruments. A ll thermocouples were connected to a NI SCXI-1303 terminal block. The data acquisi tion software used was LabVIEW 7.1. A program was written to monitor and display all thermal couples using a waveform chart to illustrate the steady state condition. Figure 7: LabVIEW Front Panel Program The waveform chart shown in Figure 7 is a temperature versus time graph. The chart displays temperatures as a function of time over a 2 minute interval. When the temperature gradient had a slope approximately equal to zero, it was used as a visual

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37 indicator of the steady state condition. The corresponding block diagram for the LabVIEW program is shown in Figure 8. Figure 8: LabVIEW Block Diagram 3.4 Boiling Surface Preparation Before and after each experiment the t op of the copper sleeve and bottom of the copper hat was cleaned. Arctic Clean 1 Thermal Material Remover was used first to remove any containments and previous thermal paste with a lint free cloth. Next, Arctic Clean 2 Thermal Surface Purifier was used to prepare the surfaces for thermal paste; again the excess fluid was removed with a lint free cloth. The boiling surface, the top of the copper hat, was prepared without using a ny chemicals. Prior experimentation using M-Prep Conditional MCA1 and M-Prep Neutra lizer MN5A-1 caused an adverse effect on the nanofluids, causing the nanoparticles to fall out of suspension once in contact with the boiling surface. Therefore, the copper hat was wet lapped with 320 grit sandpaper and de-ionized water 10 times in one direction, then 10 times in a direction perpendicular to

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38 the latter. This process was repeated again fo r a total of 20 laps in each direction after each experiment. 3.5 Surface Roughness Measurement To investigate the effects of pool boili ng with nanofluids on the boiling surface, the surface roughness was taken as a means to quantify the surface. A profilometer was used to take the roughness profile. The prof ilometer used was the Surtronics 3P, which contains a diamond tip stylus of 5 m A cutoff length of 0.8 mm was used for the copper hat. Thus any deviation greater than 0.8 mm could not be detected by the profilometer. 3.6 COMSOL Model Before the investigation began, a COMSOL model was developed for the investigation. A model was used because it was difficult to insulate the copper sleeve, thus none was used. The lack of insulati on was a concern since a 1-D conduction model would be used for analysis, so COMSOL was us ed to quantify the amount of heat lost to the environment, and to illustrate if surface temperatures of 100 oC or greater could be reached. The COMSOL model used was a 3-D Multiphysics Heat Transfer Conduction Steady State Analysis. The material properties for copper, stainless steel, and water were loaded from COMSOL’s material database. The thermal paste layer was specified to have a thermal conductivity value of 8.89 W/m*K based on the thermal properties presented for Artic Silver 5 from the manufacturer. Inside the copper sleeve the cartridge heat er was modeled as a generating cylinder with a diameter of 13 mm and height of 2 2/ 3’’. The generation rate, q’’’ was determined by

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39 C R A RV V P q1 ' '2 (2) where PR is the rated wattage, VA is the actual voltage applie d to the cartridge heaters, VR is the rated voltage of the cartridge heaters, and C is the circumferential volume of the heater. Figure 9 shows the path of heat flow with a generation rate of 4,758,644.021 W/m3, the boiling apparatus exposed to air at 293 K with a heat transfer coefficient of 30 W/m2*K Figure 9: Path of Heat Flow Figure 9 shows that although the copper sleeve is not insulated the majority of the heat flows upwards to copper hat and shows little he at flow outward into the stainless steel plate. Figure 10 shows the boiling appara tus’s boundary temperature profile. The boiling apparatus is expo sed to the same boundary conditions as Figure 9.

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40 Figure 10: Boiling Apparatus Temperature Profile. Figure 10 shows that there is ve ry little spreading of the heat from the copper hat to the surrounding stainless steel. So the embedded cartr idge heater is more than adequate to cause the copper hat to reach temperatures necessary for boiling. Figure 11 shows the boiling apparatus with water on the top surface. A slice plot was taken to show the temperatures through the water. Figure 11: Water Temperature Profile. Figure 11 shows that the water near the copper hat is hot enough to boil. However, the water around the stainless plat e and above the copper hat is at a lower temperature than

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41 the water near the copper hat, illustrating that this experiment produces the conditions associated with sub-cooled pool boiling adequately. 3.7 Thermocouple Calibration Procedure Before the investigation was started, the thermocouples were checked for accuracy and variation. All 3 thermocouples were simultaneously placed in the same water bath of constant temperature for 10 minutes. During a 10 minute interval, the temperatures were monitored and T1 = 21.7673 oC T2 = 21.9611 oC T3 = 21.6149 oC Since T3 is closest to the surface, it was taken as absolute, thus 0.1524 oC was subtracted from all temperatures recorded at T1 and 0.3462 oC from all temperatures recorded at T2. 3.8 Experimental Procedure Voltage was applied to the cartridge heat er energizing it. The energized heater heated the copper sleeve resulti ng in the heating of the fluid. During the heating process, the temperatures were monitored by the thermo couples at the specified locations. When the temperatures reached steady state, they we re recorded. Then the voltage applied to the heaters was increased again and the temp eratures were monitored until steady state was again reached. The initial voltage applie d to the cartridge heater was approximately 40 volts, and increased incrementally by approximately 3 volts. This process was repeated until 16 data points were captured, at which point the experiment was considered to be completed. Steady state was considered to be a two minute interval during which the temperatures of the ther mocouples did not increase by more than 1 oC The equipment was allowed to cool until safe to begin another experiment. A surface roughness measurement was taken of the boili ng surface after each experiment. Using

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42 the temperatures and the thermal resistances along the copper sleev e and copper hat, the heat transfer rates, then heat fluxes were calculated. The heat flux at the surface was plotted versus the excess temperature. Ne xt using the Newton’s Law of Cooling, the boiling heat transfer coefficient was calculat ed. A plot of the boiling heat transfer coefficient versus the heat flux was created For each nanofluid concentration, two experiments were done and the results analyzed.

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43 Chapter 4 Results and Discussion 4.1 Heat Transfer Calculations For this investigation a one-dimensi onal conduction analysis was performed, using the principles of thermal circuits and thermal resistance to calculate the heat flux, surface temperature, and heat transfer coeffi cient on the boiling surface. Fourier’s Law states that the one-dimensional heat tr ansfer rate conducted through a solid is dx dT kA q (3) For a 1-D steady state analysis, eq uation (3) can be expressed as: B A B AL T kA q.. .. (4) Where q is the heat transfer rate, k is the thermal conductivity, A is the cross sectional area normal to the direction of heat flow, B AL..is the distance heat travels, and B AT.. is the temperature difference between points A and B. Equation (4) suggest a similar analogy between electrical energy and resistan ce, leading to the de finition of thermal resistance q T RB A th .. (5)

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44 For conduction, equation (5) becomes: kA L RB A cond .. (6) Using equation (4), the thermal resistan ce between each thermocouple was calculated along the copper sleeve up until the copper hat. The 1-D thermal circuit analysis is for a constant cross sectional area, as you analyze the copper hat fr om top to bottom, the cross sectional area changes, negating the use of e quation (6) to find the thermal resistance of the copper hat. However, equation (5) reveals th at if a heat transfer rate was applied to a specimen and the resulting temperature diffe rence was calculated between the surfaces where the heat transfer rate enters and leav es the specimen the thermal resistance can be calculated. Using COMSOL the above pro cedure was applied to the copper hat to determine its thermal resistance. The copper hat was modeled to exact physic al dimensions and insulated along the sides of the specimen. A convectiv e heat transfer coefficient of 2500 W/ m2K was applied through a convective boundary condition to the top surface and a heat transfer rate was applied to the bottom of the coppe r hat. Figure 12 illustrates the copper hat modeled in COMSOL.

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45 Figure 12: COMSOL Copper Hat Boundary Conditions The COMSOL model used was a 3-D multi physics, steady-state, conduction heat transfer analysis. All material propertie s were loaded from COMSOL’s material database. Using COMSOL’s post processi ng boundary integration function and the surface area of the specimen, the average te mperature across the top and bottom surfaces were calculated for a particul ar heat transfer rate. The following Figures show the temperature profile on the copper hat with a heat transfer rate of 100 W. Figure 13: Boundary Temperature Pr ofile at Bottom of Copper Hat.

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46 Figure 14: Boundary Temperature Prof ile at Side View of Copper Hat. Figure 15: Boundary Temperature Profile at Top of Copper Hat.

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47 Figure 16: Heat Flux Path through Copper Hat. Thermal resistance is a material and geomet ric property, where the value is constant regardless of specimen boundary conditions. However, since the temperatures used in equation (4) as TA and TB are actually the average temper ature across the surfaces of the copper hat, the thermal resistance calculat ed will vary somewhat due to boundary conditions. To quantify the variation, the ther mal resistance was calculated for a range of heat transfer rates from experimental data. The thermal resistance as a function of the heat transfer rate is shown in the Figure 17.

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48 Figure 17: COMSOL Calcul ated Thermal Resistances Figure 17 illustrates that the ca lculated thermal resistance of the copper hat, does indeed vary with the heat transfer rate. Analyzing the calculated thermal resistance in table 2, illustrates that although variati on occurs with increasing heat transfer rate, the thermal resistance values does not begin to change until the fourth significant digit, located in the ten thousandths place, which will provide l ittle change in any temperature or fluxes calculated using the calculated thermal resistan ce of the copper hat. Refer to appendix F for all data used in the COMSOL calculated thermal resistances.

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49 Table 2: COMSOL Calculated Thermal Resistances q (W) Rth (oC/W) q (W) Rth (oC/W) q (W) Rth (oC/W) q (W) Rth (oC/W) 15 0.1114556 50 0.1114226 85 0.1115208 120 0.1115617 20 0.1114438 55 0.1114213 90 0.11151456 125 0.1115811 25 0.1114368 60 0.1115676 95 0.11150898 130 0.1115744 30 0.111432 65 0.1115554 100 0.11150395 135 0.1115683 35 0.1114287 70 0.1115449 105 0.11149941 140 0.1115626 40 0.1114261 75 0.1115358 110 0.11149527 145 0.1115572 45 0.1114242 80 0.1115278 115 0.1114915 150 0.1115523 Taking the variation of the calculated therma l resistance into account the value of the calculated thermal resistance used in furt her calculations were chosen based on the following methodology: each experime ntal heat transfer rate (expq) has a value between each heat transfer rate in Table 2 (H Lq q ,). IfW q qL5 2 exp then L th thR R_ exp and if W q qH5 2exp thenH th thR R_ exp With the thermal resistance of the coppe r hat determined the heat flux, excess temperature, and the heat transfer coefficien t at the surface could now be calculated using thermal circuits. In order to calculate the heat flux, excess temperature, and heat transfer coefficient at the boiling surface, the surface temperature at the boiling surface must be first calculated. Figure 18 is a suitable schematic for this investigation where T1, T2, T3, are the temperatures at the thermocouples 23 mm, 13 mm, and 3 mm from the surface of the copper sleeve respectively. T4 and T5 are the temperatures at the bottom and top of the thermal paste layer between the copper sleeve and copper hat, while Ts is the temperature at the top of the coppe r hat. The surface temperature Ts, is determined by using the thermal circui t shown in Figure 18.

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50 Figure 18: Thermal Circuit Schematic Figure 19: Thermal Circuit The appropriate thermal circuit for this investigation is shown in Figure 19, where inqis the heat transfer rate along the neck of the copper sleeve, generated from the embedded cartridge heater, andoutqis the heat transfer rate which is conducted out of the copper hat into the nanofluid. Arctic Silver5 was used as thermal paste in this investigation and has a thermal conductivity value of 8.89 W/m*K per 0.001 inch layer. As shown in Figure 18, L is the distance between thermocouples, where L1 = L2 = 10 mm, L3 = 3 mm, and L4 = 0.0254 mm. As illustrated in Figure 19, R is the thermal resistance for each segment of the circuit. From equation (4) 1 1 1 1A k L R 2 2 2 2A k L R 3 3 3 3A k L R 4 4 4 4A k L R and comsol thR R_ 5.

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51 The method of thermal circuits applies to composite systems, in which equation (5) becomes q T RB A B A.. .. (7) Thermal resistances add like electrical resist ances and for this investigation the thermal resistances are in series. Solving equati on (7) for the heat tr ansfer rate yields. B A B AR T q.. .. (8) Taking the heat transfer rate and dividing it by the cross s ectional area normal to the direction of heat flow, defines the heat flux, q’’ from a heated surface. The heat flux is expressed as A q q ' (9) In this investigation, all calculations are ba sed on the assumption that the heat transfer rate between the second and third thermocouple is equal to the heat tr ansfer rate between the third thermocouple and the surface of the coppe r hat as shown in equation (10). Sq q.. 3 3 .. 2 (10) Thus, from Figure 19 and equation (10), equation (7), becomes 3 .. 2 .. 3 5 .. 3q T RS (11) Solving equation (11) for TS yields

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52 ) (5 .. 3 3 .. 2 3R q T TS (12) Revisiting and using equation (7) to define the heat transfer rate between the first and second thermocouple yields 2 3 .. 2 3 .. 2R T q (13) From equation (9) the heat flux at the copper surface is calculated to be hat copper sA q q_ 3 .. 2' (14) Finally, from equation (1) and (14), the heat transfer coefficient is determined to be e sT q h (15) 4.2 Uncertainty Analysis In the current investigation, the heat fl uxes and the heat transfer coefficient for experimental data were reported. The un certainty of those calculated values are presented. Combining equation (5) and (9) defines the heat flux as A R T qth B A ..' (16) Equation (16) indicates that the heat flux is a function of the temperature difference between the thermocouples T thermal resistancethR and cross sectional area normal to

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53 the flow of heat transfer, A. Thus the un certainty of the heat flux is calculated by the following equation 2 2 2 .. '..' A U R U T U q UA th R B A T qth B A (17) The temperature change between thermocoupl es, thermal resistance, and area all have uncertainties associated with them which areB ATU..,thRU and AUrespectively. The uncertainty associated with temperature differe nce is determined as follows. First, recall that B A B AT T T .. (18) From equation (18) the uncertainty of the temperature difference is calculated to be 2 2.. B A B AT T TU U U (19) Since all temperatures were measured with type K thermocouples, the uncertainty for % 4 0 B AT TU U of the temperature reading. The uncertainty of the thermal resistance, thRU, can be calculated by first reevaluating equation (6) and it is clear that the thermal resistance is a function of the distance, L, between thermocouples, thermal conductivity, k, and cross sectional area, A, then the uncertainty of the thermal resistance is 2 2 2 A U k U L U R UA k L th Rth (20)

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54 The uncertainty of L is associated with the in strument used to measure the distance L. In this investigation L was measured with a dig ital caliper. The uncertainty of the digital caliper is taken to be half of the re solution of the measuring device, which is m UL00001 0 2 1. The value of the thermal conductivity, k, is taken to be a constant material property therefore0 kU. The uncertainty associ ated with the area is determined by first defining the area. In this investigation the cross sectional area normal to the heat flux is 42D A (21) From equation (21) the area is a function of the diameter ther efore the uncertainty of the area calculation is D U A UD A2 (22) where cDUis the uncertainty associated with meas urement of the diameter of the test specimens. The same digital calipers used to measure the distance L, were used to measure the diameter of the copper hat, thus m U UL D00001 0 2 1 With all the parameters of equation (17) de fined, the uncertainty of th e heat flux calculation can be readily determined. Refer to appendix C for heat flux uncertainty analysis data.

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55 Revisiting equation (15), the heat transfer coefficient is a f unction of the heat flux at the heated surface, ' q and excess temperatureeT Therefore, the uncertainty of the heat transfer coefficient is determined to be 2 2 '' e T q hT U q U h Ue (23) where, ' qU is calculated from equation (17). In order to findeTU, one must look at the following equation, derived from equation (1) sat S eT T T (24) From equation (24) the uncertainty of the excess temperature is 2 2sat S eT T TU U U (25) where % 4 0STU of the temperature reading and 0 satTU For a detailed list of the heat transfer coefficient uncertainty data refer to appendix D. Using the formulae above, the uncertainty of each data point was calculat ed and plotted using error bars in the experimental results section. 4.3 Surface Roughness Measurement Results As stated in chapter 1, there are several factors that influence boiling heat transfer. Of them, the number of nucleation sites might be the most important. In this investigation, surface roughness measurements we re made to characterize the surface. More specifically surf ace roughness is defined as the m easurement of vertical deviation

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56 of a real surface from its ideal surface. There ar e several roughness parameters that can be used to report this deviation. The most common parameter, which is used in this investigation, is the Ra value. The Ra valu e is defined as the arithmetic average of the vertical deviation from the mean line esta blished in a surface roughness measurement and has units of length, usually m. Table 3: Surface Roughness Data Initial Ra value Ra value after experiment Ra value after cleaning % of Initial Ra value H2O 1.25 ( m) 1.25 ( m) 1.25 ( m) 0.1% wt NF 1.25 ( m) 1.09 ( m) 1.21 ( m) 96.80% 0.2% wt NF 1.21 ( m) 1.01 ( m) 1.18 ( m) 97.52% 0.3% wt NF 1.18 ( m) 0.95 ( m) 1.13 ( m) 95.76% 0.4% wt NF 1.13 ( m) 0.85 ( m) 1.06 ( m) 93.81% As presented in the literat ure review of chapter 2, the use of nanofluids in convective heat transfer experimentation has a direct effect on the heated surface, usually leaving a smoother surface at the end of experimentation compared to preexperimentation. Table 3 shows that this phe nomenon also occurred in this investigation, as the surface of the copper hat became sm oother, indicated by a lower Ra value after experimentation. The reason the surface beco mes smoother is that the nanoparticles are several magnitudes smaller th an the surface roughness of th e copper hat. When the nanofluid evaporates at the boiling surface, it leaves behind nanoparticles that adhere to the boiling surface. This build up of na noparticles on the boiling surface introduces a

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57 new contact resistance, reduci ng heat transfer. Since aluminum oxide nanoparticles were used in this investigation which has a cons iderably lower thermal conductivity value than copper, the nanoparticle layer further reduces heat transfer performance. Also, a smoother surface reduces nucleation site dens ity, the physical locations at which the boiling process begins. It is clear that the build up on nanopa rticles on the boiling surface must be addressed. A cleaning procedure, outlined in section 3.4 was used to address the nanoparticles deposited on the boi ling surface after experimentation. Table 3 shows that the cleaning procedure restored most of the surface’s init ial roughness. Therefore the copper hat continues to serve as a base line specimen for this investigation. 4.4 Experimental He at Flux Results For this investigation, each nanofluid concentration and de-ionized water pool boiling experiment was conducted twice. Befo re applying any voltage to the heater, the fluid level was marked on the glass container. Once the experiment started, vapor began to escape slowly. When th e nanofluid level dropped 2 mm below the initial fluid level de-ionized water was added thr ough the rubber tubing in an e ffort to keep the nanofluid concentration consistent. At approximately 73 volts, or after 12 data points were taken, it became clear that no matter how much de-ion ized water was added to the nanofluid, significant vapor was escaping. Therefore, th e true concentration of the nanofluids from approximately 73 volts and above, cannot be re ported with the same level of confidence as with lower voltages.

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58 In this investigation, boiling heat flux and convective heat transfer coefficient curves using de-ionized water were generate d and compared to curves generated from nanofluids with 0.1%, 0.2%, 0.3%, and 0. 4% wt concentrations under the same conditions. The results of this investigation are presented below. Unfortunately, during the 0.1% wt nanofluid experiments, the ther mocouples malfunctioned at 67 volts causing an error in the recorded temperatures from th at point forward. Therefore, only the first ten data points of each experiment are used to compare and analyze the results. For a complete representation using all sixteen data points, please refer to appendices A and B. Figure 20: Heat Flux Cu rve of De-Ionized Water vs 0.1% wt Nanofluid The data shows a noticeable increase in the heat flux generated at the copper hat’s boiling surface when using 0.1% wt nanofluid versus de-ionized water. In general, the heat flux increased by an average of 52.9%. The highest heat flux generated by using

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59 0.1% wt nanofluids is 319,234.82 W/m2 compared to de-ionized water at 166,097.73 W/m2 which corresponds to an increase in the heat flux by 92.2%. The minimum generated heat flux for 0.1% wt nanofluids is 88,114.46 W/m2, whereas the lowest value for de-ionized water is 63,991.9 W/m2, corresponding to a 37.7% increase in the generated heat flux. The data also reveal s a slow, steady increase in the nanofluid generated heat flux, compared to water, followed by a sudden dramatic 43.2% to 64.7% increase in the heat flux from the sixth data poi nt to the seventh. Afterward, the heat flux continues to increase at a st eady rate for the remainder of the experiment. One possible reason for the sudden increase could be related to nanoparticle deposi tion. As evident by table 3 and the photos in Appendix D, nanopa rticles adhere to the boiling surface during experimentation, which suggest that nanopa rticles may fall out of suspension during experimentation. It is possible that when a certain heat flux is reached, there is enough fluid movement and there now exists c onvection currents str ong enough to prevent further nanoparticle deposition on the heated surface. The sudden dramatic increase in the heat flux might correspond to this reaching this unique heat flux.

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60 Figure 21: Heat Flux Cu rve of De-Ionized Water vs 0.2% wt Nanofluid The data shows an even more noticeable increase in the heat flux generated at the copper hat’s boiling surface when using 0.2% wt nanofluid versus de-ionized water than compared to 0.1% wt nanofluid. In genera l, the heat flux increased by an average of 85%. The maximum generated heat flux from 0.2% wt nanofluid is 340,412.42 W/m2 which is a 105% increase when compared to the maximum heat fl ux generated using deionized water. Similarly the minimum genera ted heat flux from using 0.2% wt nanofluid is 116,004.81 W/m2 which is an 81.3% increase when compared to de-ionized water. Also, akin to the generated heat flux curve from 0.1% wt nanoflui d, there is a sudden dramatic increase in the generated heat flux from 79.3% to 108.9%. However, unlike the 0.1% wt nanofluid, the sudden increase in the generated heat flux occurred at a much higher heat flux, which corresponds to gr eater input power from the variable

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61 autotransformer used to heat the copper hat. One possible explanat ion for the latter is again related to nanoparticle deposition. As evident in table 3, the surface roughness decreased by a greater amount than it in did when compared to 0.1% wt nanofluid. Thus a thicker layer of nanoparticles were deposited on the boiling su rface. It is possible that more thermal energy was required to reach the fl uid state related to the dramatic increase in the heat flux first seen in th e 0.1% wt nanofluid experiment. Figure 22: Heat Flux Cu rve of De-Ionized Water vs 0.3% wt Nanofluid Unlike the generated heat fluxes from 0.1% and 0.2% wt nanofluid the enhancement from using 0.3% wt nanofluid ove r de-ionized water is not as remarkable. The average increase in the generated heat fl ux is 4.84%, which is a 48% decrease in the effectiveness of the use of nanofluids when compared to 0.1% wt nanofluid and an 80% decrease compared 0.2% wt nanofluid. Th e minimum generated heat flux from using

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62 0.3% wt nanofluid is 56,773.9 W/m2 which is a decrease of 11.3% when compared to deionized water. Despite the initial decrease in the generated heat fl ux, the data does show a steady increase in the heat flux versus de-ion ized water as more power is supplied to the boiling apparatus via the autotransformer. The greatest generated heat flux was 235,491.59 W/m2 which is 41.8% greater than the ma ximum generated heat flux using deionized water. Identical in behavior to 0. 1% wt and 0.2% wt nanofluid experimental data, there is a sudden dramatic increase in the generated heat flux near the end of data collection. The sudden dramatic increase in th e generated heat flux for 0.3% wt nanofluid is from 11.3% to 32.6%. It is clear howeve r, that heat flux enhancement from using 0.3% wt nanofluids is not as great as 0.1% and 0.2% wt nanofluids. One possible reason the enhancement is not as great as the lower nanof luid concentrations is that, as evident by table 3, the surface roughness value decreased by an even greater amount than previous experiments. Perhaps the thickness of this nanoparticle layer depos ited on the surface is significant enough to reduce heat transfer performance. Another possible reason for lesser heat flux enhancement is that perh aps the nanoparticles be gan to fall out of suspension during the pool boili ng experiment. In this i nvestigation, nothing was done to keep the nanoparticles in suspension besides ultrasonicatio n. Ultrasonication may not be enough to keep the particles in suspension for the duration of the experiment for this particular concentration of nanoparticles. Although evidence of the nanoparticles falling out of suspension was not easily observed dur ing experimentation, the duration of the experiment increased with each increase in na nofluid concentrations as it took longer to reach steady state for each data point.

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63 Figure 23: Heat Flux Cu rve of De-Ionized Water vs 0.4% wt Nanofluid The 0.4% wt nanofluid performed similarly to 0.3% wt nanofluids when compared to the generated heat fluxes from 0.1% and 0.2% wt nanofluids, with an average heat flux enhancement of 9.81%, a decr ease in effectiveness of 43.1% from 0.1% wt nanofluid and 79.2% from 0.2% wt nanoflu id, compared to heat fluxes generated from using de-ionized water. The mi nimum heat flux generated was 60,585 W/m2, a 5.3% decrease in the heat flux when compared to the de-ionized water at the same applied wattage from the autotransformer. Nevertheless, the data did show an increase in the generated heat flux as the experiment conti nued as the highest heat flux generated was 218,716.95 W/m2, 31.7% higher than the maximum h eat flux generated from de-ionized water. For this particular nanofluid con centration, it there is not a sudden dramatic increase in the heat flux compared to the prev ious experiments of this investigation. The

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64 greatest increase is again at the end of data collection with an incr ease in the heat flux from 22.8% to 31.7%. This 8.9% increase is not as significant when compared to the sudden dramatic increase in the heat flux of lower concentrations, a ll of which have been characterized by an increase of at least 21.2% Interestingly, the heat fluxes generated from 0.4% wt nanofluid, although greater than de-ionized water, are lower than then the heat fluxes generated from 0.3% wt nanofluid This decrease in the heat flux possibly suggests that the nanoparticles might be falling out of suspension. The more concentrated nanofluid might have allowed for a greater am ount of nanoparticles to fall out suspension. Also the 0.4% wt nanofluid pool boiling experiment took the longest to complete. The latter reasons, combined with a thicker nanoparticle layer deposited on the copper hat, as evident by Table 3, might explain why the 0.4% wt nanofluid produced lesser heat fluxes than the 0.3% wt nanofluid.

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65 Figure 24: Heat Flux Curve 0.1% wt vs 0.2% wt Nanofluid As evident from Figures 20-23, the 0.2% wt nanofluid showed the greatest heat flux enhancement. Figure 24 compares 0.2% to 0.1% wt nanofluid. The 0.2% wt generated heat fluxes an average of 22.4 % greater than 0.1% wt nanofluid. The difference in performance between nanofluid s cannot be characterized by a constant factor as one might presume. Initially, the 0.2% wt nanofluid heat flux enhancement is nearly a constant 31% for 60% of the data points. The remaining 40% of data points show a near constant enhancement of about 8.8%. Varying enhancement suggest that pool boiling with nanofluids might be best desc ribed by transient-like condition as first suggested by Kwark et al. It also suggests that nanoflu id enhancement is a function of several unique variables as oppos ed to a single mechanism.

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66 Figure 25: Heat Flux Curve 0.2% wt vs 0.3% wt Nanofluid Figure 25 compares the boiling performance of 0.2% wt to 0.3% wt nanofluid. On average, the 0.2% wt nanofluid provide d heat fluxes 79% great er than 0.3% wt nanofluid. Heat flux enhancement varied from point to point, agai n suggesting transient characteristics, and decreased from 104.3% to 44.6% from beginning of experimentation to the end. The shapes of the boiling curv es are nearly identical, which might suggest that the nanofluid concentra tions are close to an optimum value as the transient conditions are parallel. Anal ogous transient boiling characteris tics also suggest that the optimum concentration is between 0.2% and 0. 3%. The 0.3% wt nanofluid can be almost be classified as a downwards shift of the 0.2% wt nanofluid curve. This possibly suggests that a single mechanism may be res ponsible for the deteri oration of the boiling curve. The increase in the thickness of the nanoparticle layer deposited on the boiling

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67 surface may be the main cause of the deterior ation as the contact resistance introduced by nanoparticle deposition became significant enough to affect heat transfer performance. Figure 26: Heat Flux Curve 0.2% wt vs 0.4% wt Nanofluid Figure 26 compares the boiling performance of 0.2% wt nanofluid to that of 0.4% wt nanofluid. In Figure 26 it is clear that 0.2% wt nanoflu id has more significant heat flux enhancement over 0.4% wt nanofluid with an average enhancement of 69.5%. Also, the boiling characteristic of the curve change d significantly when compared to previous nanofluid concentrations whic h suggest that a significant change in the mechanism of enhancement has occurred. The change of surface roughness might possibly be the most direct cause for the change in shape of the boiling curve, as the surface roughness decreased the most during the 0.4% wt nanof liud experiment. Interestingly, the heat fluxes generated for the 0.4% wt nanoflui d were the lowest of all nanoparticle

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68 concentrations. Smaller heat fluxes may be attributed to this concentration having the greatest decrease in the su rface roughness. A decrease in the surface roughness corresponds to an increase in th e nanoparticle layer deposited on the surface. 0.4% wt nanofluid had the greatest decrease in the surface roughness; hence it had the thickest nanoparticle build up on the surf ace, which creates a greater th ermal resistance for heat to conduct though at the boiling surface. This particular nanoparticle layer might have completely changed the characteristics of the boiling curve. It is worth noting that the 0.4% wt nanofluid had the lowest excess temper atures of all nanofluids, meaning that the boiling surface reached steady state at lo wer temperatures. This phenomenon can become important when dealing with devices or equipment with low melting temperatures. However, the goal of this investigation is to solely increase the boiling heat flux and boiling heat transfer coeffici ent. Addressing and quantifying the excess temperatures enhancement is outside the scope of this investigation. Overall, the addition of aluminum oxid e nanoparticles had a positive effect on the boiling curve for de-ionized wa ter as nanofluids generated hi gher heat fluxes for the same applied input power. The results of this inve stigation are agreement with the findings of Kwark et al Kwark et al. suggest that a possible r eason for nanofluid heat flux enhancement is that during pool boiling experi mentation, with very small nanoparticle concentrations, a layer of nanopaticles ar e deposited on the boiling surface. The nanoparticle layer which is created, increases the wettability of the surface by improving the contact angle of the fluid flowing acro ss the surface. The de posited nanoparticles also create a porous sorportion layer which is characterized by cavities on the order of m. Fluids flowing though the sorportion la yer become trapped in the micro sized

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69 cavities; therefore fluids are kept in cont act with the boiling surf ace longer, effectively removing more heat. However, as nanoparticle concentration increases, the sorportion layer deposited on the boiling surface become s characterized by cavit ies on the order of nm. Fluid flow is hindered by the nm sized cavities and heat transfer begins to deteriorate. Additionally, the sorportion la yer becomes thicker, creating a new thermal interface layer which introduces a greater contact resistan ce, further reducing heat transfer performance. Kwark et al. suggest that there must exist an optimum nanoparticle concentration that corresponds to maximum heat flux enhancement and minimal thermal resistance effects. This ra tionale possibly explai ns why the 0.3% and 0.4% wt nanofluid showed inferior perfor mance to 0.1% and 0.2% wt nanofluids. Perhaps the optimum nanoparticle concentration is closer to 0. 2%, and if concentrations > 0.4% were used, the heat flux enhancement mi ght become inferior to de-ionized water.

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70 4.5 Experimental Heat Tran sfer Coefficient Results Figure 27: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.1% wt Nanofluid Figure 27 compares the heat transfer coeffi cient curves of de-ionized water to that of 0.1% wt nanofluid. Compar atively speaking, the heat transfer coefficient of 0.1% wt nanofluid was on average 43.5% greater than th at of de-ionized water. De-ionized water produced lower heat transfer coefficients as the heat flux increased. Conversely, 0.1% wt nanofluids, after an initial decrease, ge nerated increasingly hi gher heat transfer coefficients as the heat flux increased. Th e initial increase in the heat flux was 12.8%, but increased to 101.3% by the end of data coll ection. The reason for the increase is that the heat flux was increasing at a greater rate than the excess temper ature, producing high heat transfer coefficients. The de-ionized water heat transfer coefficient decreased because the excess temperatures were increasi ng at a greater rate than the heat fluxes.

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71 Figure 28: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.2% wt Nanofluid Figure 28 compares the heat transfer coe fficients of de-ionize d water to those of 0.2% wt nanofluid. It is cl ear that the heat transfer coefficients from the 0.2% wt nanofluids are significantly higher than t hose of de-ionized water with an average increase of 98.6%, more than twice that of 0.1% wt nanofluid. Also, excluding 30% of the initial data points, the heat transfer coe fficients of 0.2% wt nanofluid were nearly constantly. This means that the generate d heat fluxes and excess temperatures were increasing at an almost equal rate.

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72 Figure 29: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.3% wt Nanofluid Figure 29 compares the heat transfer coeffi cients of de-ionized water to those of 0.3% wt nanofluid. The heat transfer coeffici ents of 0.3% wt nanofluid were greater than those of de-ionized water for all data points. The average increase in the heat transfer coefficient was 19.2%. This percentage is so mewhat misleading as the last 2 data points indicate remarkable enhancement of 46.5% and 64%, while in actuality 70% of the data show an enhancement in the heat transfer co efficient of 8.8%. This small enhancement shows that this particular nanofluid concentration had little effect on the heat transfer coefficient for heat fluxes in this range. Th e last two data points suggest that 0.3% wt nanofluids may be more effective in heat fluxes above 150,000 W/m2. Also, the heat transfers coefficients were mostly uniform fo r most of the data co llection indicating that both the heat fluxes and excess temperatures increased at a nearly the same rate.

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73 Figure 30: Heat Transfer Coefficient Curve of De-Ionized Water vs 0.4% wt Nanofluid Figure 30 compares the heat transfer coeffi cients of de-ionized water to those of 0.4% wt nanofluids. The average increase in the heat transfer coefficient is 47.2%. Unlike 0.2% and 0.3% wt nanofluid concentra tions, where the heat transfer coefficients were nearly constant for the duration of expe rimentation, the 0.4% wt concentration was shown to be increasing linearly throughout data collections. Although, 0.2% wt nanofluid showed the most sign ificant enhancement overall, the trend of the 0.4% wt data is arguably more significant as it represents the ideal heat transfer scenario. The increasing heat transfer coefficient is due to the fact that the heat flux is increasing at a greater rate than the excess temperatures. A greater amount of heat is being removed per increase in excess temperatures, which is wholly significant in real world applications whereas surface temperatures of high heat fl ux apparatus must always be considered.

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74 Figure 31: Heat Transfer Coefficien t Curve 0.1% wt vs 0.2% wt Nanofluid Figure 31 compares the heat transfer coeffici ents of 0.1% wt nanofluid to those of 0.2% wt nanofluid. As evident in the Fi gure, 0.2% performed better overall with an average enhancement over 0.1% of 42.7%. Howe ver, it also clear that the percentage increase is steadily decreasing over time as th e increase dropped from 67.7% to 11.2%. It is entirely possible that if more experime ntation were to occur the heat transfer coefficient of 0.1% wt nanofluid might begin to become greater than 0.2% wt. These trends suggest that although th e concentrations used are diffe rentiated by a factor of only 2, they still readily affect the boi ling performance of the nanofluids.

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75 Figure 32: Heat Transfer Coefficien t Curve 0.2% wt vs 0.3% wt Nanofluid Figure 32 compares the heat transfer coefficients genera ted from 0.2% wt nanofluids to those of 0.3% wt nanofluid. The 0.2% wt heat transfer coefficients were on average 69.1% greater than those of 0.3% wt nanofluid. This percentage of enhancement is more representative of the physical proce sses that occurred during experimentation, as both 0.2% and 0.3% wt nanofluid produced nearly constant heat transf er coefficients for the length of experimentation. It is possi ble that since both c oncentrations produced nearly constant heat transfer coefficient, th at both concentrations are affecting the same mechanism of heat transfer enhancement.

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76 Figure 33: Heat Transfer Coefficien t Curve 0.2% wt vs 0.4% wt Nanofluid Figure 33 compares the heat transfer coeffi cients of 0.2% wt nanofluid to those of 0.4% wt. The 0.2% wt nanofluid showed an average increase of 36.1% when compared to the heat transfer coefficients from 0.4% wt nanofluid. This percentage is 1.9 times less than the enhancement determined from 0.2% wt heat flux enhancement versus 0.4% heat flux generation. Perhaps heat flux and heat transfer coefficient nanofluid enhancement are inversely related. Additionally, the 0.4% wt nanofluid heat transfer curve is similar in behavior to 0.1% wt na nofluid, with increasing heat transf er coefficients over time. If the trend of the data continues, it is possible that if higher heat fluxes were generated, then 0.4% wt might surpass 0.2% wt nanofluid h eat transfer coefficients. Figures 27-33 do not readily point towards an optimum concen tration, as Figures 20-26 do with nanofluid heat flux enhancement.

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77 Chapter 5 Conclusions and Recommendations 5.1 Conclusions In conclusion, the additions of alumin um oxide nanoparticles to de-ionized water were able to improve the boiling curve of de-ionized water. Nanofluids with concentrations of 0.1%, 0.2%, 0.3%, and 0.4% on mass basis created with a base fluid of de-ionized water, were used in this investig ation. All of the na nofluids provided better performance compared to de-ionized water. The greatest heat flux enhancement was observed from the 0.2% wt nanofluid, which produced an average increase in the heat flux of 85%, followed by 0.1% wt with an av erage heat flux increase of 53%. Next was 0.4% wt with an average increase in the heat flux of 9.8%, and last ly 0.3% wt nanofluid with an average enhancement of 4.8%. One possible reason for nanofluid heat flux enhancement is that the nanoparticles depos ited on the boiling surface change the surface characteristics of the surface, improving th e wettability of the boiling surface. The improved wettability of the boiling surface allows the nanofluild to stay in contact longer with the surface, effectively removing more heat. The addition of aluminum oxide nanopartic les to de-ionized wa ter also increased the boiling heat transfer coefficients of de-ionized water. The 0.2% wt nanofluid performed the best, with an average increase in the heat transfer coefficient of 98.6%, followed by 0.4% with average increase of 47.2%. Next was 0.1% with an average

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78 increase of 43.5% and lastly was 0.3% with average an increase of 19.2%. The order of the enhancement due to different concentrations changed when comparing heat flux enhancement and heat transfer coefficient e nhancement. This observation suggests that their respective mechanisms of enhancement ar e affected differently. Therefore, it is better to choose a specific nanofluid to enha nce either the heat flux or heat transfer coefficient, unlike in this investigation, whereas a single nanofluid was used to simultaneously enhance both the heat flux and heat transfer coefficient. 5.2 Recommendations In investigations similar to the current study, it would be important to design a complete closed loop system that quickly caus es the escaping vapor to condense, in order to maintain the nanoparticle concentrations Specifically, for this investigation, a condenser could be added to prevent condensat ion of the fluid around the surrounding glass, but instead on the condenser allowing the fluid to drop dire ctly back in the liquid. Also it would be important to use a singl e piece of copper as the boiling surface, to minimize the effects of contact resistance, allowing the researcher to get better estimation of heat fluxes, heat transfer coefficients, and surface temperatures. For this investigation, a single piece of copper was insufficient, due to leakage problems from the inability to seal the copper sleeve to the st ainless steel plate. A single piece of copper that overcomes the sealant issues presented in this investigation will provide a better test specimen. Thermal paste was used in this investigat ion. Although great care was taken to use the same amount for each experiment, the fact that the thermal pa ste came in tubular

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79 form, made it difficult to apply the same am ount in a uniform thickness each time. Since the thermal paste layer resistance is critical to heat transfer calculations, thermal pads could make a better choice due to more uniform dimensions. Also, the orientation of the boiling surface could be changed. The current investigation has the boili ng surface upwards facing al lowing bubble formation to develop, detach and flow away from the surf ace. If the boiling su rface was inverted or orientated on its side then direction of bubble flow and convection currents become more important. Different orientations can provi de insight to how boiling would occur under different conditions. In this investigation, th e effects of surface roughness were reported. The average roughness value was measured before and afte r experimentation, then cleaned before another experiment, if nanofluids were used in a closed device such as a heat pipe, surface cleaning would not be possible. In the future one could investigate the pool boiling if the boiling surface was not cleaned to determine boiling performance, as surface roughness increases or decreases over time. Increasing the nanoparticles concentration beyond 0.3% starts to deteriorate the boiling curve. Any future efforts to enhance the boiling curve should explore concentrations closer to 0.2% with an upper concentration limit of 0.25%. Also, future efforts to improve the boiling h eat transfer coefficients s hould be done with nanofluid concentrations close to 0.1% and 0.4% as bot h concentrations showed promise to surpass 0.2 % at heat fluxes higher than those achieved in this investig ation. Therefore, a future boiling apparatus should be de signed to allow for much gr eater applied heat fluxes.

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80 References 1. H.U. Kang, S.H. Kim, J.M. Oh, Estimati on of Thermal Conductivity of Nanofluid using Experimental Effective Particle Volu me, Experimental Heat Transfer 19 (2006) 181-191. 2. P. Keblinski, S.R. Phillpot, S.U.S. Choi, J. A. Eastman, Mechanisms of Heat Flow in Suspensions of Nano-sized Particles (nanof luids), Int. Journal of Heat and Mass Transfer. 45 (2002) 855-863. 3. B-X. Wang, L-P. Zhou, X-F. Peng, A Fr actal Model for Predicting the Effective Thermal Conductivity of Liquid with Suspensi on of Nanoparticles, Int. Journal of Heat and Mass Transfer. 46 (2003) 2665-2672. 4. Y. Xuan, W. Roetzel, Conceptions for Heat Transfer Correlation of Nanofluids, Int. Journal of Heat and Mass Transfer. 43 (2000) 3701-3707. 5. H. Kim, J. Kim, M.H. Kim, Effect of Nanoparticles on CHF Enhancement in Pool Boiling of Nano-fluids, Int. Journal of Heat and Mass Transfer. 49 (2006) 5070-5074. 6. I.C. Bang, S.H. Chang, Boiling Heat Tr ansfer Performance and Phenomena of Al2O3-Water Nano-fluids from a Plain Surf ace in A Pool, Int. Journal of Heat and Mass Transfer. 48 (2005) 2407-2419. 7. Z-H. Liu, Y-H Qiu, Boiling Heat Transfer Characteristics of Nanofluids Jet Impingement on a Plate Surface, Heat Mass Transfer. 43 (2007) 699-706. 8. H. Kim, M. Kim, Experimental Study of th e Characteristics and Mechanism of Pool Boiling CHF Enhancement using Nanof luids, Heat Mass Transfer. 9. S.K. Das, N. Putra, W. Roetzel, Pool Boiling Characteristics of Nano-fluids, Int. Journal of Heat and Mass Transfer. 46 (2003) 851-862. 10. M. Chopkar, A.K. Das, L. Manna, P.K. Das, Pool Boiling Heat Transfer Characteristics of ZrO2-Water Nanofluids fr om a Flat Surface in a Pool, Heat Mass Transfer. 11. S-C. Tzeng, C-W. Lin, K.D. Huang, Heat Transfer Enhancement of Nanofluids in a Rotary Blade Coupling of Four-Wheel-Dri ve Vehicles, Acta Mechanica. 179 (2005) 11-23. 12. D. Wen, Y. Ding, Effect of Particle Migration on Heat Transfer in Suspensions of Nanoparticles Flowing through Minichanne ls, Microfluid Nanofluid. 1 (2005) 183189.

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81 13. K. Narayan Prabhu, Peter Fernades, Nanoque nchants for Industrial Heat Treatment, Journal of Materials Engineer ing and Performance, (2007). 14. C. Choi, H.s. Yoo, J.M. Oh, Preparat ion and Heat Transfer Properties of Nanoparticles –in-Transformer Oil Disper sions as Advanced Energy-Efficient Coolants, Current Applied Physics. (2007). 15. Min-Shen Liu, Mark Ching-Cheng Lin, C. Y. Tsai, Chi-Chuan Wang, Enhancement of Thermal Conductivity with Cu for Nanofluid s using Chemical Reduction Method, Int. Journal of Heat and Mass Transfer. 49 (2006) 3028-3033. 16. R.J. Goldstein, W.E Ibele, S.V. Patankar, T.W. Simon, T.H. Kuehn, P.J. Strykowski, K.K. Tamma, J.V.R Herberlein, J.H. Da vidson, J. Bischof, F.A. Kulacki, U. Kortshangen. S. Garrick, V. Srinivasan, H eat TransferA review of 2003 Literature, Int. Journal of Heat and Mass Transfer. 49 (2006) 451-534. 17. D.P. Kulkarni, R.S. Vajjha, D.K. Das, D. Oliva, Application of Aluminum Oxide Nanofluids in Diesel Electric Generator as Jacket Water Coolant, Applied Thermal Engineering (2007). 18. Shiro Nukiyama, The Maximum and Minimum Values of the Heat Q Transmitted from Metal to Boiling Water under Atmo spheric Pressure, Japan Society of Mechanical Engineers. 37 (1934) 367-374. 19. Incropera, Frank P., Dewitt David P., “Modes of Pool Boiling” Introduction to Heat Transfer, 4th edition John Wiley & Sons (2002) 560-563. 20. J. Haung, X. Wang, Influence of pH on the Stability Characterist ics of Nanofluids, Photonics and Optoelectronics (2009). 21. K.B. Anoop, T. Sundaraajan, Sarit K. Das, Effect of Particle Size on the Convective Heat Transfer in Nanofluid in the Devel oping Region, Internati onal Journal of Heat and Mass Transfer (2009). 22. J. Hone, M.C. Llaguno, M.J. Biercuk, A.T. Johnson, B. Batlogg, Z. Benes, J.E. Fischer, Thermal Properties of Carbon Nanotubes and Nanotube-Based Materials,l Applied Physics. 74 (2002) 339-343. 23. S. Berber, Y.K. Kwon, D. Tomanek, P hysical Review Letters 84, (2000) 4613. 24. Xiang-Qi Wang, Arun S.Mujumdar, Heat Tran sfer Characteristics of Nanofluids: A Review, International Journal of Thermal Sciences. 46 (2007) 1-19. 25. Y. Xuan, Q. Li, Heat Transfer Enhancemen t of Nanofluids, Inte rnational Journal of Heat and Fluid Transfer. 21 (2000) 58-64. 26. S.M.S. Murshed, K.C. Leong, C Yang, Enhanced Thermal Conductivity of TiO2Water Based Nanofluids, International J ournal of Thermal Sciences 44. (2005) 367373.

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82 27. Y.J. Hwang, Y.C. Ahn, H.S. Shin, C.G. Lee, G.T. Kim, H.S. Park, J.K. Lee, Investigation on Characteristics of Thermal Conductivity Enhancement of Nanofluids, Current App lied Physics, in press. 28. S. M. Kwark, R. Kumar, G. Moreno, J. Yoo, S. M. You, Pool Boiling Characteristics of Low Concentration Nanofluids, Internati onal Journal of Heat and Mass Transfer. 53 (2010) 972-981.

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83 Appendices

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84 Appendix A: Heat Flux, Heat Tr ansfer Coefficient Calculations Table A: De-Ionized Water Data Calculations De ionized Water PR VA VR PA q L1 L2 L3 L4 L5 k1 k2 k3 750 40.4 120 85.00833 85.00833 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 43.4 120 98.10208 98.10208 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 45.9 120 109.7297 109.7297 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 48.9 120 124.5422 124.5422 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 52.2 120 141.9188 141.9188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 55.1 120 158.1255 158.1255 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 58.3 120 177.0255 177.0255 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 61.4 120 196.3521 196.3521 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 64.1 120 214.0005 214.0005 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 67.2 120 235.2 235.2 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 71 120 262.5521 262.5521 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 73 120 277.5521 277.5521 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 76.2 120 302.4188 302.4188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 79.2 120 326.7 326.7 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 82.1 120 351.063 351.063 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 85.3 120 378.963 378.963 0.01 0.01 0.003 2.54E 05 0.003 401 401 401

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85 Appendix A: (continued) Table A: (continued) De ionized Water (cont) k4 k5 A1 R1 R2 R3 R4 R5 T1 T2 T3 T1 cal 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111444 160.613 155.629 150.85 160.4606 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111437 178.768 172.992 167.393 178.6156 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111432 193.11 186.382 179.934 192.9576 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111432 207.933 200.263 192.87 207.7806 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111429 223.56 215.045 206.95 223.4076 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111426 237.374 227.816 218.866 237.2216 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111424 251.624 240.594 230.818 251.4716 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111424 266.973 255.205 244.528 266.8206 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111423 279.955 267.584 256.317 279.8026 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111423 296.449 283.495 271.643 296.2966 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111421 309.566 295.747 282.767 309.4136 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111568 319.545 304.565 290.941 319.3926 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111555 331.858 316.076 301.383 331.7056 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111536 343.739 327.295 310.313 343.5866 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 356.527 338.58 321.107 356.3746 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 367.947 349.008 330.574 367.7946

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86 Appendix A: (continued) Table A: (continued) De ionized Water (cont) T2 cal T3 cal q1 2 q2 3 q3 4 % q lost q4 5 T4 T5 q3 surface A2 q''3 surface 155.2828 150.85 23.48237 20.10365 20.10365 76.35097 20.10365 149.5202 149.0123 20.10365 0.000314 63991.9 172.6458 167.393 27.07426 23.82252 23.82252 75.71661 23.82252 165.8172 165.2153 23.82252 0.000314 75829.42 186.0358 179.934 31.39177 27.6729 27.6729 74.78084 27.6729 178.1035 177.4044 27.6729 0.000314 88085.58 199.9168 192.87 35.66393 31.95867 31.95867 74.33908 31.95867 190.756 189.9486 31.95867 0.000314 101727.6 214.6988 206.95 39.49618 35.14238 35.14238 75.23767 35.14238 204.6254 203.7376 35.14238 0.000314 111861.7 227.4698 218.866 44.22639 39.01998 39.01998 75.32341 39.01998 216.2849 215.2991 39.01998 0.000314 124204.5 240.2478 230.818 50.90221 42.76606 42.76606 75.84187 42.76606 227.9891 226.9087 42.76606 0.000314 136128.6 254.8588 244.528 54.24919 46.85228 46.85228 76.13864 46.85228 241.4288 240.2451 46.85228 0.000314 149135.4 267.2378 256.317 56.98392 49.52805 49.52805 76.85611 49.52805 253.0408 251.7895 49.52805 0.000314 157652.7 283.1488 271.643 59.62794 52.18114 52.18114 77.81414 52.18114 268.1913 266.873 52.18114 0.000314 166097.7 295.4008 282.767 63.55089 57.29685 57.29685 78.17696 57.29685 278.9769 277.5294 57.29685 0.000314 182381.5 304.2188 290.941 68.81627 60.21752 60.21752 78.30406 60.21752 286.9577 285.4364 60.21752 0.000314 191678.3 315.7298 301.383 72.4535 65.06565 65.06565 78.48491 65.06565 297.079 295.4352 65.06565 0.000314 207110.4 326.9488 310.313 75.4558 75.44673 75.44673 76.90642 75.44673 305.3223 303.4163 75.44673 0.000314 240154.4 338.2338 321.107 82.27221 77.67352 77.67352 77.87477 77.67352 315.969 314.0067 77.67352 0.000314 247242.5 348.6618 330.574 86.77113 82.03185 82.03185 78.3536 82.03185 325.1477 323.0753 82.03185 0.000314 261115.5

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87 Appendix A: (continued) Table A: (continued) De ionized Water (cont) Tsurfaceexcess hboiling 146.7719 46.77186 1368.171 162.5606 62.56064 1212.095 174.3207 74.32072 1185.209 186.3874 86.38738 1177.575 199.8217 99.8217 1120.615 210.9513 110.9513 1119.451 222.1435 122.1435 1114.497 235.0247 135.0247 1104.505 246.271 146.271 1077.812 261.0589 161.0589 1031.286 271.1453 171.1453 1065.653 278.7181 178.7181 1072.518 288.1768 188.1768 1100.616 295.0013 195.0013 1231.553 305.344 205.344 1204.041 313.9265 213.9265 1220.585

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88 Appendix A: (continued) Table B: 0.1% wt Nanofl uid Data Calculations 0.1% wt Nanofluid PR VA VR PA q L1 L2 L3 L4 L5 k1 k2 k3 750 40.6 120 85.85208 85.85208 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 43 120 96.30208 96.30208 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 46.2 120 111.1688 111.1688 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 49.2 120 126.075 126.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 52.2 120 141.9188 141.9188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 54.9 120 156.9797 156.9797 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 58.4 120 177.6333 177.6333 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 61.8 120 198.9188 198.9188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 64.6 120 217.3521 217.3521 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 67.2 120 235.2 235.2 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 70.3 120 257.4005 257.4005 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 73.2 120 279.075 279.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 76.4 120 304.0083 304.0083 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 79.1 120 325.8755 325.8755 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 82.2 120 351.9188 351.9188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 85.2 120 378.075 378.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401

PAGE 100

89 Appendix A: (continued) Table B: (continued) 0.1% wt Nanofluid (cont) k4 k5 A1 R1 R2 R3 R4 R5 T1 T2 T3 T1 cal 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111432 175.501 169.18 162.73 175.3486 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111432 191.848 184.78 177.433 191.6956 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111429 208.176 199.878 191.495 208.0236 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111424 224.845 215.397 205.506 224.6926 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111423 239.277 228.914 217.825 239.1246 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111421 252.588 241.398 228.728 252.4356 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111545 269.705 258.139 242.262 269.5526 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 286.14 274.106 256.209 285.9876 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111515 296.434 285.49 265.218 296.2816 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111504 308.222 296.592 274.132 308.0696 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 321.117 309.238 290.915 320.9646 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 333.92 323.715 305.912 333.7676 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111536 350.022 338.397 321.164 349.8696 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111521 366.915 355.035 336.212 366.7626 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111515 382.608 370.471 350.543 382.4556 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111509 399.922 387.042 365.988 399.7696

PAGE 101

90 Appendix A: (continued) Table B: (continued) 0.1% wt Nanofluid (cont) T2 cal T3 cal q1 2 q2 3 q3 4 % qlost q4 5 T4 T5 q3 surface A2 q''3 surface 168.8338 162.73 29.54594 27.68197 27.68197 67.7562 27.68197 160.8989 160.1995 27.68197 0.000314 88114.46 184.4338 177.433 32.93374 31.75005 31.75005 67.03078 31.75005 175.3328 174.5307 31.75005 0.000314 101063.5 199.5318 191.495 38.51204 36.44852 36.44852 67.21334 36.44852 189.084 188.1632 36.44852 0.000314 116019.2 215.0508 205.506 43.72752 43.28761 43.28761 65.66519 43.28761 202.6426 201.549 43.28761 0.000314 137788.7 228.5678 217.825 47.87723 48.72078 48.72078 65.66995 48.72078 214.6022 213.3713 48.72078 0.000314 155083.1 241.0518 228.728 51.62785 55.89094 55.89094 64.39607 55.89094 225.0309 223.6189 55.89094 0.000314 177906.4 257.7928 242.262 53.33308 70.43533 70.43533 60.34791 70.43533 237.6028 235.8234 70.43533 0.000314 224202.6 273.7598 256.209 55.45556 79.59644 79.59644 59.98545 79.59644 250.9438 248.9329 79.59644 0.000314 253363.3 285.1438 265.218 50.51219 90.36755 90.36755 58.42343 90.36755 259.2403 256.9573 90.36755 0.000314 287648.8 296.2458 274.132 53.62334 100.2906 100.2906 57.35945 100.2906 267.4979 264.9643 100.2906 0.000314 319234.8 308.8918 290.915 54.7526 81.52844 81.52844 68.32623 81.52844 285.522 283.4623 81.52844 0.000314 259513.1 323.3688 305.912 47.16067 79.17013 79.17013 71.63123 79.17013 300.675 298.6749 79.17013 0.000314 252006.4 338.0508 321.164 53.60066 76.58507 76.58507 74.80823 76.58507 316.098 314.1632 76.58507 0.000314 243777.8 354.6888 336.212 54.75714 83.79604 83.79604 74.28587 83.79604 330.669 328.552 83.79604 0.000314 266731.1 370.1248 350.543 55.92268 88.80744 88.80744 74.76479 88.80744 344.6685 342.4249 88.80744 0.000314 282682.9 386.6958 365.988 59.29234 93.91408 93.91408 75.15993 93.91408 359.7757 357.4031 93.91408 0.000314 298937.8

PAGE 102

91 Appendix A: (continued) Table B: (continued) 0.1% wt Nanofluid (cont) Tsurfaceexcess hboiling 157.1149 57.11488 1542.758 170.9927 70.9927 1423.577 184.1018 84.10176 1379.51 196.7257 96.72571 1424.531 207.9427 107.9427 1436.716 217.3915 117.3915 1515.497 227.9667 127.9667 1752.039 240.0557 140.0557 1809.018 246.88 146.88 1958.393 253.7815 153.7815 2075.899 274.3696 174.3696 1488.293 289.8452 189.8452 1327.431 305.6212 205.6212 1185.567 319.207 219.207 1216.8 332.5216 232.5216 1215.727 346.9309 246.9309 1210.613

PAGE 103

92 Appendix A: (continued) Table C: 0.2% wt Nanofl uid Data Calculations 0.2% wt Nanofluid PR VA VR PA q L1 L2 L3 L4 L5 k1 k2 k3 750 40.3 120 84.58802 84.58802 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 43.3 120 97.65052 97.65052 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 46.5 120 112.6172 112.6172 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 49.2 120 126.075 126.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 52.3 120 142.463 142.463 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 55.3 120 159.2755 159.2755 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 58.8 120 180.075 180.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 61.9 120 199.563 199.563 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 35.5 120 65.63802 65.63802 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 68.3 120 242.963 242.963 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 71.2 120 264.0333 264.0333 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 74.5 120 289.0755 289.0755 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 77.3 120 311.213 311.213 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 80.2 120 335.0021 335.0021 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 83.4 120 362.2688 362.2688 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 86.2 120 387.0021 387.0021 0.01 0.01 0.003 2.54E 05 0.003 401 401 401

PAGE 104

93 Appendix A: (continued) Table C: (continued) 0.2% wt Nanofluid (cont) k4 k5 A1 R1 R2 R3 R4 R5 comsol T1 T2 T3 T1 cal 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111429 167.328 160.609 152.227 167.1756 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111424 182.956 175.765 165.988 182.8036 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111423 199.346 190.272 179.204 199.1936 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111421 213.279 203.103 190.204 213.1266 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111555 229.053 218.807 204.044 228.9006 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111545 245.136 232.413 216.847 244.9836 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111536 261.548 247.448 230.195 261.3956 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111521 280.559 265.013 246.143 280.4066 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111499 295.703 283.123 259.986 295.5506 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111499 307.499 293.123 269.196 307.3466 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111491 323.912 309.738 283.552 323.7596 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111581 342.506 327.652 299.616 342.3536 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111574 358.607 342.956 313.524 358.4546 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111568 376.061 359.62 329.658 375.9086 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111515 391.853 374.176 353.648 391.7006 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111504 404.117 384.761 362.021 403.9646

PAGE 105

94 Appendix A: (continued) Table C: (continued) 0.2% wt Nanofluid (cont) T2 cal T3 cal q1 2 q2 3 % qlost q3 4 q4 5 T4 T5 q3 surface A2 q''3 surface 160.2628 152.227 31.35095 36.44399 56.9159 36.44399 36.44399 149.8163 148.8956 36.44399 0.000314 116004.8 175.4188 165.988 33.49157 42.77059 56.20034 42.77059 42.77059 163.1588 162.0783 42.77059 0.000314 136143 189.9258 179.204 42.03136 48.62554 56.82227 48.62554 48.62554 175.9875 174.759 48.62554 0.000314 154779.9 202.7568 190.204 47.02915 56.9295 54.84474 56.9295 56.9295 186.4382 185 56.9295 0.000314 181212.2 218.4608 204.044 47.34661 65.38312 54.1052 65.38312 65.38312 199.719 198.0672 65.38312 0.000314 208120.9 232.0668 216.847 58.58031 69.02488 56.66322 69.02488 69.02488 212.2811 210.5373 69.02488 0.000314 219713 247.1018 230.195 64.82529 76.67577 57.42009 76.67577 76.67577 225.123 223.1859 76.67577 0.000314 244066.6 264.6668 246.143 71.38319 84.0092 57.90343 84.0092 84.0092 240.5859 238.4636 84.0092 0.000314 267409.6 282.7768 259.986 57.93178 103.3609 57.4711 103.3609 103.3609 253.1488 250.5376 103.3609 0.000314 329008 292.7768 269.196 66.077 106.9437 55.98354 106.9437 106.9437 262.1218 259.4201 106.9437 0.000314 340412.4 309.3918 283.552 65.16089 117.1887 55.61593 117.1887 117.1887 275.8001 272.8396 117.1887 0.000314 373023.4 327.3058 299.616 68.24483 125.5789 56.55846 125.5789 125.5789 291.3091 288.1366 125.5789 0.000314 399730 342.6098 313.524 71.85939 131.91 57.61424 131.91 131.91 304.7983 301.4659 131.91 0.000314 419882.6 359.2738 329.658 75.4422 134.3137 59.90662 134.3137 134.3137 320.7733 317.3801 134.3137 0.000314 427533.7 373.8298 353.648 81.04771 91.52856 74.73462 91.52856 91.52856 347.5935 345.2812 91.52856 0.000314 291344.5 384.4148 362.021 88.66231 101.5604 73.75714 101.5604 101.5604 355.3029 352.7372 101.5604 0.000314 323276.9

PAGE 106

95 Appendix A: (continued) Table C: (continued) 0.2% wt Nanofluid (cont) Tsurfaceexcess hboiling 144.8347 44.83468 2587.39 157.3126 57.31258 2375.448 169.3411 69.34106 2232.154 178.6568 78.65681 2303.834 190.7734 90.77337 2292.753 202.8379 102.8379 2136.498 214.6338 114.6338 2129.097 229.0948 129.0948 2071.42 239.0129 139.0129 2366.744 247.4959 147.4959 2307.945 259.774 159.774 2334.694 274.1244 174.1244 2295.658 286.7481 186.7481 2248.391 302.395 202.395 2112.373 335.0744 235.0744 1239.371 341.4128 241.4128 1339.104

PAGE 107

96 Appendix A: (continued) Table D: 0.3% wt Nanofluid Data Calculations 0.3% wt Nanofluid PR VA VR PA q L1 L2 L3 L4 L5 k1 k2 k3 750 40.5 120 85.42969 85.42969 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 43.5 120 98.55469 98.55469 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 46 120 110.2083 110.2083 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 49.2 120 126.075 126.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 52.6 120 144.1021 144.1021 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 55 120 157.5521 157.5521 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 58.1 120 175.813 175.813 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 61.4 120 196.3521 196.3521 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 64.2 120 214.6688 214.6688 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 67.1 120 234.5005 234.5005 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 70.6 120 259.6021 259.6021 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 73.5 120 281.3672 281.3672 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 76.1 120 301.6255 301.6255 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 79.2 120 326.7 326.7 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 82.4 120 353.6333 353.6333 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 85.1 120 377.188 377.188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401

PAGE 108

97 Appendix A: (continued) Table D: (continued) 0.3% wt Nanofluid (cont) k4 k5 A1 R1 R2 R3 R4 R5 comsol q reminder T1 T2 T3 T1 cal 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111444 17.83605 150.636 144.744 140.465 150.4836 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111444 21.27827 170.57 163.56 158.522 170.4176 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111437 24.52094 181.893 174.635 168.882 181.7406 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111432 29.22938 196.191 188.0362 181.245 196.0386 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111429 34.94283 211.898 202.909 194.858 211.7456 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111426 38.54832 224.876 215.082 206.236 224.7236 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111424 45.45997 243.91 233.738 223.368 243.7576 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111423 52.17207 259.474 248.487 236.637 259.3216 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111555 65.69605 271.517 260.585 245.753 271.3646 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111536 73.98186 282.45 270.901 254.242 282.2976 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 79.12932 298.173 285.217 267.423 298.0206 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111521 83.94117 311.141 297.387 278.532 310.9886 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111521 85.93212 322.084 308.142 288.848 321.9316 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111545 72.01358 353.177 333.19 316.965 353.0246 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111536 75.13834 369.327 349.409 332.495 369.1746 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111515 88.4673 386.551 366.062 346.209 386.3986

PAGE 109

98 Appendix A: (continued) Table D: (continued) 0.3% wt Nanofluid (cont) T2 cal T3 cal q1 2 q2 3 % qlost q3 4 q4 5 T4 T5 q3 surface A2 q''3 surface 144.3978 140.465 27.60034 17.83605 79.12196 17.83605 17.83605 139.2852 138.8346 17.83605 0.000314 56773.9 163.2138 158.522 32.6707 21.27827 78.40969 21.27827 21.27827 157.1145 156.5769 21.27827 0.000314 67730.82 174.2888 168.882 33.79543 24.52094 77.75038 24.52094 24.52094 167.26 166.6405 24.52094 0.000314 78052.56 187.69 181.245 37.8626 29.22938 76.81588 29.22938 29.22938 179.3115 178.5731 29.22938 0.000314 93040.02 202.5628 194.858 41.64586 34.94283 75.75133 34.94283 34.94283 192.5466 191.6638 34.94283 0.000314 111226.5 214.7358 206.236 45.2967 38.54832 75.53297 38.54832 38.54832 203.6861 202.7122 38.54832 0.000314 122703.1 233.3918 223.368 47.01101 45.45997 74.143 45.45997 45.45997 220.3609 219.2124 45.45997 0.000314 144703.6 248.1408 236.637 50.7072 52.17207 73.42933 52.17207 52.17207 233.1859 231.8679 52.17207 0.000314 166068.9 260.2388 245.753 50.45776 65.69605 69.39655 65.69605 65.69605 241.4073 239.7476 65.69605 0.000314 209117 270.5548 254.242 53.25598 73.98186 68.4513 73.98186 73.98186 249.3482 247.4792 73.98186 0.000314 235491.6 284.8708 267.423 59.63701 79.12932 69.519 79.12932 79.12932 262.1887 260.1896 79.12932 0.000314 251876.4 297.0408 278.532 63.25611 83.94117 70.16668 83.94117 83.94117 272.9794 270.8588 83.94117 0.000314 267193 307.7958 288.848 64.10872 85.93212 71.51033 85.93212 85.93212 283.1637 280.9928 85.93212 0.000314 273530.4 332.8438 316.965 91.52403 72.01358 77.95727 72.01358 72.01358 312.2014 310.3821 72.01358 0.000314 229226.4 349.0628 332.495 91.2111 75.13834 78.75247 75.13834 75.13834 327.5247 325.6265 75.13834 0.000314 239172.8 365.7158 346.209 93.8007 88.4673 76.54557 88.4673 88.4673 340.357 338.122 88.4673 0.000314 281600.2

PAGE 110

99 Appendix A: (continued) Table D: (continued) 0.3% wt Nanofluid (cont) Tsurfaceexcess hboiling 136.8469 36.84686 1540.807 154.2056 54.20558 1249.518 163.908 63.90796 1221.328 175.316 75.316 1235.329 187.7702 87.77018 1267.247 198.4169 98.41694 1246.768 214.1471 114.1471 1267.694 226.0547 126.0547 1317.435 232.4189 132.4189 1579.209 239.2276 139.2276 1691.415 251.3645 151.3645 1664.039 261.4976 161.4976 1654.471 271.4096 171.4096 1595.771 302.3494 202.3494 1132.825 317.2458 217.2458 1100.931 328.2566 228.2566 1233.7

PAGE 111

100 Appendix A: (continued) Table E: 0.4% wt Nanofl uid Data Calculations 0.4% wt Nanofluid PR VA VR PA q L1 L2 L3 L4 L5 k1 k2 k3 750 40.2 120 84.16875 84.16875 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 43.2 120 97.2 97.2 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 46.2 120 111.1688 111.1688 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 49.5 120 127.6172 127.6172 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 52.2 120 141.9188 141.9188 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 55.3 120 159.2755 159.2755 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 58.4 120 177.6333 177.6333 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 61.2 120 195.075 195.075 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 64.5 120 216.6797 216.6797 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 67.2 120 235.2 235.2 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 70.4 120 258.1333 258.1333 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 73.3 120 279.838 279.838 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 76.1 120 301.6255 301.6255 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 79.3 120 327.5255 327.5255 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 82.5 120 354.4922 354.4922 0.01 0.01 0.003 2.54E 05 0.003 401 401 401 750 84.7 120 373.6505 373.6505 0.01 0.01 0.003 2.54E 05 0.003 401 401 401

PAGE 112

101 Appendix A: (continued) Table E: (continued) 0.4% wt Nanofluid (cont) k4 k5 A1 R1 R2 R3 R4 R5 comsol T1 T2 T3 T1 cal 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111444 150.548 144.058 139.515 150.3956 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111437 164.284 157.003 151.5 164.1316 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111437 176.578 168.009 161.65 176.4256 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111432 188.91 179.232 171.814 188.7576 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111426 201.054 190.413 181.574 200.9016 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111424 215.128 202.945 193.043 214.9756 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111423 228.182 214.589 203.299 228.0296 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111421 238.029 223.187 210.947 237.8766 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111568 251.414 235.169 221.412 251.2616 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111545 260.422 243.037 227.54 260.2696 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111536 270.998 252.314 235.938 270.8456 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111528 281.727 262.596 244.412 281.5746 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111521 292.662 270.761 252.191 292.5096 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111515 305.729 282.831 262.524 305.5766 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111509 320.105 295.097 273.744 319.9526 8.89 401 0.000113 0.220497 0.220497 0.066149 0.025263 0.111504 330.776 305.424 282.77 330.6236

PAGE 113

102 Appendix A: (continued) Table E: (continued) 0.4% wt Nanofluid (cont) T2 cal T3 cal q1 2 q2 3 % qlost q3 4 q4 5 T4 T5 q3 surface A2 q''3 surface 143.7118 139.515 30.31239 19.03334 77.38669 19.03334 19.03334 138.256 137.7751 19.03334 0.000314 60585 156.6568 151.5 33.89974 23.38714 75.93916 23.38714 23.38714 149.953 149.3621 23.38714 0.000314 74443.56 167.6628 161.65 39.74108 27.26927 75.47038 27.26927 27.26927 159.8462 159.1573 27.26927 0.000314 86800.78 178.8858 171.814 44.77062 32.07205 74.86855 32.07205 32.07205 169.6925 168.8822 32.07205 0.000314 102088.5 190.0668 181.574 49.13802 38.51657 72.86012 38.51657 38.51657 179.0262 178.0531 38.51657 0.000314 122602.1 202.5988 193.043 56.1313 43.33749 72.79086 43.33749 43.33749 190.1763 189.0814 43.33749 0.000314 137947.5 214.2428 203.299 62.52594 49.63236 72.0591 49.63236 49.63236 200.0159 198.762 49.63236 0.000314 157984.7 222.8408 210.947 68.19041 53.9408 72.34869 53.9408 53.9408 207.3789 206.0162 53.9408 0.000314 171698.9 234.8228 221.412 74.5533 60.8207 71.93059 60.8207 60.8207 217.3888 215.8523 60.8207 0.000314 193598.3 242.6908 227.54 79.72343 68.71196 70.78573 68.71196 68.71196 222.9948 221.2589 68.71196 0.000314 218716.9 251.9678 235.938 85.61466 72.6984 71.83688 72.6984 72.6984 231.1291 229.2925 72.6984 0.000314 231406.2 262.2498 244.412 87.64189 80.89805 71.09112 80.89805 80.89805 239.0607 237.017 80.89805 0.000314 257506.5 270.4148 252.191 100.2044 82.64864 72.59892 82.64864 82.64864 246.7239 244.6359 82.64864 0.000314 263078.8 282.4848 262.524 104.726 90.52628 72.36054 90.52628 90.52628 256.5358 254.2488 90.52628 0.000314 288154.1 294.7508 273.744 114.2953 95.27011 73.12491 95.27011 95.27011 267.442 265.0352 95.27011 0.000314 303254.2 305.0778 282.77 115.8554 101.1704 72.92379 101.1704 101.1704 276.0777 273.5218 101.1704 0.000314 322035.4

PAGE 114

103 Appendix A: (continued) Table E: (continued) 0.4% wt Nanofluid (cont) Tsurfaceexcess hboiling 135.654 35.65398 1699.25 146.756 46.75595 1592.173 156.1185 56.11847 1546.742 165.3084 65.30838 1563.176 173.7614 73.76137 1662.144 184.2526 84.25259 1637.309 193.2318 93.23185 1694.536 200.006 100.006 1716.886 209.0666 109.0666 1775.046 213.5944 113.5944 1925.419 221.184 121.184 1909.544 227.9946 127.9946 2011.855 235.4189 135.4189 1942.704 244.1538 144.1538 1998.935 254.4117 154.4117 1963.932 262.2409 162.2409 1984.921

PAGE 115

104 Appendix B: Heat Flux Uncertai nty Analysis Calculations Table F: De-Ionized Water Heat Flux Error Bar Calculations De ionized Water UA A UA/A UT1 UT2 U T T U T/ T UL L UL/L Uk k 1.57E 07 0.000314 0.0005 0.621131 0.6034 0.865965 4.07814 0.212343 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.690583 0.669572 0.96189 4.832365 0.199052 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.744143 0.719736 1.035263 5.61328 0.184431 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.799667 0.77148 1.111148 6.482622 0.171404 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.858795 0.8278 1.192804 7.1283 0.167334 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.909879 0.875464 1.262663 7.914736 0.159533 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.960991 0.923272 1.332642 8.674499 0.153628 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.019435 0.978112 1.412781 9.503331 0.148662 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.068951 1.025268 1.481159 10.046 0.147438 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.132595 1.086572 1.569526 10.58413 0.14829 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.181603 1.131068 1.635696 11.6217 0.140745 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.216875 1.163764 1.683785 12.22292 0.137756 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.262919 1.205532 1.74593 13.2062 0.132205 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.307795 1.241252 1.803063 15.31174 0.117757 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.352935 1.284428 1.865526 15.76304 0.118348 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.394647 1.322296 1.92185 16.64752 0.115444 0.000005 0.006025 0.00083 0 401

PAGE 116

105 Appendix B: (continued) Table F: (continued) De ionized Water (cont) Uk/k Rth URth URth/Rth q" Uq" Uq" + Uq" % Uq"error 0 0.202856 0.000197 0.000969 63991.9 13588.42 77580.32 50403.48 21.2345958 0 0.202849 0.000197 0.000969 75829.42 15094.19 90923.61 60735.23 19.9054547 0 0.202844 0.000197 0.000969 88085.58 16246 104331.6 71839.59 18.4434213 0 0.202844 0.000197 0.000969 101727.6 17436.88 119164.5 84290.73 17.1407518 0 0.202841 0.000197 0.000969 111861.7 18718.62 130580.3 93143.06 16.7337176 0 0.202838 0.000197 0.000969 124204.5 19815.19 144019.6 104389.3 15.9536888 0 0.202836 0.000197 0.000969 136128.6 20913.63 157042.2 115215 15.3631446 0 0.202836 0.000197 0.000969 149135.4 22171.32 171306.8 126964.1 14.8665701 0 0.202834 0.000197 0.000969 157652.7 23244.59 180897.3 134408.1 14.7441752 0 0.202834 0.000197 0.000969 166097.7 24631.36 190729.1 141466.4 14.8294411 0 0.202833 0.000197 0.000969 182381.5 25670.05 208051.6 156711.5 14.0749157 0 0.202979 0.000197 0.000969 191678.3 26405.73 218084.1 165272.6 13.7760657 0 0.202967 0.000197 0.000969 207110.4 27382.04 234492.4 179728.4 13.2209875 0 0.202948 0.000197 0.000969 240154.4 28281.05 268435.5 211873.4 11.7761936 0 0.20294 0.000197 0.000969 247242.5 29261.93 276504.4 217980.6 11.8353157 0 0.20294 0.000197 0.000969 261115.5 30145.46 291260.9 230970 11.544878

PAGE 117

106 Appendix B: (continued) Table G: 0.1% wt Nanofluid Heat Flux Error Bar Calculations 0.1% wt Nanofluid UA A UA/A UT1 UT2 U T T U T/ T UL L UL/L Uk k 1.57E 07 0.000314 0.0005 0.675335 0.65092 0.937963 5.61512 0.167042 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.737735 0.709732 1.023705 6.440304 0.158953 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.798127 0.76598 1.106224 7.393238 0.149627 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.860203 0.822024 1.189821 8.780287 0.13551 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.914271 0.8713 1.262955 9.882254 0.1278 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.964207 0.914912 1.329195 11.33654 0.117249 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.031171 0.969048 1.415051 14.29533 0.098987 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.095039 1.024836 1.4998 16.15328 0.092848 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.140575 1.060872 1.557678 18.33797 0.084943 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.184983 1.096528 1.614484 20.35055 0.079334 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.235567 1.16366 1.697272 16.54536 0.102583 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.293475 1.223648 1.78056 16.06676 0.110823 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.352203 1.284656 1.865153 15.54276 0.120001 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.418755 1.344848 1.954861 17.00496 0.114958 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.480499 1.402172 2.039109 18.02138 0.113149 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.546783 1.463952 2.129717 19.05713 0.111754 0.000005 0.006025 0.00083 0 401

PAGE 118

107 Appendix B: (continued) Table G: (continued) 0.1% wt Nanofluid (cont) Uk/k Rth URth URth/Rth q" Uq" Uq" + Uq" % Uq" error 0 0.202844 0.000197 0.000969 88114.46 14719.16 102833.6 73395.3 16.70459322 0 0.202844 0.000197 0.000969 101063.5 16064.73 117128.3 84998.82 15.89566884 0 0.202841 0.000197 0.000969 116019.2 17360.02 133379.3 98659.23 14.96304829 0 0.202836 0.000197 0.000969 137788.7 18672.42 156461.1 119116.3 13.5514821 0 0.202834 0.000197 0.000969 155083.1 19820.38 174903.4 135262.7 12.78049509 0 0.202833 0.000197 0.000969 177906.4 20860.21 198766.6 157046.2 11.72538379 0 0.202957 0.000197 0.000969 224202.6 22194.48 246397.1 202008.2 9.899293748 0 0.20294 0.000197 0.000969 253363.3 23525.91 276889.3 229837.4 9.285441751 0 0.202926 0.000197 0.000969 287648.8 24435.71 312084.6 263213.1 8.494979719 0 0.202916 0.000197 0.000969 319234.8 25328.47 344563.3 293906.3 7.9341192 0 0.20294 0.000197 0.000969 259513.1 26623.12 286136.2 232890 10.25887457 0 0.20294 0.000197 0.000969 252006.4 27929.34 279935.7 224077 11.08279068 0 0.202948 0.000197 0.000969 243777.8 29254.88 273032.7 214523 12.00063309 0 0.202933 0.000197 0.000969 266731.1 30664.34 297395.4 236066.7 11.49634975 0 0.202926 0.000197 0.000969 282682.9 31986.89 314669.8 250696 11.31546852 0 0.202921 0.000197 0.000969 298937.8 33409.19 332347 265528.6 11.17596761

PAGE 119

108 Appendix B: (continued) Table H: 0.2% wt Nanofluid Heat Flux Error Bar Calculations 0.2% wt Nanofluid UA A UA/A UT1 UT2 U T T U T/ T UL L UL/L Uk k 1.57E 07 0.000314 0.0005 0.641051 0.608908 0.884147 7.392318 0.119603 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.701675 0.663952 0.966013 8.675419 0.111351 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.759703 0.716816 1.044497 9.862937 0.105901 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.811027 0.760816 1.112028 11.54719 0.096303 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.873843 0.816176 1.19572 13.27063 0.090103 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.928267 0.867388 1.27045 14.00907 0.090688 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.988407 0.92078 1.350846 15.56117 0.086809 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.058667 0.984572 1.445738 17.04821 0.084803 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.131107 1.039944 1.536518 20.97309 0.073261 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.171107 1.076784 1.590898 21.70009 0.073313 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.237567 1.134208 1.67869 23.77799 0.070598 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.309223 1.198464 1.774931 25.49162 0.069628 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.370439 1.254096 1.857649 26.77592 0.069378 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.437095 1.318632 1.950393 27.26301 0.07154 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.495319 1.414592 2.05841 18.57357 0.110825 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.537659 1.448084 2.112189 20.60822 0.102493 0.000005 0.006025 0.00083 0 401

PAGE 120

109 Appendix B: (continued) Table H: (continued) 0.2% wt Nanofluid (cont) Uk/k Rth URth URth/Rth q" Uq" Uq" + Uq" % Uq" error 0 0.202841 0.000197 0.000969 116004.8 13875.15 129880 102129.7 11.9608431 0 0.202836 0.000197 0.000969 136143 15160.33 151303.4 120982.7 11.13558842 0 0.202834 0.000197 0.000969 154779.9 16392.25 171172.2 138387.7 10.59068379 0 0.202833 0.000197 0.000969 181212.2 17452.38 198664.6 163759.8 9.630905683 0 0.202967 0.000197 0.000969 208120.9 18753.63 226874.6 189367.3 9.010928221 0 0.202957 0.000197 0.000969 219713 19926.7 239639.7 199786.3 9.069421495 0 0.202948 0.000197 0.000969 244066.6 21188.79 265255.4 222877.8 8.681561411 0 0.202933 0.000197 0.000969 267409.6 22678.98 290088.6 244730.6 8.48099193 0 0.202911 0.000197 0.000969 329008 24106.25 353114.2 304901.7 7.3269491 0 0.202911 0.000197 0.000969 340412.4 24959.41 365371.8 315453 7.33210752 0 0.202903 0.000197 0.000969 373023.4 26338.02 399361.4 346685.3 7.060689896 0 0.202993 0.000197 0.000969 399730 27835.82 427565.8 371894.1 6.963656108 0 0.202986 0.000197 0.000969 419882.6 29134.04 449016.6 390748.6 6.938615765 0 0.20298 0.000197 0.000969 427533.7 30589.27 458123 396944.4 7.154821204 0 0.202926 0.000197 0.000969 291344.5 32289.72 323634.2 259054.7 11.08300505 0 0.202916 0.000197 0.000969 323276.9 33135.35 356412.2 290141.5 10.24983589

PAGE 121

110 Appendix B: (continued) Table I: 0.3% wt Nanofluid Heat Flux Error Bar Calculations 0.3% wt Nanofluid UA A UA/A UT1 UT2 U T T U T/ T UL L UL/L Uk k 1.57E 07 0.000314 0.0005 0.577591 0.56186 0.80579 3.618144 0.222708 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.652855 0.634088 0.910103 4.316418 0.210847 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.697155 0.675528 0.970754 4.974038 0.195164 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.75076 0.72498 1.043665 5.929003 0.176027 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.810251 0.779432 1.124287 7.087823 0.158622 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.858943 0.824944 1.190931 7.819065 0.152311 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.933567 0.893472 1.292223 9.220921 0.14014 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.992563 0.946548 1.371545 10.58229 0.129607 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.040955 0.983012 1.431747 13.33415 0.107375 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.082219 1.016968 1.485066 15.01445 0.098909 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.139483 1.069692 1.562902 16.05848 0.097326 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.188163 1.114128 1.628807 17.03441 0.095619 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.231183 1.155392 1.688414 17.43844 0.096821 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.331375 1.26786 1.838485 14.61564 0.125789 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.396251 1.32998 1.928306 15.24915 0.126453 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.462863 1.384836 2.014383 17.95235 0.112207 0.000005 0.006025 0.00083 0 401

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111 Appendix B: (continued) Table I: (continued) 0.3% wt Nanofluid (cont) Uk/k Rth URth URth/Rth q" Uq" Uq" + Uq" % Uq" error 0 0.202856 0.000197 0.000969 56773.9 12644.17 69418.07 44129.734 22.27109087 0 0.202856 0.000197 0.000969 67730.82 14281.02 82011.85 53449.804 21.08496572 0 0.202849 0.000197 0.000969 78052.56 15233.3 93285.87 62819.263 19.51672142 0 0.202844 0.000197 0.000969 93040.02 16377.88 109417.9 76662.144 17.60304447 0 0.202841 0.000197 0.000969 111226.5 17643.42 128869.9 93583.072 15.86260677 0 0.202838 0.000197 0.000969 122703.1 18689.53 141392.6 104013.58 15.2315049 0 0.202836 0.000197 0.000969 144703.6 20279.42 164983 124424.16 14.01445678 0 0.202834 0.000197 0.000969 166068.9 21524.53 187593.4 144544.33 12.96120762 0 0.202967 0.000197 0.000969 209117 22454.99 231572 186662.02 10.73800404 0 0.202948 0.000197 0.000969 235491.6 23293.69 258785.3 212197.89 9.891518253 0 0.20294 0.000197 0.000969 251876.4 24515.58 276392 227360.86 9.733176055 0 0.202933 0.000197 0.000969 267193 25550.3 292743.3 241642.73 9.562488245 0 0.202933 0.000197 0.000969 273530.4 26485.29 300015.7 247045.15 9.682756573 0 0.202957 0.000197 0.000969 229226.4 28835.21 258061.6 200391.15 12.57936107 0 0.202948 0.000197 0.000969 239172.8 30245.32 269418.1 208927.44 12.64580457 0 0.202926 0.000197 0.000969 281600.2 31599.05 313199.2 250001.11 11.22124818

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112 Appendix B: (continued) Table J: 0.4% wt Nanofluid Heat Flux Error Bar Calculations 0.4% wt Nanofluid UA A UA/A UT1 UT2 U T T U T/ T UL L UL/L Uk k 1.57E 07 0.000314 0.0005 0.574847 0.55806 0.801174 3.861022 0.207503 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.626627 0.606 0.871721 4.744048 0.18375 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.670651 0.6466 0.931593 5.531534 0.168415 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.715543 0.687256 0.99213 6.50562 0.152504 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.760267 0.726296 1.051433 7.812625 0.134581 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.810395 0.772172 1.11937 8.790406 0.12734 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.856971 0.813196 1.181392 10.06715 0.117351 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.891363 0.843788 1.227398 10.94098 0.112184 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.939291 0.885648 1.290984 12.34536 0.104572 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 0.970763 0.91016 1.330704 13.94556 0.095421 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.007871 0.943752 1.380751 14.75397 0.093585 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.048999 0.977648 1.433944 16.41743 0.087343 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.081659 1.008764 1.479051 16.77211 0.088185 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.129939 1.050096 1.542551 18.37018 0.08397 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.179003 1.094976 1.609044 19.33229 0.083231 0.000005 0.006025 0.00083 0 401 1.57E 07 0.000314 0.0005 1.220311 1.13108 1.663881 20.52908 0.08105 0.000005 0.006025 0.00083 0 401

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113 Appendix B: (continued) Table J: (continued) 0.4% wt Nanofluid (cont) Uk/k Rth URth URth/Rth q" Uq" Uq" + Uq" % Uq" error 0 0.202856 0.000197 0.000969 60585 12571.76 73156.76 48013.25 20.75060499 0 0.202849 0.000197 0.000969 74443.56 13679.28 88122.84 60764.28 18.37536951 0 0.202849 0.000197 0.000969 86800.78 14618.85 101419.6 72181.93 16.8418386 0 0.202844 0.000197 0.000969 102088.5 15569.26 117657.8 86519.24 15.2507495 0 0.202838 0.000197 0.000969 122602.1 16500.49 139102.5 106101.6 13.45857235 0 0.202836 0.000197 0.000969 137947.5 17566.88 155514.4 120380.6 12.7344667 0 0.202834 0.000197 0.000969 157984.7 18540.49 176525.2 139444.2 11.73562254 0 0.202833 0.000197 0.000969 171698.9 19262.7 190961.6 152436.2 11.21888265 0 0.202979 0.000197 0.000969 193598.3 20246.15 213844.5 173352.2 10.45781499 0 0.202957 0.000197 0.000969 218716.9 20871.63 239588.6 197845.3 9.542757701 0 0.202948 0.000197 0.000969 231406.2 21657.62 253063.8 209748.6 9.359137059 0 0.20294 0.000197 0.000969 257506.5 22493.09 279999.6 235013.4 8.734959942 0 0.202933 0.000197 0.000969 263078.8 23201.41 286280.2 239877.4 8.819188275 0 0.202926 0.000197 0.000969 288154.1 24198.46 312352.6 263955.7 8.397748538 0 0.202921 0.000197 0.000969 303254.2 25242.27 328496.4 278011.9 8.323801406 0 0.202916 0.000197 0.000969 322035.4 26103.33 348138.7 295932.1 8.105732078

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114 Appendix C: Heat Transfer Coefficient Uncertainty Analysis Calculations Table K: De-Ionized Water Heat Transf er Coefficient Error Bar Calculations De ionized Water U Te Te U Te/ Te UTs UTsatUq"/q" h Uh Uh + Uh % Uh error 0.6034 46.77186 0.01290092 0.6034 0 0.212346 1368.171 291.0613 1659.232 1077.11 21.27374899 0.669572 62.56064 0.01070277 0.669572 0 0.199055 1212.095 241.6215 1453.716 970.4733 19.93420731 0.719736 74.32072 0.00968419 0.719736 0 0.184434 1185.209 218.8942 1404.103 966.3148 18.46882847 0.77148 86.38738 0.00893047 0.77148 0 0.171408 1177.575 202.119 1379.694 975.4559 17.16400031 0.8278 99.8217 0.00829279 0.8278 0 0.167337 1120.615 187.7506 1308.365 932.8642 16.75425341 0.875464 110.9513 0.00789053 0.875464 0 0.159537 1119.451 178.812 1298.263 940.6386 15.97318971 0.923272 122.1435 0.00755891 0.923272 0 0.153631 1114.497 171.4289 1285.926 943.0683 15.38172891 0.978112 135.0247 0.00724395 0.978112 0 0.148666 1104.505 164.3968 1268.902 940.1082 14.88420826 1.025268 146.271 0.00700937 1.025268 0 0.147442 1077.812 159.094 1236.906 918.7182 14.76082703 1.086572 161.0589 0.00674643 1.086572 0 0.148294 1031.286 153.0921 1184.378 878.1937 14.84477909 1.131068 171.1453 0.00660882 1.131068 0 0.140749 1065.653 150.155 1215.808 915.4982 14.09042291 1.163764 178.7181 0.00651173 1.163764 0 0.137761 1072.518 147.9157 1220.434 924.6021 13.79144713 1.205532 188.1768 0.00640638 1.205532 0 0.13221 1100.616 145.683 1246.299 954.933 13.23649978 1.241252 195.0013 0.00636535 1.241252 0 0.117762 1231.553 145.2418 1376.795 1086.311 11.7933843 1.284428 205.344 0.00625501 1.284428 0 0.118353 1204.041 142.7009 1346.742 1061.34 11.85183316 1.322296 213.9265 0.00618108 1.322296 0 0.115449 1220.585 141.1169 1361.702 1079.468 11.56141277

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115 Appendix C: (continued) Table L: 0.1% wt Nanofluid Heat Transf er Coefficient Error Bar Calculations 0.1% wt Nanofluid U Te Te U Te/ Te UTs UTsatUq"/q" h Uh Uh + Uh % Uh error 0.65092 57.11488 0.01139668 0.65092 0 0.167046 1542.758 258.3106 1801.069 1284.448 16.74342491 0.709732 70.9927 0.009997254 0.709732 0 0.158957 1423.577 226.7341 1650.311 1196.843 15.92707565 0.76598 84.10176 0.009107776 0.76598 0 0.14963 1379.51 206.7988 1586.309 1172.711 14.99074147 0.822024 96.72571 0.008498505 0.822024 0 0.135515 1424.531 193.4242 1617.955 1231.106 13.57810418 0.8713 107.9427 0.008071872 0.8713 0 0.127805 1436.716 183.9853 1620.701 1252.731 12.80595978 0.914912 117.3915 0.007793684 0.914912 0 0.117254 1515.497 178.0899 1693.587 1337.407 11.75125696 0.969048 127.9667 0.007572659 0.969048 0 0.098993 1752.039 173.9462 1925.985 1578.093 9.92821577 1.024836 140.0557 0.007317345 1.024836 0 0.092854 1809.018 168.4961 1977.514 1640.522 9.314229107 1.060872 146.88 0.007222711 1.060872 0 0.08495 1958.393 166.9653 2125.358 1791.428 8.525629356 1.096528 153.7815 0.007130431 1.096528 0 0.079341 2075.899 165.3681 2241.267 1910.531 7.966095524 1.16366 174.3696 0.006673524 1.16366 0 0.102589 1488.293 153.0048 1641.298 1335.288 10.2805577 1.223648 189.8452 0.006445503 1.223648 0 0.110828 1327.431 147.3649 1474.795 1180.066 11.10151766 1.284656 205.6212 0.006247681 1.284656 0 0.120006 1185.567 142.4683 1328.036 1043.099 12.0168852 1.344848 219.207 0.006135058 1.344848 0 0.114963 1216.8 140.0866 1356.886 1076.713 11.51270807 1.402172 232.5216 0.006030287 1.402172 0 0.113155 1215.727 137.7604 1353.488 1077.967 11.33152555 1.463952 246.9309 0.00592859 1.463952 0 0.11176 1210.613 135.488 1346.101 1075.125 11.19168145

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116 Appendix C: (continued) Table M: 0.2% wt Nanofluid Heat Transf er Coefficient Error Bar Calculations 0.2% wt Nanofluid U Te Te U Te/ Te UTs UTsatUq"/q" h Uh Uh + Uh % Uh error 0.608908 44.83468 0.013581 0.608908 0 0.119608 2587.39 311.4623 2898.852 2275.928 12.03770131 0.663952 57.31258 0.011585 0.663952 0 0.111356 2375.448 265.9477 2641.395 2109.5 11.1956864 0.716816 69.34106 0.010338 0.716816 0 0.105907 2232.154 237.5238 2469.677 1994.63 10.64101642 0.760816 78.65681 0.009673 0.760816 0 0.096309 2303.834 222.9963 2526.83 2080.838 9.679356207 0.816176 90.77337 0.008991 0.816176 0 0.090109 2292.753 207.6243 2500.377 2085.129 9.055676279 0.867388 102.8379 0.008435 0.867388 0 0.090694 2136.498 194.6041 2331.102 1941.894 9.108557325 0.92078 114.6338 0.008032 0.92078 0 0.086816 2129.097 185.6283 2314.725 1943.469 8.718640739 0.984572 129.0948 0.007627 0.984572 0 0.08481 2071.42 176.3859 2247.806 1895.034 8.515215516 1.039944 139.0129 0.007481 1.039944 0 0.073269 2366.744 174.3117 2541.056 2192.433 7.365040685 1.076784 147.4959 0.0073 1.076784 0 0.073321 2307.945 170.0577 2478.003 2137.887 7.368362361 1.134208 159.774 0.007099 1.134208 0 0.070607 2334.694 165.6765 2500.37 2169.017 7.096286015 1.198464 174.1244 0.006883 1.198464 0 0.069637 2295.658 160.6407 2456.298 2135.017 6.997587894 1.254096 186.7481 0.006715 1.254096 0 0.069386 2248.391 156.7361 2405.127 2091.654 6.971037257 1.318632 202.395 0.006515 1.318632 0 0.071548 2112.373 151.7618 2264.135 1960.611 7.184423229 1.414592 235.0744 0.006018 1.414592 0 0.11083 1239.371 137.5619 1376.933 1101.809 11.09932972 1.448084 241.4128 0.005998 1.448084 0 0.102498 1339.104 137.4908 1476.595 1201.613 10.26737262

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117 Appendix C: (continued) Table N: 0.3% wt Nanofluid Heat Transf er Coefficient Error Bar Calculations 0.3% wt Nanofluid U Te Te U Te/ Te UTs UTsatUq"/q" h Uh Uh + Uh % Uh error 0.56186 36.84686 0.015248519 0.56186 0 0.222711 1540.807 343.958 1884.765 1196.849 22.32323144 0.634088 54.20558 0.011697836 0.634088 0 0.21085 1249.518 263.8655 1513.383 985.652 21.1173903 0.675528 63.90796 0.010570326 0.675528 0 0.195167 1221.328 238.7125 1460.04 982.6152 19.54532509 0.72498 75.316 0.009625843 0.72498 0 0.17603 1235.329 217.7804 1453.109 1017.548 17.62934325 0.779432 87.77018 0.008880374 0.779432 0 0.158626 1267.247 201.3332 1468.58 1065.914 15.88744485 0.824944 98.41694 0.008382135 0.824944 0 0.152315 1246.768 190.1889 1436.957 1056.579 15.25455156 0.893472 114.1471 0.007827375 0.893472 0 0.140145 1267.694 177.9373 1445.631 1089.757 14.03629855 0.946548 126.0547 0.007509026 0.946548 0 0.129612 1317.435 171.0418 1488.477 1146.393 12.98294103 0.983012 132.4189 0.007423505 0.983012 0 0.10738 1579.209 169.9802 1749.189 1409.228 10.76363391 1.016968 139.2276 0.007304359 1.016968 0 0.098915 1691.415 167.7622 1859.177 1523.653 9.918450984 1.069692 151.3645 0.007066993 1.069692 0 0.097332 1664.039 162.3902 1826.429 1501.649 9.758798083 1.114128 161.4976 0.006898728 1.114128 0 0.095625 1654.471 158.6197 1813.09 1495.851 9.587340921 1.155392 171.4096 0.006740534 1.155392 0 0.096828 1595.771 154.8886 1750.66 1440.883 9.706189924 1.26786 202.3494 0.006265698 1.26786 0 0.125794 1132.825 142.6788 1275.504 990.146 12.59495592 1.32998 217.2458 0.006122004 1.32998 0 0.126458 1100.931 139.3847 1240.316 961.5467 12.66061463 1.384836 228.2566 0.006067013 1.384836 0 0.112212 1233.7 138.6387 1372.338 1095.061 11.23763753

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118 Appendix C: (continued) Table O: 0.4% wt Nanofluid Heat Transf er Coefficient Error Bar Calculations 0.4% wt Nanofluid U Te Te U Te/ Te UTs UTsatUq"/q" h Uh Uh + Uh % Uh error 0.55806 35.65398 0.015652 0.55806 0 0.207506 1699.25 353.6062 2052.856 1345.643 20.80955293 0.606 46.75595 0.012961 0.606 0 0.183754 1592.173 293.2945 1885.468 1298.878 18.42102218 0.6466 56.11847 0.011522 0.6466 0 0.168418 1546.742 261.1087 1807.851 1285.633 16.88120567 0.687256 65.30838 0.010523 0.687256 0 0.152507 1563.176 238.9629 1802.139 1324.213 15.28701236 0.726296 73.76137 0.009847 0.726296 0 0.134586 1662.144 224.2988 1886.443 1437.846 13.49454399 0.772172 84.25259 0.009165 0.772172 0 0.127345 1637.309 209.0419 1846.351 1428.267 12.76740411 0.813196 93.23185 0.008722 0.813196 0 0.117356 1694.536 199.4128 1893.949 1495.123 11.76799139 0.843788 100.006 0.008437 0.843788 0 0.112189 1716.886 193.1593 1910.045 1523.726 11.25056534 0.885648 109.0666 0.00812 0.885648 0 0.104578 1775.046 186.1898 1961.236 1588.856 10.48929351 0.91016 113.5944 0.008012 0.91016 0 0.095428 1925.419 184.3846 2109.804 1741.035 9.576335638 0.943752 121.184 0.007788 0.943752 0 0.093591 1909.544 179.3345 2088.878 1730.209 9.391482224 0.977648 127.9946 0.007638 0.977648 0 0.08735 2011.855 176.4053 2188.26 1835.449 8.76829208 1.008764 135.4189 0.007449 1.008764 0 0.088192 1942.704 171.9408 2114.644 1770.763 8.850592601 1.050096 144.1538 0.007285 1.050096 0 0.083977 1998.935 168.4959 2167.431 1830.439 8.429283924 1.094976 154.4117 0.007091 1.094976 0 0.083238 1963.932 164.066 2127.998 1799.866 8.353953062 1.13108 162.2409 0.006972 1.13108 0 0.081057 1984.921 161.4864 2146.407 1823.435 8.135657662

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119 Appendix D: Surface Roughness Images Figure A: Copper Hat after 0.1% wt Nanofluid Experiment

PAGE 131

120 Appendix D: (continued) Figure B: Copper Hat after 0.2% wt Nanofluid Experiment

PAGE 132

121 Appendix D: (continued) Figure C: Copper Hat after 0.3% wt Nanofluid Experiment

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122 Appendix D: (continued) Figure D: Copper Hat after 0.4% wt Nanofluid Experiment

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123 Appendix E: COMSOL Thermal Resistance Data Table P: Thermal Resistance Data from COMSOL A COMSOL q q" top boundary bottomboundary Atop A bottom T top T bottom RthCOMSOL 0.000113 15 132629.1192 0.100247 0.036278 0.000314 0.000113 319.0961 320.7679 0.111455617 0.000113 20 176838.8257 0.102247 0.037061 0.000314 0.000113 325.4623 327.6912 0.111443828 0.000113 25 221048.5321 0.104247 0.037844 0.000314 0.000113 331.8285 334.6144 0.111436754 0.000113 30 265258.2385 0.106247 0.038627 0.000314 0.000113 338.1947 341.5377 0.111432039 0.000113 35 309467.9449 0.108247 0.03941 0.000314 0.000113 344.5609 348.4609 0.11142867 0.000113 40 353677.6513 0.110247 0.040193 0.000314 0.000113 350.9271 355.3841 0.111426144 0.000113 45 397887.3577 0.112247 0.040976 0.000314 0.000113 357.2933 362.3074 0.111424179 0.000113 50 442097.0641 0.114247 0.041759 0.000314 0.000113 363.6595 369.2306 0.111422607 0.000113 55 486306.7706 0.116247 0.042542 0.000314 0.000113 370.0257 376.1539 0.111421321 0.000113 60 530516.477 0.118247 0.043326 0.000314 0.000113 376.3919 383.0859 0.111567615 0.000113 65 574726.1834 0.120247 0.044109 0.000314 0.000113 382.7581 390.0092 0.111555372 0.000113 70 618935.8898 0.122247 0.044892 0.000314 0.000113 389.1243 396.9324 0.111544879 0.000113 75 663145.5962 0.124247 0.045675 0.000314 0.000113 395.4905 403.8557 0.111535784 0.000113 80 707355.3026 0.126247 0.046458 0.000314 0.000113 401.8567 410.7789 0.111527826 0.000113 85 751565.009 0.128247 0.047241 0.000314 0.000113 408.2229 417.7021 0.111520805 0.000113 90 795774.7155 0.130247 0.048024 0.000314 0.000113 414.5891 424.6254 0.111514563 0.000113 95 839984.4219 0.132247 0.048807 0.000314 0.000113 420.9553 431.5486 0.111508979 0.000113 100 884194.1283 0.134247 0.04959 0.000314 0.000113 427.3215 438.4719 0.111503953

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124 Appendix E: (continued) Table P: (continued) A COMSOL q q" top boundary bottomboundary Atop A bottom T top T bottom RthCOMSOL 0.000113 105 928403.8347 0.136247 0.050373 0.000314 0.000113 433.6877 445.3951 0.111499406 0.000113 110 972613.5411 0.138247 0.051156 0.000314 0.000113 440.0539 452.3183 0.111495272 0.000113 115 1016823.248 0.140247 0.051939 0.000314 0.000113 446.4201 459.2416 0.111491498 0.000113 120 1061032.954 0.142247 0.052723 0.000314 0.000113 452.7863 466.1737 0.11156172 0.000113 125 1105242.66 0.144246 0.053506 0.000314 0.000113 459.1493 473.0969 0.111581055 0.000113 130 1149452.367 0.146246 0.054289 0.000314 0.000113 465.5155 480.0202 0.111574417 0.000113 135 1193662.073 0.148246 0.055072 0.000314 0.000113 471.8817 486.9434 0.11156827 0.000113 140 1237871.78 0.150246 0.055855 0.000314 0.000113 478.2479 493.8666 0.111562563 0.000113 145 1282081.486 0.152246 0.056638 0.000314 0.000113 484.6141 500.7899 0.111557249 0.000113 150 1326291.192 0.154246 0.057421 0.000314 0.000113 490.9803 507.7131 0.111552289

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125 Appendix F: Heat Flux Curv es for All Data Points Figure E: Heat Flux De-Ionized Water All Data Points Figure F: Heat Flux 0.1% wt Nanofluid All Data Points

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126 Appendix F: (continued) Figure G: Heat Flux 0.2% wt Nanofluid All Data Points Figure H: Heat Flux 0.3% wt Nanofluid All Data Points

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127 Appendix F: (continued) Figure I: Heat Flux 0.4% wt Nanofluid All Data Points

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128 Appendix G: Heat Transfer Coeffici ent Curves for All Data Points Figure J: Heat Transfer Coefficien t De-Ionized Water All Data Points Figure K: Heat Transfer Coefficien t 0.1% wt Nanofluid All Data Points

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129 Appendix G: (continued) Figure L: Heat Transfer Coefficien t 0.2% wt Nanofluid All Data Points Figure M: Heat Transfer Coefficien t 0.3% wt Nanofluid All Data Points

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130 Appendix G: (continued) Figure N: Heat Transfer Coefficien t 0.4% wt Nanofluid All Data Points


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Rice, Elliott Charles.
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Sub-cooled pool boiling enhancement with nanofluids
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by Elliott Charles Rice.
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[Tampa, Fla] :
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2011.
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ABSTRACT: Phase-change heat transfer is an important process used in many engineering thermal designs. Boiling is an important phase change phenomena as it is a common heat transfer process in many thermal systems. Phase change processes are critical to thermodynamic cycles as most closed loop systems have an evaporator, in which the phase change process occurs. There are many applications/processes in which engineers employ the advantages of boiling heat transfer, as they seek to improve heat transfer performance. Recent research efforts have experimentally shown that nanofluids can have significantly better heat transfer properties than those of the pure base fluids, such as water. The objective of this study is to improve the boiling curve of de-ionized water by adding aluminum oxide nanoparticles in 0.1%, 0.2%, 0.3% and 0.4% wt concentrations in a sub-cooled pool boiling apparatus. Enhancement to the boiling curve can be quantified in two ways: (i) the similar heat fluxes of de-ionized water at smaller excess temperature, indicating similar quantity of heat removal at lower temperatures and (ii) greater heat fluxes than de-ionized water at similar excess temperatures indicating better heat transfer at similar excess temperatures. In the same fashion, the secondary objective is to increase the convective heat transfer coefficient due to boiling by adding different concentrations of aluminum oxide nanoparticles.
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Advisor:
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Heat Transfer Coefficient
Nanoparticles
Phase Change
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