Effect of CuO Nanoparticle Concentration

on R134a/Lubricant Pool Boiling Heat

Transfer with Extensive Analysis

Mark A. Kedzierski

NISTIR 7450
2
3

NISTIR 7450

Effect of CuO Nanoparticle Concentration on

R134a/Lubricant Pool Boiling Heat Transfer

with Extensive Analysis

Mark A. Kedzierski

U.S DEPARTMENT OF COMMERCE

National Institute of Standard and Technology

Building Environment Division

Building and Fire Research Laboratory

Gaithersburg, MD 20899-8631

September 2007

U.S. Department of Commerce

Carlos M. Gutierrez, Secretary

National Institute of Standards and Technology

James M. Turner, Acting Director
4

Effect of CuO Nanoparticle Concentration on R134a/Lubricant Pool 

Boiling Heat Transfer with Extensive Analysis 

M. A. Kedzierski 

National Institute of Standards and Technology 

Bldg. 226, Rm B114 

Gaithersburg, MD 20899 

Phone: (301) 975-5282 

Fax: (301) 975-8973 

ABSTRACT 

This paper quantifies the influence of copper (II) oxide (CuO) nanoparticle concentration on 

the boiling performance of R134a/polyolester mixtures on a roughened, horizontal flat 

surface. Nanofluids are liquids that contain dispersed nano-size particles. Two lubricant 

based nanofluids (nanolubricants) were made with a synthetic polyolester and 30 nm 

diameter CuO particles to a 4 % and a 2 % volume fraction, respectively. As reported in a 

previous study for the 4 % volume fraction nanolubricant, a 0.5 % nanolubricant mass 

fraction with R134a resulted in a heat transfer enhancement relative to the heat transfer of 

pure R134a/polyolester (99.5/0.5) of between 50 % and 275 %. The same study had shown 

that increasing the mass fraction of the 4 % volume fraction nanolubricant resulted in 

smaller, but significant, boiling heat transfer enhancements. The present study shows that 

use of a nanolubricant with half the concentration of CuO nanoparticles (2 % by volume) 

resulted in either no improvement or boiling heat transfer degradations with respect to the 

R134a/polyolester mixtures without nanoparticles. Consequently, significant 

refrigerant/lubricant boiling heat transfer enhancements are possible with nanoparticles; 

however, the nanoparticle concentration is an important determining factor. Further research 

with nanolubricants and refrigerants are required to establish a fundamental understanding of 

the mechanisms that control nanofluid heat transfer. 

Keywords: additives, boiling, copper (II) oxide, enhanced heat transfer, nanotechnology, 

refrigerants, refrigerant/lubricant mixtures 
5

TABLE OF CONTENTS 

ABSTRACT............................................................................................................................. 4

Table of Contents .................................................................................................................... 5

LIST of figures ........................................................................................................................ 5

List of tables............................................................................................................................. 5

INTRODUCTION................................................................................................................... 7

APPARATUS .......................................................................................................................... 8

TEST SURFACE .................................................................................................................... 8

MEASUREMENTS AND UNCERTAINTIES .................................................................... 8

EXPERIMENTAL RESULTS............................................................................................... 9

DISCUSSION ........................................................................................................................ 12

CONCLUSIONS ................................................................................................................... 14

ACKNOWLEDGEMENTS ................................................................................................. 14

NOMENCLATURE.............................................................................................................. 15

English Symbols ................................................................................................................. 15 

Greek symbols .................................................................................................................... 15 

English Subscripts............................................................................................................... 15 

REFERENCES...................................................................................................................... 16

LIST OF FIGURES 

Fig. 1 Schematic of test apparatus...................................................................................... 24

Fig. 2 OFHC copper flat test plate with cross-hatched surface and thermocouple 

coordinate system...................................................................................................... 25

Fig. 3 R134a/RL68H with 2 % volume CuO nanoparticle mixtures boiling curves for 

plain surface .............................................................................................................. 26

Fig. 4 R134a/RL68H with 4 % volume CuO nanoparticle mixtures boiling curves for 

plain surface (Kedzierski and Gong, 2007) ............................................................ 27

Fig. 5 R134a/RL68H mixtures boiling curves for plain surface (Kedzierski and Gong, 

2007) ........................................................................................................................... 28

Fig. 6 Heat flux of R134a/RL68H mixtures with CuO nanoparticles relative to that of 

R134a/RL68H mixtures without CuO nanoparticles for the 99.5/0.5 composition

..................................................................................................................................... 29

Fig. 7 Heat flux of R134a/RL68H mixtures with CuO nanoparticles relative to that of 

R134a/RL68H mixtures without CuO nanoparticles for the 99/1 composition .. 30

Fig. 8 Heat flux of R134a/RL68H mixtures with CuO nanoparticles relative to that of 

R134a/RL68H mixtures without CuO nanoparticles for the 98/2 composition .. 31

Fig. A.1 Expanded relative uncertainty in the heat flux of the surface at the 95 % 

confidence level.......................................................................................................... 32

Fig. A.2 Expanded uncertainty in the temperature of the surface at the 95 % confidence 

level............................................................................................................................. 33

LIST OF TABLES 

Table 1 Conduction model choice....................................................................................... 17
6

Table 2 Pool boiling data..................................................................................................... 18

Table 3 Number of test days and data points.................................................................... 22

Table 4 Estimated parameters for cubic boiling curve fits for plain copper surface.... 22

Table 5 Residual standard deviation of ΔTs ...................................................................... 23

Table 6 Average magnitude of 95 % multi-use confidence interval for mean Tw -Ts (K)

..................................................................................................................................... 23
7

INTRODUCTION 

In recent years, the U.S. National Nanotechnology Initiative (NNI) has, in large part, driven 

the deluge of heat transfer property investigations of liquids with dispersed nano-size 

particles called nanofluids. Much of the justification for nanofluids heat transfer research 

rests on the potential improvement in the thermal conductivity of the fluids due to 

nanoparticles. For example, Eastman et al. (2001) found that more than a 40 % increase in 

the thermal conductivity of a liquid could be achieved by adding nanoparticles to a volume 

fraction of approximately 0.4 %. Most nanofluid boiling heat transfer studies have been 

conducted with water based nanofluids (Bang and Chang (2004), Wen and Ding (2005), and 

You et al. (2003)). Although, You et al. (2003) and Bang and Chang (2004) did not observe 

a pool-boiling enhancement with water-based nanofluids, Wen and Ding (2005) did. 

Kedzierski and Gong (2007) also obtained a boiling heat transfer enhancement with 

nanofluids for refrigerant/lubricant mixtures by using a lubricant-based nanofluid 

(nanolubricant). The study obtained between 50 % and 275 % improvement in the boiling 

heat transfer with a nanolubricant where 4 % of volume was occupied by 30 nm diameter 

CuO nanoparticles. Not much is presently known about how the material of the particles, 

their shape, size, distribution, and concentration affect refrigerant/lubricant boiling 

performance. Consequently, this study is a first step toward the understanding of how one of 

the aforementioned parameters influence heat transfer: nanoparticle concentration. 

In order to investigate the influence of nanoparticle concentration on refrigerant/lubricant 

pool boiling, the boiling heat transfer of three R134a/nanolubricant mixtures on a roughened, 

horizontal flat (plain), copper surface was measured. A commercial polyolester lubricant 

(RL68H1

) with a nominal kinematic viscosity of 72.3 μm

2

/s at 313.15 K was the base lubricant 

that was mixed with nominally 30 nm diameter copper (II) oxide (CuO) nanoparticles. Copper 

(II) oxide (79.55 g/mol) has many commercial applications including use as an optical glasspolishing agent. A manufacturer used a proprietary surfactant at a mass between 5 % and 

15 % of the mass of the CuO as a dispersant for the RL68H/CuO mixture (nanolubricant). 

The manufacturer made the mixture such that 40 % of the volume was CuO particles. The 

mixture was diluted at NIST to a 2 % volume fraction of CuO by adding neat RL68H and 

ultrasonically mixing the solution for approximately 24 h. The particle size and dispersion 

were verified by a light scattering technique and were found to be approximately 35 nm and 

well dispersed with little particle agglomeration (Sung, 2006). The RL68H/CuO (98/2)2

volume fraction mixture, a.k.a. RL68H2Cu, was mixed with pure R134a to obtain three 

R134a/RL68H2Cu mixtures at nominally 0.5 %, 1 %, and 2 % mass fractions for the boiling 

tests. The present measurements were compared to measurements from Kedzierski and 

Gong (2007) that were obtained with an identical experimental method with the exception 

that the volume fraction of the nanolubricant was 4 % (R134a/RL68H4Cu) rather than 2 %. 

In addition, the boiling heat transfer of three R134a/RL68H mixtures (0.5 %, 1 %, and 2 % 

 

1

 Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the 

experimental procedure adequately. Such identification is not intended to imply recommendation or 

endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the 

materials or equipment identified are necessarily the best available for the purpose. 2

 The equivalent mixture is RL68H/CuO (95.6/4.4) in terms of mass. 
8

mass fractions), without nanoparticles, was obtained from the previous study to serve as a 

baseline for comparison. 

APPARATUS 

Figure 1 shows a schematic of the apparatus that was used to measure the pool boiling data 

of this study. More specifically, the apparatus was used to measure the liquid saturation 

temperature (Ts), the average pool-boiling heat flux (q"), and the wall temperature (Tw) of the 

test surface. The three principal components of the apparatus were the test chamber, the 

condenser, and the purger. The internal dimensions of the test chamber were 25.4 mm × 

257 mm × 1.54 m. The test chamber was charged with approximately 7 kg of refrigerant, 

giving a liquid height of approximately 80 mm above the test surface. As shown in Fig. 1, the 

test section was visible through two opposing, flat 150 mm × 200 mm quartz windows. The 

bottom of the test surface was heated with high velocity (2.5 m/s) water flow. The vapor 

produced by liquid boiling on the test surface was condensed by the brine-cooled, shell-andtube condenser and returned as liquid to the pool by gravity. Further details of the test 

apparatus can be found in Kedzierski (2002) and Kedzierski (2001). 

TEST SURFACE 

Figure 2 shows the oxygen-free high-conductivity (OFHC) copper flat test plate used in this 

study. The test plate was machined out of a single piece of OFHC copper by electric 

discharge machining (EDM). A tub grinder was used to finish the heat transfer surface of the 

test plate with a crosshatch pattern. Average roughness measurements were used to estimate 

the range of average cavity radii for the surface to be between 12 μm and 35 μm. The 

relative standard uncertainty of the cavity measurements were approximately ± 12 %. 

Further information on the surface characterization can be found in Kedzierski (2001). 

MEASUREMENTS AND UNCERTAINTIES 

The standard uncertainty (ui) is the positive square root of the estimated variance ui

2

. The 

individual standard uncertainties are combined to obtain the expanded uncertainty (U), which 

is calculated from the law of propagation of uncertainty with a coverage factor. All 

measurement uncertainties are reported at the 95 % confidence level except where specified 

otherwise. For the sake of brevity, only an outline of the basic measurements and 

uncertainties is given below. Complete detail on the heat transfer measurement techniques 

and uncertainties can be found in Kedzierski (2000) and Appendix A, respectively. 

All of the copper-constantan thermocouples and the data acquisition system were calibrated 

against a glass-rod standard platinum resistance thermometer (SPRT) and a reference voltage 

to a residual standard deviation of 0.005 K. Considering the fluctuations in the saturation 

temperature during the test and the standard uncertainties in the calibration, the expanded 

uncertainty of the average saturation temperature was no greater than 0.04 K. Consequently, 

it is believed that the expanded uncertainty of the temperature measurements was less than 

0.1 K. 

Twenty 0.5 mm diameter thermocouples were force fitted into the wells of the side of the test 

plate shown in Fig. 2. The heat flux and the wall temperature were obtained by regressing 

the measured temperature distribution of the block to the governing two-dimensional 
9

conduction equation (Laplace equation). In other words, rather than using the boundary 

conditions to solve for the interior temperatures, the interior temperatures were used to solve 

for the boundary conditions following a backward stepwise procedure given in Kedzierski 

(1995)3

. Fourier's law and the fitted constants from the Laplace equation were used to 

calculate the average heat flux (q") normal to and evaluated at the heat transfer surface based 

on its projected area. The average wall temperature (Tw) was calculated by integrating the 

local wall temperature (T). The wall superheat was calculated from Tw and the measured 

temperature of the saturated liquid (Ts). Considering this, the relative expanded uncertainty in 

the heat flux (Uq") was greatest at the lowest heat fluxes, approaching 20 % of the 

measurement near 10 kW/m2

. In general, the Uq" remained approximately within 6 % for 

heat fluxes greater than 40 kW/m2. The average random error in the wall superheat (UTw) 

was between 0.05 K and 0.2 K. Plots of Uq" and UTw versus heat flux can be found in 

Appendix A. 

EXPERIMENTAL RESULTS 

The heat flux was varied approximately between 10 kW/m2

 and 120 kW/m2 to simulate a 

range of possible operating conditions for R134a chillers. All pool-boiling tests were taken 

at 277.6 K saturated conditions. The data were recorded consecutively starting at the largest 

heat flux and descending in intervals of approximately 4 kW/m2. The descending heat flux 

procedure minimized the possibility of any hysteresis effects on the data, which would have 

made the data sensitive to the initial operating conditions. Table 2 presents the measured 

heat flux and wall superheat for all the data of this study. Table 3 gives the number of test 

days and data points for each fluid. 

The mixtures were prepared by charging the test chamber (see Fig. 1) with pure R134a to a 

known mass. Next, a measured mass of nanolubricant or lubricant was injected with a 

syringe through a port in the test chamber. The refrigerant/lubricant solution was mixed by 

flushing pure refrigerant through the same port where the lubricant was injected. All 

compositions were determined from the masses of the charged components and are given on 

a mass fraction basis. The maximum uncertainty of the composition measurement is 

approximately 0.02 %, e.g., the range of a 2.0 % composition is between 1.98 % and 2.02 %. 

Nominal or target mass compositions are used in the discussion. For example, the “actual” 

mass composition of the RL68H2Cu in the R134a/ RL68H2Cu (99.5/0.5) mixture was 

0.51 % ± 0.02 %. Likewise, the RL68H2Cu mass fractions for R134a/ RL68H2Cu (99/1) 

and the R134a/ RL68H2Cu (98/2) mixtures were 0.99 % ± 0.02 % and 2.00 % ± 0.02 %, 

respectively. 

The effect of mass fraction on R134a/RL68H2Cu pool boiling for the 2 % volume fraction 

nanolubricant (RL68H2Cu) is shown in Fig. 3. Figure 3 is a plot of the measured heat flux 

(q") versus the measured wall superheat (Tw - Ts) for the R134a/RL68H2Cu mixtures at a 

saturation temperature of 277.6 K. The solid lines shown in Fig. 3 are cubic best-fit 

regressions or estimated means of the data. Five of the 243 measurements were removed 

before fitting because they were identified as “outliers” based on having both high influence 

and high-leverage (Belsley et al., 1980). Table 4 gives the constants for the cubic regression 

 

3

 For the record, Table 1 provides functional forms of the Laplace equation that were used in this study in the 

same way as was done in Kedzierski (1995) and in similar studies by this author. 
10

of the superheat versus the heat flux for all of the fluids tested here. The residual standard 

deviation of the regressions - representing the proximity of the data to the mean - are given in 

Table 5. The dashed lines to either side of the mean represent the lower and upper 95 % 

simultaneous (multiple-use) confidence intervals for the mean. From the confidence 

intervals, the expanded uncertainty of the estimated mean wall superheat was on average 

approximately 0.27 K. Table 6 provides the average magnitude of 95 % multi-use 

confidence interval for the fitted wall superheat for all of the test data. 

Figure 3 shows that the means of the R134a/RL68H2Cu (99/1) and the R134a/RL68H2Cu 

(98/2) superheat measurements are within approximately 1 K for the entire heat flux range 

that was tested. For heat fluxes less than approximately 75 kW/m2

, the R134a/RL68H2Cu 

(99/1) mixture mean superheat is less than that of the R134a/RL68H2Cu (98/2) mixture. For 

heat fluxes larger than 75 kW/m2

, the R134a/RL68H2Cu (98/2) mixture exhibits the unusual 

characteristic of having an enhanced boiling performance as compared to the 

R134a/RL68H2Cu (99/1) mixture. However, the confidence intervals coincide for heat 

fluxes larger than 75 kW/m2 indicating that no difference can be discerned between the two 

data sets. For most heat fluxes, the R134a/RL68H2Cu (99.5/0.5) superheat measurements, 

represented by the closed triangles, are as much as 4 K less than those of the 99/1 and the 

98/2 mixtures. For comparison, the mean of the pure R134a boiling curve taken from 

Kedzierski and Gong (2007) is provided as a coarsely dashed line. 

The effect of the 4 % volume fraction nanolubricant (RL68H4Cu) mass fraction on R134a/ 

RL68H4Cu pool boiling is shown in Fig. 4. Figure 4 is a plot of the measured heat flux (q") 

versus the measured wall superheat (Tw - Ts) for the R134a/RL68H4Cu mixtures at a 

saturation temperature of 277.6 K taken from Kedzierski and Gong (2007). The means of the 

R134a/RL68H4Cu (99/1) and the R134a/RL68H4Cu (98/2) superheat measurements are 

within approximately 1 K for the entire heat flux range that was tested. For heat fluxes less 

than approximately 30 kW/m2, and greater than approximately 60 kW/m2, the 

R134a/RL68H4Cu (99/1) mixture mean superheat is less than that of the R134a/RL68H4Cu 

(98/2) mixture. For heat fluxes between these limits, the R134a/RL68H4Cu (98/2) mixture 

exhibits the unusual characteristic of having an enhanced boiling performance as compared 

to the R134a/RL68H4Cu (99/1) mixture. For most heat fluxes, the R134a/RL68H4Cu 

(99.5/0.5) superheat measurements, represented by the open triangles, are significantly less 

that those of the 99/1 and the 98/2 mixtures. The average expanded uncertainty of the 

estimated mean wall superheat for the three refrigerant/nanolubricant mixtures was 0.23 K. 

Figure 5 is a plot of the measured heat flux (q") versus the measured wall superheat (Tw - Ts) 

for three R134a/RL68H mixtures at a saturation temperature of 277.6 K taken from 

Kedzierski and Gong (2007). Figure 5 illustrates the effect of the pure lubricant mass 

fraction on R134a/lubricant pool boiling. Comparison of the three mean boiling curves 

shows that the superheats are within approximately 1 K of each other for heat fluxes between 

approximately 30 kW/m2 and 90 kW/m2. For the same heat flux range, the superheat for the 

pure R134a is roughly 3 K less than that for the mixtures translating into a heat transfer 

degradation with respect to R134a. 
11

A more precise examination of the effect of CuO nanoparticle concentration on boiling 

performance, for a given R134a/nanolubricant mass fraction, is given in Figs. 6 through 8. 

Each figure compares the relative performance of the R134a/RL68H4Cu and the 

R134a/RL68H2Cu for one of the target mass fractions. A heat transfer enhancement exists 

where the heat flux ratio is greater than one and the 95 % simultaneous confidence intervals 

(depicted by the shaded regions) do not include the value one. 

Figure 6 plots the ratio of the R134a/RL68H2Cu heat flux to the R134a/RL68H heat flux 

(q"np/q"PL) versus the R134a/RL68H2Cu mixture heat flux (q"CuO) at the same wall superheat 

for the 99.5/0.5 mixture composition. The heat flux ratio varies between roughly 0.73 and 

1.12 for the R134a/RL68H2Cu (99.5/0.5) mixture for heat fluxes between 7 kW/m2

 and 

93 kW/m2

. The R134a/RL68H2Cu (99.5/0.5) mixture shows a maximum heat flux ratio of 

approximately 1.12; however, the maximum resides in a region between 50 kW/m2 and 

93 kW/m2

 where no difference can be established between the two fluids because the 

confidence intervals include the value of one. Overall, the average heat flux ratio for the 

R134a/RL68H2Cu (99.5/0.5) mixture from approximately 7 kW/m2

 to 93 kW/m2 was 0.91. 

In contrast, Fig. 6 shows that a significant boiling heat transfer enhancement over that of the 

R134a/RL68H (99.5/0.5) mixture without nanoparticles is obtained when the nanoparticle 

volume faction of the lubricant is increased from 2 % to 4 %. More specifically, the heat flux 

ratio for the R134a/RL68H4Cu (99.5/0.5) mixture varies between roughly 1.5 and 3.75 for 

heat fluxes between 10 kW/m2 and 110 kW/m2. Overall, the average heat flux ratio for the 

R134a/RL68H4Cu (99.5/0.5) mixture from approximately 8 kW/m2 to 94 kW/m2 was 2.15. 

Consequently, the average heat flux ratio for the 4 % CuO volume fraction mixture was 

nearly 2.4 times larger than that for the 2 % CuO volume fraction mixture for approximately 

the same heat flux range. 

Figure 7 plots the ratio of the R134a/RL68H2Cu heat flux to the R134a/RL68H heat flux 

(q"np/q"PL) versus the R134a/RL68H mixture heat flux (q"PL) at the same wall superheat for 

the 99/1 mixture. The heat flux ratio varies between roughly 0.78 and 0.33 for the 

R134a/RL68H2Cu (99/1) mixture for heat fluxes between 9 kW/m2 and 93 kW/m2. The 

R134a/RL68H2Cu (99/1) mixture shows a maximum heat flux ratio of approximately 0.78 at 

a heat flux of approximately 9 kW/m2

. Overall, the average heat flux ratio for the 

R134a/RL68H2Cu (99/1) mixture from approximately 9 kW/m2

 to 93 kW/m2 was 0.44. In 

contrast, Fig. 7 shows that a significant boiling heat transfer enhancement over that of the 

R134a/RL68H (99/1) mixture without nanoparticles is obtained when the nanoparticle 

volume faction of the lubricant is increased from 2 % to 4 %. More specifically, the heat flux 

ratio for the R134a/RL68H4Cu (99/1) mixture varies between roughly 1.54 and 1.05 for heat 

fluxes between 5 kW/m2

 and 85 kW/m2. Overall, the average heat flux ratio for the 

R134a/RL68H4Cu (99/1) mixture from approximately 5 kW/m2

 to 85 kW/m2 was 1.19. For 

a shared heat flux range between 9 kW/m2 and 85 kW/m2, the average heat flux ratio for the 

4 % CuO volume fraction mixture was approximately 2.6 times larger than that for the 2 % 

CuO volume fraction mixture. 

Figure 8 plots the ratio of the R134a/RL68H2Cu heat flux to the R134a/RL68H heat flux 

(q"np/q"PL) versus the R134a/RL68H mixture heat flux (q"PL) at the same wall superheat for 

the 98/2 mixture. The heat flux ratio varies between roughly 0.88 and 0.33 for the 
12

R134a/RL68H2Cu (98/2) mixture for heat fluxes between 9 kW/m2

 and 76 kW/m2. The 

R134a/RL68H2Cu (98/2) mixture shows a maximum heat flux ratio of approximately 0.88 at 

a heat flux of approximately 13 kW/m2

. Overall, the average heat flux ratio for the 

R134a/RL68H2Cu (98/2) mixture from approximately 9 kW/m2

 to 76 kW/m2 was 0.51. In 

contrast, Fig. 8 shows that when the nanoparticle volume faction of the lubricant is increased 

from 2 % to 4 % a boiling heat transfer enhancement over that of the R134a/RL68H (98/2) 

mixture is obtained for heat fluxes less than approximately 60 kW/m2

. More specifically, the 

heat flux ratio for the R134a/RL68H4Cu (98/2) mixture varies between roughly 1.53 and 

0.70 for heat fluxes between 7 kW/m2

 and 100 kW/m2. Overall, the average heat flux ratio 

for the R134a/RL68H4Cu (98/2) mixture from approximately 9 kW/m2

 to 76 kW/m2 was 

1.17. As a result, the average heat flux ratio for the 4 % CuO volume fraction mixture was 

nearly 2.3 times larger than that for the 2 % CuO volume fraction mixture for approximately 

the same heat flux range. 

 

DISCUSSION 

The heat transfer results summarized in Figs. 6 through 8 show that use of the 2 % CuO 

volume fraction nanolubricant with R134a results in a significantly smaller pool boiling heat 

flux than that exhibited with R134a and the nanolubricant with the 4 % CuO volume fraction. 

Overall, mixtures with the 4 % volume fraction nanolubricant had boiling heat fluxes (for a 

given superheat) that were on average 140 % larger than those for mixtures with the 2 % 

CuO volume fraction nanolubricant. In fact, for most heat fluxes, the 2 % CuO volume 

fraction nanolubricant caused a heat transfer degradation with respect to the R134a/POE 

boiling performance, while the 4 % CuO volume fraction nanolubricant caused an 

enhancement relative to R134a/POE. Kedzierski and Gong (2007) have shown that 

improvement in nanolubricant thermal conductivity appears to be of secondary importance in 

its influence on boiling enhancement. Of primary importance in the enhancement of 

refrigerant/lubricant boiling appears to be the interaction of nanoparticles with bubbles. The 

fact that nanoparticles at a 2 % volume fraction in the lubricant did not provide a boiling heat 

transfer enhancement while a 4 % volume fraction did, may suggest a critical nanoparticle 

volume fraction that is necessary for boiling enhancement. It may be necessary for a 

nanoparticle volume fraction threshold to be exceeded before there is a sufficient number of 

nanoparticles to influence bubble growth and formation. A similar synergistic behavior of 

nanofluids was also seen by Prasher (2006) in his examination of a maximum enhancement 

of nanofluid thermal conductivity being achieved if there were sufficient nanoparticles to 

give an agglomeration rate of approximately 35 %. 

The critical volume fraction of nanoparticles depends on the distribution of the nanoparticles 

among the excess layer, the surface, and the bulk of the boiling refrigerant/nanolubricant 

mixture. For this reason, it is speculated that a boiling enhancement or a boiling degradation 

is realized based on the coupling of the following three heat transfer mechanisms: (1) boiling 

enhancement via nanoparticle interaction with bubbles, (2) improved thermal conductivity of 

the lubricant excess layer by the accumulation of highly conductive nanoparticles, and (3) 

loss of nano-size nucleation sites due to nanoparticle filling of cavities. The last mechanism, 

the loss of nano-size sites, induces a loss in boiling performance. The surface cavities 

become saturated with nanoparticles leaving the remaining particles not trapped by the 

surface to be available for use in the first and second mechanisms. Some volume fraction 
13

greater than what is necessary to attain the saturated surface state is believed to be the critical 

or threshold nanoparticle volume fraction for achieving a boiling enhancement. The second 

mechanism, improved thermal conductivity of the excess layer, may improve boiling or even 

degrade it by the loss of wall superheat due to improved conduction from the surface. The 

first mechanism, the nanoparticle interaction with bubbles, is believed to be the primary 

contributor to improved boiling. 

 

The above discussion brings to light the likelihood that filling of the cavities of the surface 

caused the boiling heat transfer degradation measured for the R134a/RL68H2Cu mixtures of 

this study. Das et al. (2003) have conjectured that the boiling heat transfer degradation that 

they measured for a water-based nanofluid was caused by nanoparticles plugging the surface, 

which caused a decrease in nucleation sites. Their conclusion was drawn from an analogy 

with water deposits that are typically found on surfaces used for boiling water. In order for a 

smoothing of the surface to decrease boiling, it must reduce the number of active boiling 

sites. 

Following the critical radius criterion given by Carey (1992), the calculated range of active 

cavity radii for saturated R134a boiling at the present test conditions is between 50 μm and 

0.1 μm. The Ra roughness of the new, clean test surface before its use was 3.39 μm as 

measured by the NIST Precision Engineering Division (Kedzierski, 2002). In addition, Ra

roughness measurements were also made after boiling tests with a portable contacting stylus 

device while the boiling surface was in the test apparatus. The average Ra roughness of the 

surface after boiling the (98/2) RL68H4Cu mixture, with the test fluid removed from the 

apparatus, and while the nanolubricant excess layer was still on the surface, was 

approximately 2.9 μm. The average Ra roughness of the surface after it was then cleaned 

with acetone was approximately 3.0 μm. A statistical comparison supported the conclusion 

that no difference between the clean and the dirty roughness values could be claimed. In 

addition, given that the original roughness measurement and the measurements made on the 

installed test surface were done with different instruments, there would be little or no 

justification for claiming that Ra roughness has changed as far as the resolution and 

methodology of these instruments are concerned. Consequently, it is speculated that the 

nanoparticles smoothed the surface on the nano-scale without changing the gross roughness 

characteristics because the cavities are an order of magnitude larger than the nanoparticles. 

This suggests that cavities smaller than those predicted by the critical radius criterion for 

R134a are active sites for boiling R134a/lubricant mixtures. 

Future research is required to investigate the influence of the particle material, its shape, size, 

distribution, and concentration on refrigerant boiling performance. Not only should the bulk 

concentration be studied, the distribution of the concentration of the nanoparticles within a 

particular system should be investigated along with the influence of nanoparticles on boiling 

surface roughness. Further investigation into the above effects may lead to a theory that can be 

used to develop nanolubricants that improve boiling heat transfer for the benefit of the 

refrigeration and air-conditioning industry. 
14

CONCLUSIONS 

The influence of CuO nanoparticle concentration on the boiling performance of 

R134a/polyolester mixtures on a roughened, horizontal flat surface was investigated. The 

measurements show that use of the 2 % CuO volume fraction nanolubricant with R134a 

results in a significantly smaller pool boiling heat flux than that exhibited with R134a and the 

nanolubricant with the 4 % CuO volume fraction. Overall, mixtures with the 4 % volume 

fraction nanolubricant had boiling heat fluxes (for a given superheat) that were on average 

140 % larger than those for mixtures with the 2 % volume fraction nanolubricant. It was 

speculated that the 4 % CuO volume fraction was greater than some threshold CuO volume 

fraction resulting in sufficiently more nanoparticles for interaction with bubbles, thus 

resulting in a significant boiling heat tranfer enhancement as compared to boiling without 

nanoparticles. Conversely, the 2 % CuO volume fraction apparently was less than the 

required threshold CuO volume fraction, which resulted in reduced active boiling sites 

causing a corresponding degradation in the boiling heat transfer as compared to boiling 

without nanoparticles. For example, the average heat flux ratio for the R134a/RL68H2Cu 

(99.5/0.5), the R134a/RL68H2Cu (99/1), and the R134a/RL68H2Cu (98/2) mixtures from 

approximately 10 kW/m2

 to 90 kW/m2 was 0.91, 0.44, and 0.51, respectively. 

It was speculated that enhancement or a boiling degradation is realized based on the coupling 

of the following three heat transfer mechanisms: (1) boiling enhancement via nanoparticle 

interaction with bubbles, (2) improved thermal conductivity of lubricant excess layer by the 

accumulation of highly conductive nanoparticles, and (3) loss of nanosize nucleation sites 

due to nanoparticle filling of cavities. The total number of nanoparticles in the test fluid are 

split between those within the nano-size cavities of the surface, those in the lubricant excess 

layer, those on the adiabatic surfaces, and those in the bulk liquid. Nanoparticles not trapped 

on the surfaces are available to interact with bubbles and posssibly induce a boiling 

enhancement. An overall improvement in the boiling heat transfer will result if the 

enhancement due to nanoparticle interactions more than compensates for the boiling heat 

transfer degradation as caused by the filling of boiling cavities with nanoparticles. 

ACKNOWLEDGEMENTS 

This work was funded by NIST. Thanks go to the following NIST personnel for their 

constructive criticism of the first draft of the manuscript: Dr. S. Treado, and Dr. P. 

Domanski. Thanks go to Mr. N. Sawant of the Catholic University of America for his 

constructive criticism of the second draft of the manuscript. Furthermore, the author extends 

appreciation to W. Guthrie and Mr. A. Heckert of the NIST Statistical Engineering Division 

for their consultations on the uncertainty analysis. Boiling heat transfer measurements were 

taken by Mr. David Wilmering of KT Consultants. The RL68H (EMKARATE RL 68H) was 

donated by Dr. S. Randles of Uniqema. The RL68H2Cu was manufactured by Nanophase 

Technologies with a copper (II) oxide and dispersant in RL68H especially for NIST. 
15

NOMENCLATURE 

English Symbols

An regression constant in Table 4 n=0,1,2,3 

Ly length of test surface (Fig. 2), m 

q" average wall heat flux, W m-2 

T temperature, K 

Tw temperature at roughened surface, K 

U expanded uncertainty 

ui standard uncertainty 

X model terms given in Table 2 

Greek symbols

ΔTs wall superheat: Tw - Ts, K 

English Subscripts

CuO R134a/RL68H2Cu or R134a/RL68H4Cu mixture 

L nanolubricant 

PL R134a/RL68H mixture 

q" heat flux 

s saturated state 

Tw wall temperature 

w wall, heat transfer surface 
16

REFERENCES 

Bang, I. C. and, Chang, S. H., 2004, “Boiling Heat Transfer Performance and Phenomena of 

Al2O3–Water Nanofluids From a Plain Surface in a Pool,” Proceedings of ICAPP, Pittsburgh, 

PA, pp. 1437-1443. 

Belsley, D. A., Kuh, E., and Welsch, R. E., 1980, Regression Diagnostics: Identifying 

Influential Data and Sources of Collinearity, New York: Wiley. 

Carey, V. P., 1992, Liquid-Vapor Phase-Change Phenomena, Hemisphere, NY, pp. 186 –

191. 

Das S. K., Putra, N. and Roetzel, W., 2003, “Pool Boiling of Nano-Fluids on Horizontal 

Narrow Tubes,” Int. J. Multiphase Flow, Vol. 29, pp. 1237-1247. 

Eastman, J. A, Choi, S. U. S., Li, S., Yu, W., and Thompson, L. J., 2001, “Anomalously 

Increased Effective Thermal Conductivities of Ethylene Glycol-Based Nanofluids Containing 

Copper Nanoparticles,” Appl. Phys. Lett. 78, p.718-720. 

Kedzierski, M. A, and Gong, M., 2007, “Effect of CuO Nanolubricant on R134a Pool Boiling 

Heat Transfer with Extensive Measurement and Analysis Details,” NISTIR 7454, U.S. 

Department of Commerce, Washington, D.C. 

Kedzierski, M. A., 2002, “Use of Fluorescence to Measure the Lubricant Excess Surface 

Density During Pool Boiling,” Int. J. Refrigeration, Vol. 25, pp.1110-1122. 

Kedzierski, M. A., 2001, “Use of Fluorescence to Measure the Lubricant Excess Surface 

Density During Pool Boiling,” NISTIR 6727, U.S. Department of Commerce, Washington, 

D.C. 

Kedzierski, M. A., 2000, “Enhancement of R123 Pool Boiling by the Addition of 

Hydrocarbons,” Int. J. Refrigeration, Vol. 23, pp. 89-100. 

Kedzierski, M. A., 1995, “Calorimetric and Visual Measurements of R123 Pool Boiling on 

Four Enhanced Surfaces,” NISTIR 5732, U.S. Department of Commerce, Washington, D.C. 

Prasher, R., 2006, “Effect of Colloidal Chemistry on the Thermal Conductivity of 

Nanofluids,” Proceedings of ASME IMECE, IMECE2006-13142. 

Sung, L., 2006, Private Communications, National Institute of Standards and Technology, 

Building and Fire Research Laboratory, Gaithersburg, MD. 

Wen, D, and Ding, Y., 2005, “Experimental Investigation into the Pool Boiling Heat Transfer 

of Aqueous Based γ-Alumina Nanofluids,” J. of Nanoparticle Research, Vol. 7, pp. 265-274. 

You, S. M., Kim, J. H., and Kim, K. H., 2003, “Effect of Nanoparticles on Critical Heat Flux 

of Water in Pool Boiling Heat Transfer,” Applied Physics Letter, 83 (16) pp. 375-377
17

 

Table 1 Conduction model choice 

X0= constant (all models) X1= x X2= y X3= xy 

X4=x

2

-y

2

X5= y(3x2

-y

2

) X6= x(3y2-x

2

) X7= x

4

+y4-6(x2)y2 

 X8= yx

3

-xy

3

R134a/RL68H2Cu 

(99.5/0.5) 

(file: RL2Cu5.dat) 

X1,X2,X4,X6 (95 of 243) 39 % 

X1,X2,X4 (81 of 243) 33 % 

X1,X2 (40 of 243) 17 % 

R134a/RL68H2Cu (99/1) 

(file: RL2Cu1.dat) 

X1,X2,X4,X6 (113 of 291) 39 % 

X1,X2 (27 of 291) 9 % 

X1,X2,X4 (25 of 291) 8 % 

X1,X2,X4,X6,X7 (25 of 291) 8 % 

R134a/RL68H2Cu (98/2) 

(file: RL2Cu2.dat) 

X1,X2,X3,X4,X6 (61 of 208) 29 % 

X1,X2,X3,X4 (49 of 208) 23 % 

X1,X2,X3,X4,X5,X6 (20 of 208) 9 % 

X1,X2 (18 of 208) 8 % 
18

Table 2 Pool boiling data 

 R134a/RL68H2Cu (99.5/0.5) 

File: RL2Cu5.dat 

 

ΔTs

(K) 

q" 

(W/m

2

) 

 12.09 116062. 

 12.14 116834. 

 12.04 118800. 

 11.56 107105. 

 11.51 107384. 

 11.46 106020. 

 11.46 106020. 

 11.11 97450. 

 11.14 95819. 

 10.79 85973. 

 10.82 84869. 

 10.89 84455. 

 10.44 73303. 

 10.42 70473. 

 10.51 69348. 

 10.42 63799. 

 10.37 60320. 

 10.37 59423. 

 9.83 49768. 

 9.80 48415. 

 9.86 48287. 

 9.18 38654. 

 9.23 37767. 

 9.22 36896. 

 8.52 29555. 

 8.50 28838. 

 8.49 28478. 

 7.73 22185. 

 7.75 21891. 

 7.71 21621. 

 6.67 17071. 

 6.59 16619. 

 6.56 16418. 

 5.27 11078. 

 4.97 10280. 

 4.88 9990. 

 3.33 6456. 

 3.23 6391. 

 10.72 122172. 

 10.58 122510. 

 10.43 122798. 

 9.92 113828. 

 9.77 111100. 

 9.71 111160. 

 9.45 104688. 

 9.52 106670. 

 9.47 103176. 

 9.19 90667. 

 9.42 89231. 

 9.55 88706. 

 9.32 76039. 

 9.38 75007. 

 9.43 73995. 

 9.12 63872. 

 9.15 63185. 

 9.20 63089. 

 8.78 52947. 

 8.79 51234. 

 8.84 50086. 

 4.75 12813. 

 4.62 12461. 

 4.55 12027. 

 3.01 7217. 

 2.81 6715. 

 11.63 115191. 

 11.82 114396. 

 11.88 114094. 

 11.50 103751. 

 11.36 103616. 

 11.18 103820. 

 10.63 93622. 

 10.56 93378. 

 10.51 92885. 

 10.18 83975. 

 10.31 84252. 

 10.47 84767. 

 10.02 72126. 

 10.15 71840. 

 10.29 71112. 

 9.90 59313. 

 10.01 58686. 

 10.09 58305. 

 9.55 47596. 

 9.57 46712. 

 9.55 45620. 

 9.01 37906. 

 9.08 36661. 

 9.10 36176. 

 8.39 29238. 

 8.36 28351. 

 8.40 28239. 

 7.71 22604. 

 7.67 22048. 

 7.68 21903. 

 6.55 15368. 

 6.38 14521. 

 6.42 14666. 

 5.11 11589. 

 4.95 11253. 

 4.92 11161. 

 3.11 6405. 

 2.84 3111. 

 11.89 101733. 

 11.79 101684. 

 11.67 101830. 

 11.15 90046. 

 11.04 88356. 

 11.02 89751. 

 10.46 77202. 

 10.48 76672. 

 10.51 76540. 

 10.10 66977. 

 10.16 66239. 

 10.20 65987. 

 9.76 56010. 

 9.80 55725. 

 9.86 54988. 

 9.39 45602. 

 9.46 44515. 

 9.55 44037. 

 9.13 36980. 

 9.38 38216. 

 9.59 39938. 

 8.45 26181. 

 8.45 25627. 

 8.46 25629. 

 7.77 21164. 

 7.73 20620. 

 7.68 20195. 

 6.33 13437. 

 6.29 13235. 

 6.19 12748. 

 4.93 10279. 

 4.66 9801. 

 4.55 9475. 

 2.94 5860. 

 2.91 5683. 

 11.45 91666. 

 11.34 91714. 

 11.23 91753. 

 10.56 77318. 

 10.46 76340. 

 10.52 78265. 

 10.06 68178. 

 10.19 68474. 

 10.23 68618. 

 9.70 56961. 

 9.75 56300. 

 9.82 56231. 

 9.30 45676. 

 9.40 44727. 

 9.53 44110. 

 9.01 36013. 

 9.04 34915. 

 9.09 34495. 

 8.44 26712. 

 8.44 25236. 

 8.42 24810. 

 7.79 20439. 

 7.75 19967. 

 7.71 19668. 

 6.91 14587. 

 6.67 13541. 

 6.73 13864. 

 5.52 11737. 

 5.56 11862. 

 5.54 11668. 

 3.44 6101. 

 3.09 5904. 

 10.60 107879. 

 10.42 108444. 

 10.26 109251. 

 9.75 99210. 

 9.68 98497. 

 9.65 98550. 

 9.30 87155. 

 9.36 86508. 
19

 9.43 86291. 

 9.26 77012. 

 9.44 77827. 

 9.67 80041. 

 9.43 67781. 

 9.58 67154. 

 9.71 66848. 

 9.11 50344. 

 9.21 49092. 

 9.29 48770. 

 8.66 37631. 

 8.65 34900. 

 8.70 34541. 

 8.18 27567. 

 8.19 27088. 

 8.20 26988. 

 7.46 20973. 

 7.42 20448. 

 7.37 19403. 

 6.18 13122. 

 6.00 12482. 

 5.97 12318. 

 4.42 9059. 

 4.17 8734. 

 4.05 8437. 

 3.12 6268. 

 2.98 6060. 

 11.73 117032. 

 11.64 117390. 

 11.50 117533. 

 10.97 108167. 

 10.79 107370. 

 10.60 108217. 

 9.99 95594. 

 9.90 94709. 

 9.87 94455. 

 9.47 83541. 

 9.51 82936. 

 9.59 82942. 

 9.35 72226. 

 9.44 71371. 

 9.54 70749. 

 9.11 56067. 

 9.18 52366. 

 9.28 51504. 

 9.01 43638. 

 9.13 43863. 

 9.24 44304. 

 8.55 34211. 

 8.72 33827. 

 8.73 33382. 

 7.88 24584. 

 7.86 23757. 

 7.81 22969. 

 6.93 17068. 

 6.86 16511. 

 6.88 16540. 

 6.04 12459. 

 5.87 11933. 

 5.90 12034. 

 4.83 10172. 

 4.50 9537. 

 4.45 9405. 

 3.11 6190. 

 2.91 5837. 

 2.87 5708. 

R134a/RL68H2Cu (99/1) 

File: RL2Cu1.dat ΔTs

(K) 

q" 

(W/m

2

) 

15.57 90431. 

15.60 89536. 

15.66 90451. 

15.19 77293. 

15.36 76117. 

15.46 75578. 

14.88 65611. 

14.97 64785. 

15.02 64399. 

14.27 54909. 

14.31 53920. 

14.33 53449. 

13.46 45308. 

13.53 45245. 

13.81 46823. 

12.51 36970. 

12.83 38198. 

13.12 39595. 

11.44 29649. 

11.46 28844. 

11.52 28830. 

10.22 22234. 

10.13 21521. 

10.09 21323. 

9.06 16960. 

8.87 16071. 

8.73 15661. 

7.57 12077. 

7.48 11769. 

7.41 11500. 

5.63 8947. 

5.44 8679. 

5.39 8522. 

3.69 5407. 

3.35 5187. 

13.97 91247. 

14.27 91504. 

14.42 90861. 

13.96 77913. 

14.05 76767. 

14.12 76044. 

13.59 65042. 

13.78 63894. 

13.91 63451. 

13.40 55572. 

13.47 54881. 

13.52 54406. 

12.75 46400. 

12.79 45800. 

12.82 45531. 

11.92 37861. 

11.95 37381. 

11.95 37149. 

10.90 29101. 

11.09 29545. 

11.36 30509. 

9.84 21877. 

9.84 21468. 

9.87 21408. 

8.66 15935. 

8.59 15496. 

8.55 15273. 

7.21 11730. 

7.03 11391. 

7.03 11286. 

5.35 8962. 

5.10 8685. 

5.04 8536. 

3.64 5823. 

3.32 5601. 

13.98 88452. 

14.00 87820. 

14.00 87230. 

13.33 72350. 

13.43 70518. 

13.58 69346. 

13.23 60665. 

13.38 59550. 

13.54 59226. 

13.05 50264. 

13.15 49805. 

13.25 49590. 

12.57 42549. 

12.60 41564. 

12.62 41093. 

11.64 33743. 

11.68 33187. 

11.70 32891. 

10.84 27163. 

10.81 26333. 

10.84 26132. 

9.79 20732. 

9.78 20404. 

9.77 20199. 

8.78 17139. 

8.64 16393. 

8.64 15480. 

7.23 11842. 

7.04 11461. 

6.99 11202. 

5.61 9769. 

5.43 9703. 

5.49 9771. 

3.91 6246. 

3.60 6014. 

15.20 100449. 

15.30 99935. 

15.33 99467. 

14.77 86042. 

14.71 84652. 

14.69 84407. 

14.22 76082. 

14.36 75210. 

14.53 74413. 

14.23 66355. 

14.36 65033. 

14.48 64379. 

14.05 56282. 

14.18 55526. 

14.27 54977. 

13.47 46288. 

13.49 45529. 
20

13.55 45048. 

12.63 37457. 

12.65 36947. 

12.84 37570. 

11.96 31505. 

11.99 31315. 

12.02 31216. 

10.90 24617. 

10.85 24136. 

10.84 23907. 

9.87 19581. 

9.75 19122. 

9.80 19122. 

7.95 14104. 

7.88 13778. 

7.87 13608. 

6.16 9303. 

5.92 10850. 

5.91 10749. 

3.87 6425. 

3.69 6256. 

14.18 97100. 

14.80 95412. 

14.96 94812. 

14.50 80208. 

14.50 79513. 

14.44 79686. 

14.14 70883. 

14.32 70088. 

14.45 68986. 

13.94 58383. 

13.87 55577. 

14.08 55668. 

13.62 49628. 

13.83 50425. 

13.96 50865. 

12.95 41690. 

13.01 41396. 

13.09 41393. 

12.05 34383. 

12.04 33848. 

12.04 33455. 

10.77 25856. 

10.75 25315. 

10.72 24996. 

9.78 20872. 

9.68 20160. 

9.63 19972. 

8.57 15822. 

8.47 15502. 

8.53 15514. 

7.12 12229. 

6.81 10390. 

6.83 10389. 

5.08 9467. 

5.01 9246. 

16.26 97545. 

16.35 97208. 

16.34 96890. 

15.77 85720. 

15.68 85361. 

15.56 85473. 

14.98 76110. 

15.29 74304. 

15.52 73703. 

15.23 64687. 

15.29 63606. 

15.32 63154. 

14.62 55086. 

14.66 54331. 

14.73 54141. 

13.90 45903. 

13.95 45315. 

14.01 44879. 

13.16 37982. 

13.22 37563. 

13.18 37096. 

11.98 29848. 

12.03 29895. 

12.24 30636. 

11.07 24698. 

11.07 24434. 

11.06 24106. 

9.83 19221. 

9.75 18693. 

9.72 18488. 

8.39 14212. 

8.27 14690. 

8.21 14517. 

5.74 10004. 

5.45 9638. 

13.63 111999. 

13.64 110201. 

13.67 110012. 

13.19 99108. 

13.15 98706. 

13.10 98779. 

12.55 88573. 

12.73 87504. 

12.93 86551. 

12.62 76206. 

12.79 74919. 

12.96 73811. 

12.58 64587. 

12.72 63614. 

12.82 63088. 

12.21 53589. 

12.36 52857. 

12.44 51889. 

11.84 44165. 

11.90 43596. 

11.81 42042. 

10.97 34485. 

11.17 34870. 

11.33 35648. 

10.28 28813. 

10.32 28398. 

10.35 28000. 

9.23 22779. 

9.20 22252. 

9.16 21948. 

8.15 17778. 

8.06 17311. 

8.09 17308. 

4.07 6869. 

3.50 6141. 

13.06 113088. 

13.25 113437. 

13.34 113001. 

12.89 101946. 

12.86 101267. 

12.79 101821. 

12.21 92085. 

12.47 89656. 

12.77 88020. 

12.40 76899. 

12.58 75429. 

12.69 74752. 

12.24 66222. 

12.31 65415. 

12.36 65183. 

11.60 55652. 

11.63 54760. 

11.76 55353. 

10.71 44622. 

10.73 37470. 

10.69 36501. 

9.84 30072. 

9.95 29869. 

10.00 29957. 

8.69 22283. 

8.59 21474. 

8.62 21402. 

7.27 15545. 

7.01 14656. 

6.95 14331. 

14.82 104337. 

14.92 104090. 

14.94 104079. 

14.17 93011. 

14.18 92405. 

15.86 98960. 

15.90 99047. 

15.84 99525. 

15.71 98751. 

15.23 91161. 

14.99 86850. 

14.71 80979. 

14.80 75557. 

R134a/RL68H2Cu (98/2) 

File: RL2Cu2.dat ΔTs

(K) 

q" 

(W/m

2

) 

14.90 102684. 

14.87 102593. 

14.95 102828. 

14.24 94679. 

14.32 93683. 

14.42 93407. 

13.86 84447. 

13.97 83987. 

14.13 83943. 

13.55 76976. 

13.59 76293. 

13.57 73727. 

13.03 66472. 

13.03 65734. 

13.15 65565. 

12.38 55631. 

12.25 54820. 

12.24 53255. 

11.67 45974. 
21

11.79 46527. 

12.01 47550. 

11.07 36645. 

11.08 36293. 

11.16 35968. 

10.40 28366. 

10.36 27808. 

10.44 27765. 

9.47 20917. 

9.46 20770. 

9.46 20431. 

8.38 15638. 

8.30 15198. 

8.26 14983. 

6.27 9837. 

6.01 9372. 

16.52 111885. 

16.58 111998. 

16.72 111872. 

16.06 101352. 

16.17 99547. 

16.31 98764. 

15.63 88273. 

15.71 87408. 

15.82 86758. 

15.27 78576. 

15.33 78079. 

15.50 74860. 

14.64 68441. 

14.61 64098. 

14.63 63421. 

13.41 52030. 

13.37 51699. 

13.41 51409. 

12.54 42904. 

12.52 42782. 

12.44 41645. 

11.76 34712. 

11.79 34490. 

12.03 35616. 

10.87 26229. 

10.81 25939. 

10.83 25845. 

9.11 17691. 

8.96 17044. 

8.91 16698. 

7.07 12051. 

6.89 11762. 

6.84 11529. 

4.68 6221. 

4.39 5925. 

14.88 106368. 

15.01 105716. 

15.18 105067. 

14.53 95385. 

14.44 92577. 

14.59 91898. 

14.14 84049. 

14.35 84502. 

14.52 84303. 

13.72 74102. 

13.75 73407. 

13.80 72763. 

12.88 62496. 

12.84 62173. 

12.87 61855. 

12.26 54233. 

12.24 54306. 

12.33 54613. 

11.52 43697. 

11.59 41981. 

11.62 41216. 

10.86 32399. 

10.88 32354. 

10.92 31999. 

9.93 24073. 

9.91 24682. 

9.88 24197. 

8.94 18895. 

8.85 18242. 

8.87 17989. 

7.17 11779. 

6.88 12447. 

6.89 12323. 

4.53 5949. 

4.13 5892. 

15.34 97913. 

15.29 99656. 

15.39 99973. 

14.80 91273. 

14.85 89705. 

14.93 88944. 

13.98 76410. 

13.92 74810. 

13.99 74085. 

13.28 64939. 

13.42 65858. 

13.55 67458. 

12.71 58221. 

12.73 57805. 

12.76 56949. 

11.91 48494. 

11.93 48169. 

11.94 47695. 

11.36 41047. 

11.38 41727. 

11.40 41302. 

10.48 30798. 

10.43 30106. 

10.43 29651. 

9.65 23284. 

9.59 22507. 

9.56 22089. 

8.54 17384. 

8.55 17243. 

8.58 17287. 

7.04 11264. 

6.91 10815. 

6.86 10669. 

4.30 5641. 

4.06 5545. 

14.02 101904. 

14.06 101203. 

14.13 99544. 

13.72 90059. 

13.98 91461. 

14.30 93656. 

13.86 79065. 

14.09 80676. 

14.35 81913. 

13.15 68612. 

13.17 69390. 

13.25 70321. 

12.11 57108. 

12.07 57088. 

12.14 56792. 

11.40 47127. 

11.31 44581. 

10.67 36995. 

10.44 31626. 

10.52 32029. 

10.64 33136. 

9.54 23685. 

9.47 22610. 

9.48 21944. 

8.72 18905. 

8.71 17851. 

8.67 17308. 

7.14 13432. 

7.23 12150. 

7.38 12554. 

5.33 7848. 

5.17 7786. 

14.91 87968. 

15.06 87572. 

15.21 86930. 

14.84 80539. 

14.79 77125. 

14.88 76599. 

14.23 68151. 

14.20 67968. 

14.23 67973. 

13.41 59512. 

13.38 59128. 

13.39 59355. 

12.61 51825. 

12.57 52099. 

12.62 52518. 

11.75 44030. 

11.77 44021. 

11.85 44163. 

10.91 35135. 

10.93 34971. 

10.98 34815. 

10.11 27469. 

10.16 27220. 

10.18 27221. 

9.21 20416. 

9.16 20221. 

9.21 20318. 

7.94 14252. 

7.90 14795. 

7.91 14755. 

6.68 12082. 

6.63 11915. 

6.56 11669. 

4.27 5644. 

4.08 5620. 

3.99 5510. 
22

Table 3 Number of test days and data points 

Fluid (% mass fraction) Number of days Number of data points 

R134a/RL68H2Cu (99.5/0.5) 

4.3 K ≤ ΔTs ≤ 10.2 K 

7 243 

R134a/RL68H2Cu (99/1) 

4.7 K ≤ ΔTs ≤ 11.3 K 

9 291 

R134a/RL68H2Cu (98/2) 

4.1 K ≤ ΔTs ≤ 10.3 K 

6 208 

Table 4 Estimated parameters for cubic boiling curve fits for plain copper surface 

ΔTs = A0 + A1 q” + A2 q”2

 + A3 q”3 

ΔTs in Kelvin and q” in W/m2

Fluid Ao A1 A2 A3

R134a/RL68H2Cu (99.5/0.5) 

4.3 K ≤ ΔTs ≤ 8.5 K 

8.5 K ≤ ΔTs ≤ 10.2 K 

-7.73305x10-1

4.88641 

7.30718x10-4

1.77973x10-4

-1.90704x10-8

-2.19853x10-9

1.65931x10-13

9.84361x10-15

R134a/RL68H2Cu (99/1) 

4.7 K ≤ ΔTs ≤ 9.5 K 

9.5 K ≤ ΔTs ≤ 11.3 K 

-1.35217 

4.66547 

9.95281x10-4

3.07933x10-4

-2.86155x10-8

-3.31686x10-9

3.07861x10-13

1.21057x10-14

R134a/RL68H2Cu (99/2) 

4.1 K ≤ ΔTs ≤ 9.5 K 

9.5 K ≤ ΔTs ≤ 10.3 K 

1.13092 

7.13263 

5.80570x10-4

1.21866x10-4

-5.24572x10-9

-2.70100x10-10

-1.77277x10-13

-1.61515x10-15
23

Table 5 Residual standard deviation of ΔTs

Fluid u (K) 

R134a/RL68H2Cu (99.5/0.5) 

4.3 K ≤ ΔTs ≤ 8.5 K 

8.5 K ≤ ΔTs ≤ 10.2 K 

0.30 

0.53 

R134a/RL68H2Cu (99/1) 

4.7 K ≤ ΔTs ≤ 9.5 K 

9.5 K ≤ ΔTs ≤ 11.3 K 

0.49 

0.88 

R134a/RL68H2Cu (99/2) 

4.1 K ≤ ΔTs ≤ 9.5 K 

9.5 K ≤ ΔTs ≤ 10.3 K 

0.20 

0.53 

Table 6 Average magnitude of 95 % multi-use confidence interval for mean Tw -Ts(K) 

Fluid u (K) 

R134a/RL68H2Cu (99.5/0.5) 

4.3 K ≤ ΔTs ≤ 8.5 K 

8.5 K ≤ ΔTs ≤ 10.2 K 

0.22 

0.26 

R134a/RL68H2Cu (99/1) 

4.7 K ≤ ΔTs ≤ 9.5 K 

9.5 K ≤ ΔTs ≤ 11.3 K 

0.31 

0.37 

R134a/RL68H2Cu (99/2) 

4.1 K ≤ ΔTs ≤ 9.5 K 

9.5 K ≤ ΔTs ≤ 10.3 K 

0.19 

0.28 
24

 

Fig. 1 Schematic of test apparatus 
25

 

Fig. 2 OFHC copper flat test plate with cross-hatched surface and thermocouple 

coordinate system 
26

Fig. 3 R134a/RL68H with 2 % volume CuO nanoparticle mixtures boiling 

curves for plain surface 
27

Fig. 4 R134a/RL68H with 4 % volume CuO nanoparticle mixtures boiling curves for 

plain surface (Kedzierski and Gong, 2007) 
28

Fig. 5 R134a/RL68H mixtures boiling curves for plain surface (Kedzierski and Gong, 

2007) 
29

Fig. 6 Heat flux of R134a/RL68H mixtures with CuO nanoparticles relative to that of 

R134a/RL68H mixtures without CuO nanoparticles for the 99.5/0.5 composition 
30

Fig. 7 Heat flux of R134a/RL68H mixtures with CuO nanoparticles relative to that of 

R134a/RL68H mixtures without CuO nanoparticles for the 99/1 composition 
31

Fig. 8 Heat flux of R134a/RL68H mixtures with CuO nanoparticles relative to that of 

R134a/RL68H mixtures without CuO nanoparticles for the 98/2 composition 
32

APPENDIX A: UNCERTAINTIES 

Figure A.1 shows the relative (percent) uncertainty of the heat flux (Uq") as a function of 

the heat flux. Figure A.2 shows the uncertainty of the wall temperature as a function of 

heat flux. The uncertainties shown in Figs. A.1 and A.2 are "within-run uncertainties." 

These do not include the uncertainties due to "between-run effects" or differences 

observed between tests taken on different days. The "within-run uncertainties" include 

only the random effects and uncertainties associated with one particular test. All other 

uncertainties reported in this study are "between-run uncertainties" which include all 

random effects such as surface past history or seeding. 

 

Fig. A.1 Expanded relative uncertainty in the measured heat flux at the 95 % 

confidence level 
33

 

Fig. A.2 Expanded uncertainty in the temperature of the surface at the 95 % 

confidence level