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NIST Technical Note 1836

Effect of Acoustic Excitation on 

R134a/Al2O3 Nanolubricant Mixture 

Boiling on a Reentrant Cavity 

Surface with Extensive Measurement 

and Analysis Details

Mark A. Kedzierski

Steven E. Fick

http://dx.doi.org/10.6028/NIST.TN.1836
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NIST Technical Note 1836

Effect of Acoustic Excitation on 

R134a/Al2O3 Nanolubricant Mixture 

Boiling on a Reentrant Cavity Surface 

with Extensive Measurement and 

Analysis Details

Mark A. Kedzierski

Energy and Environment Division

Engineering Laboratory

Steven E. Fick

Semiconductor and Dimensional Metrology Division

Physical Measurement Laboratory

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August 2014

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
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Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept 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

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1836

Natl. Inst. Stand. Technol. Tech. Note 1836, 41 pages (August 2014) 

CODEN: NTNOEF

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iii

Effect of Acoustic Excitation on R134a/Al2O3 Nanolubricant Mixture 

Boiling on a Reentrant Cavity Surface with Extensive Measurement 

and Analysis Details

M. A. Kedzierski and S. E. Fick

National Institute of Standards and Technology

Gaithersburg, MD 20899

ABSTRACT

This paper quantifies the influence of acoustic excitation of Al2O3 nanoparticles on the pool 

boiling performance of R134a/polyolester mixtures on a commercial (Turbo-BII-HP) boiling 

surface. A nanolubricant with 10 nm diameter Al2O3 nanoparticles at a 5.1 % volume 

fraction in the base polyolester lubricant was mixed with R134a at a 1 % mass fraction. The 

study showed that high frequency ultrasound at 1 MHz can improve R134a/nanolubricant 

boiling on a reentrant cavity surface by as much as 44 %. This maximum enhancement 

occurred for an applied power level to the fluid of approximately 6 W and a heat flux of 

approximately 6.9 kW/m2. Applied power levels larger and smaller than 6 W resulted in 

smaller boiling heat transfer enhancements. In total, five different applied power levels were

studied: 0 W, 4 W, 6 W, 8 W, and 12 W. The largest and smallest enhancement averaged 

over the tested heat flux range was approximately 12 % and 2 % for applied power levels of 

6 W and 4 W, respectively. In-situ insonation at 1 MHz resulted in an improved dispersion 

of the nanolubricant on the test surface. An existing pool boiling model for

refrigerant/nanolubricant mixtures was modified to include the effect of acoustic excitation. 

For heat fluxes greater than 25 kWm-2, the model was within 4.5 % of the measured heat flux 

ratios for mixtures at all applied power levels. The average agreement between 

measurements and predictions was approximately 1 % for all power levels.

Keywords: acoustics, additives, aluminum oxide, boiling, enhanced heat transfer, 

nanolubricant, nanotechnology, refrigerants, refrigerant/lubricant mixtures, structured 

surface, ultrasound
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iv

TABLE OF CONTENTS

ABSTRACT............................................................................................................................iii

TABLE OF CONTENTS ...................................................................................................... iv

LIST OF FIGURES............................................................................................................... iv

LIST OF TABLES.................................................................................................................. v

INTRODUCTION................................................................................................................... 1

TEST FLUIDS......................................................................................................................... 3

APPARATUS .......................................................................................................................... 3

TEST SURFACE .................................................................................................................... 4

MEASUREMENTS AND UNCERTAINTIES .................................................................... 4

EXPERIMENTAL RESULTS............................................................................................... 5

DISCUSSION/MODEL DEVELOPMENT.......................................................................... 7

CONCLUSIONS ................................................................................................................... 11

ACKNOWLEDGEMENTS ................................................................................................. 11

NOMENCLATURE.............................................................................................................. 12

English Symbols.................................................................................................................... 12

Greek symbols....................................................................................................................... 12

English Subscripts................................................................................................................. 12

REFERENCES...................................................................................................................... 14

APPENDIX A: UNCERTAINTIES .................................................................................... 40

LIST OF FIGURES

Fig. 1 Schematic of test apparatus...................................................................................... 29

Fig. 2 Schematic of ultrasonic piezoelectric transducer showing presumed amplitude 

variations with respect to the boiling surface......................................................... 30

Fig. 3 OFHC copper flat test plate with Turbo-BII-HP surface and thermocouple 

coordinate system...................................................................................................... 31

Fig. 4 Photograph of Turbo-BII-HP surface ..................................................................... 32

Fig. 5 TEM image of Al2O3 nanolubricant (Sarkas, 2009)............................................... 33

Fig. 6 R134a/5AlO (99/1) mixture boiling curves for Turbo-BII-HP and P = 0 W ....... 34

Fig. 7 Effect of extended acoustic excitation on the boiling heat flux of R134a/5ALO 

(99/1) Turbo-BII-HP................................................................................................. 35

Fig. 8 R134a/RL68AlO mixtures boiling curves for Turbo-BII-HP ............................... 36

Fig. 9 Boiling heat flux of R134a/nanolubricant mixtures relative to that of 

R134a/RL68H without nanoparticles for Turbo-BII-HP...................................... 37

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

confidence level.......................................................................................................... 40

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

level............................................................................................................................. 41
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v

LIST OF TABLES

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

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

Table 3 Number of test days and data points.................................................................... 24

Table 4 Estimated parameters for cubic boiling curve fits for Turbo-BII-HP copper 

surface ........................................................................................................................ 25

Table 5 Residual standard deviation of Ts ...................................................................... 26

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

..................................................................................................................................... 27
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1

INTRODUCTION

For nearly a century, it has been known that ultrasound can be used to create bubbles via 

cavitation and/or influence the motion of bubbles or of the fluid for physical or chemical 

benefit (Leong et al., 2011). More recently, the interaction between nanoparticles and 

bubbles have resulted in measureable enhancement of the boiling heat transfer of 

refrigerant/lubricant mixtures (Kedzierski, 2011, 2012a). The enhancement of refrigerant 

boiling with nanolubricants (lubricants with nanoparticles) is mainly due to an exchange of 

momentum between the nanoparticles and the bubbles (Kedzierski, 2011, 2012a). The 

purpose of the present study was to determine if refrigerant/nanolubricant boiling could be 

further enhanced with acoustic excitation based on the premise that the ultrasound would 

contribute to the exchange of momentum between nanoparticles and bubbles. 

There are far more boiling studies of acoustic enhancement of neat fluids like water and 

refrigerant than there are for nanofluids, and even fewer studies with 

refrigerant/nanolubricant mixtures. A review article by Legay et al. (2011) identified sixteen 

acoustically enhanced boiling studies, only two of which were for nanofluids, while no 

refrigerant/nanolubricant studies were cited. In addition, the present authors were unable to 

find another acoustically enhanced refrigerant/nanolubricant boiling study in the open 

literature. Nevertheless, there is good reason to believe that ultrasound can improve

refrigerant/nanolubricant boiling heat transfer. For example, Kim et al. (2004) showed that 

the pool boiling heat flux for FC-72 on a wire can be increased by approximately 133 % by 

exciting the test fluid with 48 kHz from a piezoelectric transducer spaced 10 mm to 40 mm 

below the heated wire. They attributed the enhancement to “the reduction of bubble size 

departing the heating wire and to the acoustic streaming sweeping the bubbles over the 

heating wire.” Riley (1998) showed that acoustic streaming is driven by Reynolds stresses 

that result in stokeslet to jet-like fluid flow. These incompressible flows take place in the 

main body of the fluid and act on all things in it including, presumably, bubbles and 

nanoparticles. Thus, gradients in momentum flux, as caused by acoustic streaming, are likely 

to increase the exchange of momentum between nanoparticles and bubbles resulting in 

improved boiling heat transfer. This line of reasoning is by no means new. For example, 

ultrasound was used in the first half of the last century by Wood and Loomis (1927) to move 

particles in oil in order to induce flocculation.

Bonekamp and Bier (1997) also realized a 90 % increase in the pool boiling heat transfer 

coefficient on a smooth tube for mixtures of R32 and R134a and attributed the improvement 

to “better mixing in the liquid boundary near the heating wall.” They also show that the 

enhancement is not as pronounced for larger boiling heat fluxes due to the absorption of the 

ultrasound in the greater bubble density associated with larger boiling heat fluxes. 

Conversely, Zhou et al. (2002) observed an increase in the boiling heat transfer of acetone 

with increasing heat flux for fixed acoustic excitation. They attribute the boiling 

enhancement to “cavitation bubbles providing nucleated embryos to the boiling surface.” 

Heffington and Glezer (2004) describe an observed 425 % boiling enhancement by 

ultrasound as caused by bubbles being ejected from the surface in a jet-like formation. The 

above research demonstrates both the complexity of the effect and the potential benefit of 

ultrasound on boiling.

 
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Zhou and Liu (2004) have shown that even when nanoparticles, in the absence of lubricant, 

fail to enhance the boiling of fluids, acoustic excitation can enhance the boiling heat transfer 

of an acetone nanofluid by as much as 38 %. Zhou and Liu (2004) claim that the 

enhancement is due in part to the ultrasound keeping the calcium carbonate nanoparticles 

from filling boiling cavities and by activating “smaller vapor embryos.” For boiling of 

refrigerant/nanolubricants, the lubricant excess layer that resides on the boiling surface keeps 

the nanoparticles suspended in Brownian motion and prevents them from filling the boiling 

cavities (Kedzierski, 2011). Consequently, part of the 38 % increase observed by Zhou and 

Liu (2004) would not have been realized had the acetone nanofluid been dilutely mixed with 

lubricant. For this reason, the resulting acoustic enhancement of boiling 

refrigerant/nanolubricants may not be as large as 38 %. Zhou (2004) performed an acetone 

nanofluids study similar to the aforementioned, but with copper nanoparticles rather than 

calcium carbonate. Zhou (2004) observed as much as a 52 % enhancement of the nanofluid 

boiling with ultrasound excitation. 

Studies by Henderson et al. (2010), Bi et al. (2007), Peng et al. (2011), Hu (2013), and 

Kedzierski (2009) have explored the use of nanolubricants as a means for improving 

efficiencies of air-conditioning and refrigeration equipment. For low flow qualities, 

Henderson et al. (2010) have shown that CuO nanoparticles can improve the flow boiling 

heat transfer of refrigerant/lubricant mixtures by as much as 76 % and that the lubricant can 

act as a necessary dispersant. Peng et al. (2010) have shown that diamond nanolubricants 

can improve refrigerant/lubricant pool boiling by as much as 63 %. Similarly, copper-oxide 

nanoparticles have also been shown to improve refrigerant/lubricant pool boiling by as much 

as 245 % (Kedzierski and Gong, 2009). These preceding studies suggest that it is worth

investigating the potential benefits of acoustic excitation of nanolubricants for large 

commercial chillers. 

Consequently, the scope of the present investigation was to determine if 

refrigerant/nanolubricant boiling on a reentrant cavity type surface could be enhanced by 

acoustic excitation. With externally mounted transducers of the type and resonance 

frequencies typically used in ultrasonic cleaners, tens of watts of acoustic excitation can be 

achieved easily. However, for the associated wavelengths (e.g., approximately 12 mm at 

50 kHz), a spherical sound wave would be generated that could not be focused near the test 

section. A uniform plane wave parallel and above the entire length of the test section was 

achieved by choosing a wavelength that was significantly smaller than the diameter of the 

transducer. For this task, a 1 MHz transducer with a nominal circular active area 14 mm in 

diameter, and a near field zone length of 8 cm in the R134a/nanolubricant mixture was 

chosen. The single internal transducer assembly excites a cylindrical volume of fluid parallel 

to the horizontal, flat, copper, Turbo BII HP (commercial) finned surface. For liquid R134a 

at the saturated test temperature, the acoustic wavelength is approximately 0.6 mm.

To achieve the above scope, boiling tests of a R134a/nanolubricant mixture at four 

ultrasound power levels were made on the horizontal, flat, copper, Turbo-BII-HP-finned 

surface. A commercial polyolester lubricant (RL68H)1 with a nominal kinematic viscosity of 

72.3 m2/s at 313.15 K was the base lubricant that was mixed with nominally 10 nm diameter 
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3

Al2O3 nanoparticles. Al2O3 nanoparticles have the advantages of a well-established, successful 

dispersion technology and being relatively inert with respect to lubricated compressor parts.

TEST FLUIDS

A manufacturer used a proprietary surfactant at a mass of 6.2 % of the mass of the Al2O3 as a 

dispersant for the RL68H/Al2O3 mixture (nanolubricant). The manufacturer made the 

mixture such that approximately 24.7 % of the mass was Al2O3 particles. From this mixture, 

a nanolubricant with a 16.7 % mass fraction of Al2O3 was made by adding neat RL68H and 

ultrasonically mixing the solution for approximately 24 h. The 16.7 % Al2O3 mass fraction in 

the nanolubricant corresponds to a volume fraction () of 5.1 %. In this study, the 5.1 %

volume fraction nanolubricant is identified as 5AlO. The R134a/5AlO (99/1) mixture 

produced a nanoparticle surface density of the lubricant excess layer, Nnp/As (Kedzierski, 

2012a), of approximately 2.8 x 1020 m-2. The value of Nnp/As was calculated assuming that 

all of the nanoparticles charged to the test rig reside uniformly distributed on the heat transfer 

surface.

Boiling tests with the refrigerant/5AlO (99/1) mixture with increasing acoustic excitation 

power were designed to determine the effect of ultrasound power level on the boiling. The 

applied power levels (Pa) were nominally 4 W, 6 W, 8 W, and 12 W. In addition, the boiling 

heat transfer of R134a/5AlO (99/1) mixture without acoustic excitation (Pa = 0) was 

measured to serve as a baseline for comparison to the tests with excitation. 

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. Also visible through the 

window, as shown in Fig. 1, is the ultrasonic piezoelectric transducer. Figure 2 shows the 

transducer and its theoretical 8 cm near field zone. The beam axis is parallel to and 

approximately 23 mm above the boiling surface. Further details of the test apparatus can be 

found in Kedzierski (2002) and Kedzierski (2001). 

The ultrasonic transducer was a cylindrical assembly approximately 25 mm in diameter and 

51 mm long. It was attached to the end of a length of nominally 8 mm diameter tubing 

supported by a compression fitting at the top of the test tank. Ultrasonic output was taken 

from the circular active area, nominally 14 mm in diameter, of a disc-shaped piezoelectric 

element that constitutes the end face of the transducer. The piezoelectric element was airbacked, made of Type 4 lead zirconate titanate, and designed to operate at 1 MHz. The 

sinusoidal radiofrequency (RF) drive signal was provided by a 100 W power amplifier driven 
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4

by a function generator set to 1.0 MHz, the measured resonance frequency of the transducer. 

The power level applied to the fluid was measured using an RF power meter interposed in the 

feedline linking the power amplifier and the transducer. The reflected power was also 

measured and used to calculate the net power (Pn) absorbed by the fluid by subtracting it 

from the applied power. The net power was nominally 1.9 W, 3.6 W, 5.4 W, and 7.7 W for 

the 4 W, 6 W, 8 W, and 12 W applied power levels, respectively. The net power along with 

the surface area of the transducer was used to calculate a net acoustic intensity (In) of 

approximately 12.3 kWm-2, 23.4 kWm-2, 35.1 kWm-2, and 50.0 kWm-2for the applied power 

levels 4 W, 6 W, 8 W, and 12 W, respectively. 

TEST SURFACE

Figure 3 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). The internal fins of a commercial 25 mm (outer-diameter) 

Turbo-BII-HP tube were removed by EDM. The tube was then cut axially, annealed, 

flattened, and soldered onto the top of the test plate. Figure 4 shows a photograph of the fin 

surface. The Turbo-BII-HP has approximately 826 fins per meter (fpm) oriented along the short 

axis of the plate. The overall height and tip-width of a fin are 0.76 mm and 0.36 mm, 

respectively.

MEASUREMENTS AND UNCERTAINTIES

The standard uncertainty is the positive square root of the estimated variance. 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 a summary 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.

A Dynamic Light Scattering (DLS) technique was used to measure the average nanoparticle 

size on a number basis. The diameter of most of the particles (Dnp) was approximately 10 nm 

(10.1 nm ± 1.3 nm) and the particles were well dispersed in the lubricant (Kedzierski, 2010). 

Figure 5 shows a Transmission Electron Microscopy (TEM) image of the nanoparticles as 

taken by Sarkas (2009). The image confirms the good dispersion and shows that the particles 

are spherical with most of them having diameters of approximately 10 nm or less and a few 

having diameters close to 50 nm. 

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 estimated that the expanded uncertainty of the temperature measurements was less than 

0.1 K. 
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5

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

plate shown in Fig. 3. The heat flux and the wall temperature were obtained by regressing 

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

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)1. As shown in Fig. 3, the origin of the coordinate system was centered on the surface 

with respect to the y-direction at the heat transfer surface. Centering the origin in the ydirection reduced the uncertainty of the wall heat flux and temperature calculations by 

reducing the number of fitted constants involved in these calculations.

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 9 % of the 

measurement near 10 kW/m2. In general, the Uq" remained approximately between 3 % and 

7 % for heat fluxes greater than 20 kW/m2. The average random error in the wall superheat 

(UTw) remained mainly between 0.06 K and 0.08 K with an average value of approximately 

0.07 K. Plots of Uq" and UTw versus heat flux can be found in Appendix A. 

EXPERIMENTAL RESULTS

The heat flux was varied between approximately 10 kW/m2and 120 kW/m2to simulate a 

range of possible operating conditions for R134a chillers. All pool-boiling measurements 

were made 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. A total of 952 measurements were made 

over 49 days.

The test fluid was prepared by charging the test chamber (see Fig. 1) with pure R134a of 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 lubricant mass fraction (xb) 

measurement is approximately 0.02 %, e.g., the range of a 1.0 % mass fraction is between 

0.98 % and 1.02 %. The nominal or target mass composition is used in the discussion. For 

example, the “actual” mass composition of the RL68H in the R134a/5AlO (99/1) mixture 

was 1.00 % ± 0.02 %. 

 

1 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.
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6

Figure 6 is a plot of the measured boiling heat flux of the refrigerant/nanolubricant (

"

qnp

) 

versus the measured wall superheat (Tw - Ts = Ts) for the Turbo-BII-HP at a saturation 

temperature of 277.6 K for tests with no applied acoustic excitation (Pa = 0 W). The open 

circles represent the measured data while the solid line is a cubic best-fit regression or 

estimated means of the data. Four days of Pa = 0 boiling R134a/5ALO (99/1) produced 194

measurements over a period of approximately one week. Seven of the 194 measurements 

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

high influence and high leverage (Belsley et al., 1980). The data sets for each test fluid 

presented in this manuscript exhibited a similar number of outliers and were regressed in the 

same manner. Table 4 gives the constants for the cubic regression 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 and are, on average, 

approximately 0.05 K. The dashed lines to either side of the mean represent the lower and 

upper 95 % simultaneous (multiple-use) confidence intervals for the mean and are, for the 

most part, concealed by the data symbols. From the confidence intervals, the expanded 

uncertainty of the estimated mean wall superheat was, on average, 0.03 K. Table 6 provides 

the average magnitude of the 95 % multi-use confidence interval for the fitted wall superheat 

for all of the test data.

The Pa = 0 measurements serve as a baseline for comparison to the R134a/5ALO (99/1) 

measurements that are acoustically excited. “Break-in” data had to be taken in order to 

remove the effect of the quality of the nanoparticle dispersion on the boiling heat transfer. 

Kedzierski (2012b) showed that a refrigerant/nanolubricant with well dispersed nanoparticles 

leads to better boiling heat transfer than for mixtures with poorer dispersed nanoparticles. 

This is illustrated in Fig. 6 by comparing the present measurements for R134a/5ALO (99/1)

with no ultrasound excitation to those taken by Kedzierski (2013) for identical test 

conditions: the same test apparatus, the same test surface, and the same test fluid. 

Essentially, the nanoparticle dispersion improved with excitation over eight days of testing. 

As a result of the improving dispersion, the boiling curve moved incrementally to the left

after each test day. The Pa = 0 boiling curve became repeatable, presumably, once the 

quality of the dispersion no longer was improved by acoustic excitation. A clearer 

illustration of the effect of improved nanolubricant dispersion is shown in Fig. 7. Figure 7 

plots the ratio of the R134a/5ALO (99/1) mixture heat flux after extended sonication to the 

heat flux prior to sonication (q"np /q"pe) versus the pre-excitation heat flux (q"pe) at the same 

wall superheat. On average, sonication improved the boiling heat flux by approximately 

15 %.

Figure 8 is a plot of the acoustically excited measured heat flux (q"ae) versus the measured 

wall superheat (Tw - Ts) for the R134a/5ALO (99/1) mixture at a saturation temperature of 

277.6 K for five different acoustically net power levels: 0 W, 1.9 W, 3.6 W, 5.4 W, and 

7.7 W. Comparison of the mean boiling curves shows that the superheats for all the 

excitation levels are within 0.5 K of each other for the entire tested heat flux. For all power 

levels, acoustic excitation has enhanced the pool boiling heat transfer relative to that for no 

acoustic excitation. 
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7

A more precise illustration of the effect of ultrasound on the boiling heat transfer is given in 

Fig. 9. Figure 9 plots the ratio of the heat flux for R134a/5ALO (99/1) with incident 

ultrasound excitation to the heat flux for R134a/5ALO (99/1) without excitation (q"ae/q" np) 

versus the heat flux without excitation (q" np) at the same wall superheat. Figure 9 illustrates 

the influence of ultrasound power on the R134a/5ALO (99/1) boiling curve with solid and 

dashed lines representing the mean heat flux ratios for each mixture and shaded regions 

showing the 95 % multi-use confidence level for each mean. A heat transfer degradation 

exists where the heat flux ratio is less than one and the 95 % simultaneous confidence 

intervals (depicted by the shaded regions) do not include the value one. Figure 9 shows that

no heat transfer degradations were observed as caused by the ultrasound for the entire heat 

flux range for the R134a/5ALO (99/1) mixture. The smallest enhancement occurred for the 

smallest excitation power of 4 W, which had an average heat flux ratio of approximately 1.02 

between the heat fluxes of 20 kW/m2and 95 kW/m2. The largest heat flux ratio, 1.44 ± 0.08, 

occurred for an intermediate net power level of 3.6 W at a heat flux of approximately 

6.9 kW/m2. Averaged over the entire heat flux test range, the enhancement for the 3.6 W net

power level was approximately 12 %. In general, the heat flux ratio for all excitation levels 

was relatively constant for heat fluxes larger than 20 kW/m2. The average measured heat 

flux ratio (q"ae/q" np), between roughly 20 kW/m2and 95 kW/m2, was approximately 1.1, 

1.07, and 1.04 for nominal net power levels of 3.6 W, 5.4 W, and 7.7 W, respectively. 

DISCUSSION/MODEL DEVELOPMENT

The following describes the development of a pool boiling model to predict the combined 

nanoparticle agitation and acoustic streaming effects. The refrigerant/nanolubricant boiling 

theory developed in Kedzierski (2012a) is modified here to predict the acoustic excitation of 

nanoparticles. While a Collier’s (1981) suppression model was used to predict the effect of 

acoustic streaming on the nanolubricant pool boiling:

 

"

" 6 1.17

s

1

1 2.56 10 Re

c

q

S

q



 

 

(1)

Here the suppression factor, S, is the ratio of the boiling heat flux with convection (q”) to that 

without convection. Because the face of the acoustic transducer is perpendicular to the 

boiling surface, the streaming jet-flow induced by the acoustic transducer is parallel to the 

surface. The streaming Reynolds number (Res) is calculated from the net acoustic scattering 

intensity (In) as:

l s l n

s

l l l l

0.175 0.175 2 Re u L L I

c

 

  

 

(2)

Pure liquid refrigerant properties were used to calculate the Reynolds number based on the 

length of the plate (L). The liquid properties of the refrigerant were the density (l), the 

dynamic viscosity (l), and the speed of sound (cl). The 0.175 factor in eq. (2) accounts for

the decay of the centerline streaming velocity (us) to the velocity near surface (ū) of the test 

plate, 20 mm from the jet-centerline. Görtler’s (1942) analysis of a circular jet was used:
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8

2

2

c

1

2

u y

u x





   

           

 (3)

Here, y and x are the perpendicular and parallel coordinate distances, respectively, measured 

from the origin that is located at the center of the face of the transducer. In order to improve 

the fit of eq. (1) to the measurements, a flow constant, , of approximately 6.0 was used 

instead of the traditional value of 13.5 or the more recent value of 11 (Schetz and Fuhs, 

1999). It is likely that the presence of the bubbles in the jet flow resulted in the need for a 

modified . Using the modified  in eq. (3) produced a velocity ratio of 0.175 when y and x

were set to L/2 and 20 mm, respectively.

Sound reaches the top of the plate surface by scattering from the field of bubbles that resides 

above the heat transfer surface. It was assumed that the net scattering intensity (In) was 

proportional to the number of bubbles per unit surface area (Nb/A) as follows:

2

1 l l

n

l

K c Nb

I

A







(4)

where K1 is a dimensionless constant having a value of approximately 0.43. 

Only a portion (f) of the scattered sound makes its way to the boiling cavities of the surface 

because the cavity openings are 1/8th of the surface area, and liquid is sometimes present at 

the cavity opening. When liquid is at the cavity opening, it blocks the sound because the 

0.6 mm wavelength of the sound in liquid is four times larger than the approximately 

0.15 mm wide cavity opening. Only when vapor is at the cavity opening can sound find its 

way to the cavity because its wavelength (0.15 mm) is comparable to the approximate width 

of the cavity opening. Consequently, the scattering intensity is reduced by f in order to 

determine the nanoparticle velocity. The f is grouped with other unknown constants into a 

single constant for the model. 

Equation 5 gives the boiling heat transfer model for refrigerant/aluminum oxide 

nanolubricant mixtures: (Kedzierski, 2012c):

   

9 1 np

" L v b

np s G

" 3/2 " 2

PL np n L np L b

1.45 10 [s m ]

1

(1 )

N

x

q A

q D q g x

 

  

   

 

 

(5)

where 

"

n q

is equal to

"

PL q

normalized by 1

Wm-2

. Properties included in eq. (1) are the 

refrigerant surface tension (), the refrigerant vapor density (v), the neat lubricant liquid 

density (L) and liquid kinematic viscosity (L), and the nanoparticle density (np).

Equation 5 can be applied to a plain, a rectangular-finned, or the Turbo-BII-HP surface by 

choosing the appropriate surface geometry dependent nanoparticle surface density; (Nnp/As)G. 

The (Nnp/As)G for the Turbo-BII-HP is (Kedzierski, 2013):
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9

 

1.47

2.53 np np 8 " 20 "

n n

s s G

4.15 10 1 10 0.00017

N N

q q

A A



 

        

(6)

The model above is based on the assumption that the enhancement is due to surface work on 

bubbles as caused by momentum transfer between growing bubbles and nanoparticles 

suspended within the lubricant excess layer. The Nnp/As is obtained by calculating the entire 

active surface area of the evaporator (As) and dividing it into the total number of 

nanoparticles (Nnp) charged to the evaporator, which can be obtained by assuming a spherical 

particle diameter of an average size. 

The re-derivation of eq. (5) can be tailored to acoustic excitation by starting with its 

fundamental governing equation, which is a momentum balance between a colliding

nanoparticle and bubble:

bi

b np np np

bi b bi

1 1 M N M u

M N M u

 

  

(7)

The masses of the nanoparticle (Mnp) and the bubble before impact (Mbi) are calculated from 

the density of the nanoparticle (np) and the refrigerant vapor (v), respectively, while 

assuming a spherical shape. The change in velocity of the nanoparticle (unp) after impact 

with the bubble can be represented using the coefficient of restitution (e) as done in Beer and 

Johnston (1977):

    u e u u np np b (1 ) 

(8)

Here the bubble velocity is assumed to be constant and to remain unchanged by the impact so 

that the energy exchange is in the form of surface work on the bubble, which is responsible 

for the boiling enhancement. The constant e becomes part of a group of constants that was

determined from the regression of the complete model to the measured boiling heat flux. The 

number of bubbles (Nb) was obtained from eq. (4). 

The ratio of the acoustically enhanced refrigerant/nanolubricant boiling heat flux (

"

ae q

) to the 

refrigerant/nanolubricant boiling heat flux without acoustic enhancement (

"

np q

) is the sum of 

the nanoparticle effects from eq. (7) and the suppression effects from eq. (1):

bi

"

ae b np np np

"

np bi b bi

1 1 q M N M uS S

q M N M u

 

     

(9)

Substitution of the previously described parameters into eq. (9) and regressing with the 

boiling measurements to obtain the constants yields:
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10

 

" 4 2

ae np np np l l L np

" 3 6 1.17

np s v l b L L np L np s

12832 1 0.353

Re Re L 1 2.56 10 Re

q N D c D

q A D c c

  

    

   

              

(10)

where cL is the speed of sound in the lubricant. 

The nanoparticle Reynolds number Renp is calculated as:

L np np L np n

np

L L L L

2

Re

u D D I

c

 

  

 

(11)

The bubble diameter is calculated from the refrigerant/lubricant pool-boiling model of 

Kedzierski (2003): 

a L b L b     b

b v b v

0.75 1 18.75[ ] 1

2

D l x x

x x

 

 

 

 

Å

(12)

where xb is the mass fraction of the lubricant in the bulk mixture, and the la represents the 

thickness of the lubricant excess layer that is removed by a departing bubble and is taken as 

two monolayers, which is approximately 25 Å for lubricant. Equation (12) is valid for xb > 0.

Figure 10 compares the acoustically enhanced refrigerant/nanolubricant boiling predictions 

of eq. (10) to the measured means for the 5AlO (99/1) mixture. For heat fluxes greater than 

25 kWm-2, the predictions are within ± 2 % of the measured means for the 4 W, 6 W, and 

8 W power levels. The average underprediction for the 4 W and 6 W was approximately 

0.8 % and 0.4 %, respectively. The average overpredition for the 8 W power level was 

approximately 0.8 %. The boiling measurements for the 12 W power level were 

underpredicted by no more than 4.5 %, with an average underprediction of 1 %, for heat 

fluxes greater than 25 kWm-2. 

Figure 11 illustrates the effect of the net power level on the acoustically obtained boiling 

enhancement for the R134a/nanolubricant mixture. Equation 10 was used to produce the 

solid line that shows the maximum boiling enhancement occurs near a net power level of 

approximately 3.6 W. According to the model, increases in the net power past 3.6 W caused 

an increasing of the acoustic streaming and corresponding suppression of boiling, which 

leads to a decrease in the boiling enhancement. Net power levels less than 3.6 W have 

sufficiently small streaming velocities to negate the agitating effect of the ultrasound on the 

nanoparticles. Consequently, as the net power is increased from 1.9 W to 3.6 W the 

enhancement also becomes larger. Measured means for a 

"

np q

of approximately 30 kWm-2are 

plotted as circles for the various power levels. The agreement with the model is shown to be 

within 1 % for this condition.

Future research is required to verify the effect of the distance and orientation of the acoustic 

transducer on the acoustic streaming. In addition, the scattering of the sound from the 

bubbles needs to be better understood along with how the sound interacts with the boiling 
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11

surface. This additional work would include direct quantification of f ; the fraction of In that 

reaches the boiling cavities.

CONCLUSIONS

The effect of acoustic excitation on the boiling performance of a R134a/nanolubricant on a 

flattened, horizontal Turbo-BII-HP was investigated. A nanolubricant containing roughly 

10 nm diameter Al2O3 nanoparticles at 5.1 % volume fraction with a polyolester lubricant 

was mixed with R134a to a 1 % mass fraction. The study showed that acoustic excitation can 

improve R134a/nanolubricant boiling on a Turbo-BII-HP, thus demonstrating that the boiling 

performance of reentrant cavity surfaces can be enhanced if a sufficient acoustic excitation is 

provided. For example, applied power levels of 4 W, 6 W, 8 W, and 12 W produced average 

boiling enhancements of 2 %, 10 %, 7 %, and 4 %, respectively. The study also illustrated the 

importance of a having a sufficiently dispersed nanolubricant for improved boiling heat 

transfer.

A model was developed to predict the effects of acoustic excitation on R134a/nanolubricant 

boiling on the Turbo-BII-HP surface. The model accounts for acoustic streaming effects and 

the nanoparticle agitation by the ultrasound. An existing model for predicting 

refrigerant/nanolubricant boiling was modified to model the interaction between the 

nanoparticles and the bubbles, while a suppression factor was used to model the acoustic 

streaming effects. For heat fluxes greater than 25 kWm-2, the model is within 4.5 % of the 

measured heat flux for all acoustic power levels. The average agreement between 

measurements and predictions was approximately 1 % for all power levels. The model 

predicts and optimum power level that is a consequence of the balance between increasing 

nanoparticle-bubble interaction and convective suppression with increasing acoustic power.

ACKNOWLEDGEMENTS

This work was funded by the U.S. Department of Energy (project no. DE-EE0002057/004) 

under Project Managers Antonio Bouza and Bahman Habibzadeh. Thanks go to Junemo Koo 

of Kyung Hee University and to the following NIST personnel for their constructive criticism 

of the draft manuscript: A. Persily, and P. Domanski. Furthermore, the author extends 

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

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

taken by D. Wilmering of Dakota Consulting Inc. at the NIST laboratory. The RL68H 

(EMKARATE RL 68H) was donated by K. Lilje of CPI Engineering Services, Inc. The 

RL68H1AlO was manufactured by Nanophase Technologies with an aluminum oxide and 

dispersant in RL68H especially for NIST. 
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

12

NOMENCLATURE

English Symbols

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

As heat transfer surface area, m

c speed of sound, m·s-1

Dnp nanoparticle diameter, m

e coefficient of restitution

f fraction of In that reaches boiling cavities

In acoustic scattering intensity, W·m-2

L test surface length shown in Fig. 3, m

M mass, kg

Nb number of bubbles

Nnp number of nanoparticles

Nnp/As nanoparticle surface density, m-2

Pa applied power level to acoustic transducer, W

Pn net power level from acoustic transducer, W

q" average wall heat flux, W·m-2

"

n q

"

PL

-2 1W m

q





Res streaming Reynolds number given by eq. (2)

S suppression factor given by eq. (1)

T temperature, K

Tw temperature at roughened surface, K

u velocity, m·s-1

U expanded uncertainty

X model terms given in Table 1

xb bulk lubricant mass fraction

Greek symbols

 flow constant given in eq. (3)

Ts wall superheat: Tw - Ts, K 

 dynamic viscosity, kg·m-1·s-1

 kinematic viscosity, m2·s-1

 surface tension of refrigerant, N·m-1

 density, kg·m-3

 nanoparticle volume fraction

English Subscripts

ae acoustically enhanced/excited

Al R134a/nanolubricant mixture

bi bubble before impact

G surface geometry dependent

l liquid refrigerant

L pure lubricant without nanoparticles

nL nanolubricant 

np nanoparticle/with nanoparticles
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13

p pure R134a

PL refrigerant/pure lubricant (R134a/RL68H) mixture

q" heat flux

s saturated state, streaming

Tw wall temperature

v refrigerant vapor 
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14

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Table 1 Conduction model choice

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

X4=x2-y

2

X5= y(3x2-y

2

) X6= x(3y2-x

2

) X7= x4+y4-6(x2)y2

 X8= yx3-xy3

Fluid Most frequent models

R134a/5AlO (99/1)

Pa = 0 W 

(file:ACOAL0.dat)

X1,X3 (190 of 194) 98 %

X1,X2,X3 (4 of 194) 2 %

R134a/5AlO (99/1)

Pa = 4 W 

(file:ACOAL4.dat)

X1,X2,X3 (129 of 243) 53 %

X1,X2,X5 (71 of 243) 29 %

X1,X2,X8 (28 of 243) 12 %

R134a/5AlO (99/1)

Pa = 6 W 

(file:ACOAL6.dat)

X1,X2,X3 (86 of 219) 39 %

X1,X2,X8 (73 of 219) 33 %

X1,X2,X5 (30 of 219) 14 %

R134a/5AlO (99/1)

Pa = 8 W 

(file:ACOAL8.dat)

X1,X2,X3 (185 of 248) 75 %

X1,X2,X5 (23 of 248) 9 %

X1,X2,X8 (18 of 248) 7 %

R134a/5AlO (99/1)

Pa = 12 W 

(file:ACOAL12.dat)

X1,X2,X3 (44 of 48) 92 %

X1,X2,X8 (3 of 48) 6 %
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18

Table 2 Pool boiling data

R134a/5Al0 (99/1)

Pa = 0 W

File: Acoal0.dat

Ts

(K)

q" 

(W/m2)

 5.63 103 666.

 5.64 103 788.

 5.65 103 756.

 5.38 97 854.

 5.39 97 969.

 5.40 97 949.

 5.14 92 304.

 5.14 92 256.

 5.14 92 319.

 4.87 86 783.

 4.87 86 819.

 4.88 86 852.

 4.60 81 204.

 4.61 81 262.

 4.60 81 291.

 4.31 75 719.

 4.30 75 806.

 4.31 75 881.

 4.03 70 526.

 4.02 70 538.

 4.03 70 583.

 3.71 64 910.

 3.71 64 930.

 3.71 64 948.

 3.42 59 762.

 3.42 59 839.

 3.42 59 828.

 3.14 54 802.

 3.15 54 865.

 3.15 54 880.

 2.87 49 997.

 2.88 49 987.

 2.87 49 960.

 2.61 45 235.

 2.60 45 174.

 2.61 45 232.

 2.34 40 575.

 2.33 40 472.

 2.33 40 473.

 2.08 36 186.

 2.07 36 138.

 2.08 36 182.

 1.83 31 956.

 1.83 31 900.

 1.83 31 884.

 1.61 27 924.

 1.58 27 780.

 1.59 27 788.

 1.35 23 952.

 1.33 23 759.

 1.32 23 723.

 1.09 19 853.

 1.08 19 660.

 1.08 19 682.

 0.87 16 250.

 0.85 16 060.

 5.76 105 157.

 5.75 104 896.

 5.75 104 784.

 5.49 98 967.

 5.49 99 015.

 5.50 99 073.

 5.24 93 588.

 5.24 93719.

 5.25 93 800.

 4.98 88 015.

 4.97 87 977.

 4.98 87 927.

 4.70 82 560.

 4.70 82 585.

 4.71 82 631.

 4.43 76 910.

 4.43 76 919.

 4.43 76 962.

 4.14 71 468.

 4.13 71 588.

 4.14 71 649.

 3.86 66 426.

 3.87 66 472.

 3.87 66 544.

 3.56 61 104.

 3.55 61 082.

 3.54 61 086.

 3.24 56 026.

 3.25 56 204.

 3.25 56 305.

 2.97 51 465.

 2.98 51 531.

 2.98 51 584.

 2.69 46 658.

 2.68 46 563.

 2.69 46 563.

 2.40 41 879.

 2.39 41 832.

 2.41 41 926.

 2.15 37 448.

 2.14 37 311.

 2.15 37 432.

 1.88 33 215.

 1.87 33 022.

 1.87 32 980.

 1.68 29 241.

 1.64 28 873.

 1.64 28 840.

 1.41 25 016.

 5.92 104 185.

 5.92 104 103.

 5.92 104 024.

 5.62 97 800.

 5.62 97 916.

 5.62 97 911.

 5.33 92 443.

 5.34 92 512.

 5.34 92 565.

 5.07 87 136.

 5.07 87 225.

 5.08 87 250.

 4.79 81 720.

 4.79 81 794.

 4.79 81 761.

 4.49 76 011.

 4.49 76 083.

 4.49 76 163.

 4.20 70 716.

 4.20 70 880.

 4.21 70 889.

 3.92 65 731.

 3.93 65 826.

 3.93 65 806.

 3.63 60 562.

 3.64 60 561.

 3.63 60 460.

 3.35 55 404.

 3.35 55 438.

 3.35 55 419.

 3.07 50 390.

 3.06 50 416.

 3.07 50 412.

 2.79 45 681.

 2.78 45 542.

 2.78 45 677.

 2.51 41 033.

 2.50 40 946.

 2.50 40 943.

 2.23 36 410.

 2.22 36 234.

 2.21 36 272.

 1.95 32 013.

 1.95 32 001.

 5.67 104 441.

 5.69 104 503.

 5.70 104 449.

 5.44 98 563.

 5.45 98 712.

 5.46 98 747.

 5.20 93 199.

 5.20 93 296.

 5.21 93 403.

 4.95 87 643.

 4.94 87 563.

 4.95 87 502.

 4.66 81 944.

 4.66 81 983.

 4.66 82 007.

 4.37 76 661.

 4.38 76 689.

 4.38 76 739.

 4.10 71 371.

 4.09 71 417.

 4.10 71 424.

 3.81 66 173.

 3.81 66 138.

 3.81 66 136.

 3.53 60 969.

 3.53 61 001.

 3.54 61 090.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

19

 3.26 56 082.

 3.25 56 000.

 3.26 56 049.

 2.98 51 030.

 2.97 51 050.

 2.97 51 014.

 2.70 46 174.

 2.69 46180.

 2.70 46215.

 2.44 41546.

 2.43 41527.

 2.44 41545.

 2.17 37097.

 2.16 37002.

 2.16 37017.

 1.90 32747.

 1.89 32626.

 1.89 32581.

R134a/5Al0 (99/1)

Pa = 4 W

File: Acoal4.dat

Ts

(K)

q" 

(W/m2)

 5.66 103826.

 5.67 104019.

 5.67 103993.

 5.39 97739.

 5.38 97858.

 5.39 97914.

 5.11 92059.

 5.11 92126.

 5.12 92217.

 4.85 86515.

 4.85 86613.

 4.86 86663.

 4.57 81059.

 4.56 81120.

 4.58 81104.

 4.31 75536.

 4.31 75537.

 4.31 75643.

 4.02 70192.

 4.03 70235.

 4.02 70140.

 3.74 64966.

 3.75 65082.

 3.76 65208.

 3.48 59956.

 3.49 59973.

 3.49 59984.

 3.21 54955.

 3.20 54899.

 3.21 54962.

 2.93 50209.

 2.93 50206.

 2.93 50235.

 2.68 45665.

 2.68 45659.

 2.67 45619.

 2.42 41135.

 2.41 41060.

 2.41 41051.

 2.15 36669.

 2.14 36634.

 2.14 36470.

 1.89 32184.

 1.87 32106.

 1.87 32120.

 1.63 28315.

 1.62 28159.

 1.63 28200.

 1.39 24151.

 1.36 23892.

 1.36 23839.

 1.12 20210.

 1.10 20213.

 5.69 102793.

 5.69 102784.

 5.68 102668.

 5.40 96869.

 5.40 96953.

 5.40 97138.

 5.11 91100.

 5.12 91275.

 5.13 91338.

 4.85 85802.

 4.84 85772.

 4.86 85904.

 4.58 80228.

 4.58 80301.

 4.59 80297.

 4.31 74755.

 4.30 74604.

 4.30 74516.

 4.01 69278.

 4.03 69386.

 4.03 69526.

 3.77 64455.

 3.77 64504.

 3.76 64513.

 3.49 59370.

 3.49 59402.

 3.49 59391.

 3.22 54514.

 3.21 54518.

 3.21 54554.

 2.95 49874.

 2.94 49871.

 2.94 49887.

 2.69 45256.

 2.68 45130.

 2.68 45129.

 2.42 40572.

 2.41 40602.

 2.41 40539.

 2.16 36321.

 2.14 36203.

 2.14 36251.

 1.89 32035.

 1.87 31887.

 1.86 31776.

 1.62 27993.

 1.61 27847.

 1.61 27903.

 1.36 23882.

 5.48 102590.

 5.48 102839.

 5.48 102781.

 5.20 96890.

 5.20 96946.

 5.21 97090.

 4.94 91160.

 4.94 91278.

 4.94 91298.

 4.68 85737.

 4.68 85774.

 4.68 85798.

 4.41 80111.

 4.41 80032.

 4.13 74638.

 4.14 74763.

 4.14 74872.

 3.89 69640.

 3.88 69631.

 3.87 69545.

 3.60 64412.

 3.61 64525.

 3.61 64529.

 3.34 59465.

 3.34 59488.

 3.34 59504.

 3.07 54607.

 3.07 54613.

 3.08 54661.

 2.82 49990.

 2.81 49874.

 2.82 49952.

 2.56 45291.

 2.56 45225.

 2.57 45250.

 2.30 40782.

 2.30 40675.

 2.30 40706.

 5.63 103677.

 5.63 103826.

 5.63 103857.

 5.35 97628.

 5.36 97813.

 5.35 97795.

 5.08 91934.

 5.08 92021.

 5.08 92127.

 4.81 86235.

 4.80 86346.

 4.81 86334.

 4.55 80975.

 4.54 81096.

 4.55 81039.

 4.28 75498.

 4.27 75440.

 4.27 75426.

 3.99 70065.

 4.00 70130.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

20

 3.99 70168.

 3.72 64883.

 3.72 64977.

 3.72 64946.

 3.45 59819.

 3.45 59829.

 3.45 59979.

 3.19 54907.

 3.17 54900.

 3.18 54916.

 2.92 50095.

 2.92 50001.

 2.91 50019.

 2.66 45391.

 2.64 45307.

 2.65 45440.

 2.38 40836.

 2.38 40983.

 2.38 40844.

 2.12 36260.

 2.10 36295.

 2.10 36234.

 1.86 32122.

 1.85 32049.

 1.85 32035.

 1.61 28008.

 1.59 27914.

 1.60 27887.

 1.36 23797.

 1.33 23590.

 1.33 23567.

 1.14 20424.

 1.11 20044.

 5.52 105142.

 5.53 105171.

 5.54 105173.

 5.28 99082.

 5.28 99168.

 5.30 99216.

 5.04 93482.

 5.05 93554.

 5.07 93592.

 4.80 87832.

 4.80 87859.

 4.81 87892.

 4.54 82335.

 4.54 82417.

 4.54 82483.

 4.27 76904.

 4.26 76938.

 4.27 77087.

 3.99 71551.

 4.00 71706.

 4.00 71698.

 3.72 66253.

 3.72 66224.

 3.72 66161.

 3.43 60812.

 3.42 60863.

 3.43 60909.

 3.15 55937.

 3.15 55963.

 3.15 56018.

 2.88 51008.

 2.88 50986.

 2.88 50950.

 2.61 46159.

 2.60 46036.

 2.59 46074.

 2.33 41496.

 2.33 41458.

 2.33 41472.

 2.07 37076.

 2.07 36996.

 2.06 36955.

 1.81 32548.

 1.80 32416.

 1.80 32300.

 1.57 28252.

 1.54 28053.

 1.55 28363.

 1.33 24466.

 1.32 24341.

R134a/5Al0 (99/1)

Pa = 6 W

File: Acoal6.dat

Ts

(K)

q" 

(W/m2)

 5.35 104525.

 5.36 104562.

 5.36 104756.

 5.09 98646.

 5.10 98854.

 5.11 98861.

 4.85 92980.

 4.86 93089.

 4.86 93046.

 4.60 87212.

 4.60 87305.

 4.60 87272.

 4.35 81854.

 4.36 81876.

 4.36 81969.

 4.10 76348.

 4.10 76399.

 4.11 76480.

 3.84 71076.

 3.84 71165.

 3.85 71207.

 3.57 65722.

 3.57 65783.

 3.58 65798.

 3.31 60751.

 3.31 60720.

 3.31 60702.

 3.04 55487.

 3.03 55625.

 3.03 55666.

 2.77 50642.

 2.79 50688.

 2.78 50707.

 2.54 46425.

 2.53 46540.

 2.54 46319.

 2.27 41843.

 2.27 41761.

 2.27 41792.

 2.02 37399.

 2.00 37205.

 2.00 37129.

 1.76 32789.

 1.74 32675.

 1.74 32753.

 1.51 28817.

 1.50 28537.

 5.28 105131.

 5.29 105124.

 5.29 105233.

 5.04 99299.

 5.04 99480.

 5.06 99638.

 4.79 93668.

 4.80 93840.

 4.81 93836.

 4.56 88080.

 4.57 88163.

 4.58 88349.

 4.31 82568.

 4.31 82685.

 4.31 82690.

 4.05 76952.

 4.05 76991.

 4.05 77036.

 3.79 71752.

 3.79 71828.

 3.79 71755.

 3.53 66284.

 3.51 66412.

 3.52 66477.

 3.25 61339.

 3.25 61235.

 3.25 61240.

 2.98 56244.

 2.99 56152.

 2.99 56121.

 2.73 51419.

 2.74 51465.

 2.75 51497.

 2.49 46809.

 2.48 46801.

 2.48 46783.

 2.22 42244.

 2.22 42193.

 2.23 42178.

 1.98 37506.

 1.97 37490.

 1.96 37490.

 1.73 33196.

 1.71 33081.

 1.72 33038.

 1.47 28703.

 1.46 28766.

 1.45 28494.

 1.24 24858.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

21

 1.23 24651.

 1.22 24549.

 1.01 20883.

 0.98 20609.

 0.98 20549.

 0.79 17099.

 0.76 16827.

 5.32 105406.

 5.31 105441.

 5.32 105523.

 5.07 99230.

 5.07 99332.

 5.07 99400.

 4.83 93499.

 4.84 93698.

 4.84 93786.

 4.59 87711.

 4.58 87817.

 4.59 87885.

 4.34 82296.

 4.34 82346.

 4.34 82345.

 4.08 76796.

 4.08 77011.

 4.09 77107.

 3.83 71567.

 3.83 71609.

 3.83 71591.

 3.56 66343.

 3.56 66410.

 3.57 66445.

 3.30 60938.

 3.29 61086.

 3.29 61063.

 3.04 56249.

 3.03 56195.

 3.04 56336.

 2.80 51518.

 2.78 51346.

 2.78 51370.

 2.53 46774.

 2.52 46708.

 2.52 46708.

 2.25 42152.

 2.22 42163.

 2.26 42103.

 2.01 37404.

 1.99 37365.

 1.99 37324.

 1.74 33037.

 1.72 32938.

 1.72 33024.

 1.47 28866.

 1.45 28677.

 1.45 28682.

 1.21 24544.

 1.18 24435.

 1.18 24297.

 0.97 20567.

 0.92 20221.

 0.92 20479.

 0.73 17183.

 0.70 16827.

 0.69 16671.

 0.48 13197.

 0.46 12889.

 5.23 105953.

 5.23 106049.

 5.22 105864.

 4.97 99583.

 4.97 99680.

 4.98 99774.

 4.73 93675.

 4.73 93711.

 4.73 93792.

 4.48 87964.

 4.49 87993.

 4.49 87881.

 4.23 82289.

 4.24 82348.

 4.23 82383.

 3.98 76727.

 3.98 76730.

 3.98 76740.

 3.72 71390.

 3.72 71449.

 3.73 71488.

 3.48 66196.

 3.47 66173.

 3.47 66192.

 3.19 61026.

 3.20 61132.

 3.19 61055.

 2.94 56077.

 2.93 56065.

 2.94 56106.

 2.70 51520.

 2.69 51464.

 2.71 51478.

 2.45 46789.

 2.45 46702.

 2.45 46717.

 2.19 42132.

 2.19 42105.

 2.20 42138.

 1.96 37593.

 1.94 37546.

 1.94 37536.

 1.69 33144.

 1.68 33011.

 1.68 32892.

 1.44 28881.

 1.44 28776.

 1.43 28681.

 1.19 24498.

 1.18 24216.

 1.18 24135.

 0.95 20575.

 0.94 20433.

 0.94 20434.

 0.74 16984.

 0.71 16719.

 0.71 16632.

R134a/5Al0 (99/1)

Pa = 8 W

File: Acoal8.dat

Ts

(K)

q" 

(W/m2)

 5.29 105740.

 5.31 105741.

 5.30 105581.

 5.04 99211.

 5.05 99241.

 5.05 99263.

 4.80 93162.

 4.80 93144.

 4.81 93293.

 4.55 87376.

 4.55 87466.

 4.56 87498.

 4.31 82018.

 4.31 82050.

 4.31 82080.

 4.05 76404.

 4.05 76409.

 4.05 76400.

 3.79 71041.

 3.79 71109.

 3.79 71129.

 3.53 65784.

 3.52 65779.

 3.52 65734.

 3.26 60695.

 3.25 60699.

 3.26 60750.

 2.99 55673.

 2.99 55667.

 2.98 55664.

 2.72 50889.

 2.72 50992.

 2.73 51013.

 2.47 46197.

 2.47 46136.

 2.46 46119.

 2.20 41501.

 2.20 41467.

 2.21 41469.

 1.96 37053.

 1.95 36981.

 1.95 36985.

 1.71 32725.

 1.70 32651.

 1.70 32658.

 1.46 28364.

 1.45 28253.

 1.45 28192.

 1.24 24427.

 1.23 24431.

 1.21 24066.

 1.01 20817.

 0.98 20481.

 0.98 20298.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

22

 0.78 16891.

 0.75 16617.

 5.70 104169.

 5.70 104138.

 5.70 104169.

 5.41 98168.

 5.42 98473.

 5.43 98738.

 5.16 93033.

 5.17 93240.

 5.18 93338.

 4.91 87429.

 4.91 87619.

 4.91 87677.

 4.61 81603.

 4.62 81586.

 4.61 81587.

 4.33 75947.

 4.32 76069.

 4.33 76066.

 4.05 70729.

 4.06 70834.

 4.07 70815.

 3.78 65531.

 3.78 65577.

 3.79 65616.

 3.49 60322.

 3.50 60305.

 3.50 60325.

 3.23 55173.

 3.22 55145.

 3.22 55170.

 2.94 50364.

 2.94 50421.

 2.94 50438.

 2.65 45666.

 2.66 45599.

 2.67 45642.

 2.40 41032.

 2.40 40977.

 2.40 41017.

 2.12 36682.

 2.12 36602.

 2.12 36644.

 1.86 32055.

 1.85 32207.

 1.85 32199.

 1.60 28072.

 1.59 28150.

 1.59 28171.

 1.37 24480.

 5.49 105143.

 5.50 105339.

 5.50 105418.

 5.24 99079.

 5.27 99304.

 5.27 99442.

 5.01 93537.

 5.02 93609.

 5.03 93770.

 4.76 87891.

 4.76 87963.

 4.77 88023.

 4.49 82363.

 4.49 82630.

 4.51 82747.

 4.24 76978.

 4.24 76950.

 4.24 76975.

 3.94 71403.

 3.95 71582.

 3.96 71633.

 3.68 66310.

 3.67 66316.

 3.68 66266.

 3.38 61045.

 3.39 61072.

 3.39 61111.

 3.11 56170.

 3.09 56143.

 3.10 56175.

 2.83 51191.

 2.82 51188.

 2.82 51204.

 2.53 46338.

 2.51 46229.

 2.51 46205.

 2.27 41730.

 2.27 41734.

 2.27 41785.

 2.02 37387.

 2.02 37287.

 2.01 37209.

 1.76 32860.

 1.75 32870.

 1.76 32808.

 1.52 28827.

 1.51 28730.

 1.50 28421.

 1.30 24945.

 5.37 105619.

 5.39 105803.

 5.39 105873.

 5.14 99655.

 5.15 99831.

 5.16 99952.

 4.89 93937.

 4.91 94119.

 4.92 94288.

 4.66 88227.

 4.66 88371.

 4.67 88413.

 4.40 82686.

 4.40 82842.

 4.41 82873.

 4.13 77230.

 4.13 77312.

 4.14 77349.

 3.86 71730.

 3.86 71768.

 3.86 71832.

 3.58 66394.

 3.58 66410.

 3.58 66422.

 3.32 61392.

 3.31 61396.

 3.32 61393.

 3.05 56307.

 3.05 56257.

 3.05 56119.

 2.77 51226.

 2.77 51224.

 2.77 51269.

 2.51 46573.

 2.51 46505.

 2.50 46387.

 2.24 41738.

 2.24 41774.

 2.24 41745.

 1.99 37320.

 1.98 37258.

 1.98 37197.

 1.75 33021.

 1.73 32927.

 1.73 32941.

 1.55 28745.

 1.53 28570.

 1.53 28495.

 1.30 24540.

 5.37 105586.

 5.40 105650.

 5.41 105501.

 5.14 99100.

 5.15 99230.

 5.16 99315.

 4.93 93549.

 4.94 93563.

 4.95 93598.

 4.69 87844.

 4.69 87838.

 4.70 87972.

 4.45 82570.

 4.45 82677.

 4.46 82711.

 4.18 77113.

 4.18 77164.

 4.17 77150.

 3.90 71636.

 3.91 71717.

 3.91 71755.

 3.63 66308.

 3.62 66349.

 3.63 66346.

 3.34 61047.

 3.35 61122.

 3.35 61168.

 3.07 56152.

 3.08 56157.

 3.08 56181.

 2.81 51049.

 2.80 51087.

 2.81 51133.

 2.55 46333.

 2.54 46288.

 2.54 46274.

 2.29 41617.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

23

 2.29 41526.

 2.29 41608.

 2.04 37194.

 2.03 37238.

 2.02 37220.

 1.80 32808.

 1.77 32612.

 1.77 32542.

R134a/5Al0 (99/1)

Pa = 12 W

File: Acoal12.dat

Ts q" 

(K) (W/m2)

 5.41 105116.

 5.42 105234.

 5.43 105156.

 5.19 98932.

 5.20 99014.

 5.22 99003.

 4.98 93199.

 4.99 93202.

 5.00 93116.

 4.75 87427.

 4.76 87440.

 4.77 87536.

 4.50 81883.

 4.50 81922.

 4.51 81980.

 4.24 76430.

 4.24 76454.

 4.25 76463.

 3.98 70911.

 3.97 70942.

 3.97 70946.

 3.71 65805.

 3.71 65856.

 3.71 65857.

 3.43 60723.

 3.44 60683.

 3.44 60680.

 3.16 55701.

 3.16 55710.

 3.15 55729.

 2.88 50751.

 2.88 50815.

 2.88 50778.

 2.61 46082.

 2.60 45939.

 2.61 45961.

 2.35 41405.

 2.33 41372.

 2.33 41377.

 2.08 37112.

 2.07 37004.

 2.07 36778.

 1.81 32399.

 1.80 32319.

 1.79 32321.

 1.58 28519.

 1.56 28319.

 1.55 28295.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

24

Table 3 Number of test days and data points

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

R134a/5AlO (99/1)

Pa = 0 W 

0.8 K  Ts  5.8 K

4 194

R134a/5AlO (99/1)

Pa = 4 W 

1. 1 K  Ts  5.6 K

5 243

R134a/5AlO (99/1)

Pa = 6 W 

0.4 K  Ts  5.3 K

4 219

R134a/5AlO (99/1)

Pa = 8 W 

0.8 K  Ts  5.4 K

5 248

R134a/5AlO (99/1)

Pa = 12 W 

1.0 K  Ts  6.0 K

1 48
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

25

Table 4 Estimated parameters for cubic boiling curve fits for Turbo-BII-HP copper 

surface

Ts = A0 + A1 q” + A2 q”2 + A3 q”3

Ts in kelvin and q” in W/m2

Fluid Ao A1 A2 A3

R134a/5AlO (99/1)

Pa = 0 W 

0.8 K  Ts  5.8 K

-0.150578 6.32198x10-5-1.36492x10-12-6.03840x10-16

R134a/5AlO (99/1)

Pa = 4 W 

1. 1 K  Ts  5.6 K

-0.158376 6.45856x10-5-5.78231x10-11-3.01352x10-16

R134a/5AlO (99/1)

Pa = 6 W 

0.4 K  Ts  5.3 K

-0.375243 6.75659x10-5-1.26793x10-10-3.30088x10-17

R134a/5AlO (99/1)

Pa = 8 W 

0.8 K  Ts  5.4 K

-0.197504 5.97647x10-53.29158x10-11-8.80686x10-16

R134a/5AlO (99/1)

Pa = 12 W 

1.0 K  Ts  6.0 K

-0.111051 5.68618x10-51.15503x10-10-1.48576x10-15
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

26

Table 5 Residual standard deviation of Ts

Fluid (K)

R134a/5AlO (99/1)

Pa = 0 W 

0.8 K  Ts  5.8 K

0.06

R134a/5AlO (99/1)

Pa = 4 W 

1. 1 K  Ts  5.6 K

0.06

R134a/5AlO (99/1)

Pa = 6 W 

0.4 K  Ts  5.3 K

0.04

R134a/5AlO (99/1)

Pa = 8 W 

0.8 K  Ts  5.4 K

0.09

R134a/5AlO (99/1)

Pa = 12 W 

1.0 K  Ts  6.0 K

0.006
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.TN.1836

27

Table 6 Average magnitude of 95 % multi-use confidence interval for mean Ts

Fluid U (K)

R134a/5AlO (99/1)

Pa = 0 W 

0.8 K  Ts  5.8 K

0.03

R134a/5AlO (99/1)

Pa = 4 W 

1. 1 K  Ts  5.6 K

0.02

R134a/5AlO (99/1)

Pa = 6 W 

0.4 K  Ts  5.3 K

0.02

R134a/5AlO (99/1)

Pa = 8 W 

0.8 K  Ts  5.4 K

0.04

R134a/5AlO (99/1)

Pa = 12 W 

1.0 K  Ts  6.0 K

0.04
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Fig. 1 Schematic of test apparatus
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Fig. 2 Schematic of ultrasonic piezoelectric transducer showing presumed 

amplitude variations with respect to the boiling surface
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Fig. 3 OFHC copper flat test plate with Turbo-BII-HP surface and thermocouple 

coordinate system
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Fig. 4 Photograph of Turbo-BII-HP surface
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Fig. 5 TEM image of Al2O3 nanolubricant (Sarkas, 2009)
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Fig. 6 R134a/5AlO (99/1) mixture boiling curves for Turbo-BII-HP and P = 0 W
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Fig. 7 Effect of extended acoustic excitation on the boiling heat flux of R134a/5ALO 

(99/1) Turbo-BII-HP
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Fig. 8 R134a/nanolubricant mixtures boiling curves for Turbo-BII-HP for various 

ultrasonic applied levels
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Fig. 9 Acoustically excited boiling heat flux of R134a/nanolubricant mixtures relative 

to that without excitation for Turbo-BII-HP
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Fig. 10 Prediction of acoustically excited boiling heat flux of R134a/nanolubricant 

mixture relative for Turbo-BII-HP
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Fig. 11 Effect of net power on acoustically excited boiling heat flux of a 

R134a/nanolubricant mixture
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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 

the 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 heat flux of the surface at the 95 % 

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

confidence level