International Journal of Refrigeration(2013) in press

Flow boiling heat transfer of carbon dioxide with polyalkylene
glycol–type lubricating oil in pre-dryout region inside horizontal
tube
Minxia LI1, Chaobin DANG2*, Eiji HIHARA2
1
Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, MOE, Tianjin
University, Tianjin, 300072, China
2
Department of Human and Engineered Environment, Graduate School of Frontier Sciences,
The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8563, Japan*
Corresponding author: dangcb@k.u-tokyo.ac.jp; Fax: 81-47-136-4647
ABSTRACT
A flow boiling heat transfer model for horizontal tubes is proposed for CO2 withentrained polyalkylene glycol (PAG oil) in the pre-dryout region. A general power law–typemodel with a power number of 3 is used together with the average thermodynamic propertiesof the CO2–oil mixture. A convective enhancement factor (F) is recommended according to
the relationship between the Lockhart–Martinelli parameter and the ratio αtp/αl, which was
obtained based on previous experimental results for CO2 and oil. A new suppression factor (S)is introduced that comprises a suppression term for forced convection and oil concentration
term for bubble generation. A comparison of six correlations showed that the proposedcorrelation can depict the influence of the mass and heat fluxes on both nucleate andconvection boiling reasonably well.
Keywords: Carbon dioxide; heat transfer coefficient; correlation; lubricating oil; boiling heattransfer
Nomenclature
Bo boiling number, Bo  q  Ghlv 

Bd bond number, Bd = g  D2 

cp specific heat (J kg-1 k-1)
El mix two-phase heat transfer multiplier
F convective enhancement factor
g gravitational acceleration (m s-2)
hlv latent heat of vaporization (J kg-1)
m mass flow rate (kg s-1)
Pc critical pressure (Pa)D,d hydraulic diameter (m)EF enhancement factor
Fr Froude number, Fr = ν2/(gd)G mass flux (kg m-2 s-1)
M molecular weight (kg kmol-1)
n total number of data points
Pr Prandtl number Pr     a 1

q heat flux (W m-2)

pr reduced pressure pr  P Pc
Re Reynolds number Re  Gd 

S boiling suppression factor

T temperature (°C or K )Tb oil normal boiling point temperature (K)Xtt Lockhart–Martinelli parametery measured value

Tc critical temperature ( K)x vapor quality
y calculated value

Greek symbols
 heat transfer coefficient (W m-2 K-1)
 void fraction

 film thickness (m) dry dry angle
 dynamic viscosity (kg m-1 s-1)
 density (kg m-3)

 thermal conductivity (W m-1 K-1)
 kinematic viscosity (m2 s-1)
 surface tension (N m-1)

ω oil concentration (wt%)

Subscripts
b bubblecb convective boilingl liquidm refrigerant–lubricant mixtureo, oil oil
ref reference
tp two phasew, wall surface
0 known parameters
2 second parametercal calculated result
exp experimental data
local local parameternb nucleate boilingr refrigerant
sat saturationv vaporwet wet wall
1 first parameter

1. INTRODUCTION
Carbon dioxide (CO2) was rediscovered by Lorentzen and Pettersen (1993, 1994, 1995) tobe a promising alternative refrigerant for heat pump, water heater, and mobile air-conditioning
applications because of its favorable properties such as zero ODP (ozone depletion potential),
negligible GWP (global warming potential), non-toxicity, and non-flammability. CO2 also has
excellent thermodynamic properties such as low viscosity and surface tension because of its
low critical temperature and high operating pressure, which cause it to exhibit uniquecharacteristics in flow and heat transfer. Many previous studies on flow boiling heat transfer
of CO2 (e.g., Pettersen et al., 2002, Tanaka and Hihara, 2000, Dang et al., 2010) showed that
nucleate boiling is the dominant mechanism in CO2 flow boiling because of the high density
ratio of vapor to liquid.
2

In an actual heat pump cycle, the lubricating oil in the compressor is carried to the othercomponents of the system along with the circulating refrigerant. The entrainment of a small
amount of lubricating oil in the refrigerant can cause a profound deterioration in the heattransfer performance of the working fluids. Despite the importance of understanding the
behavior of oil inside the CO2 heat pump cycle, only limited information is available
regarding the characteristics of flow boiling heat transfer of CO2 in the presence of oil.
Katsuta et al. (2003) measured the boiling heat transfer coefficient of CO2 in a 3-mm I.D. tube
at heat fluxes ranging from 5 to 15 kW m-2 and mass fluxes ranging from 200 to 600 kg m-2
s-1. A sharp decrease in the heat transfer coefficient was reported when the oil concentration
exceeded 0.3%. Gao and Honda (2004a, 2004b) visually observed the flow pattern of
lubricating oil flowing with CO2 at heat fluxes of 5–40 kW m-2 and mass fluxes of 230–1200
kg m-2 s-1. The formation of an oil-rich layer near the heat transfer surface was reported to be
the main cause of the nucleate boiling suppression. Dang et al. (2013) presented a systemic
experimental study of CO2 with a polyalkylene glycol (PAG)-type lubricating oil entrained
from 0% to 5% in horizontal smooth tubes with inner diameters of 2–6 mm at mass fluxes of
360–1440 kg m-2 s-1 and heat fluxes of 4.5–36 kW m-2. Experimental results showed that thepresence of oil causes the mass flux to significantly influence the heat transfer coefficient at a
low heat flux until dryout, while the mass flux was not observed to have a significant
influence at a high heat flux.
Shen and Groll (2003) reviewed numerous methods for predicting the heat transfer of arefrigerant in the presence of lubricating oil. However, available literature on predicting theflow boiling heat transfer of CO2 and oil is limited. Zhao et al. (2009) attempted to use two
methods to predict the flow boiling coefficient: they chose the correlations of Kandlikar(1990), Liu and Winterton (1991), and Kattan et al. (1998) and compared their predictions
with the CO2–lubricant mixture data by replacing the viscosity of pure CO2 with the viscosityof the mixture. The enhancement factor (EF) proposed by Eckels et al. (1994, 1998),
correlation for R134a/369-SUS and two-phase heat transfer multiplier (El,mix) introduced byTichy et al. (1986), and correlation for R12/300-SUS were applied in these predictions. Zhao
et al. concluded that an overall prediction based on different correlations is inaccurate,
especially under low vapor quality and low oil concentration conditions.
This report presents a prediction model of the flow boiling heat transfer of CO2–PAG oil
mixtures that considers the local properties of a mixture based on the previous experimental
work of Dang et al. (2013). The proposed correlation and five other available correlations
were compared using an experimental database that was set up according to the experimental
results of four independent research groups.
2. EXPERIMENTAL APPARATUS AND DATA REDUCTION
The theoretical model is mainly based on previously published experimental results3

(Dang et al., 2013); a brief description is given here to explain how the data were obtained.
Figure 1 shows a schematic diagram of the experimental apparatus. The main devices include
a magnetic gear pump to circulate the refrigerant and oil, a mass flow meter (±0.5% accuracy),a heater, an oil sampling section, a test section, an oil separator, and a heat exchanger forcooling the refrigerant. The test section was a stainless steel tube heated electrically by a DC
power supply. The outside wall temperature of the test section was measured using T-type
thermocouples with an uncertainty of ±0.1 °C. Pressures of the working fluid at the inlet andoutlet of the test section were monitored using a precision pressure sensor with an uncertainty
of ±0.1% F.S. The vapor quality at the inlet of the test section was controlled by a heater. The
oil concentration was measured by the oil sampling section and controlled by the opening of aneedle valve at the exit of the oil separator. The oil separator was kept at 60 °C to reduce
dissolution of CO2 into the oil. The sampling was usually conducted three times at the
beginning, middle, and end of each run. The uncertainty of the oil concentration was
estimated to be ±0.1 %.
The local heat transfer coefficient α was determined from the measured heat flux q and thetemperature difference between the saturation temperature Tsat and inside wall temperature Tw.
q
(1)
Tw  Tsat
Tsat was estimated from the measured pressure, and Tw was calculated from the measured
outside wall temperature using a one-dimensional heat conduction model.
The experimental conditions are summarized in Table 1. The thermodynamic properties of
CO2 were obtained from REFPROP 7.0. The measurement accuracies of some parameters arepresented in Appendix 1.
3. THEORETICAL ANALYSIS
3.1 Available correlation for CO2–oil mixture
Most of the CO2–oil mixture correlations available in the literature were developed basedon the properties of pure refrigerants. Katsuta et al. (2008a), Gao et al. (2008), andAiyoshizawa et al. (2006) proposed correlations to predict the flow boiling heat transfer
coefficient of CO2 and PAG-type oil based on the properties of pure CO2. The main
characteristics of the above models are introduced below. More details on these models arepresented in Appendix 2.
Katsuta et al. (2008a) and Gao et al. (2008) sought to depict the trend of the heat transfer
characteristics of the CO2–oil mixture by revising the boiling suppression factor correlation in
the flow boiling heat transfer model for CO2. Katsuta et al. (2008a) proposed their correlation
by introducing a new suppression factor into the Stephan–Abdelsalam correlation (Stephan
and Abdelsalam, 1980), which reflects the depression on nucleate boiling caused by thepresence of oil.
4 nb  S SA  (2)
Bo 0.518 Bd 1.27 Fr 0.964 S  ln  2.332

Re0.834
tp



(3)

where  is defined as




1

1  OCR

0.698

Bo0.207 Bd 0.912

(4)

In addition, the factor  , which is the function of the oil concentration, is employed to
predict the dryout quality in the presence of oil.

Re0.0571 Fr 0.0697
xdry  0.269 Bo0.0519
OCR  1 1

(5)

OCR  1
  1.169e0.17OCR
The predicted results were reported to agree with 80% of their test results within a prediction
deviation of ±20%.
Gao et al. (2008) employed Cheng’s correlations based on their own experimental data. In
their analysis, they redefined suppression factor S′ as a function of the vapor quality; thethreshold of the vapor fraction was set to 0.7. The correlation was reported to capture 81% of
their experimental data within a deviation of ±30%.
 S   0.5 x  0.35 
S'  
0 x  0.7 
 x  0.7 (6)Considering the suppression of nucleate boiling by flow boiling of CO2 in the presence ofoil, Aiyoshizawa et al. (2006) adopted the superposition form of Chen and proposed new
constants and exponents for convection enhancement and boiling suppression factor equations
based on the previous experimental data of Dang et al. (2013). They used the properties ofpure CO2 in their correlation.
1.2

 1 
F  0.8  0.5  X tt S1

6  Re tp  10



4 3

(7)

(8)

3.2 Proposed correlation
3.2.1 Properties of CO2–oil mixture
CO2 fluid is partly miscible with PAG oil according to Kawaguchi et al. (2000) and Kaneko
et al. (2004). Figure 2 shows the two-phase separation temperature of PAG oil with CO25

under subcritical conditions (Dang et al. 2012). CO2 and PAG oil separate under theevaporation temperature from -50 °C to 30 °C when the oil concentration is less than 30%.
When the oil separates from liquid CO2, a certain amount of CO2 may dissolve into the oil. In
this study, the inlet oil concentration of the evaporator was below 5%, and the evaporation
temperature was kept at 15 °C. Therefore, three phases exist during the evaporation process: avapor CO2 phase, liquid CO2 phase (with negligible dissolved oil), and an oil layer with
dissolved CO2. Based on the experimental measurements of Youbi-Idrissi et al. (2003) andGarcia et al. (2008), the solubility of CO2 in PAG oil layer is about 50% when thetemperature is 15 °C and pressure is 5.0 MPa.
PAG oil flowing with bulk CO2 affects both bubble generation on the surface of the tube
and the wall superheating temperature. Thermodynamic properties of the oil, such as its
viscosity, also influence the flow boiling heat transfer of CO2. Using just the properties of
CO2 to predict the heat transfer coefficient of the CO2–oil mixture in the model without
considering the contribution of the oil’s properties seems unreasonable. In this study, thethermodynamic properties of the CO2–oil mixture was introduced into the conventional flow
boiling heat transfer correlation in order to predict the refrigerant–oil flow boiling coefficient.
The oil concentration in the refrigeration cycle ωo is defined from the ratio of the mass flowrate of oil to the mass flow of the mixture.
moil
(9)
o 
mco  moil
2

Inside the evaporator, the local oil concentration ωol is the function of the vapor quality andincreases with the progress of evaporation:

ol 

o
1 x

(10)

where x is the local vapor quality.
Figure 3 shows that the local oil concentration increases with vapor generation; at an inlet
oil concentration ωo of 1%, ωol may reach 5 % at a vapor quality of 0.8 and increase sharply
at higher vapor quality. However, considering the relatively low dryout quality, which changes
from 0.4 to 0.8 based on the mass flux, limiting the scope to low local oil concentrations
seems a reasonable constraint.
The average thermal physical properties of CO2–oil were used in this analysis based on the
mixing rules shown in Appendix 3 (Eqs. (A-35)–(A-39)). The thermodynamic properties of
PAG used in this study were provided by the manufacturer and are presented in Table 2. The
data were correlated as a function of the temperature, as shown in Eqs. (A-40)–(A-43). Table
3 compares the properties of PAG oil, CO2, and the calculated properties of the CO2–oil
mixture using the mixing rules. Even a tiny amount of entrained oil may lead to a dramaticincrease in liquid viscosity and surface tension compared to pure CO2. At a local oilconcentration of 1%, the liquid viscosity and surface tension increase 9.9 and 2.1 times,
6

respectively; when the oil concentration is 5%, these increase 16.9 and 3.6 times, respectively.
In contrast, the changes in liquid conductivity, liquid density, and specific heat compared tothose of CO2 are negligible when the oil concentration is small.
The average thermal properties of the mixture defined in this study were applied to
calculating the nucleate boiling and force convective heat transfer. For example, the force
convection component is given by the following formula:0.4
 l  0.023Re0.8m Prm  m / d (11)where Rem   G(1  x) D  F 1.25 and subscript “m” denotes the average properties of the mixture


m



being used.
3.2.2 Proposed correlation
Based on the experimental data from Dang et al. (2013), a new correlation is proposed. The
correlation of Steiner and Taborek (1992) was used in this study; this is a general power
law–type model with a power number of 3. The terms for nucleate boiling and convective
heat transfer are shown in Eqs. (13) and (14), respectively.
3 1/ 3

 tp   S nb    cb  (12)

 m0.79 cpm 0.45 m0.49 
0.24
0.75
 nb  0.00122  0.5 0.29 0.24 0.24  tw  tsat  pl    psat  tw   pl   m m hlv  v 



(13)

 cb  F l

(14)

X tt  10
1.0,F 
1.05
2.55  0.413  1/ X tt  , X tt  10

(15)

3





F and S are defined as

S  Soil1

0.9  0.4 Re tpm 10

3  Bo 10 
0.3

3

0.23

(16)

where Re tpm   G (1  x) D  F 1.25 and Soil is introduced to reflect the suppression of nucleate


m



boiling due to the presence of oil:
Soil  ol

2 ln(1  ol ), 0.0005  ol  1(17)

3.2.2.1 Discussion of conventional heat transfer component
In general, flow boiling data can be correlated using the form (αtp/αl) ∞ (1/Xtt)n, where αtp
and αl are the flow boiling heat transfer coefficient and heat transfer coefficient, respectively,
for liquid flowing alone and Xtt is the Lockhart–Martinelli parameter. Figure 4 shows that the7αtp/αl regulations of the CO2–oil mixture and pure CO2 are similar and not linear to 1/Xtt in alogarithmic coordinate system. The data of αtp were obtained from Dang et al. (2013). When
1/Xtt and the oil concentration are increased, αtp/αl of the CO2–oil mixture deviates from thatof pure CO2. However, the αtp/αl values of CO2–oil mixtures with the same oil concentrations
under different conditions tend to be identical at high 1/Xtt values, as shown in Fig. 5.
Figure 6 compares the forced convective heat transfer results from using the mixture
properties and those of pure CO2. The liquid-phase forced convective heat transfer of the
mixture is much smaller than that of the pure CO2 due to the reduction in Rem with theaddition of oil to the refrigerant. The forced heat transfer of the two-phase flow is also
affected by the velocity of the vapor and increases with vapor generation. Chen introduced atwo-phase multiplier F that relates the ratio of the two-phase Reynolds number to that of theliquid Reynolds number: F ∞ Retp/Rel. The Lockhart–Martinelli number is used to calculate F,
as shown in Eqs. (18) and (19).
X tt  10
1.0,F 
0.736, X tt  10
 2.0  0.213  1/ X tt 
0.5

0.9

 1  x    v   m 
X tt     x   m   v 

(18)

0.1

(19)

Similar to the F factor proposed by Chen (1966), a new definition for the F factor isintroduced in Eq. (15) based on the experimental data shown in Fig. 5. In addition, F
proposed by Yoshida et al. (1994) is shown in Eq. (20) for comparison.
1.0,F 
0.88
1  2.0 1/ X tt  ,X tt  10
X tt  10(20)

Figure 7 compares the F factors from four correlations (Chen, 1966, Yoshida et al., 1994,
Aiyoshizawa et al., 2006, and the present study). The values of the F factor proposed in thisstudy as calculated by using the mixture’s properties were similar to those of the correlations
by Yoshida et al. and Chen, which were calculated by using the properties of pure CO2. The F
factor of Aiyoshizawa et al. (2006) was much lower than that of Chen (1966). Aiyoshizawa etal. (2006) apparently tried to decrease the value of F to reflect the suppression of oil. Thus, F
was less than 1.0 when the vapor quality was very low, which is physically impossible.
3.2.2.2 Discussion of nucleate boiling component
The nucleate boiling component was constructed using Forster and Zuber’s (1955)
correlation as modified by Chen (1966), which is shown in Eq. (13).
As noted in Section 3.2.1, three phases exist during the evaporation process: a vapor CO2
phase, liquid CO2 phase (with negligible dissolved oil), and oil layer with dissolved CO2. The
8

proportion of PAG oil dissolved into CO2 is usually less than 0.2%; its effect on the change insaturation pressure of CO2 was negligible in our preliminary experiment. Thus, as long as aseparate liquid CO2 phase exists, the saturated temperature of the CO2–oil mixture is assumed
to be equivalent to that that of pure CO2 and can be determined by the measured local
pressure. In addition, the vapor properties used in Eq. (13) are the same as those of pure CO2.
The latent heat of pure CO2 was used without considering the effect of the oil because the oilconcentration was small.
Figure 6 compares the αnb values calculated by Eq. (13) for both pure CO2 and the CO2–oil
mixture against the vapor quality. αnb of the CO2–oil mixture decreased with increasing vapor
quality due to the change in mixture properties caused by increases in the local oilconcentration.
Chen (1966) introduced the factor S to express the suppression of nucleate boiling by
convective heat transfer and related it to the two-phase flow Reynolds number as follows:S

1   2.56 10

1
6

(21)

 Re F 

1.25 1.17l

As shown in Fig. 5, scattering of αtp/αl at low 1/Xtt (i.e., low vapor quality) at different
heat fluxes showed the contribution of nucleate boiling to heat transfer. At a small mass fluxof 360 kg m-2 s-1, αtp/αl at low vapor quality increased with the heat flux. In addition, αtp/αltook similar values at G = 360 kg m-2 s-1 and q = 9 kW m-2 compared to at G = 720 kg m-2 s-1
and q = 18 kW m-2. The relative significance of nucleate boiling against convective heattransfer in flow boiling heat transfer can be expressed by the boiling number (Bo = q/(hlvG)).
Yoshida et al. (1994) introduced Bo in their S model as follows:S

1
4 0.5
1  0.9  Re tp 10  Bo 104   X tt 0.5 






(22)

In this study, a similar expression was proposed based on the experimental data in thepresence of oil. The expression of S shown in Eq. (16) considers the suppression of nucleateboiling due to both convective heat transfer and the presence of oil. Since the effect of oil on
the nucleate boiling is sophisticated—involving bubble generation suppression, retarded
bubble departure, increased surface tension, separation of the oil layer from liquid refrigerant,
and the corresponding heat transfer resistance of the oil film—a new factor Soil was
introduced into Eq. (16) to reflect all of the other effects that could not be represented by the
mixture properties. According to Gao et al. (2007), the heat transfer coefficient of a mixture
with an oil concentration of less than 0.05% is similar to that of pure CO2. Therefore, thelower limit for the oil concentration was set to 0.05% in Eq. (17).
Figure 8 compares the four S values. The regulation of S calculated by Eq. (16) and thecorrelation proposed by Chen (1966) decreased quickly at the beginning and then slowly
decline after the vapor reached 0.5. The comparison shows that the proposed S had similar
9values as the original Chen’s model at low vapor qualities. When the vapor quality wasincreased, the decrease in S of the proposed model was more smooth than that of Chen’smodel. In contrast, the S values of Aiyoshizawa et al.’s model (2006) were much lower thanthose of the other models because they represented the suppression of nucleate boiling due tothe presence of oil by reducing the S value. In addition, the S of Yoshida et al.’s model (1994)
in Eq. (22) was much higher than that of the other models.
Figure 9 shows the tendencies of the predicted heat transfer coefficient based on thecontributions of different constituent terms to the variation in the overall heat transfer
coefficient, against the vapor quality. The contribution of nucleate boiling decreased and thatof convective heat transfer increased with increasing vapor quality before the onset of dryout.
The total heat transfer coefficient (αtp) increased with the mass flux, heat flux, and vapor
quality. All of these behaviors were consistent with the regulation based on the experimental
results of Dang et al. (2013). According to the experimental results, the nucleate boiling andconvective heat transfer alternated dominance over the flow boiling at heat transfer undervarious conditions. At a mass flux of 360 kW m-2 s-1, the nucleate boiling term (Sαnb)
increased significantly with the heat flux. At a heat flux of 18 kW m-2, the nucleate boilingand convective heat transfer were dominant in the lower and higher vapor quality regions,
respectively. The overall heat transfer coefficient (αtp) was almost constant over the vapor
quality range of 0.2–0.8. At a high heat flux of 36 kW m-2, the nucleate boiling term was large
and decreased linearly with increasing vapor quality until the vapor quality reached 0.8. At asmall heat flux of 4.5 kW m-2, the convective part of the heat transfer coefficient (Fαl)
dominated almost the entire pre-dryout flow boiling region.
4. COMPARISON of PREDICTED VALUE with EXPERIMENTAL RESULTS
A database was set up based on data from four independent research groups, as listed inTable 4. The database covers experimental data obtained using a mini-channel of 0.86 mm by
Zhao et al. (2002) to data by Gao et al. (2008) using a tube diameter of 3.76 mm. Because
Zhao et al. (2002) did not explain the type of oil they used, PAG-type oil was considered forthis calculation. For the data from Katsuta et al. (2008a, 2008b), an average oil concentration
was used in the calculation because they presented their groups of measured values fordifferent oil ranges rather than exact oil concentrations. Since the regulation of flow boilingheat transfer observed in the post-dryout region was not similar to that in the pre-dryout
region, only the predicted flow boiling heat transfer coefficient in the pre-dryout region wasconsidered in this analysis.
This experimental database was used to compare six prediction methods: namely, thecorrelations presented by Katsuta et al. (2008), Gao et al. (2008), Aiyoshizawa et al. (2006),
Hwang et al. (1997), Thome et al. (2004), and the newly proposed model. The correlations
proposed by Hwang et al. (1997) and Thome et al. (2004) for pure CO2 were used, and the10mixture properties of CO2 and oil were applied in these correlations. In contrast, theproperties of CO2 were employed in the correlations of Katsuta et al. (2008), Gao et al.
(2008), and Aiyoshizawa et al. (2006) following the original proposal.
In order to compare the accuracies of the prediction methods with the experimental
database, the deviation was defined as shown in Eq. (23). Two characteristic parameters wereused to provide a global quantitative measure of the performance of the new correlation; theirdefinitions are shown in Eqs. (24) and (25). The relative mean absolute error (RMAE) andrelative standard deviation (RSD) in Eqs. (24) and (25) indicate how close the predictions
were to the eventual outcomes.
Deviation

ED 

yexp  ycal

(23)

yexp

Relative mean absolute error
RMAE 

1 n yexp,i  ycal,i yn i 1exp,i(24)

Relative standard deviation

 yexp,i  ycal,i
-RMAE 
yexp,ii 1 

RSD 
n 1
n

2

(25)

Figure 10 shows the previous experimental results (Dang et al., 2013, Katsuta et al.,2008a, 2008b, Gao et al., 2007, 2008) for CO2 with a small amount of lubricating oil and theprediction results of the new correlations. Figure 10(a) shows that the results predicted by the
newly proposed correlation illustrated similar regulations at different oil concentrations withincreasing vapor quality. However, the oil concentration affected the calculated heat transfer
coefficient more significantly than that indicated by the experimental results. Aiyoshizawa etal.’s (2006) results were independent of the oil concentration and did not reflect the influenceof oil on the heat transfer. Figures 10(b) and (c) show that the prediction by the newly
proposed correlation tended to be similar to the experimental values of Katsuta et al. (2008a)
and Gao et al. (2007), and the prediction results reflected the regulation of the effect of the oilconcentration on the heat transfer coefficients.
Figure 11(a) compares the predicted and measured heat transfer coefficients in a 2-mm I.D.
tube with an oil concentration of 1% and mass flux of 360 kg m-2 s-1 to demonstrate the effectof the heat flux based on the data of Dang et al. (2013). The heat flux had a strong positive
influence on the heat transfer coefficient for a majority of the vapor quality region when theheat flux was increased from 4.5 kW m-2 to 36 kW m-2, and the mass flux stayed at 360 kg m-2
11

s-1. The theoretical results predicted by the newly proposed correlation depicted thecharacteristics of the heat transfer coefficient well: they showed the tendency of the heattransfer coefficients against the vapor quality to change from an upward movement to adownward movement when the heat flux rose and the mass flux remained constant. At heat
fluxes of 4.5 and 9 kW m-2, the theoretical heat transfer coefficients from the newly proposedcorrelation matched the measurements well at low vapor quality and gradually deviated fromthe experimental coefficients at high vapor quality. For both the heat fluxes, the predicted
results tended to increase with the vapor quality in the pre-dryout region. This shows thestrong influence of convective boiling on the heat transfer coefficients. At a heat flux of 18
kW m-2, both the measured pre-dryout heat transfer coefficients and the prediction by the new
proposed correlation barely changed with variations in the vapor quality. The nucleate boiling
was considered to be the dominant mechanism affecting the contribution of the heat transfer
coefficient to the flow boiling of CO2. When the heat flux was increased to 36 kW m-2, thetheoretical heat transfer coefficient decreased steadily with increasing vapor quality andmatched the dynamics of the measured results well. The prediction results from thecorrelation of Aiyoshizawa et al. (2006) increased with the heat flux and vapor quality.
However, the tendencies of some prediction data were different from those of theexperimental results when the heat flux reached 18 kW m-2 and especially 36 kW m-2.
Figure 11(b) compares the measured and calculated results for a CO2–oil mixture at aheat flux of 18 kW m-2, mass flux of 360–1440 kg m-2 s-1, and oil concentration of 1%. In Fig.
11(b), both the theoretical and experimental heat transfer coefficients showed that the massflux had a positive influence on the heat transfer coefficient of the CO2–oil mixture in the
pre-dryout region; this differs from the case under oil-free conditions. Under oil-free
conditions, the heat transfer coefficient with a large mass flux was slightly lower than thatwith a small mass flux (Dang et al., 2010). The difference was attributed to the suppression of
nucleate boiling due to oil entrainment. In oil-free CO2, the density difference between theliquid and vapor phases is very small; thus, nucleate boiling is predominant for the overall
heat transfer coefficient compared to convective heat transfer. However, the presence of oilretards bubble generation and bubble departure due to the increased surface tension and liquid
viscosity, which suppresses nucleate boiling. The convective heat transfer of the CO2–oil
mixture thus becomes prominent. The values predicted by the new proposed correlation
almost matched the trend of the measured data. When the mass flux was 360 kg m-2 s-1, thenucleate boiling and convective heat transfer were comparable, and the heat transfer
coefficients showed a gentle tendency with an increase in vapor quality. When the mass flux
was increased to 720 and 1440 kg m-2 s-1 at a heat flux of 18 kW m-2, the influence of thedominant convective heat transfer caused the heat transfer coefficient to increase with thevapor quality.
Figures 12(a)–(d) compare the measured and calculated flow boiling heat transfer
12coefficients of the CO2–oil mixture using the above correlations. The comparisons showedthat the best-fitting correlations were those of Aiyoshizawa et al. (2006) and the proposedmodel, which were based on almost the same experimental results. These correlations were ingood agreement with approximately 95% of the data of Dang et al. (2013) and also
corroborated about 90% of the data of Gao et al. (2007, 2008) and approximately 70% of thedata of Katsuta et al. (2008a, 2008b) within a deviation of ±30%. Because of the smaller innerdiameter of the test tube (0.86 mm) used by Zhao et al. (2002) and the unknown oil type, theprediction deviations of these two correlations are large. The correlations proposed by Katsuta
et al. (2008) and Gao et al. (2008) partially corroborated the data from Dang et al. (2013),
Gao et al. (2007, 2008), and Katsuta et al. (2008a, 2008b). Table 5 presents the RMAE andRSD of the six correlations. The correlations of Hwang et al. (1997) and Thome et al. (2004)
for pure CO2, when used with the properties of the mixture, could not obtain acceptabledeviations. The correlations for pure CO2 must therefore be corrected before they can beutilized to predict the heat transfer coefficient of a CO2–oil mixture. The correlations of the
proposed model and Aiyoshizawa et al. (2006) outperformed the other correlations in terms ofpredicting the flow boiling heat transfer of the CO2–oil mixture. The main differences
between these two correlations were that the proposed model considered the properties of the
mixture during calculation and provided superior performance when based on the measured
results of Dang et al. (2013).
5. CONCLUSIONSThis paper proposed a prediction model for the flow boiling heat transfer of CO2 with asmall amount of PAG-type lubricating oil. The main conclusions are as follows:(1) A prediction model for the heat transfer coefficient is proposed that considers theproperties of the CO2–oil mixture using new F and S factors. The proposed F factor with the

mixture properties is similar to that proposed for pure refrigerant. Factor S was revised byintroducing factor Soil to consider the influence of oil on nucleate boiling.
(2) The proposed correlation matched most (95%) of the experimental results of Dang et al.
(2013) with a deviation of ±30%. It also matched 90% of the data from Gao et al. (2007,2008) within a deviation of ±30%.
(3) Based on this model, increasing the oil concentration causes a gentle decrease in thepredicted heat transfer coefficient of a CO2–oil mixture.
(4) The proposed correlation can describe the tendency of the heat transfer coefficient of a
CO2–oil mixture at various heat and mass fluxes and agrees well with the experimental
results.
ACKNOWLEDGMENTS
The authors express their gratitude to Panasonic Corp. for their financial support. The first13

author is grateful for the support of the JSPS and NSFC No. 50976075.
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16

Appendix 1
Measured accuracy of the parameters in the experiment of Dang et al. (2013)
Item

Uncertainties

oil concentration (ωo)

0.1%Heat flux (qw)

±3%

Tw

±0.1 °C

Tsat±0.03 °C

Vapor fraction±3.3%

Heat transfer coefficient (α)

From ±8.9% to ±13%.

Appendix 2

1) Correlation of Hwang et al. (1997) for pure CO2

 tp   nb  cb(A-1)

 l0.79 c pl 0.5 l0.49 
0.4
0.75
 nb  0.00122S  0.6 0.29 0.24 0.24  tw  tsat  pl    psat  tw   pl   l hlv  v 



(A-2)




1  exp( F l X / l )S
, X  0.05 F l X / l
 g ( l   v ) 

0.5

(A-3)

0.4
cb   l F Prl0.6 ,  l  0.023Re0.8
 l / d 
l Prl

(A-4)

F  1.0, X tt  10

(A-5)

F  2.00.213  1 / X tt 

0.736

, X tt  10

0.5

0.9

 1  x    v   l 
X tt      
 x   l    v 

(A-6)

0.1

(A-7)

2) Correlation of Thome et al. (2004) for pure CO2

 tp 

ddry v  d  2  dry   wet
2 d wet   S nb,CO    cb3 
3



2

17



1/ 3

(A-8)
(A-9)

 nb  55 pr0.12   log10 pr 
 4G 1  x    cb  0.0133  1    l 

0.55

M 0.5 q 0.67

0.69

0.4

 cpL l  l 

0.8

 Gxd   cpv  v 
 v  0.023    v   v 

0.4

(A-10)l

(A-11)

v(A-12)d nb,CO  0.71 nb  3790

(A-13)

21  x 

1/ 2

S(A-14)

0.121Re0.225
l





1/ 4




 x 1  x  1.18  g l   v
x 1  x 
1  0.12 1  x         G 
2

v 



l 
 v
l





1

(A-15)

3) The correlations of Gao et al. (2008) are the same as those of Cheng et al. (2006) except
for additional suppression factors for the CO2–oil mixture.
1/ 3

 tp   S nb   cb3 (A-16)

 S   0.5 x  0.35   x  0.7 
S'  
 x  0.7 0(A-17)34) Correlation of Katsuta et al. (2008b) for CO2–oil mixture

 tp   nb  cb(A-18)

  G 1  x  D 
0.4
 cb  F  0.023 l 
 Pr
l
D

0.8

 1 
F  1  0.258   X tt 

0.886

 nb  S SA  

183

   1 
 92.32  v   l   X tt 

(A-19)
0.9(A-20)(A-21)  qD  SA  207 l  b 
Db  lTsat 0.745

 v   l 

0.581

Prl0.533

Bo0.518 Bd 1.27 Fr 0.964 S  ln  2.332

0.834

Re
tp





1  OCR

1
0.698

Bo 0.207 Bd 0.912



Re0.0571 Fr 0.0697
Bo0.0519
OCR  1   1

xdry  0.269 (A-22)(A-23)(A-24)(A-25)(A-26)OCR  1   1.169e0.17OCR(A-27)

5) Correlation of Aiyoshizawa et al. (2005)

 tp  Fcb  S nb(A-28)

  G 1  x  d 0.4
 cb  0.023 l 
 Pr
l
d 

0.8

  qD  nb  207 l  b 
Db  lTb 

0.745

 v   l 

(A-29)

0.581



2
Db  0.51  g  l   v  

Prl0.533(A-30)

0.5

 G (1  x ) D  1.25
Re tp  
F
l



(A-31)(A-32)

1.2

 1 
F  0.8  0.5  X tt S1
6   Retp  10

19



4 3

(A-33)(A-34)

Appendix 3 m   o  ol   r  1  ol (A-35)ln m  ol ln o  1  ol  ln  r(A-36)

cpm  (1  ol )cpr  olcpo(A-37)

 m   r    o   r  1  r

(A-38)

m  rr  o 1  r   0.72 1  r  r  o  r 

(A-39)

Conde (1996) presented an equation to predict the density of oil against temperature: (T )  0 (T )  A(T0  T )(A-40)

where T0 is a reference temperature and was taken as 15 °C here; ρ0(T0) is the density at thereference temperature (kg m-3); and A = 0.8 for the oil proposed by Conde.
Liley et al. (1973) proposed a formula to calculate the specific heat:Cp 

4.1861 9 0.388  0.00045  5 t  32  
S0 



(A-41)

where S0 is the relative density. S0 = ρo/ρw, which is the ratio of the liquid density and purewater density (999.0 kg m-3 at a temperature of 15 °C).
Robbins and Kingrea (1962) proposed a correlation to calculate the conductivity of a
fluid as a function of the molecular weight, density, specific heat, and critical temperature.



1
M

1/ 2

 T
3  20 1   Tc 

2/3

 T 
3  20 1  sb  Tc 

2/3(A-42)

where Δ1 = 1.5 for lubricants. Tsb and Tc are the standard boiling point and critical temperature,
respectively, of PAG oil (K) as shown in Table 2. Based on the data in Table 2, the maximum
deviation between the properties calculated by Eq. (A-42) and those provided by the
manufacturer of the oil is less than ±5%.
The viscosity of PAG oil is correlated to the temperature in the following function:(A-43)
lg(lg(  A))  B lg T  C
where A, B, and C have recommended values of 0.6, 2.468, and 6.46, respectively, to best fit
the viscosity data given in Table 2.

20

P

T

Gear Pump

Refrigerant
ChargeP

Pressure indicator

T

P
Wind tunnel

Mass
Flow
Meter

CoolerTeflon packing
Electrode

HeaterOil Sampling
SectionT

P

Thermocouple

F low of CO 2

Insulation layer

Sight
Glass
Oil

TEST SECTIONP

Test tube

T

P

T

FlangeOil
Separator

T
Thermocouple(a) Experimental system

DC power supply(b) Detailed structure of test section

Figure 1 Schematic diagram of experimental apparatus.

21

50
40

Tc

30

PVE10020
Separate10

PAG1000-10
ECP100

Soluble-20-30

Separate

-40
-50
0

10

20

30

40

50

60

70

80

90

100

Oil concentration (wt%)

Figure2 Two-phase separation temperature.

2260
o = 1%50

Local  o (%)

40

30

20

10

0

0

0.2

0.4

0.6

0.8

1

Vapor quality

Figure 3 Variation in local oil concentration against vapor quality.

23

30
20

 tp/ l

10

5

2

1
0.1

G=360kgm-2 s-1 , q=9kWm-2 , o=0%
G=360kgm-2 s-1 , q=9kWm-2 , o=0.5%
G=360kgm-2 s-1 , q=9kWm-2 , o=1%
G=360kgm-2 s-1 , q=9kWm-2 , o=5%

1

10

1/Xtt
Figure 4 Variation in boiling heat transfer coefficient ratio at various oil concentrations
against Lockhart–Martinelli parameter.

24

Figure 5 Variation in boiling heat transfer coefficient ratio against Lockhart–Martinelliparameter at ωo = 1%.

25

30000G = 360 kgm-2s -1
nb pure CO2
nb CO2-oil (o = 1%)

 l and  nb (Wm-2K-1)

10000

1000

l pure CO2
lCO2-oil (o = 1%)
100
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality
Figure 6 Comparison of αl and αnb calculated for pure CO2 and for CO2–oil.

26

300
Chen (1966)
Yoshida et al. (1994)
Aiyoshizawa et al. (2006)
This work (o = 1%)

Factor F

100

10

1
0.5
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality
Figure 7 Variations in factor F values of four correlations at 15 °C against vapor quality.

27

1

Yoshida et al. (1994)
G=360kgm-2 s-1 , q=18 kWm-2
G=360kgm-2 s-1 , q=36kWm-2
G=720kgm-2 s-1 , q=36kWm-2

0.9
0.8

Chen (1966)
G=360kgm-2 s-1 , q=18kWm-2
G=360kgm-2 s-1 , q=36kWm-2
G=720kgm-2 s-1 , q=36kWm-2

Factor of S

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality

Fig. 8(a)1
0.9Aiyoshizawa et al.(2006)
G=360kgm-2 s-1 , q=18kWm-2

0.8

G=360kgm-2 s-1 , q=36kWm-2

Factor S

0.7

G=720kgm-2 s-1 , q=36kWm-2

0.6( o =1%)-2-1
G=360kgm s , q=18 kWm-2

0.5

G=360kgm-2 s-1 , q=36kWm-2

This work

G=720kgm-2 s-1 , q=36kWm-2

0.4
0.3
0.2
0.1
0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality

Fig. 8(b)Figure 8 Variations in factor S values of four correlations at different mass and heat fluxes
against vapor quality.

28

7
o = 1%

6

G = 360 kWm-2 s-1 , q = 4.5 kWm-2

Fl
S nb
tp

-2

-1

 (kWm K )54
3

G = 360 kgm-2 s-1 , q = 18 kWm-2

Fl
S nb
tp

tp
S nb

G = 360 kgm-2 s-1 , q = 36 kWm-2

Fl

2

Fl
S nb

1
0
0

tp
0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality

Figure 9 Contributions of different constituent terms to variation in total heat transfer
coefficient (αtp) from nucleate boiling (Sαnb) and convective heat transfer (Fαl) with the vapor
quality29

8
G = 360 kgm-2 s-1

Heat transfer coefficient (kWm-2K-1)

7exp,  o = 0.5%

q = 9 kWm-2

exp,  o = 1%
exp,  o = 5%6This work5 o = 0.5%
 o = 1%
 o = 5%

4

Aiyoshizawa et al. (2006)3 o = 0.5%
 o = 1%
 o = 5%

2
1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality

Fig. 10 (a) Data of Dang et al. (2013)

Heat transfer coefficient (kWm-2K-1)

7
G = 400 kgm-2 s -1
q = 10 kWm-2
T = 10o C

6exp, o = 0.3%
exp, o = 1%
exp, o = 2%
exp, o = 3.5%5

4cal, o = 0.3%
cal, o = 1%

3cal, o = 2%
cal, o = 3.5%

2

1
0

0.2

0.4

0.6

Vapor quality

0.8

Fig. 10 (b) Data of Katsuta et al. (2008a)
30

1Heat transfer coefficient (kW/m-2K-1)

7
G = 380 kWm-2 s -1
q = 20 kWm-2
T = 20o C

6exp, o = 0.11%
exp, o = 0.3%
exp, o = 0.57%

5cal, o = 0.11%

4cal, o = 0.3%
cal, o = 0.57%

3

2

1
0

0.2

0.4

0.6

0.8

1

Vapor quality

Fig. 10 (c) Data of Gao et al. (2007)

Figure 10 Comparison of measured heat transfer coefficients and those calculated using
proposed correlation at different oil concentrations.

31Heat transfer coefficient (kWm-2K-1)

8exp, 4.5 kWm-2
exp, 9 kWm-2
exp, 18 kWm-2

6exp, 36 kWm-2
This work
4.5 kWm-2
9 kWm-2
18 kWm-2
36 kWm-2

4

Aiyoshizawa et al. (2006)
4.5 kWm-2
9 kWm-22-2 -1

G = 360 kgm s

18 kWm-2

o = 1%36 kWm-2

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Vapor quality

Fig. 11 (a) Various heat fluxes and constant mass flux
10

-2 -1

Heat transfer coefficient (kWm K )q = 18 kWm-2
 o = 1%8

exp, 360 kgm-2 s-1-2 -1
exp, 720 kgm s
exp, 1440 kgm-2 s-1
This work
360 kgm-2 s-1

6

720 kgm-2 s-1
1440 kgm-2 s-1
Aiyoshizawa et al. (2006)

4

360 kgm-2 s-1
720 kgm-2 s-1
1440 kgm-2 s-1

2

0
0

0.1

0.2

0.3

0.4

0.5

0.6

Vapor quality

0.7

0.8

0.9

1

Fig. 11 (b) Various mass fluxes and constant heat flux
Figure 11 Comparison of theoretical heat transfer coefficients based on correlations of authors
and Aiyoshizawa et al. (2006) and measured data in 2-mm I.D. tube at different mass and heat
fluxes based on data of Dang et al. (2013).

32

16

o>0.05%14 cal (kWm-2K-1)

12
10


8
6
4
2013
Dang et al. (2012)
Gao et al. (2007, 2008)
Zhao et al. (2002)
Katsuta et al. (2008a, 2008b)

2
0
0

2

4

6

8

10

-2 -1

12

14

16

 ex p (kWm K )Fig. 12 (a) Correlations proposed by Gao et al. (2008)12

o>0.05%
Gao et al. (2007, 2008)-2 -1

 cal (kW/m K )10Dang et al. (2012)
2013


8


6

4

2
Zhao et al. (2002)
Katsuta et al. (2008a, 2008b)

0
0

2

4

6

8

-2 -1)

10

12

K-1）
(kW/m
 ex p (kWm-2K

Fig. 12 (b) Correlations proposed by Katsuta et al. (2008)

33

12

Katsuta et al. (2008a, 2008b)
Zhao et al. (2002)
Gao et al. (2007, 2008)
Dang et al. (2012)
2013

8

-2 -1

 cal (kWm K )10

 o>0.05%



6

4

2

0
0

2

4

6

8
-2

10

12

-1

 exp (kWm .K )Fig. 12 (c) Correlations proposed by Aiyoshizawa et al. (2006)
12

10

Katsuta et al. (2008a, 2008b)
Gao et al. (2007, 2008)
Zhao et al. (2002)
Dang et al. (2012)
2013


-2 -1

cal (kWm K )

8

6
o>0.05%
4

2

0
0

2

4

6

-2

-1

8

10

12

 ex p (kWm K )Fig. 12 (d) New correlations proposed by authors
Figure 12 Comparison of heat transfer coefficients predicted using six correlations anddatabase.

34

Table 1 Experimental conditions
Tube Material

SUS316

Tube I.D. [mm]2.0

Oil type

PAG100Oil concentration [%]0.5–5.0-2 -1

Mass velocity [kg m s ]360, 720, 1440

Heat flux [kW m-2]

4.5, 9, 18, 36

Evaporating temperature [°C]15

Quality

Approximately 0–1.0

Table 2 Properties of PAG-type oil
Temperature [°C]

0

15

40

-1

Molecular weight [kg kmol ]

100

1600

Density
Specific heat [kJ kg-1 K-1]
-1

50

-1

Conductivity [W m K ]---1000.6---1028.6---1.7577------1.9251

2.09250.1478------0.1465

---

---

106.1

---

2 -1

Kinematic viscosity [mm s ]Standard boiling point [K]845.41

Critical temperature [K]968.57

Eccentricity factor

1.352

20.29

Table 3 Comparison of the properties of the oil–CO2 mixture and pure CO2
Items

CO2–oil mixture
1 % oil-1-1

Liquid conductivity [W m k ]oilPure CO2

5% oil

0.09195

0.09355-40.1232-4

0.09186-1-3

Liquid viscosity [Pa s ]7.35 × 10

12.56 × 10

3.41 × 10

7.44 × 10-5

Liquid density [kg m-3]

822.73828.851006.84821.21-13425.163356.61

1728.583442.3

Surface tension [N m ]0.004220.00701

0.02450.00196-1

Specific heat [J kg K ]-1

The saturation temperature of pure CO2 is 15 °C

35Table 4 Experimental studies on flow heat transfer of CO2–oil mixtures

Tube Inner

Heat flux

Mass flux

Oil concentration

Saturatedtemperature

Number ofdata items

D (mm), L (m)(kW m-2)(kg m-2 s-1)

(%)

(°C)

ωo > 0.05%

Katsuta etal. (2008a)

Smooth stainless
D = 3 mm, L = 0.5 m

5, 10, 15Electric heating400

PAG 100, 0%,
0.05%–0.5%, 0.6%–1.2%,
1.8%–2.5%, 3.0%–4.0%

Oil supply system,
oil mass flow meter,and oil samplingtank–10, 0, 10

55

Katsuta etal. (2008b)

Smooth stainless
D = 3 mm, L = 0.5 m

10Electric heating

200, 400, 600

PAG 0%–0.05%,
0.5%–1.0%, 1.0%–2.0%,
2.0%–3.0%, 3.0%–5.0%,
7.0%–9.0%

034

Gao et al.
(2008)

Smoothstainlessinner
D = 3.76 mm

10, 20, 30
Electric heating

100, 190, 380

PAG 1.01% PAG < 0.01%,
0.09%, 0.17%, 0.48%,
0.72%

Measured from the
size
and
time
interval of an oil
droplet falling into
the sight glasschamber

1051

Gao et al.
(2007)

Smooth stainless
D = 3 mm, L = 2.185m; Micro-fin D =3.04 mm, Fin height= 0.11 mm

5, 10, 20, 30
Electric heating

190, 380, 770,
390, 780, 1180

PAG
0.01%,
0.30%, 0.57%

1053

Zhao et al.
(2002)

Mini tube D = 0.86
mm

11

300

Oil type N/A 0%, 1%, 3%,
4%, 5%, 7%

N/A

10

54

Dang et al.
(2013)

Smooth stainless
D = 2 mm4.5, 9, 18, 36
Electric heating

360, 720, 1440

PAG 100,
0%, 1%, 3%, 5%

Oil sampling tube

15

274

References

Oil test methods

36

0.11%,

Table 5 Deviations of methods when predicting heat transfer data at ωo > 0.05%

Data

Katsuta et al.

Zhao et al.

Dang et al.

Gao et al.

(2008a, 2008b)(2002)(2013)(2007, 2008)

Mean
absolute
Model
Thome et al.
(2004)
Hwang et al.
(1997)
Katsuta etal. (2008a)
Gao et al.
(2008)Aiyoshizawa
et al. (2006)
This work

errorStandard
deviation

Mean
absolute
errorStandard
deviation

Mean
absolute
errorStandard
deviation

Mean
absolute
errorStandard
deviation

–146%

290%

–141%

255%

194%

400%

196%

399%

63%

136%

32%

57%

123%

243%

109%

234%

42%

25%

46%

24%

21%

39%

21%

11%

53%

112%

61%

118%

62%

129%

64%

141%

32%

21%

40%

22%

10%

9%

14%

11%

32%

13%

49%

20%

10%

7%

17%

10%

37