Intelligent dimensional and thermal performance analysis of Al2O3 nanofluid

Energy Conversion and Management 138 (2017) 686–697

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Intelligent dimensional and thermal performance analysis of Al2O3 nanofluid Rong-Tsu Wang a, Jung-Chang Wang b,⇑ a b

Department of Airline and Transport Service Management, Vanung University, No. 1, Van-Nung Rd., Chung-Li, Tao-Yuan 32061, Taiwan, ROC Department of Marine Engineering, National Taiwan Ocean University (NTOU), Keelung 20224, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 22 November 2016 Received in revised form 13 January 2017 Accepted 6 February 2017 Available online 28 February 2017 Keywords: Nanofluid Two-step synthesis Thermal performance Dimensional analysis

a b s t r a c t This study incorporated a two-step synthesis assisted by a microemulsion ultrasound technique to prepare Al2O3 nanofluid. 0.5–2.5 wt.% concentration (wt.%) nanofluid was tested for grain size, zeta potential, pH value, thermal conductivity coefficient, viscosity, and light absorption. A sedimentation experiment verified suspendibility, stability, and thermal conductivity to determine the beat mixing method. And an empirical formula for the nanofluid thermal conductivity was derived by using the intelligent dimensional analysis to examine the functional relationships between the experimental parameters. The results of the property verification and experimentation indicated that nanofluid and emulsifying agent mixture at a concentration of 1 wt.% had the best thermal conductivity, and that this decreases as the concentration increases. The suspendability and stability were the best at a concentration of 2 wt.%; there was no sedimentation at three weeks. The results also indicated that for 0.5–2.5 wt.% Al2O3 nanofluid between 20 and 40 °C, inserting the temperature and concentration parameters can estimate the thermal conductivity within an error rate of 3%. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction At the beginning of the 20th century, Maxwell discovered that suspending particles on the nano-level within a liquid increases thermal conductivity [1]. This began the era of nanoliquid research, making nanotechnology the emerging technology of the modern age. Improving the efficiency of electrical components often creates large amounts of waste heat. This waste heat influences efficiency and consumes extra energy; therefore, more efficient heat dissipation methods are needed. Thus, research into the thermal properties of nanofluids as heat exchanging working fluids has good potential for development [2–7]. Milanese et al. [8,9] investigated on a promising new technology of DASC (Direct Absorber Solar Collector) and CSP (Concentrated Solar Power) utilizing several metal oxide nanoparticles (Al2O3, ZnO, CeO2, TiO2, and Fe2O3) as a function of temperature in the range 25–500 °C. They found different optical and thermal behaviors of the nanofluids and how heating cycles affected nanoparticle structural stability and absorption characteristics depending on nanoparticles material and concentration. Nanoliquids are a type of working fluid that contain suspended nanoparticles that have a strong size effect for

⇑ Corresponding author. E-mail address: [email protected] (J.-C. Wang). http://dx.doi.org/10.1016/j.enconman.2017.02.010 0196-8904/Ó 2017 Elsevier Ltd. All rights reserved.

thermal conductivity and fluid behavior on the micro-scale. They are widely used with regard to continuous physical measures such as temperature, pressure, internal energy, entropy and enthalpy, and thermal properties such as thermal conductivity, specific heat capacity, and viscosity. The geometry, cohesion situation, and surface obstruction of nanoparticles are the main variables controlling thermal conductivity enhancement in nanofluids. The particles in the liquid are not easily affected by gravity and do not aggregate or precipitate [10–15]. Chauhan and Singhvi [16,17] employed the artificial neural network (ANN) technique for calculating the effective thermal conductivity (ETC) of nanofluids. They found that the enhancement in the ETC of nanofluids may be due to the distribution of volume fraction at the liquid particle interface. And the base fluid layering between the liquid particle interface and aggregation of nanoparticles plays an important role in enhancement of ETC of nanofluids. Milanese et al. [18] carried out the layering phenomenon in order to explain the differences in thermal conductivity between nanofluids based on metal (Cu) and metal oxide (CuO) nanoparticles employing molecular dynamics simulations. The numerical results revealed two shell-like formations of water molecules (layers) close to the Cu nanoparticle surface, differently from CuO nanoparticle, where no significant layering phenomenon has been observed. A two-step synthesis method, also known as the direct mixing method, was used to prepare Al2O3 nanofluid, adding nanoparticles

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687

Nomenclature A C K Q T W b c t Abs Io Ie EK Knf,f Knf,e

area (m2) specific heat (kJ/kg °C) thermal conductivity (W/m °C) heat transfer rate (W) temperature (°C) weight percent concentration (%) thickness (m) concentration (%) time (s) absorbance incident light intensity (W/m2) intensity of the light passing through (W/m2) error of the measured and calculated thermal conductivity calclulated thermal conductivity (W/m °C) measured thermal conductivity (W/m °C)

into the working fluid in the present study. However, directly adding nanoparticles often results in clumping, reducing suspendibility. Therefore, the three following methods were used to stabilize the suspended matter: (1) The pH value of the nanofluid was changed to prevent sedimentation due to isoelectric points (IEP); (2) an ultrasonicator was used to disperse the particles; (3) a surfactant/ dispersant was added (such as sodium laureth sulfate or oleic acid) to promote dispersal and suspension. Selection of the surfactant and dispersant was based on solvent and particle properties. The advantage of the two-step synthesis method is that the grain size and concentration can be better controlled as they can be selected based on usage needs [19–26]. In addition, this study also used emulsifying agent to increase the suspendibility of the particles in the aluminum oxide nanofluid. However, higher concentrations of the emulsifying agent reduce the zeta potential of the suspended particles and the thermal conductivity coefficient for the nanofluid. According to past literature, with a fixed 2% concentration of aluminum oxide powder and dispersant, an emulsifying agent concentration of 2% is optimal [22,27]. The effective thermal conductivities or heat transfer behaviors of buoyancy-driven nanofluids were depended on nanoparticle volume fraction varied between 0% and 9%, operating temperature between 25 and 40 °C, nanoparticle shape and nanoparticle size and were measured by transient hot wire method. The nanofluids are exhibiting higher thermal conductivity compared to base fluid and show an increase in the effective thermal conductivity with an increase in particle volume fraction and with a decrease in particle size. Furthermore, the relative increase in thermal conductivity was found to be more important at higher temperatures. It is observed that the nanoparticle enhances heat transfer rate even at a small volume fraction. Material with higher thermal conductivity is not a decisive factor and not always effective to improve the thermal transport properties of nanofluids [28–32]. Eventually, this study had two goals. The first was to use a microemulsion ultrasonic technique to prepare an aluminum oxide nanofluid. As the suspendibility of the particles in the nanofluid determine the properties of the nanofluid, if the nanofluid used in a heat exchanger does not have good suspendibility, it may cause obstruction or wear. Manipulation of Van Der Waals’ force, electrostatic force, stochastic force, and hydrodynamics can change the surface area of the particles and the collision frequency, which can alter the boiling heat transfer, thermal conductivity, and suspendibility. Iacobazzi et al. [33] studied the effect of mass difference scattering on the thermal conductivity of alumina based

Greek

a

b

c

k

e l q jB

constant constant constant constant molar absorption coefficient (m
1) viscosity (m2/s) density (kg/m3) Boltzmann’s constant (1.381  10
23 J/K)

Subscripts p nanoparticle bf base fluid nf nanofluid

nanofluid using theoretical and experimental approaches. Nanofluid based on liquid water, frozen water and diathermic oil has been experimentally compared. They found the most intensive mechanism reducing the nanofluid thermal conductivity with respect to the microfluid one. Moraveji and Razvarz [34] investigated on the thermal efficiency enhancement of a heat pipe using aluminum oxide nanofluid compared with pure water under the different operating state. Zhao et al. [35] present a threedimensional numerical analysis to study the thermal conductivity and viscosity of Al2O3–water nanofluids through a flat and circular tube at constant heat flux boundary condition. Colangelo et al. [36] exhibited diathermic oil based nanofluids more suitable application in renewable energy, cogeneration and cooling systems than water nanofluids. And thermal conductivity enhancement of nanofluids with diathermic oil is higher than those with demineralized water in the range 20–60 °C. Wang and Wang [37] used the alumina nanofluid as an electrolyte compared with the other aqueous solutions according to the Zeta potential, pH value, thermal conductivity, and viscosity to demonstrate the most favorable stability, particle fraction, thermal conductivity, and stable current output. Therefore, the properties of the Al2O3 nanofluid were first analyzed to confirm the optimal mixing method. The second goal was to derive an empirical formula via dimensional analysis [38–46] of experimental data. Because there has been no theoretical formula regarding the thermal conductivity and electrical generation characteristics of nanofluids with emulsifying agents in past studies, this study aimed to derive an empirical formula using dimensionless parameters and to use dimensional analysis to verify the formula. 2. Methods 2.1. Nanofluid preparation Fig. 2.1 introduced the preparation method and the manufacturing process of the alumina nanofluids involving the experimental instruments for testing the thermal performances. The density and radii of the individual Al2O3 nanoparticles (from Yong-Zhen Technomaterial Co., Taiwan) were 0.08 g/cm3 and between 15 and 35 nm, respectively. Moreover, the SEM (Scanning Electron Microscope) image of the prepared alumina nanoparticles was exhibited in Fig. 2.1. The mean size of alumina nanoparticles was about 27 nm. Furthermore, the thermal conductivity of the Al2O3 nanoparticles was approximately 38 W/(m K). And Al2O3

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Select alumina nanoparticles & Emulsifying proportion (Weight percentage concentration are 0.5% to 2.5%)

Emulsified mixed deionized water was heated and stirred. (80°C, 500 rpm, and stirring 40-60 min)

Add

dispersant

(Dispersant

after

Weight

stirring

percentage

concentration are 0.5% to 2.5%)

The sample is ultrasonically vibrated

Finished Fig. 2.1. Al2O3 Nanofluid preparation procedure and experimental apparatus.

nanoparticles with aqueous solution has a very high thermal conductivity 32–42 W/(m K). A dispersant named QF-DTK-190 (from Yong-Zhen Technomaterial Co., Taiwan) comprising propylene glycol mono-methyl ether, 1-methoxy-2-hydroxypropane, 2-methoxy-1-methylethanol, and propylene glycol methyl ether (PGME) for dispersing the Al2O3 nanoparticles uniformly in the de-ionized water and emulsifying agent of the non-ionic surfactants Tween 20, Tween 80, Span 20, and Span 80 (from First Chemical Co., Taiwan) were incorporated to enhance the suspension stability of the nanofluids using ultrasonic vibration. The concentrations of emulsifying agents and the oscillatory time of supersonic waves affect the suspended stability and the size of Al2O3 nanofluid. The thermal conductivity and pH value of the QF-DTK190 dispersant were approximately 0.51 W/(m K) and 2.8, respectively. An electromagnetic hot plate with a stirrer (PC-620D model, CORNING Co., USA) was used for stirring the nanofluids. The rotational velocity of the stirrer was between 60 and 1150 rpm, and the maximum heating temperature was 550 °C. A supersonic homogenizer (ultrasonic 250 model, He-Yu Technology Co., Taiwan) was employed to prepare the various Al2O3-nanoparticle concentrations of the nanofluids. The maximum power, supersonic frequency, and processing capacity of the supersonic homogenizer are 250 W, 23 kHz and 0.2 –400 ml, respectively [22,27,46]. The thermal conductivities of nanofluids with different concentrations were analyzed, and changes were observed after resting. Afterward, intelligent dimensional analysis was used to derive an empirical formula using the experimental data. An analytical

balance (Shimadzu Co., Japan; Maximum value 220 g; The lowest precision was 10
4 g) was employed to measure the slight weight of nanoparticles and emulsifying agent. The scales had to be considered when determining whether to maintain the level, surrounded by vibration and wind; otherwise, it affects the accuracy of the measurements. Five nanofluids (ranging from 0.5 wt.% to 2.5 wt.%) with different weight percent concentrations were used. The nanofluid preparation procedure is shown below. (1) Mix Tween20 (HLB = 16.7) and Span20 (HLB = 8.6) into a HLB = 12 emulsifying agent. (First use a stirrer at 200 rpm. When the emulsion becomes less viscous, adjust the rotational speed to 400 rpm. Use the stirrer for 120 min until the emulsion becomes clear. Do not heat.) (2) Mix the emulsifying agent and deionized water and then heat while stirring (80 °C with a rotational speed of 500 rpm for approximately 40–60 min). (3) Use a constant temperature bath at 25 °C or natural cooling to cool the samples. A circulating water bath (He-Yu Technology Co., Taiwan) was employed at constant temperature to preserve the temperature of the nanofuilds; the water bath had a heating power of 1 kW, a frozen force of 1/3 HP, a volume capacity of 20 L, and the operating temperature ranged from
20 to 100 °C. (Attention must be paid to whether the water surface is over condensing tube or not and the water flow must not contaminate the samples.)

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(4) Add the nanoparticles into the mixed and cooled emulsified deionized water. (a respirator and gloves must be worn to avoid inhalation of nanoparticles or remnants on hands.) (5) Stir the samples from step 4 for 10 min. Do not heat. (6) While waiting for step 5 to be complete, add the dispersant into the samples and stir for approximately 10 min. (7) Move the stirred samples into a sonicator acoustic hood. (make sure the probe is below the sample surface or it might break.) Fix the probe and start oscillation at 80% power output. Because accumulated heat will generate charring in the nanofluid, the oscillation time should not last over 30 min. The nanofluid can be cooled to continue oscillation. The total oscillation time is approximately 60 min. (8) Repeat these seven steps to prepare mixtures with concentrations of 0.5–2.5 wt.% Al2O3 nanoparticle powder, emulsifying agent, and dispersant. 2.2. Nanofluid property test This section describes the nanofluid testing as shown in Fig. 2.2. To confirm the application in future studies and obtain the empirical formula, the stability and suspendibility of nanofluids with different concentrations must be explored. The baseline was determined by the grain size, zeta potential, absorbance, pH value, viscosity, and thermal conductivity of the nanofluid. The steps for testing each property were as follows: 2.2.1. Zeta potential and pH value The nanofluid zeta potential changes along with pH value and isoelectric points (IEPs), which is a critical factor for determining the stability of nanofluids, occur at certain pH values. The zeta potential of nanofluids at an IEP is zero. Scilicet, along with pH value testing, the zeta potential of the nanofluid was tested to confirm the presence of sufficient electrostatic repulsion between the nanoparticles. Thus, the pH values of the samples must be tested as an initial evaluation of zeta potential employed the back scattering light measurements through the particle size and potential analyzer (Malvern Co., Switzerland) and the desktop pH meter (Metrohm Co., Switzerland), respectively.

2.2.2. Grain size The mean grain size directly affects the suspendibility of the nanoparticles in the nanofluid. Therefore, a particle size and zeta potential analyzer tested the mean grain size to determine the influence of different concentrations and oscillation times on grain size. The grain size can also be used to determine the suspension stability. 2.2.3. Absorbance When a light source passes through the sample, it will absorb a portion of the light. Therefore, a spectrophotometer was used to measure changes in absorption. A calibration curve that relates the weight concentrations of the Al2O3 nanoparticles (ranging from 0.5 wt.% to 2.5 wt.%) in the de-ionized water, which were added without any surfactant or dispersant, to the light absorbance value was established using a spectrophotometer (U-1900, Hitachi Inc., Japan). Its wavelength range is between 190 nm and 1100 nm with bandwidth 1.5 nm and error range of ±0.5 nm. Considering this calibration curve enabled obtaining the concentration of the alumina nanoparticles in the nanofluid from the light absorbance data compiled by the spectrophotometer. The absorbed light was used to calculate changes in suspendibility, concentration, and sedimentation along with time. The absorption principle is shown in Eq. (2.1).

Abs ¼ log

I0 ¼ecb Ie

ð2:1Þ

Abs = absorbance, I0 = incident light intensity, Ie = intensity of the light passing through, ee = molar absorption coefficient, b = thickness, c = concentration. 2.2.4. Viscosity Viscosity is also closely related to the quality of the nanofluid. The viscosity seems also to be a crucial factor for the thermal performance. As highly viscous liquids have poor fluidity, this affects the Brownian movement of the nanoparticles in the liquid, causing aggregation and sedimentation. Each nanofluid’s viscosity was measured with a capillary viscosity meter (K 698,Cannon Inc., USA) in a water tube at a constant temperature of 40 °C ± 0.02 °C,

Alumina nanofluid testing

Grain size

Thermal conductivity

Zata potential

pH Value

Fig. 2.2. Al2O3 Nanofluid testing instruments.

Viscosity

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based on the ASTM D445 method and the deviation was less than ± 3%. The ASTM D445 standard test for the kinematic viscosity of transparent and opaque liquids has a repeatability of 0.11% in most cases. 2.2.5. Thermal conductivity The thermal conductivity coefficient is a numerical value that determines the thermal conductivity of the nanofluid. The probe is inserted into the liquid (the entire probe must be immersed) and left alone for several minutes for the data to be recorded. A portable thermal conductivity meter (KD2, Decagon Devices Inc., USA) was employed to measure the nanofluids’ thermal conductivity coefficients. Its operating environment is 5–40 °C. The measuring range of thermal conductivity is 0.02–2 W/(m K) and the accuracy is ±2.5%. The transient hot wire principle derived from one dimensional Fourier’s transform was used as shown in Eq. (2.2).

K¼


 Q t2 AðT 2
T 1 Þ t1

ð2:2Þ

K is thermal conductivity, Q is heat, A is area, T is temperature, t is time. When t = 0, thermal equilibrium is reached. Given one unit of Q, time t changes from t1 to t2 , and temperature changes from T1 ? T2 . In this thermal process, temperature increases or decreases after being subjected to changes in thermal energy; thus, K can be calculated. 2.3. Experimental parameters input into theoretical thermal conductivity formulas from past literature Theoretical models from which parameters can be obtained for this experiment were selected from past literature and incorporated to calculate the results in order to confirm these formulas and the differences in the thermal conductivities obtained for the aluminum oxide nanofluid in the present study. The theoretical models that could be used in this study are shown in Table 2.1. These are the basic nanofluid thermal conductivity model developed by Maxwell [1] and the model modified by Xuan [47] to take into account stochastic force, grain size, concentration, and temperature. Nevertheless, emulsifying agents were not taken into account for. In those models, Knf is the thermal conductivity for the nanofluid, Kbf is the thermal conductivity for the base fluid, Kp is the thermal conductivity for the particles, u is the particle volume fraction, qp is the particle density, C p;p is the particle specific heat, kB is the Boltzmann’s constant, T nf is the nanofluid temperature, lnf is viscosity, and r is the particle radius. 2.4. Empirical formula deduction Generally, most engineering problems in fluid mechanics can be solved via equations and theories. However, the conclusions derived from the equations and theories can only be used for initial evaluations; many conclusions should be tested experimentally in order to obtain actual results. The present study applied VashyBuckingham Pi (P) Theorem dimensional analysis to acquire an empirical formula for the nanofluid thermal conductivity. The analysis procedure is shown as follows:

Table 2.1 Formulas from past literature [1,47]. Methods

Theoretical formula

Maxwell [1]

K nf K bf

¼

K p þ2K bf þ2ðK p
K bf Þu K p þ2K bf
ðK p
K bf Þu

K nf K bf

¼

K p þ2K bf þ2ðK p
K bf Þu K p þ2K bf
ðK p
K bf Þu

Xuan [47]

þ

qp uC p;p 2K bf

rffiffiffiffiffiffiffiffiffiffiffiffiffiffi kB T nf 3plnf r

1. Definition of the nine correlated variables: thermal conductivity of nanofluid K nf , nanopowder thermal conductivity K p , nanofluid specific heat C nf , nanofluid grain size dp , nanofluid temperature T nf , thermal conductivity of base fluid K bf , nanofluid viscosity lnf , nanofluid density qnf , and diluted Al2O3 weight percent concentration Wnf. K nf nanofluid thermal conductivity was determined by the other eight variables, four of which were independent physical quantities, namely, mass (M), length (L), time (T), and temperature (H). These can be used in Eq. (2.3).

n o K nf ¼ Function K p ; C nf ; dp ; T nf ; K bf ; lnf ; qnf ; W nf

ð2:3Þ

2. Expression of all variables using M, L, T, and H resulted in the following. Knf = [MLT
3H
1], Kp = [MLT
3H
1], Cnf = [L2T
2H
1], dp = [L], Tnf = [H], Kbf = [MLT
3H
1], lnf = [ML
1T
1], qnf = [ML
3], W nf = [–] 3. There are 5 dimensionless p numbers for the thermal conductivity; this study selected four repeating variables, Kbf, Tnf, lnf , and qnf , for extrapolation. These four repeating variables were multiplied by other non-repeating variables to obtain dimensionless P parameters. i. First, P1 was determined using the non-repeating variable Knf . P1 = (MLT
3H-1)  (MLT
3H-1)a  (H)b  (ML
1T
1)c  (ML
3)d ii. When M, L, T, and H are all 0, the simultaneous equations obtain a = -1 and b = c = d = 0. iii. Similarly, P2 =

Kp , K bf

P3 =

C nf lnf , K bf

1

P4 =

1

dp K 2bf qnf T 2nf 3 2 nf

l

P5 =W nf .

iv. Examine whether the above are dimensionless parameters 4. The five pi-groups obtained above represent the functional relationship and the results are shown in Eq. (2.4).

8 9k 1 1  b   C nf  lnf c
ð2:4Þ

nf

As P2 and P3 share the denominators, they can be combined into Eq. (2.5).

9c ( )b 8 1 1 K p  C nf  lnf
ð2:5Þ

nf

5. Substituting the nanofluid fluid properties obtained from the experiment into Eq. (2.5) obtains values for a, b, c, and k; which, after simplification, yields the thermal conductivity empirical formula for the nanofluid. Eventually, the present study utilizes intelligent dimensional methods to calculate the effective thermal conductivity values of alumina nanofluids with emulsifying agents. Therefore, certain error necessarily exist between the data measured during experiment, value deriving from experimental data and actual values due to artificial operation and limitation of accuracy of experimental apparatus. For this reason, it is necessary take account of experimental error to create confidence of experiments before analyzing experimental results. The concept of the error propagation is introduced to calculate experimental error and fundamental functional relations for propagation of error. During the experiment, various items of thermal conductivities are adopted to analyze the effective thermal conductivity values of alumina nanofluids with emulsifying agents. The effective thermal conductivity values belong to derived variable and includes nano-powder thermal conductivity K p , nanofluid specific heat C nf , nanofluid grain size dp , nanofluid

R.-T. Wang, J.-C. Wang / Energy Conversion and Management 138 (2017) 686–697

temperature T nf , thermal conductivity of base fluid K bf , nanofluid viscosity lnf , nanofluid density qnf , and diluted Al2O3 weight percent concentration Wnf, which are measured with experimental instruments. The error of experimental instruments is propagated to the result value during deduction and thus become the error of effective thermal conductivity values. An experimental error is represented with a relative error and the maximum relative error of effective thermal conductivities are within ±1.5%. 3. Results and discussion 3.1. Nanofluid property test results This section describes the influence of adding HLB = 12 emulsifying agent and dispersant to change the powder concentration on the particles and the stability of the nanofluid. Measurements of grain size, zeta potential, pH value, thermal conductivity coefficient, viscosity and absorbance were used to determine the stability of the nanofluid. The concentrations of the nanofluid powder were 0.5–2.5 wt.%; the dispersant and emulsifying agent were adjusted according to the concentration of the aluminum oxide powder. The results indicated that 2 wt.% was the optimal concentration. The experimental results were used in intelligent dimensional analysis to extrapolate an empirical formula, which takes into account the emulsifying agents.

Table 3.1 Changes in nanofluid particle grain size after four weeks. Mean nanofluid grain size (nm)

Al2O3 powder, dispersant, and emulsifying agent concentrations (wt.%) 1 7 14 21

0.5

1

1.5

2

2.5

157.2 160.9 159.6 –

155.2 159.2 186.5 –

169.3 173.7 175.1 –

168.9 170.1 167.1 175.9

194.7 207.4 208.4 210.9

3.1.1. Nanofluid particle grain size The mean grain diameter of the nanofluid particles used in the present study is shown in Table 3.1. After standing for one week, the mean grain size only slightly increased and no precipitation occurred. At two weeks, aside from the 1 wt.% sample for which the mean grain size increased nearly 30 nm, the difference in grain size for the other samples was negligible. However, the 0.5%, 1%, and 1.5% nanofluids had aluminum oxide precipitation noticeable by the naked eye. As the 0.5%, 1%, and 1.5% nanofluids had fewer particles, they were more susceptible to gravity. As shown in Fig. 3.1(a), at each time of measurement, the mean grain size did not always increase compared to the previous measurement. This was because measurements were taken in the middle of the sample as shown in Fig. 3.1(a) and most of the heavier, clustered particles precipitated to the bottom due to gravity. At the third week, Fig. 3.2 exhibited that the 0.5%, 1%, and 1.5% nanofluids experienced substantial precipitation, such that the nanofluid lost its original properties. The 2% and 2.5% nanofluids had no noticeable precipitation; however, the 2.5 wt.% nanofluid had larger grain sizes, thus, it was initially determined that 2 wt.% was the optimal concentration. Two layers, an emulsified layer and a sediment layer, were observed in the Al2O3 nanofluid after it remained motionless for a certain period. This implies a faster nanoparticle precipitation rate after the nanofluid has remained motionless for some time or been centrifuged. The above results show that the grain size cannot accurately portray the precipitation of the nanofluids; as such, absorbance measurements and observations with the naked eye must be done in complement to determine optimal nanofluid suspendibility. 3.1.2. pH value and zeta potential According to past work, the IEP for most aluminum oxide nanofluids is at pH = 9. As such, when producing the nanofluid, it was ensured that the pH level did not approach 9. It also found that adding an emulsifying agent decreased the zeta potential; for a 2 wt.% Al2O3 powder and dispersant mixture, emulsifier concentration between 8% and 10% created an IEP. Therefore, this study determined the Al2O3 powder, emulsifying agent, and dispersant

250 Average Size Vs Days 0.5%

240

1% 1.5% 230

2% 2.5%

220

210

Average Size(nm)

Day Day Day Day

200

190

180

170

160

150

0

5

10

15

20

691

25

Days

(a) The influence of time Fig. 3.1. Alumina nanofluid grain size.

(b) Measurement position

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(a) 0.5%

(b) 1.0%

(c) 1.5%

(d) 2.0%

(e) 2.5% Fig. 3.2. Al2O3 nanofluid precipitation after three weeks.

concentration standards and used these results as a reference when mixing the nanofluids. The nanofluid zeta potential results in this study changed along with the mixture concentrations. Fig. 3.3 shows the IEP of alumina nanofluids of different concentrations. The 0.5% nanofluid had the highest zeta potential, and the general trend was that this

decreased as the concentrations increased as shown in Fig. 3.3(b). The main reason for this was because the emulsifier concentration increased, causing the emulsion layer on the surface of the particles to become thicker, lowering the zeta potential. In addition, the pH value for the emulsifying agent was also relatively higher (approximately 6–7). Therefore, the 2.5 wt.% nanofluid had the

693

R.-T. Wang, J.-C. Wang / Energy Conversion and Management 138 (2017) 686–697 5.4

25 pH Vs Concentration

5.35

Zeta Potential Vs Concentration

0.5~2.5% Al2O3 Nanofluid

0.5~2.5% Al2 O 3 nanofluid

20

5.3

Zeta potential(mV)

5.25

pH

5.2

5.15

15

10

5.1

5.05 5 5

0 0

1

2 Concentration(%)

(a) Zeta potentials

3

0

1

2

3

Concentration(%)

(b) pH values

(c) Correlation between pH and zeta potential Fig. 3.3. IEP of alumina nanofluids of different concentrations.

highest pH value as shown in Fig. 3.3(a). Fig. 3.3(c) exhibits the correlation between pH and zeta potential. It can be seen that addition of emulsifying agent with higher pH value to the aluminum oxide nanofluid caused the IEP to be between 5.4 and 5.8 and that as aluminum oxide concentration increased, the IEP occurred at lower pH values. The pH values for the nanofluid samples used in the present study did not approach the IEPs. 3.1.3. Viscosity Fig. 3.4 shows that the viscosity of the nanofluid increases as the concentration increases at a concentration of 2.5%, and more

obvious changes in viscosity were observed. Increases in viscosity lower the dispersibility of the nanofluid. This increased viscosity probably impedes from the curd of the suspended Al2O3 nanoparticles in the deionized water over a longer motionlessness, rendering the repulsive forces among the nanoparticles smaller than the attractive forces. Because the flow behavior of a solid-liquid suspension depends on the hydrodynamic force acting on the surface of solid particles. The consequence was that the least increase in viscosity was observed for the Al2O3 nanofluid with both a dispersant and surfactant. The aluminum oxide powder used in this study had a low density; small increases in concentration greatly

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1.48

Table 3.3 Changes in absorbance for Al2O3 nanofluids at different concentrations.

Viscosity Vs Concentration 0.5~2.5% Al2O3 Nanofluid

Wavelength (nm)

0.5%

1%

1.5%

2%

2.5%

Day 1

350 400 450

2.163 2.275 2.267

2.255 2.326 2.364

2.179 2.386 2.463

2.366 2.477 2.51

2.351 2.549 2.613

Week 1

350 400 450

2.048 2.179 2.191

2.189 2.223 2.254

2.147 2.321 2.426

2.166 2.441 2.507

2.211 2.468 2.55

Week 2

350 400 450

1.974 2.062 2.082

2.018 2.18 2.201

2.096 2.387 2.415

2.161 2.44 2.501

2.197 2.452 2.511

Week 3

350 400 450

1.615 1.636 1.701

1.704 1.634 1.727

1.764 2.129 2.164

2.286 2.432 2.453

2.105 2.239 2.421

1.44

Viscosity(cP)

1.4

1.36

1.32

1.28

0

1

2

3

Concentration(%)

Fig. 3.4. Viscosities of nanofluids of different concentrations.

Table 3.2 Nanofluid thermal conductivities W/(m K) at 20–40 °C.

20 °C 25 °C 30 °C 35 °C 40 °C

0.5%

1%

1.5%

2%

2.5%

0.60 0.61 0.62 0.63 0.63

0.61 0.62 0.63 0.64 0.64

0.60 0.60 0.62 0.63 0.64

0.58 0.60 0.61 0.61 0.63

0.57 0.58 0.59 0.60 0.60

increased the percentage by volume, thickening the nanofluid. A working fluid with greater viscosity for two-phase thermal conductance units, such as heat pipes or vapor chambers, generally has lower thermal diffusion and conductance rates and is thus not preferable. Viscosity was shown to increase with an increase in the weight concentration of the Al2O3 nanoparticles in the deionized water primarily due to the increase in attractive forces among the nanoparticles of a larger number [46]. The explanation is that the addition of the dispersant disturbed the surface charges of the nanoparticles, whereas the addition of the surfactant further decreased their surface tension. Therefore, only low concentrations were used to mix the Al2O3 nanofluid. 3.1.4. Thermal conductivities Table 3.2 indicates that when the nanofluid emulsifying agent concentration increases, the thermal conductivity for the resulting nanofluid may not increase. The trend in the thermal conductivities over concentrations is the reverse of the viscosity trend shown in Fig. 3.4, which implies that an increase in viscosity may reduce the thermal conductivity of alumina nanofluids. This is because the thermal conductivity for the emulsifying agent is only 0.1–0.2 W/(m K); therefore, while the emulsifying agent can shroud the aluminum oxide particles, increasing their surface tension so that they do not aggregate, it does not affect thermal conductivity. The thermal conductivities for the nanofluids change along with temperature. The concentration of aluminum oxide powder was the highest at 2.5 wt.%, but because the emulsifying agent concentration also increased, the increasing trend in thermal conductivity along with temperature became less apparent. Therefore, the thermal conductivity measured for the 2.5 wt.% nanofluid

was the lowest in the present study. However, the presence of the dispersant and surfactant enhanced the suspension stability of the Al2O3 nanoparticles in deionized water, thus leading to their more even dispersion. That prepared with both the dispersant and surfactant with a 2.5 wt.% was found to undergo the least decline in thermal conductivity. 3.1.5. Absorbance Table 3.3 shows the changes in absorbances over three weeks for the nanofluids at different concentrations. Larger absorbance values indicate the sample absorbed and obstructed more light; in other words, larger absorbance values indicate that the nanoparticles suspended in the nanofluid were more evenly dispersed and had better suspendibility. Looking at the 2 wt.% nanofluid in Fig. 3.5(a), at a wavelength of 350 nm, the absorbance did not decrease along with time. At the third week, the absorbance increased; however, at 400 nm and 450 nm wavelengths, the absorbances decreased slightly. Therefore, it is not suitable to compare the 350 nm absorbance with the aluminum oxide nanofluids at other concentrations. Fig. 3.5(b) shows the changes in absorbance at 400 nm illumination for different concentrations of nanofluids. At the first day, higher concentrations had higher absorbance values because the nanopowder was more evenly suspended throughout the liquid and there were more particles to block light. Overall, absorbances at later days gradually decreased because as time passed, the particles in the nanofluid slowly aggregated, and as particle size increased, so did the interval between particles; thus, the light passed through more easily. Also, larger aggregate particles precipitated out of suspension. At the third week, as the 0.5%, 1%, and 1.5% nanofluids had less particles, they experienced more precipitation, greatly reducing the absorbances. There was no evident change in absorbance for the 2% nanofluid, whereas that for the 2.5% nanofluid decreased slightly. The nanofluid with 2 wt.%, in contrast, had the highest light absorbance values among the nanofluids, but also the greatest rate of decrease in these values. In terms of absorbance, the 2 wt.% nanofluid was optimal. 3.2. The empirical formula derived using dimensional analysis In this section, the measured data was substituted into the thermal conductivity empirical formula derived in Section 2. Table 3.4 shows the parameters to be input. Nanofluid density (qnf) and specific heat (Cnf) were calculated using the water and Al2O3 nanoparticle weight percent concentration ratios. The thermal conductivity empirical formula validation methods used in this study consisted of substituting other experimental data to confirm error and comparing theoretical formulas established in the relevant literature.

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The formulas from past literature did not include emulsifying agents, and, as such, are not applicable in predicting the aluminum oxide nanofluid made in this study. Therefore, another empirical formula must be made that takes emulsifying agents into consideration.

2.7

Absorbency Vs Days 2.65

350nm 400nm 450nm

2.6 2.55

3.2.2. Thermal conductivity empirical formula This study used intelligent dimensional analysis to create a thermal conductivity empirical formula so that, under certain conditions, only a few simple parameters are required for insertion into the formula and there is no need to use instruments in order to estimate the thermal conductivity of a nanofluid with emulsifying agent. According to the formula created using the experimental results, it is applicable for aluminum oxide nanofluids between 20 °C and 40 °C, with concentrations between 0.5% and 2.5%, with grain sizes between 170 nm and 210 nm, and with an emulsifying agent. First, known parameters Kbf and Kp were substituted into Eq. (2.5) to obtain Eq. (3.1):

Absorbency(Abs)

2.5 2.45 2.4 2.35 2.3 2.25 2.2 2.15


3

1

c

K nf ¼ 0:61að102:12  C nf  lnf Þb ð0:78  r n  qnf  lnf2  T 2nf Þ ðW nf Þk ð3:1Þ

0

4

8

12 Days

16

20

24

(a) 2 wt.% of different illuminations 2.9

Absorbency Vs Days

2.8

0.5% 1%

2.7

1.5% 2%

2.6

Parameters awaiting substitution include C nf , lnf , rn , qnf , T nf , and W nf . For simplification, the mean values were used for parameters with smaller variances (C nf , lnf , rn , and qnf ). As the product of ðC nf  lnf Þ has negligible change as concentration increases, it is ignored because it has no impact on the thermal conductivity coefficient and it is assumed that b = 0. Thus, Eq. (3.1) can be simplified into Eq. (3.2):

2.5%

1

2.4

Absorbency(Abs)

c

K nf ¼ 0:61að2:99T 2nf Þ ðW nf Þk

2.5

ð3:2Þ

1

Next, T 2nf is taken out of the parenthesis and combined with c,

2.3

rearranging Eqs. (3.2) into (3.3):

2.2

K nf ¼ 0:61að8:94T nf Þc ðW nf Þk

2.1

ð3:3Þ

Eq. (3.3) shows that Knf is determined by the aluminum oxide nanofluid temperature and concentration. Concentration was temporarily not considered. Setting k = 0 and using 1 wt.% nanofluid as a benchmark revealed that Knf changed along with temperature at 1 wt.%. Substituting values from Table 3.4 obtains Eq. (3.4):

2 1.9 1.8 1.7

K nf ¼ ð9:8875  10
3 ÞT 0:7271 nf

1.6

0

4

8

12 Days

16

20

24

(b) Over 3 weeks under 400 nm illumination Fig. 3.5. Absorbance of Al2O3 nanofluids of different concentrations.

3.2.1. Experimental parameters input into theoretical thermal conductivity formulas from past literature Table 3.5 shows a comparison of the values measured by the equipment and those derived from the empirical formula. The highest thermal conductivity was that of the nanofluid without any surfactant, which was close to these two formulas [1,47]. It is clear that the results from substituting the parameters into the formula and the equipment measurements differ greatly. This was mainly due to the addition of the emulsifying agents into the nanofluids because they created the emulsion layer on the surface of the particles that prevented them from aggregating. However, this also lowers the thermal conductivity of the nanofluid.

ð3:4Þ

The parameters in Table 3.4 show that the changes in temperature and thermal conductivity are similar for nanofluids at different weight-percent concentrations. Therefore, 30 °C is used at the benchmark to find the k values for different concentrations. The weight-percent concentration was then a function of k to obtain Eq. (3.5). Summarily, the empirical function of Eq. (3.5) is validated in the present study and it is not a general expression, which is applicable for Al2O3 nanofluids between 20 and 40 °C, with concentrations between 0.5 and 2.5 wt.%, and with an emulsifying agent having a relative error within ±1.5%.

K nf ¼ ð9:8875  10
3 ÞT 0:7271 W knf nf

ð3:5Þ

k ¼
0:063W 4nf þ 0:379W 3nf
0:774W 2nf þ 0:57W nf
0:112 Table 3.6 shows a comparison of the measured and calculated values for the 0.5–2.5 wt.% aluminum oxide nanofluids. Initial estimations show that the calculated and measured values were similar. Error rates can also be calculated as shown in Table 3.7. The largest error rate, 2.82%, was for 2% nanofluid at 20 °C, indicating that the overall error rates were small. Eq. (3.6) shows the formula used to

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Table 3.4 Parameters substituted into the empirical formula. Wnf

0.5%

1.0%

1.5%

2.0%

2.5%

Cnf (kJ/kg K)

4.163 Cnf mean: 4.129

4.146

4.129

4.112

4.095

qnf (kg/m3)

992.39 qnf mean: 983.17

987.78

983.17

978.56

973.95

lnf (cP)

1.31 lnf mean: 1.374

1.36

1.37

1.38

1.45

dp (nm)

182.9 dp mean: 184.78

177.8

172.0

189.5

201.7

0.6 0.61 0.62 0.63 0.63

0.61 0.62 0.63 0.64 0.64

0.6 0.6 0.62 0.63 0.64

0.58 0.6 0.61 0.61 0.63

0.57 0.58 0.59 0.6 0.6

20 °C 25 °C 30 °C 35 °C 40 °C

Knf(W/m K)

(293 K) (298 K) (303 K) (308 K) (313 K)

Kbf (W/m K)

0.61

Kp (W/m K)

38

Table 3.5 Comparison of thermal conductivity theoretical equation values and actual measurements. Wt (%)

Vol (%)

Maxwell [1]

Xuan [47]

0.5 1 1.5 2 2.5

3.2 6.3 9.2 11.9 14.6

0.6682 0.7268 0.7858 0.8452 0.9050

0.6741 0.7382 0.8027 0.8661 0.9291

Equipment Surfactants

No Surfactants

0.61 0.62 0.6 0.6 0.58

– 0.64 – 0.75 0.82

Table 3.6 Comparison of measured and calculated thermal conductivities W/(m K) of 0.5%, 1%, 1.5%, 2% and 2.5% nanofluid. 20

25

30

35

40

0.5%

Temperature (°C) Equipment Formula

0.60 0.6051

0.61 0.6126

0.62 0.6201

0.63 0.6275

0.63 0.6349

1%

Equipment Formula

0.61 0.6148

0.62 0.6224

0.63 0.6300

0.64 0.6375

0.64 0.6451

1.5%

Equipment Formula

0.60 0.6053

0.60 0.6128

0.62 0.6203

0.63 0.6277

0.64 0.6351

2%

Equipment Formula

0.58 0.5963

0.60 0.6037

0.61 0.6111

0.61 0.6184

0.63 0.6257

2.5%

Equipment Formula

0.57 0.5800

0.58 0.5872

0.59 0.5944

0.60 0.6015

0.60 0.6086

4. Conclusion Table 3.7 Measured and calculated thermal conductivity error rates. Temperature (°C)

20 25 30 35 40

The results can be summarized as follows in the present study.

Error EK (%) 0.5%

1%

1.5%

2%

2.5%

0.85 0.43 0.01
0.40 0.77

0.79 0.39 0.00
0.38 0.79

0.89 2.14 0.05
0.36
0.76

2.82 0.62 0.18 1.38
0.69

1.76 1.24 0.74 0.25 1.43

calculate error rates, where EK is the error of the measured and calculated thermal conductivity, Knf,e is the measured thermal conductivity, and Knf,f is the calclulated thermal conductivity.

EK ¼

K nf ;e
K nf ;f  100% K nf ;e

ð3:6Þ

1. The concentration of the aluminum oxide nanofluid has an immense influence on the suspendibility; at a concentration of 1.5 wt.%, precipitation began at the second week. 2. The nanofluid became murky after being mixed with the emulsifying agent and being left to stand for several days. It was inferred that after standing for a certain amount of time, the emulsifying agent with the higher density would precipitate to the bottom; thus, the HLB value would change. If it is needed for multiple uses, to return the HLB to 12, it must be mixed again until the emulsifying agent becomes clear. 3. The viscosity of the nanofluid began to noticeably increase at a concentration of 2.5 wt.%. Converting weight-percent concentration to volume-percent concentration shows that the volume fraction exceeds 25%. Viscosity also had a negative effect on the dispersibility of the nanofluid.

R.-T. Wang, J.-C. Wang / Energy Conversion and Management 138 (2017) 686–697

4. According to the discussion and evaluation, an alumina nanofluid with a weight-percent concentration of 2% was the best. 5. This study used intelligent dimensional analysis to determine an empirical formula for the thermal conductivity of an Al2O3 nanofluid with an emulsifying agent, kept at temperatures between 20 °C and 40 °C, and an aluminum oxide powder concentration between 0.5 wt.% and 2.5 wt.%; the error rate was below 3%.

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