An experimental study on the effect of nanoparticle shape ...The nanoparticle deposition on the heated surface, during the early stages of droplet impact, was explained as the reason

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An experimental study on the effect of nanoparticle shape on the dynamics of Leidenfrost droplet impingement

Liju Ulahannan, K. Krishnakumar, Anjan R. Nair, S. Kumar Ranjith ()

Micro/nanofluidics Research Lab, Department of Mechanical Engineering, College of Engineering Trivandrum, Engineering College P.O, Sreekaryam, Thiruvananathapuram, Kerala, PIN 695016, India Abstract Extensive investigations have been carried out on the thermo-hydrodynamics of nanofluid droplet interaction with heated and non-heated flat surfaces. However, the influence of shape of

nanoparticles on the dynamics of droplet impingement on heated flat surfaces is yet to be explored in detail. In this study, hydrodynamics of nanofluid droplet impingement process on heated and mechanically polished aluminum substrate was studied using dissolved Al2O3 nanoparticles having

spherical as well as cylindrical shapes. Nanofluids of 0.3% volume fractions were prepared from spherical Al2O3 particles of mean size less than 50 nm and from cylindrical Al2O3 particles of 2–6 nm in diameter and 200–400 nm in length. It was observed that, the impact dynamics is different

from that of base pure fluid owing to the presence of nanoparticles. Leidenfrost temperatures of both nanofluids were dropped drastically in comparison with pure liquid. Further, the residence time, spread factor as well as retraction height also exhibit a different behavior against the base

fluid. Detailed investigations were carried out for different Weber numbers (We = 18–159) and surface superheat and results obtained were compared with de-ionized (DI) water.

Keywords droplet impingement

nanofluid

particle shape

droplet boiling

Leidenfrost temperature

Weber number

Article History Received: 9 August 2019

Revised: 13 October 2019

Accepted: 20 October 2019

Research Article © Tsinghua University Press 2019

1 Introduction

Investigation of droplet interaction with a solid substrate kept at different temperatures is an important area of research owing to its relevance in various engineering applications (Yarin, 2006; Duursma et al., 2009; Moreira et al., 2010; Marengo et al., 2011; Josserand and Thoroddsen, 2016; Weisensee et al., 2017). Leidenfrost temperature is an important parameter that governs the boiling heat transfer of droplets over hot surfaces (Liang and Mudawar, 2017). At the Leidenfrost temperature, a stable vapor layer forms between the hot solid surface and the liquid which prevents the liquid from coming in contact with the solid surface (Mitra et al., 2013; Quéré, 2013). This contributes to a high thermal resistance which results in low heat transfer characteristics. Thus, an effective heat transfer is possible only in the temperature range below the Leidenfrost tem-perature.

Different research groups reported that, the Leidenfrost temperature is influenced by various parameters such as droplet properties, surface textures, laboratory conditions,

substrate temperature, and so on (Bernardin and Mudawar, 1999; Huang and Lin, 2007; Huang and Carey, 2007; Mitra et al., 2013). In an experimental study, Bernardin and Mudawar (1999) have shown that, the Leidenfrost temperature increases with increase in the surface roughness. In another experimental study, it was reported that, the dynamics of droplet bouncing on curved surfaces are entirely different in comparison with a flat counterpart (Simhadri Rajesh et al., 2019). Further, a study on the effect of dissolved salts on the Leidenfrost temperature demonstrated that, salt solutions exhibit higher Leidenfrost temperature than a pure solvent (Huang et al., 2007).

It is noteworthy that, addition of second phase particles like nanoparticles was found to enhance the heat transfer properties drastically (Hsieh et al., 2016). In an investigation involving nanofluid, an increase of Leidenfrost temperature is noted with increase in nanoparticle concentration (Huang et al., 2007). The nanoparticle deposition on the heated surface, during the early stages of droplet impact, was explained as the reason for this behavior. However, investigations on the effect of shape of nanoparticles on the

Vol. 3, No. 1, 2021, 47–58Experimental and Computational Multiphase Flow https://doi.org/10.1007/s42757-019-0053-7

L. Ulahannan, K. Krishnakumar, A. R. Nair, et al.

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Leidenfrost temperature are scarce in the literature. Therefore, the Leidenfrost temperature of nanofluids generated from nanoparticles having different shapes has to be investigated in detail.

Numerous studies have been done on the evaporation and the subsequent deposition patterns of sessile nanofluid droplets on solid surfaces (Hu and Larson, 2002; Shin et al., 2014; Kim, 2015; Liang and Mudawar, 2017). It is interesting to note that, the presence of nanoparticles has altered the sessile contact angle as well as concurrent hydrodynamics significantly (Vafaei et al., 2006; Liang and Mudawar, 2017). Moreover, the factors such as nanoparticle deposition and aggregation, fluid properties like density, viscosity, surface tension, and thermal conductivity, evaluated in the bulk of the nanofluid is entirely different than those at the interfaces (Chengara et al., 2004; Nikolov et al., 2010; Kondiparty et al., 2011; Wasan et al., 2011).

In addition, the deposition of nanoparticles modifies the surface characteristics and concurrent impingement thermo-hydrodynamics. Thus the droplet impact pheno-menon of a nanofluid is much more complex than that of pure base liquid. It becomes further complicated when the impinging surface is heated and the evaporation of the base fluid happens during impingement. Some experimental studies and theoretical explanations are found in literature addressing these issues (Duursma et al., 2009; Shen et al., 2009; Murshed and Nieto de Castro, 2011; Kahani et al., 2016). Interestingly, the nanofluid droplets have larger spreading diameters than that of its base fluid (Shen et al., 2009). Furthermore, it is reported that the spreading diameter increases with increase in volume fraction while the droplet rebounding height increases with rise in temperature (Murshed and Nieto de Castro, 2011). However, increasing the volume fraction of nanofluids resulted in rise in viscosity (Hwang et al., 2007; Ghadimi et al., 2011), reduction of secondary droplet production, spreading, and rebounding (Duursma et al., 2009).

An experimental study on nanofluid evaporation kinetics on super-hydrophobic patterned surfaces had demonstrated that nanofluids display stronger pinning effects and slower evaporation rates compared to base fluid (Xu and Choi, 2009). Study on evaporation of nanofluid droplet on uncoated surface showed that, evaporation process includes a uniform contact area phase for the majority of phase change time. Further, it was found that the presence of nanoparticles enhances the evaporation rate in comparison with its base fluid (Kim, 2015). Investigation on the effect of surface wettability on nanofluid droplet heat transfer demonstrated that nanofluid results in higher heat transfer coefficient compared to the base fluid and the effect elevates with the surface wettability (Jackson et al., 2014).

An experimental study on the boiling of liquid drops

impinging on a heated wall exhibited that, for single-phase fluids the effect of surface temperature on the wetting characteristics is insignificant (Liang et al., 2016). It is interesting to note from literature that, observations on the temperature dependence of nanofluid wetting dynamics employing nanoparticles of different shape are contradictory in nature. The droplet impingement of nanofluid prepared from single-walled carbon nanotubes (SWCNT) having 0.5–10 nm diameter and 100–2000 nm length dispersed in water, on a gold coated nanostructured porous silicon substrates at different substrate temperatures showed interesting results. It is observed that the nanofluid causes larger spreading diameters as surface temperature is increased; in addition, it gives lower equilibrium contact angles at high temperatures (Shen et al., 2009).

On the other hand, experimental study on the droplet impingement and spreading characteristics of nanofluid having spherical TiO2 particles (15 nm in diameter) in ethylene glycol on aluminum substrate demonstrated that the spreading diameter is decreased when surface tem-perature is increased (Murshed and Nieto de Castro, 2011). These studies indicate that nanoparticles of different properties lead to contradicting results for the change of spreading diameter with increase in substrate temperature. In particular, the shape of nanoparticle drastically alters the thermo-hydrodynamic properties of the droplet. However, literature on systematic quantification of effect of shape of nanoparticles on spreading and wetting dynamics on heated flat surface is scarce.

The objective of present study is to experimentally examine and compare the impact dynamics of Al2O3/water nanofluid prepared from nanospheres and nanorods onto a heated aluminum surface. The effect of nanoparticle shape on the Leidenfrost temperature is also studied. Experiments were carried out for different Weber numbers (18–159) and surface temperatures (30–200 °C). Note that, for the selected range of temperatures, the secondary droplet generation was avoided for the range of Weber numbers. Variations of maximum spread factor, non-dimensional rebounding height, and residence time with Weber number and temperature were compared for the both nanofluids.

2 Experimental details

2.1 Nanofluid preparation

The major objective of current investigation is to study the influence of shape of nanoparticles on the physics of fluids during impact. Nanoparticles of Al2O3 having spherical and cylindrical shapes are chosen for the examination. The SEM images of the dry particles are given in Fig. 1. The nanofluids are prepared using Al2O3 nano-sphere and


49

nano-rod in base fluid water. The Al2O3 particles are chosen for investigation owing to their high heat transfer capabilities. Thermophysical properties of Al2O3 are given in Table 1.

Note that, the aluminum particles have high thermal conductivity in comparison with the base fluid water while having a low specific heat capacity as in Table 1. Addition of a small fraction of nanoparticles drastically alters the thermo-hydrodynamic properties. Indeed, such changes are expected to influence the droplet formation and its impact dynamics.

A two-step procedure is deployed for the nanofluid preparation (Hwang et al., 2008; Yu and Xie, 2012). At first, the spherical Al2O3 nanoparticles having average diameter < 50 nm and rod type Al2O3 nanoparticles having diameter 2–6 nm and length 200–400 nm (Sigma Aldrich India) are procured. As the second step, the dry nano-powder is dispersed in the base fluid. Both nanofluids of 0.3% volume fraction was prepared by dispersing the nanoparticles in de-ionized (DI) water. Note that, the range of volume fraction considered for the present investigation is of engineering relevance (Hwang et al., 2007; Ghadimi et al., 2011). Further, sodium dodecylbenzenesulfonate surfactant in the proportion 1 mg/4 mg of the nanoparticles was added for stability. Volume fraction (φ) was computed using equation:

( )np np

np np bf bf

Volume of nanoparticles ( / )Total volume of nanofluid / ( / )

ω ρφ

ω ρ ω ρ= =

+ (1)

where ω and ρ are weight and density respectively of nanoparticles and base fluid.

Table 1 Thermophysical properties of Al2O3 spherical particles and base fluid

Property Al2O3 (30 °C) Water (30 °C)

Density (kg/m3) 3970 995.3

Specific heat capacity (J/(kg·K)) 760 4176

Thermal conductivity (W/(m ·K)) 36 0.619

Source: Holman (2010).

The mixtures were ultrasonicated at 20 kHz for 180 min

using a probe type ulrasonicator to achieve uniform dis-persion. Prepared nanofluids were again ultrasonicated for 180 min to ascertain uniform dispersion before each trail. The stability of the nanofluids was checked by allowing it to sediment for 60 h and noticed a uniform dispersion as shown in Fig. 2.

2.2 Nanofluid characterization

The thermo-physical properties of both nano-sphere and nano-rod based nanofluids were characterized prior to the experiments. Initially, the surface tension and viscosity of nanofluids were determined. Surface tension of de-ionized water, Al2O3 nanosphere/water nanofluid, and Al2O3 nanorod/ water nanofluid were measured using a capillary tube surface tensiometer equipped with a capillary of 0.5 mm inner diameter. The capillary rise (h) was noted and surface tension was estimated using equation (de Gennes et al., 2013):

2

rhρgσ = (2)

where r is the radius of tube and g is the acceleration due to gravity.

Fig. 2 Prepared nanofluids. Left: Al2O3 nanosphere/water nanofluid. Right: Al2O3 nanorod/water nanofluid.

Fig. 1 SEM images of Al2O3 nanoparticles having (a) spherical and (b) rod shapes for a magnification of 200000×.


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The surface tension of all the three fluids at room temperature (29.5 ± 1) °C is tabulated for as in Table 2. It is noticed that, the surface tension of water is reduced to half when nanoparticles was dispersed. The presence of surfactant and nanoparticles were attributed to this drastic modification in the key property of droplet.

On the other hand, another important property, dynamic viscosity of the nanofluids was calculated using an empirical correlation by Brinkman (Ghozatloo et al., 2015). The viscosity of nanofluid is given by the expression:

2.5nf bf /(1 )μ μ= -Æ (3)

where bfμ is the viscosity of base fluid. Moreover, Æ= (1 )φ mf- , where mf is the morphology factor of the

particles. For spherical shaped nanoparticles 0.21mf = and that for cylindrical particles 0.11mf = . Estimated viscosity values are given in Table 3. Since the volume fraction is too low, the influence of nanoparticles on viscosity is insignificant.

2.3 Experimental setup and procedure

Experimental setup for droplet impact consists of a heated aluminum plate with mechanically polished surface whose temperature is kept constant using a temperature controller and relay. A K-type thermocouple placed 2 mm below the test surface is used to continuously measure the temperature of the surface. Moreover, the surface temperature is measured separately using a calibrated digital thermometer and variation noticed is ±1.5 °C owing to the high thermal conductivity of aluminum.

Mono-dispersed droplets were generated using a burette fitted with a hypodermic needle with flat tip. The flow rate is adjusted such that the inertia is negligible so as to obtain

Table 2 Surface tension of test liquids measured at laboratory conditions

Test liquid Capillary rise (mm)

Surface tension (10−3 N/m)

De-ionized water 59 72

Al2O3 nanosphere/water nanofluid 25 31

Al2O3 nanorod/water nanofluid 28 34

Table 3 Viscosity of test fluids at 30 °C

Test liquid Viscosity (cP)

De-ionized water 0.8030§

Al2O3 nanosphere/water nanofluid 0.8078†

Al2O3 nanorod/water nanofluid 0.8084† §Source: Holman (2010).

†Values calculated using Brinkman correlation (Ghozatloo et al., 2015).

drops with uniform diameter due to the sole effect of gravity. Since the surface tension values of all the test fluids are different, suitable needle sizes were chosen to obtain drops with uniform diameters as given in Table 4.

Schematic illustration and actual photograph of the experimental setup is given in Fig. 3. The test area is illuminated using an LED back light source equipped with a translucent diffuser. The shadow images of the droplet impingement were recorded at 2500–2750 fps using a high speed camera (Fastec HiSpec2) equipped with a Tamron macro lens. Sequence of images were captured and stored in a computer for further data analysis. The tests were per-formed for a temperature range of 30–200 °C. Leidenfrost temperature was estimated by conducting single droplet

Table 4 Details of needles used for obtaining droplets for different fluids

Liquid Needle gauge used

Droplet diameter (mm)

DI water 23 2.74

Al2O3 nanosphere/water nanofluid 18 2.68

Al2O3 nanorod/water nanofluid 18 2.59

Fig. 3 (a) Schematic diagram and (b) photograph of experimental setup.


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boil-off test described by Bernardin and Mudawar (1999). The droplet boil-off time was recorded for a single droplet of known volume on a hot surface (Simhadri Rajesh et al., 2019). The surface has a shallow depression to avoid the droplet run-off. The evaporation process is recorded using a digital camera at 50 fps and the droplet evaporation time was estimated from the sequence of images. The experiment was repeated with surface temperatures in the range of 180–300 °C for DI water and 80–300 °C for the nanofluids. All the experiments were repeated for five times in order to keep the statistical accuracy.

Droplet impingement studies were carried out by allowing the single droplets generated from the test fluids to impinge on the test surface kept at a pre-determined temperature. The shadow images of the droplet impingement process were captured at 2500 fps using a high speed camera. Thereafter, the images were analyzed using ImageJ 1.50i (Schneider et al., 2012) software developed by Wayne Rasband at the National Institutes of Health, USA to quantify the shape parameter of the drop. The pre-impact droplet diameter (Do), maximum spreading diameter (Dmax), maximum retraction height (Hmax), and relaxation time (tr) were estimated from the image analysis as depicted in the schematic given in Fig. 4.

Important non-dimensional parameters relevant to nanofluid droplet impingement dynamics are as given below. The most important non-dimensional number in connection with droplet impact is Weber number, which is the ratio of inertia to the surface tension forces and is given by

2

d oρv DWeσ

= (4)

where, dv is the droplet velocity at impact. The amount of extension of the droplet is studied by monitoring the spread factor:

max*

o

DdD

= (5)

which is the ratio of maximum and initial diameters of the droplet. A non-dimensional retraction height *h also defined to compare the relative strength of retraction height to the

Fig. 4 Schematic depiction of droplet impact and spreading on a flat surface. The time taken for the complete rebound from the surface is denoted as residence time (tr).

droplet diameter and is expressed as

max*

o

HhD

= (6)

In addition, a dimensionless residence time pertinent to the droplet morphology change is defined as

rr

o d/tτ

D v= (7)

Finally, the surface temperature is non-dimensionalized ( θ ) by means of saturation temperature satT and fluid room temperature fT and is expressed as

s sat

sat f

T TθT T

-=

- (8)

2.4 Uncertainty analysis

Inherent experimental errors in the measurement of independent variables result in uncertainties in calculated parameters. The degree of uncertainty was estimated following the Kline–McClintok method (Kline and McClintock, 1953). Suppose, a derived variable R is computed from “n” independent variables:

1 2 3( , , , , )nR f x x x x= ¼ (9)

Then the uncertainty associated with the derived variable, Rω , can be determined from the uncertainties of the

independent variables as given by the following expression:

1/22 2 2

R 1 21 2

nn

R R Rω ω ω ωx x x

é ùæ ö æ ö æ ö¶ ¶ ¶÷ ÷ ÷ê úç ç ç= + +¼+÷ ÷ ÷ç ç ç÷ ÷ ÷ç ç çê úè ø è ø è ø¶ ¶ ¶ë û (10)

where 1 2, , , nω ω ω¼ are the uncertainties associated with the independent variables 1 2, , , nx x x¼ respectively. Maximum uncertainties associated with * *

o r, , ,D d h τ , and We are esti-mated to be ±1.31%, ±2.20%, ±10.07%, ±4.48%, and ±9.06%, respectively.

3 Results and discussion

3.1 Estimation of Leidenfrost temperature

Leidenfrost temperatures of all the three test fluids were estimated by monitoring the droplet evaporation time by increasing the surface temperature. From the droplet evaporation curve of each fluid the maximum evaporation time (i.e., minimum heat transfer) was noted as the Leidenfrost point and the corresponding surface temperature was taken as the Leidenfrost temperature (Quéré, 2013). The droplet evaporation curves for all test liquids were plotted in Fig. 5. The droplet evaporation curves of DI water and the two


52

Fig. 5 Droplet evaporation curve for DI water and nanofluids.

nanofluids clearly show the different boiling regimes. When the surface temperature is equal to the saturation temperature, the nanofluid droplets evaporate slowly. However, as the surface temperature exceeds the saturation temperature, the evaporation time decreases rapidly for the nanofluids. This denotes that the nucleate boiling is predominant in this regime. For DI water, the nucleate boiling was initiated at 170 °C while that of nanofluid commenced at a lower tem-perature of 110 °C. Thus it is evident that the presence of nanoparticles aids nucleate boiling even at a low surface temperature.

Further, the Leidenfrost points were noticed and tabulated in Table 5. It is noteworthy that, the Leidenfrost temperature of the base fluid (DI water) was 270 °C and was much more than that for nanofluids. It is further observed that the Leidenfrost temperature of Al2O3 nanosphere/water nanofluid was 200 °C while that of Al2O3 nanorod/water nanofluid was 210 °C. These observations exhibited that, nanoparticle addition reduces the Leidenfrost temperature. This difference in Leidenfrost point is attributed to enhanced heat transfer owing to the presence of high conductivity nano metal particles as well as nanoparticle deposition on the superheated surface during boiling (Duursma et al., 2009). The dissolved nanoparticles enhanced the heat flux and advanced the vapor layer formation and concurrent droplet levitation. Subsequently, time taken for complete evaporation of the nanofluid droplets is less in comparison with pure fluid as evident in Fig. 5.

Table 5 Leidenfrost temperature for different working fluids

Liquid Leidenfrost temperature (°C)

DI water 270

Al2O3 nanosphere/water nanofluid 200

Al2O3 nanorod/water nanofluid 210

3.2 Impact dynamics of DI water droplets

Experiments were conducted to examine the impingement dynamics of base fluid as explained in Section 2.3. The monodisperse droplets generated were allowed to collide on the hot aluminum surface. The temperatures of the substrate were 30, 130, and 200 °C ( 1, 0.43, 1.43θ =- ).

Sequence of images of DI water droplet evolution was recorded using high-speed camera as shown in Fig. 6. At a surface temperature of 30 °C (room temperature) the droplet spreads over the surface due to the inertial forces. Similar trend is observed for a superheat of 130 °C; however, the drop boils-off after certain duration owing to the pool-boiling.

In contrast, for 200 °C the droplet spreads, retracts, and rebounds as in Fig. 6. Owing to the inertial forces the droplet extends radially to its maximum diameter and meanwhile a vapor layer is formed instantaneously between the drop and solid substrate. The surface tension forces try to compress the liquid in the inward radial direction and retraction com-mences. Subsequently, the radial momentum was converted into to the vertically upward momentum and the rebounding occurs thereafter.

The spread factor, non-dimensional retraction height, and non-dimensional droplet residence time were studied for droplet impingement of DI water on flat aluminum surface at different surface temperatures and different Weber numbers as shown in Fig. 7.

It is observed from Fig. 7(a) that the spread factor is almost uniform against the temperature. This is expected since the extension pattern is depended on the surface tension and radial momentum of the fluid rather than the vapor layer formation. Similar behavior is observed in prior studies as well (Liang and Mudawar, 2017; Simhadri Rajesh et al., 2019). Further, the non-dimensional retraction height is observed to be constant at low temperatures ( 1 0.28θ- < < ) and

Fig. 6 Image sequence of droplet impingement of DI water on a hot aluminum surface at 11We = for different superheat.


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Fig. 7 Plots of (a) spread factor (d*), (b) non-dimensional retraction height (h*), and (c) non-dimensional residence time ( rτ ) against non-dimensionalized surface temperature ( θ ) for the DI water.

thereafter it rises along with the surface temperature as in Fig. 7(b).

The observed behavior is due to the reduction in surface tension of water with increase in temperature. This results in higher droplet spreading and lower retraction heights. However, as the temperature of the surface comes closer to the superheat temperature required for transition boiling, an unstable vapor film forms on the solid surface, thereby decreasing surface wettability, and this increases droplet

retraction compared to that at the room temperature. The droplet is lifted off from the surface and the retraction height reached maximum value. Note that, the droplet residence time is the time elapsed between the droplet contact and droplet bounce-off during the collision process.

From the droplet residence time curve given in Fig. 7(c), it is observed that the droplet residence time increases highly with increase in Weber number at low surface temperatures ( 0.7θ = ). However at higher temperatures, due to the high vaporization in the transient boiling regime, an unstable vapor layer is formed at the surface which reduces surface wettability and thus the effect of Weber number is less.

3.3 Impact dynamics of nanofluid droplets

In this section, impingement dynamics of droplets dispersed with nanoparticles is discussed in detail. It is observed that, the nanofluid droplet impacted on flat surfaces exhibits entirely different dynamics than that of its base fluid. The presence of nanoparticles significantly alters the thermo-hydrodynamic properties of the droplet, and subsequently the impact dynamics is also modified.

The image sequences of droplet impact on surfaces kept at different temperatures were recorded. The spread factor and non-dimensional retraction height plots against non- dimensionalized surface temperature for different Weber numbers for both nanofluids was estimated. The nanorod droplets are allowed to hit the surface such that 24We = and the droplet evolution was given in Fig. 8.

It was noticed that, the droplet spreads maximum and is retracted due to the change in direction of radial momentum into vertical. However, at low temperature ( 1θ = ) the vapor layer formation is weak and droplet sticks on the surface. In contrast, at 1.48θ = the vapor cushion formation was rapid in comparison to base fluid and bouncing was observed as seen in Fig. 8.

Note that, the maximum droplet bouncing was occurred at a time 33.45 mst = for a pure liquid drop while that at

25.45 mst = for a nanorod drop. This is owing to the fact that the heat transfer and concurrent formation of vapor layer was rapid in nanofluid droplet due to enhanced local contact spots. Moreover, similar behavior is observed for nanosphere droplets as well and was depicted in Fig. 9.

Nanorod–water nanofluid has slightly higher viscosity than the nanosphere counterpart and thus at room tem-perature ( 1θ =- ) nanorod–water nanofluid droplets take more time to reach maximum spreading and maximum retraction stages compared to the nanosphere counterpart. The surface tension of nanofluids is reduced with increase of temperature. This results in the reduction of contact angle. This is evident from the maximum spread stage at

1θ =- and 0.4285θ = for both nanofluids. This increases


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Fig. 8 Droplet impingement stages of Al2O3 nanorod/water nanofluid at 24We = .

Fig. 9 Droplet impingement stages of Al2O3 nanosphere/water nanofluid at 24We = .

the maximum spread diameter. Also at 0.4285θ = ( S 130T = °C) nucleate boiling occurs for the nanofluids as is seen from Fig. 5. As vapor bubbles form at the heated surface and rise up and escape at the liquid–gas interface, the nanofluid around it is pushed further out and thus the maximum spread stage is reached faster. But it is observed that only nanorod–water nanofluid demonstrates a drastic reduction in spreading time from the room temperature value. This denotes the higher vapor bubble nucleation rate in the case of nanorod–water nanofluid. At 1.4285θ = (200 °C) the boiling regime is film boiling. Boiling happens on the bottom side of the droplet without the formation of vapour bubbles inside the droplet. Thus the spreading is influenced mainly by the fluid viscosity. Thus nanorod–water nanofluid droplet takes longer than nanosphere–water nanofluid droplet to reach the maximum spread stage as in the case of 1θ =- .

The maximum spreading factor, retraction height, and

residence time were quantified from the image sequences. Variation of spread factor for the nanofluids was plotted in Figs. 10(a) and 10(b) and it exhibited that the spread factor is uniform against the rise in surface temperature and is in line with the earlier reported results (Quéré, 2013). However, the spread factor increased with respect to the Weber number. At higher Weber numbers the inertial force is high and the increased momentum will deform the droplet further.

It is to be noted that the effect of Weber number on the spread factor is more when compared with surface tem-perature. Figure 10(a) shows that for Al2O3 nanosphere/ water nanofluid, a change of θ from 0 to 0.7143 produces maximum increase in *d of 16%, 13%, 1.2%, 2.6%, and 1.5% for the Weber numbers 18, 24, 79, 119, and 159, respectively. Correspondingly, Fig. 10(b) shows that the increase in the case of Al2O3 nanorod/water nanofluid was 14.8%, 16.9%, 18.5% 7.98%, and 1.73%. However, it was noted that, the spread factor of both nanofluids was increased by 76% as the Weber number was elevated from 18 to 159.

Fig. 10 Spread factor ( *d ) vs. non-dimensionalized surface temperature ( θ ) for (a) Al2O3 nanosphere/water and (b) Al2O3 nanorod/water nanofluids.


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The plots for the nanofluids show that, near the saturation temperature, retraction height is minimum (Figs. 11(a) and 11(b)). This is due to the lowering of surface tension near the saturation temperature. It is observed that as surface temperature increases from room temperature, the spread factor increases to a maximum around 0.5θ = and then decreases.

On comparing the spread factor and non-dimensional retraction height plots for both the nanofluids in Figs. 12, 13, 14, 15, and 16, it was observed that the effect of the shape of the nanoparticles on the droplet impingement dynamics is negligible for the volume fraction investigated.

The maximum spread factor is correlated to Weber number and non-dimensional surface temperature using non-linear least square method. The correlations are given in Table 6 and is applicable for 0.3% volume concentration and for 18 159We = - .

In addition, the droplet residence time on the hot surface was also investigated for both the test nanofluids. Variation of droplet residence time with differing surface superheat

Fig. 11 Spread factor ( *h ) vs. non-dimensionalized surface tem-perature ( θ ) for (a) Al2O3 nanosphere/water and (b) Al2O3 nanorod/water nanofluids.

Fig. 12 *d and *h vs. non-dimensionalized surface temperature ( θ ) for investigated nanofluids at 18We = .


was given in Fig. 17. The responses of residence time with increasing temperature for both the nanofluids are similar. Both the fluids exhibit decrease in residence time as the surface temperature increases from 0.43θ = to 1.43θ = . This is comparable to the thermal response of the residence time of DI water. It is observed that the residence time of the nanofluids is lower than that of the pure water. This is due to the sedimentation of nanoparticles on the heated surface,


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which enhances the density of nucleation sites (Narayan et al., 2007), thereby increasing the rate of boiling. Owing to this enhanced vaporization, the vapor pressure below the droplet increases quickly and drives the droplet upwards. Subsequently, the residence time is lowered considerably.

The spread factor for all tested fluids at 200 °C ( 1.4286θ = ) is plotted against Weber number in Fig. 18. Akao et al. (1980), Hatta et al. (1995), and Liang and Mudawar


Table 6 Correlations for maximum spread factor obtained from experiments

Surface temperature

limit

Maximum spread

factor *d

2 3Al O nanorod–water

nanofluid

2 3Al O nanosphere–water

nanofluid

1 0θ- £ < ( )ca b We+ 0.9449a = 0.2b = 0.4679c =

1.207a = 0.1049b = 0.5712c =

0 0.5θ£ <( )( )

c

n

a b Wed θ

+

+

1a = 0.2231b = 0.4407c = 0.9106d = 1.483n =

1a = 0.2232b = 0.4546c = 0.126d = 1.004n =

0.5 1.43θ£ <

( )( )

c

n

a b Wed θ

+

+

1a = 0.46b = 0.3419c =

0.4269d =- 0.5n =

1.439a = 0.18b = 0.4636c =

0.1848d =- 0.5n =

(2017) have given correlations for the case of water droplet impingement on hot surfaces:

Akao et al. (1980): * 0.390.613d We= ´

Hatta et al. (1995): * 0.740.093 1d We= ´ + Liang and Mudawar (2017): * 0.3060.788d We= ´

and used them to compare the current results. It is seen that the DI water test results are in agreement with the correlation given by Liang and Mudawar (2017). Further, the presence of nanoparticles modified the fluid properties; consequently the droplet extension was lowered as shown in Fig. 18.


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Fig. 17 Non-dimensional droplet residence time ( rτ ) vs. non- dimensionalized surface temperature ( θ ) for (a) Al2O3 nanosphere/ water and (b) Al2O3 nanorod/water nanofluids.

Fig. 18 Spread factor ( *d ) against Weber number (We) at 80 °C.

4 Conclusions

Single droplet impingement experiments were carried out for DI water, Al2O3 nanosphere/water nanofluid, and Al2O3 nanorod/water nanofluid on heated flat aluminum surface.

The spread factors of all fluids are affected significantly by Weber number. Temperature does not have much effect on the spreading of DI water. The spread factor of nanofluids is not affected by temperature variations in the range θ = –1 to 0, which is below the saturation temperature of the base fluid, water. However, in the temperature range θ = 0 to 1.5, the spread factor first increases and then decreases. This increasing trend is caused by the lowering of surface tension in the bulk of the droplet mass and also due to the nucleate boiling happening during the spreading phase. The bubble formation during this nucleate boiling pushes the oncoming fluid radially and thus increases the spreading. But, as the surface temperature increases, the high rate of boiling creates stable vapor layer on the substrate which reduces wettability and thus reduces spreading.

For the nanofluids investigated, the spread factor *d , is affected significantly by variations in temperature (15%–20% increase in the nucleate boiling regime) at lower Weber numbers (18–79) only. Also it is noted that for 79We = , as θ is varied from 0 to 0.5, *d of Al2O3 nanorod/water nanofluid, increases by a maximum of 18.5%. However for Al2O3 nanosphere/water nanofluid, the corresponding increase in *d only amounts to 1.23%. Leidenfrost tem-peratures of nanofluids are lower by about 60 °C when compared to that of the base fluid. The droplet residence time decreases for increase in temperature and nanofluids have lower residence time than that of DI water. There is no appreciable change in the droplet residence time with Weber number. The maximum spread factor, the non- dimensional retraction height, and the non-dimensional residence time of both nanofluids show similar variations with change in temperature and Weber number. Further studies are required to understand the influence of volume fraction on the dependence of droplet impingement dynamics on nanoparticle shape. The current investigation was confined to the case of macro-droplets. Further research using micro-droplets needs to be performed to understand the effect of the shape of the nanoparticles on the dynamics of nanofluid micro-droplet impingement.

Acknowledgements

The authors acknowledge the financial support received from CERD-KTU, Kerala, India (Research Grant: KTU/RESEARCH 3/2645/2016), and SARD-KSCSTE, Kerala, India, for this investigation.

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