An experimental investigation on pool boiling heat transfer enhancement using sol-gel derived nano-CuO porous coating

Experimental Thermal and Fluid Science 103 (2019) 37–50

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An experimental investigation on pool boiling heat transfer enhancement using sol-gel derived nano-CuO porous coating

T

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Albin Josepha,b, Sreejith Mohanc, , C.S. Sujith Kumard, Arun Mathewe, Shijo Thomasa, B.R. Vishnuf, S.P. Sivapirakasamf a

School of Nanoscience and Technology, National Institute of Technology, Calicut, Kerala 673601, India Department of Mechanical Engineering, Vimal Jyothi Engineering College, Kannur, Kerala 670 632, India c Department of Mechanical Engineering, Sree Buddha College of Engineering, Nooranad, Alappuzha, Kerala 690 529, India d Department of Mechanical Engineering, National Institute of Technology, Calicut, Kerala 673601, India e Department of Mechanical Engineering, Curtin University, Bentley, Perth 6102, Australia f Department of Mechanical Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu 620 015, India b

A R T I C LE I N FO

A B S T R A C T

Keywords: Nano-CuO Sol-gel Pool boiling Heat transfer coefficient Topography Chemistry

This paper presents an experimental investigation aimed at studying the influence of nano-CuO coating on the pool boiling heat transfer enhancement of SS 316 LN stainless steel. The coating was developed using the sol-gel dip coating technique. The Scanning Electron Microscopic and X-Ray Diffraction apparatus were used to analyze morphological and structural characteristics of the coating. The complex interaction of coating process parameters on the boiling heat transfer coefficient was studied using the concept of design of experiment. The results reveal as much as 30% increase in the boiling heat transfer coefficient for the sol-gel derived CuO coated boiling surface compared to its uncoated counterpart. The coating was found to tailor the porosity, roughness and wettability of the surface. The heat transfer coefficient increased with an increase in porosity, roughness and decrease in wettability of the boiling surface. Analysis of variance results revealed that the molar concentration of the CuO sol was the most significant parameter influencing the heat transfer coefficient while sintering temperature was the least significant parameter.

1. Introduction Functionalization of boiling surfaces to enhance heat transfer efficiency is of worth interest in energy conversion devices [1]. With the rapid miniaturization and high density integration of electronic components and the consequent increase in power density, the conventional air cooling methods or single-phase liquid cooling technologies for the thermal management of electronic components are becoming inadequate. In such situations, liquid to vapor phase change heat transfer processes such as pool boiling, gas-assisted evaporative cooling, jet impingement and spray cooling, which make use of the latent heat of vaporization of the cooling liquid, are feasible alternatives. Among them, the immersion pool boiling is considered relatively easy for implementing as well as to enhance the heat transfer coefficient (HTC) [2]. In pool boiling, the heat dissipation from a surface proceeds through the following steps: natural convection from the heated surface to the

fluid, bulk convection ensued by bubble growth and detachment and latent heat transfer from the liquid phase to vapor bubbles at the surface [3]. In the pool boiling curve, the most favorable regime is nucleate boiling on account of its high heat flux removability and low superheats [4]. The intention of enhancing the nucleate boiling is to decrease the minimum heat flux (MHF) for boiling incipience, to reduce the superheat and to post-pone the critical heat flux (CHF). The heat transfer coefficient during pool boiling primarily depends on the topography and chemistry of the boiling surface. In general, the surface topography is quantified in terms of porosity and roughness while the surface chemistry is characterized in terms of the wettability of the solid-liquid contact surface (hydrophilic or hydrophobic). The effect of these parameters on the boiling heat transfer coefficient has been a topic of great interest in boiling research. In the recent decades, attempts have been made to modify the surface topography and chemistry by the fabrication of micro/ nanostructures on heating surfaces using various methods and to study its effect on the boiling heat

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Corresponding author at: Department of Mechanical Engineering, Sree Buddha College of Engineering, Pattoor P.O. Nooranad, Alappuzha, Kerala 690 529, India. E-mail addresses: [email protected], [email protected] (S. Mohan), [email protected] (S. Thomas), [email protected] (B.R. Vishnu), [email protected] (S.P. Sivapirakasam). https://doi.org/10.1016/j.expthermflusci.2018.12.033 Received 7 March 2017; Received in revised form 30 December 2018; Accepted 31 December 2018 Available online 03 January 2019 0894-1777/ © 2019 Elsevier Inc. All rights reserved.

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Nomenclature A D Dc h j I k L_1 L_2 M Na Nc Q S

t T_1 T_2 T_3 T_4 T_5 T_avg T_s T_sat V α β

cross sectional area of the substrate (mm2) dipping time (sec) cavity mouth diameter (m) heat transfer coefficient (kW/m2 °C) number of process parameters considered for the study. input Current (A) thermal conductivity of the substrate (W/mK) distance between points 1 and 2 (mm) distance from the point 1 to the boiling surface (mm) molar concentration of the CuO sol (M) nucleation density (/m2) number of pores or cavities heat input (W) sintering temperature (°C)

δ θ λ

mean crystallite size (nm) temperature at point 1 (°C) temperature at point 2 (°C) temperature at point 3 (°C) temperature at point 4 (°C) temperature at point 5 (°C) average temperature (°C) boiling surface temperature (°C) saturation temperature of the fluid (°C) input voltage (V) factorial points full width at half maxima of the peak corresponding to the diffraction angle, θ contact angle (°) diffraction angle (°) X-ray wavelength (nm)

are placed on it. These are wenzel state, cassie state and sunny side up state. A droplet of water on a hydrophilic rough surface is in the wenzel state, while on a hydrophobic rough surface, it is in the cassie state. The droplet of water on a hydrophilic surface with relatively higher roughness was turned into sunny side up state. A droplet in the cassie state promoted entrapment of gas/vapor inside cavities and was recommended for better boiling heat transfer applications. Dharmendra et al. [12] compared the boiling behavior of a plain copper surface with that of the sandblasted copper surface and the carbon nano tube (CNT) coated copper surface. The CNT coating favored the increase of effective heat transfer area and roughness of the boiling surface when compared to the sandblasted microscale modified surface. Further, the hydrophobic nature of the coating favored bubble detachment and contributed to an enhancement in the heat transfer. The maximum enhancement in the boiling heat transfer coefficient value accounting to 80% was observed for the CNT coated surface. Adam et al. [13] used hot alkali solutions to promote cupric and cuprous oxide growth on copper surfaces, which resulted in different levels of wettability and roughness. The study showed that the boiling heat transfer coefficient had an inverse relationship to wettability in that the former increased with a decrease in the latter. Recently, Das et al. [1] prepared a nanoSiO2 film on Cu substrate using the electron beam evaporation technique and noted an increase in the wettability and roughness of the boiling surface which profoundly reflected as an increase in the heat transfer coefficient. The increase in wettability was hypothesized to improve the nucleation site density and bubble departure frequency. The review of these studies reveals that micro/ nano scale modification of a boiling surface could enhance the porosity and roughness of the boiling surface and tailor its wettability characteristics. It could be inferred that surface coating is a promising technique to achieve boiling surface modification. Further, it was apparent from those studies that, a nano structured surface could yield a better heat transfer characteristic than a microscale surface due to the larger surface to volume ratio. Among the methods used to achieve nano structured surface, the most economical and convenient is the sol-gel dip coating technique [14]. However, there is limited number of literature describing this coating technique for boiling heat transfer studies. Hence, in this work, the sol-gel dip coating technique was employed to achieve a thin film of CuO over 316 LN stainless steel boiling surface. The microstructure, porosity, and roughness of the sol-gel derived CuO coating depends on several interlinked coating process parameters such as molar concentration of the sol, dipping time and sintering temperature. Because of the complex interaction of these parameters, a ‘one variable at a time’ approach cannot give a clear indication of the correlation between them and their effect on the output. In such multivariate problems, several methods such as design of experiments (DoE), fuzzy logic and

transfer coefficient. Bergles and Chyu [5] prepared a porous coating of micron-sized copper particles by brazing it over a heated cylindrical copper surface and observed a maximum enhancement of 800% and 250% in the boiling heat transfer coefficients for R-113 and water respectively. They insisted that the improvement of heat transfer performance was attributed to the nucleation occurring within the cavities inside the porous layer and capillary action that favored the liquid return to dry spots. Jung et al. [6] developed boiling surfaces by depositing metal particles and observed 2–3 times enhancement in heat transfer rate compared to a plain surface. The modified surface was found to possess high roughness which was responsible for increasing the nucleation site density of the boiling surface and hence the heat transfer. Bong et al. [7] converted a plain boiling surface into a nano porous surface by developing alumina sponge-like nano-porous structures (ASNPS) using the anodization technique and investigated its boiling behavior. The modified surface was enhanced up to 200% of heat transfer coefficient when compared to the plain surface, which was ascribed to the enhanced surface area of the nano porous surface leading to increase in active nucleation site density. They (Bong et al. [7]) also reduced the wettability of the boiling surface with a hydrophobic self-assembled monolayer (SAM) coating and found it to yield a better boiling performance than the ASNPS and the plain surface. The hypothesis was that a hydrophobic surface could increase the number of active pores and the bubble departure frequency. Gao et al. [8] modified the surface topography of a stainless steel twisted tube by mechanical machining technique. The resultant boiling surface was found to be porous and yielded a large number of nucleation sites compared to the plain surface. About 1.8–2.0 times enhancement in the heat transfer coefficient was attained from their method. Hai et al. [9] used three different nano coating techniques viz; Metal-Organic Chemical Vapor Deposition (MOCVD), Plasma Enhanced Chemical Vapor Deposition (PECVD) and Nanofluids Nucleate Boiling Deposition (NNBD) to vary the surface wettability of a boiling surface. They observed a higher heat transfer coefficient value on the super hydrophilic surface having a static contact angle close to 0°. Brusly et al. [10] used the anodization technique to prepare a thin porous coating on the inner wall of a two phase closed thermosyphon (TPCT) and observed a superior thermal performance. The porous nature of the coating was found to increase the number of active nucleation sites and the bubble release frequency in the boiling surface. In a systematic review carried out by Dong et al. [11] the effect of altering the surface wettability of the boiling surface on the boiling heat transfer enhancement was reported. They concluded that a hydrophobic surface favored the heat transfer coefficient while a hydrophilic surface favored the critical heat flux (CHF). The review further pointed out that though hydrophilic and hydrophobic rough surfaces exhibited the same surface texture and geometry, three typical condition or state exists when water droplets 38

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30 × 30 × 40 mm. Table 3 shows the chemical composition of the substrate. Prior to coating, the substrates were polished with different grades of emery paper and cleaned in acetone. The procedure adopted for the deposition of nano-CuO film is shown in Fig. 2. The precursor used was Copper (II) Acetate (98% pure, Changshu Merck). It was mixed with ethanol solution in a suitable proportion to attain different molar concentrations (0.5 M, 0.7 M, 1 M, 1.3 M, 1.5 M) that are required for the study. The resultant solution was stirred for 1 h followed by stabilization (ageing) for 24 h so as to obtain the final sol. The substrates were dipped in the prepared sol for the scheduled time duration (60 min, 84 min, 120 min, 156 min, 180 min). Later, it was withdrawn and air dried for 1 h. Following this, the sintering was carried out at the required temperature (450 °C, 491 °C, 550 °C, 609 °C, 650 °C) for 1 h which enhances the adhesion of the CuO film to the substrate [32]. The sintering was carried out in 5 steps inside a muffle furnace equipped with a PID controller. Firstly, the specimen was kept inside the furnace maintained at the ambient temperature. The furnace was then programmed to 1/5th of the target temperature (Say 100 °C for a target temperature of 500 °C). Once this temperature has been attained, the furnace was set to 2/5th of the target temperature. Likewise, the temperature inside the furnace was slowly increased up to the final target temperature. The heating rate was calculated based on the time taken to attain the target temperature and was found to be 1 °C/ min (approx.). The specimen was kept at the target temperature for one hour following which the furnace was turned off to ensure gradual cooling. The specimen was taken out only after the temperature inside the furnace dropped back to the ambient temperature. This had ensured the slow cooling of the specimen, as it was required to avoid the possible formation of cracks resulting from the thermal expansion mismatch of CuO and stainless steel [19,20].

Artificial Neural Networks are employed. Among them, DoE is the most appropriate technique to evaluate the individual and interaction effects of the process parameters on the output response by conducting a minimum number of experiments [15]. Nevertheless, there are no available reports describing the use of DoE for boiling heat transfer studies. In the present study, the pool boiling heat transfer coefficient of nano-CuO coated stainless steel surfaces were measured and compared with that of an uncoated boiling surface under identical conditions. The experiments were designed using the response surface methodology of DoE. The optimum range of process parameters that could enhance the boiling heat transfer coefficient was also determined through the statistical analysis. 2. Experimental methods 2.1. Design of experiments Literature revealed that the properties of a CuO film depended on several process parameters such as the molar concentration of the sol, dipping time and sintering temperature [16]. The workable range of these process parameters was fixed through the literature survey and conduct of preliminary experimental trials in the laboratory. The process parameter ranges were fixed as follows; molar concentration of the sol: 0.5–1.5 M, dipping time: 60–180 min, sintering temperature: 450–650 °C. The workable range indicated the lower and upper limit values of process parameters within which a uniform coating was obtained. For instance, no coating was observed for a molar concentration value less than 0.5 M and the coating was peeling off for a molar concentration value above 1.5 M and so on. Since there exists a complex interaction among the selected process parameters, varying one parameter-at-a-time and analyzing its effect on the heat transfer coefficient is a tedious task. For this reason, in the present work, the response surface methodology of Central Composite Design (CCD), a standard technique of DoE was employed to incorporate the variation of process parameters at five different levels [17]. CCD comprises an embedded factorial design having center points in addition to a group of ‘star points’. Fig. 1 shows the CCD for studying three factors. There are 8 factorial points (colored black), six axial points (colored gray) and three to five center points (colored white). The center points are usually replicated to get an unbiased estimate of pure error as well as to hone the precision of the experiment [18]. In Fig. 1, the axial points are marked as ± α which is nothing but the distance of axial lines from the center. The precise value of α depends on the number of factors considered in the study and is found using the Eq. (1).

α = (2 j)1/4

2.3. Pool boiling experiments The pool boiling experiments were carried out using demineralized water as the working fluid in the atmospheric pressure. The boiling chamber used for the study was made of polycarbonate sheets of dimension 90.0 × 30.0 × 6.10 mm. Except the boiling face, all the sides of the test specimen were insulated with an alumina-silicate ceramic of thermal conductivity 0.85 W/mK. The test specimens were drilled at several positions to make provision for holding the cartridge heaters and thermocouples. Three DC cartridge heaters (Prusa Reprap) each of 40 W capacities with a diameter of 6 mm and length of 27.5 mm were used as the heat source. The surface temperature of both the test specimen and the boiling chamber were measured using J-type thermocouples. A high thermal conductive paste (thermal conductivity 4.3 W/ mK) was used for fixing the thermocouple in the holes provided on the

(1)

Since, the present study had three process parameters; the value of j becomes 3 and α attains a value of 1.6817. The five levels of each process parameters were then coded as −1.6817, −1, 0, +1, and 1.6817 as shown in Table 1. A set of twenty experimental runs based on the combined level of each process parameters was then used to form the design matrix as presented in Table 2. The coated boiling surfaces were prepared as per this combination. 2.2. Preparation of nano coated boiling surface The sol-gel dip coating method was adopted to deposit the nanoCuO film. The coating process involved the following procedures. At first, the substrates were dipped and withdrawn from the fluid sol prepared for gravitational draining. Secondly, as a result of solvent evaporation, the deposition of the solid film took place [15]. The substrate (test specimen) was 316 LN austenitic stainless steel of dimension

Fig. 1. Central composite design for studying three factors. 39

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Table 1 Levels of coating process parameters. Sl. No.

Parameter

1 2 3

Unit

Molar concentration Dipping time Sintering temperature

M Min °C

Levels −1.6817

−1

0

+1

+1.6817

0.5 60 450

0.7 84 491

1.0 120 550

1.3 156 609

1.5 180 650

Table 2 Design matrix. Run

Molar concentration (M)

Dipping time (min)

Sintering temperature (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1.00 1.00 1.30 0.70 1.00 1.50 1.30 1.30 1.00 1.00 0.70 0.50 1.00 1.00 1.00 0.70 1.00 0.70 1.00 1.30

120 60 84 156 180 120 156 84 120 120 84 120 120 120 120 156 120 84 120 156

550 550 609 609 550 550 491 491 550 450 491 550 550 550 650 491 550 609 550 609

Table 3 Chemical composition of the substrate (wt.%). Fe

Cr

Ni

Mo

Mn

Si

N

P

C

S

Balance

16–18

10–14

2–3

2

1

0.1–0.3

0.045

0.03

0.03

specimen. Fig. 3 shows the schematic of the pool boiling experimental setup and the position of thermocouples and heaters in the substrate. As seen in Fig. 3, five thermocouples were positioned on the substrate at points 1, 2, 3, 4 and 5. Table 4 shows the temperature readings (T_1, T_2, T_3, T_4, and T_5) for the bare uncoated substrate and the experimental run 5 at 90 kW/m2 of applied heat flux. As evident from the Table 4, the temperature difference between the points 1 and 2 was approximately the same as it maintained between 2 and 3. Further, two thermocouples were placed at points 4 and 5 in order to estimate the average surface temperature (i.e. the average of T_1, T_4, and T_5) for the calculation of average heat transfer coefficient [12,20]. As noticed from Table 4, the variation in temperature measurements at points 1, 4 and 5 was within ± 4%. The sixth thermocouple was kept 10 mm above the boiling surface to measure the pool reference temperature. A proper condensation setup was employed at the top of the boiling chamber to enable recirculation of the working fluid. The thermal conductivity of the substrate was estimated using the Eq. (2):

k= (Q× L_1 × 10−3)/(A× 10−6 × (T_2 − T_1))

Fig. 2. Flow chart showing the procedure of nano-CuO coating using sol-gel dip coating technique.

(2)

literature [21]. The higher estimate of thermal conductivity was attributed to the larger area of substrate in the radial direction because of which the heat that effectively arrives at the boiling surface will be lower than the applied power. Due to which a smaller heat loss in radial direction is expected and was calculated to be 2% (maximum) of the heat flux even at higher input.

where Q = V × I is the heat input, T_2 and T_1 are the temperature values at locations 2 and 1 respectively. The thermal conductivity was estimated to be 22 W/mK (uncertainty ± 4.01% calculated in accordance with Kline and McClintock [23]) which was higher than the value of 17 W/mK reported in the 40

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Fig. 3. Schematic of experimental setup and location of thermocouples.

small amount of heat loss in the radial direction is expected. This heat loss in the radial direction was found to be 2% even at higher heat flux with an estimated uncertainty of ± 3.04%. Measurements on a similar apparatus set forth by Saeidi et al. [22] revealed that heat loss account to merely 6% of the power consumed. However, the calculated heat loss has been deducted from the input heat flux during the calculations. Prior to commencing the boiling experiments, the working fluid was degassed for 20 min by heating it using a cartridge heater kept inside the pool. This was done to enable the escape of dissolved gases. Temperature at the boiling surface was found out using the following equation:

Table 4 Temperature values at specific points. Surface identity

T_1

T_2

T_3

T_4

T_5

Bare surface Run5

139.25 135.15

200.27 196.51

264.39 258.37

137.60 133.56

140.01 132.86

In order to get an insight into this effect of thermal conductivity difference, the HTC values were calculated for each run (at various heat flux) by assuming a thermal conductivity of 17 W/mK (reported in literature) and the results were compared with the HTC values obtained in the present study. The HTC values were found 4% less than that of the value obtained in the present study. This variation could still be reduced by improving the insulation. A circuit diagram of the experimental set up is shown in Fig. 4. As seen in Fig. 4, the thermocouples were connected to a data acquisition system (National Instruments, USB-6009). The temperature data was delogged using LabVIEW software. A DC battery of 36 V (3 × 12 V), 100 A specification was used as the heater power source. The power of the heater was varied from 20 kW/m2 to 90 kW/m2 using a 100 Ω, 5 A rheostat. Even though the insulation is high and effective,

T_s = −T_avg − (Q×L_2 × 10−3)/(k× A× 10−6)

(3)

where T_avg = (T_1 + T_4 + T_5)/3 is the average temperature and T_1, T_4 and T_5 represents the temperature values at locations 1, 4 and 5 respectively. k is the thermal conductivity calculated from Eq. (2). Finally, the pool boiling heat transfer coefficient was calculated using the Newton’s law of cooling as below.

h= Q/ A× 10−6 (T_ s− T_sat)

(4)

where h is the Heat transfer coefficient (HTC), Q is the heat input, A is the cross sectional area of the substrate and T_sat is the saturation

Fig. 4. Circuit diagram of the experimental setup. 41

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specific surface area obtained through the BET analysis was multiplied with the weight of the sample to determine its actual surface area.

temperature. The uncertainty analysis was carried out as per the Kline and McClintock [23] method. Table 5 presents the uncertainty values of various measurements in this study. The estimated uncertainty in heat input was ± 1.2% and that of thermal conductivity was ± 4.01%. The estimated uncertainties for the surface temperature (Eq. (3)) and boiling heat transfer coefficient was found to be ± 6.91% and ± 13.91% respectively. The calibration of the J type thermocouple with an accuracy of ± 0.5 °C was done using the constant temperature oil bath. The calibration was done at a temperature range of 50–200 °C and the measured temperature values were compared with the standard temperature value of the oil bath, calibration curve is presented as Fig A1 in the (Electronic Supplementary Information (ESI)). Each boiling experiments were repeated thrice (each with a fresh specimen). Hence a total of 60 samples were fabricated and tested under identical operating conditions to ensure the reproducibility of data. However, taking a fresh coated specimen each time does not imply that the coating is unstable. The experiments were repeatable and the coating remained intact even after continuous operation. Fig A2 (ESI) show the SEM images of the boiling surface corresponding to run no. 5 (as an example) prior to and after 50 h of continuous operation which reveals negligible degradation of the coating. Further, to ensure reproducibility of the porous nature of coating, the morphological analysis of the experimental run 5 was carried out. The SEM images of 3 samples of run no 5 is presented in Fig A3 in the ESI. The images reveal similar surface texture for the three samples. The porosity of samples was found comparable at 59%, 60% and 59%. Since the porosity of each reproduced samples, which are the prime governing factors causing enhancement in boiling heat transfer rate, are similar, the reproducibility of the experimental data could be guaranteed.

2.5. Contact angle measurement The contact angle of both plain and CuO coated boiling surfaces were measured to characterize the wettability of the surface. Under standard atmospheric condition (Pressure of 1 bar and temperature of 28 °C), 0.1 ml droplet of demineralized water was carefully placed on the surface of the specimen. The image of the droplet was captured after 5 min using a high resolution camera and was analysed further using the imageJ software. The contact angle was measured thrice on each sample and an average value was reported. The standard deviation in the measurement of contact angle was estimated to be ± 5°. 3. Results and discussion 3.1. Pool boiling performance To estimate the boiling heat transfer coefficient, the pool boiling experiments were conducted on all coated samples in an applied heat flux range of 20–90 kW/m2. The results were then compared with the boiling heat transfer coefficient values of the plain boiling surface. For all the experimental runs HTC increased with an increase in the heat flux value and the maximum enhancement was noted at 90 kW/m2 heat flux as compared to the plain surface. However the enhancement in HTC was not worth mentioning for the experimental runs 3, 6, 7, 8 and 12, due to which the boiling curves of these runs were almost near to the boiling curve of the plain surface. Since the maximum enhancement in HTC was observed at 90 kW/m2 heat flux, in the present study boiling heat transfer coefficient at 90 kW/m2 heat flux was selected as the output response. Table 6 shows the design matrix along with the pool boiling heat transfer coefficients for the nano-CuO coated boiling surfaces at 90 kW/m2 heat flux. The boiling heat transfer coefficient of the plain boiling surface at 90 kW/m2 heat flux was found to be 4.99 kW/m2 °C. As evident from the Table 6, the heat transfer coefficient values varied with the experimental runs. The highest heat transfer coefficient of 6.50 kW/m2 °C was observed for the experimental run no. 5 (1 M molar concentration, 180 min dipping time, 550 °C sintering temperature) which was 30% more than the plain boiling surface. Whereas, the experimental run no. 6 (1.50 M, 120 min, 550 °C) yielded the lowest heat transfer coefficient value of 5.03 kW/m2 °C. Run no. 12 corresponding to 0.5 M molar concentration (which was the lower limit of molar concentration) yielded an HTC value of 5.09 kW/m2 °C. Similarly, it could be observed that run no. 4 had an HTC value of 6.33 kW/ m2 °C which was closely matching to that of run no. 5. Also, run no. 14 exhibited an HTC value of 5.83 kW/m2 °C close to the median value of 5.92 kW/m2 °C among the series of tested samples. In order to identify the reason behind the variation in HTC, the aforementioned runs hereafter referred to as critical runs were subjected to further structural, morphological, roughness and wettability analyses which will be discussed in the following sections.

2.4. Coating characterization Square pieces of 316LN stainless steel with dimension of 10 × 10 × 10 mm were coated with the CuO film at par and under identical conditions as that of the substrate for the structural and morphological analysis of the coating. The structural analysis of the coating was carried out using the X-Ray Diffraction (XRD) apparatus (Rigaku Xtalabmini N) augmented with a computer controlled processor under Cu-Kα radiation (λ = 1.5405 Å). From the XRD spectra, the average grain size (t) of the coating was found out using the following Scherrer’s equation:

t= (0.9λ)/(βcosθ)

(5)

where λ is the X-Ray wavelength, β is the full width at half maxima of the peak at diffracting angle θ. For the morphological analysis of the coating, a Scanning Electron Microscope (SEM) [TESCAN VEGA SBH] was employed. From the SEM images, the porosity of the coated surface was estimated using image analysis software, imageJ. The roughness and thickness of the coating was estimated using a surface roughness tester [Mitutoyo Surftest SJ 210]. For the measurement of thickness, a part of the sample was covered with a mechanical mask to induce a step between the coated and non-coated regions. The stylus of the roughness tester was traversed crosswise this step and the thickness was approximated from the peak height measured using a piezoelectric pickup. A set of three measurements were taken in each sample at different locations across the step and the average these values was taken. An approximate measurement of the surface area of the coated and uncoated samples was carried out using the BET surface area analyser [BEL Belsorp max] by the adsorption of nitrogen at −170 °C in accordance with the reports of Yuxin Liu et al. [34]. A cut section of dimension 2.0 mm × 2.0 mm × 1.0 mm was made from the sample and subjected to BET surface area analysis. As BET permits only the measurement of specific surface area, the sample was weighed prior to the analysis. The

3.1.1. Pool boiling curves Figs. 5(a) and 6 (a) shows the boiling and HTC curves of the critical Table 5 Uncertainty values of various parameters.

42

Sl No

Parameter

Uncertainty (%)

1 2 3 4 5

Heat Input Heat loss Thermal conductivity Surface temperature Heat transfer coefficient

± 1.2 ± 3.04 ± 4.01 ± 6.91 ± 13.91

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runs compared with the uncoated (plain) boiling surface. In addition Figs. 5(b) and 6(b) shows the behavior of run no. 3, 7 and 8 which had minimal enhancement in HTC compared to the plain boiling surface as mentioned in Section 3.1. The data presented here is the average of three replicates of wall superheat measurements and were within ± 4% variation. As evident, for all the five critical runs, the maximum reduction in wall superheat occurred at 90 kW/m2 heat input. Further, the reduction in wall superheat was more for run no. 5 compared to the other four critical runs. The estimated percentage reduction in wall superheat for run no.5 was 27%. Fig. 6(a) shows the variation of heat transfer coefficient with the applied heat flux for the critical runs and the plain boiling surface. The data were within ± 3% variation. It could be observed from Fig. 6(a) that the heat transfer coefficient increased with the heat flux. This might be attributed to the consequent increase in the active nucleation site density as reported by Gheitaghy et al. [29]. The maximum enhancement of heat transfer coefficient occurred at 90 kW/m2heat flux. Further, it was observed that run no.5 exhibited higher heat transfer coefficient values compared to the other critical runs.

Table 6 Estimated pool boiling heat transfer coefficient value for nano-CuO coated boiling surfaces. Run

Molar concentration (M)

Dipping time (min)

Sintering temperature (°C)

Heat transfer coefficient (kW/ m2 °C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1.00 1.00 1.30 0.70 1.00 1.50 1.30 1.30 1.00 1.00 0.70 0.50 1.00 1.00 1.00 0.70 1.00 0.70 1.00 1.30

120 60 84 156 180 120 156 84 120 120 84 120 120 120 120 156 120 84 120 156

550 550 609 609 550 550 491 491 550 450 491 550 550 550 650 491 550 609 550 609

6.31 6.01 5.19 6.33 6.50 5.03 5.17 5.19 6.13 5.83 5.60 5.09 6.07 5.83 6.28 6.07 6.28 5.64 6.23 5.69

3.1.2. Characterization of critical runs Fig. 7 shows the XRD patterns of these runs and the bare surface. The peaks obtained at 2θ values of 32.62°, 35.63°, 38.87°, 48.87°, 50.73°, 53.53°, 58.33°, 61.47°, 65.73°, 66.27°, 68.07°, 72.40° and 82.33°

Fig. 5. Boiling curve of (a) critical runs compared with the plain boiling surface, (b) run no. 3, 7and 8 compared to plain boiling surface. 43

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Fig. 6. Variation of heat transfer coefficient with heat flux for (a) critical runs and plain boiling surface, (b) run no. 3, 7 and 8 compared to plain boiling surface.

porosity in each squares were found to be within 42% to 46% and 44% to 48% respectively. Hence, the coating corresponding run no. 5 was having more homogeneous distribution of pores compared to the other critical runs. Still, a better comparison of homogeneity of the boiling surfaces corresponding to the critical runs could be made from the lower magnification SEM images shown in Fig A4 of ESI. Table 7 also shows the results of the surface roughness measurements of the critical runs. The average roughness for run no. 5 was noted to be 2.26 μm. The roughness decreased to 2.2 μm for run no. 4 (which was however very close to that of run no.5). The roughness of run no. 14 was 1.81 μm and run no. 12 was 1.02 μm. The lowest roughness value of 0.98 μm was noted for run no. 6. The roughness of the plain boiling surface was found to be 1.03 μm. In order to estimate the wettability of the boiling surface, its static contact angles were measured as per the procedures outlined in Section 2.5. The results of the static contact angle measurement for the various runs are presented in Table 7. The static contact angle of the plain boiling surface was found to be 61°. Fig. 10 shows the measurement of static contact angle of run no. 5 and the plain boiling surface as an example. Among the critical runs, as evidenced from Table 7, the experimental run no. 5 with the highest HTC value had the highest contact angle while run no. 6 with the lowest HTC value had the lowest contact

confirmed the presence of CuO (JCPDS: 45-0937). The average grain size of the coatings was estimated from the XRD spectra. The least grain size was estimated for run 5 (25 nm) followed by runs 4, 14, 12 and 6. This finding indicated that the boiling surface corresponding to run no. 5 was supplemented with the nano structured morphology to a greater extent compared to the other critical runs. Fig. 8 shows the SEM images of the boiling surfaces corresponding to the critical runs. There was hardly any variation in surface morphology for the critical runs. From the SEM images, the percentage porosity was measured using the image analysis software. The results of percentage porosity measurements are shown in Table 7. Among the critical runs, the highest porosity of 59% was noted for run no. 5. Run no. 4 had a porosity of 55% which was close to that of run no. 5. Further, run no. 14 had an intermediate porosity value of 51%. The lowest porosity of 46% was noted for run no. 6 with the lowest HTC value. To estimate the homogeneity in pore distribution, the SEM image was divided into small squares each of length 0.2 times the vertical length of the SEM image as shown in Fig. 9 and the number of the pores in each square was counted [10]. Hence, the percentage porosity was measured in each individual squares. The variation in pore distribution for run no. 5 was found to be in the range 58% to 61%. For run no. 12 and run no.6, the percentage 44

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[20]. In order to strengthen this argument, an approximate estimate of the coating surface area was made using the BET analyser. Table 7 shows the ratio of surface area of the critical samples to that of the uncoated sample. As evident, run no. 5 which was more nano sized and rough had the maximum enhancement in surface area while the minimum enhancement was for run no. 12. This enhancement in surface properties could overcome any effect of thermal resistance of the deposited layer. The thermal resistance offered by the coating is a function of its thickness. Lower coating thickness rules out the effect of thermal resistance [33]. As evident from Table 7, the coated samples had low thickness values (in the range of 77–158 µm) and hence the thermal resistance offered by the coating layer will be less. Further, due to the hydrophobicity and porosity of sol-gel derived CuO coating, the cavity will be filled with vapor/gas and volume of water entrapped inside the pore or cavity would be less compared to a hydrophilic surface. This lesser entrapped vapor/gas quantity gets heated quickly and promotes local pumping action on the water above it and facilitates early detachment of bubbles even at lower superheat. Moreover as the hydrophobicity and porosity increases, the bubble diameter decreases and promotes the accommodation of maximum number of nucleation sites which in turn favor heat transfer [7,12,24–26]. To confirm the above hypothesis, the static images of the boiling surface corresponding to the experimental run no. 5 (which yielded the highest heat transfer coefficient), run no. 6 (which yielded the lowest Heat Transfer Coefficient) and the plain boiling surface at an applied heat flux of 35 kW/m2were taken as shown in Fig. 11. It is apparent from the Fig. 11 that there was a decrease in bubble diameter and increase in bubble density (implying an increase in nucleation site density) for the boiling surface corresponding to run no. 5 compared to the plain boiling surface. The bubble diameter was more for run no.6. An estimate of the nucleation site density from the boiling surface could be made from a correlation proposed by Wang and Dhir [27] given in Eq. (6):

N_ a= N_c (1 − cosδ) (T_ S− T_sat)

(6)

where N_c is the number of pores on the coated specimen (obtained from SEM image) and δ is the contact angle. Using this equation, the nucleation site density of the boiling surface corresponding to the critical runs was predicted as shown in Table 7. As evident, the predicted nucleation site density of run no. 5 (4.34e11/m2) was higher than that of run no. 6 (3.63e10/m2) and run no. 12 (4.70e10/m2). Thus, to summarize from Table 7, a reduction in average crystallite size, 15% increase in porosity, 53% increase in contact angle and nearly one fold increase in roughness of the nano-CuO coated boiling surface from run no. 6 to run no. 5 resulted in about tenfold increase in nucleation site density. Thus, run 5 was found to be the better coated surface within the scope of tested samples with the increase in heat transfer coefficient up to 30% compared to the plain surface. Another interesting result from the present study was that heat transfer coefficient values of the experimental runs 6 and 12 were comparable with that of the plain boiling surface. The following reasons may explain this; the roughness of the above mentioned surfaces were more or less same viz; 1.03 µm for the bare surface, 0.98 µm and 1.02 µm in that order for run nos. 6 and 12. In addition, the percentage porosity on the boiling surface of run no. 6 (1.5 M, 180 min, 550 °C) and run no. 12 (0.5 M, 120 min, 550 °C) were found less (44% for run no. 6 and 46% for run no. 12) which would have resulted in minimal variation in the heat transfer coefficient values. Another possible reason might be the nature of deposition of CuO on their boiling surface. The higher molar concentration (1.5 M) of run no. 6 could have caused bulk deposition of CuO particles and blocked the micro/nano pores on the boiling surface thereby reducing the heat transfer [28]. Similarly, since the boiling surface associated with run no. 12 was prepared from a relatively lower molar concentration value of 0.5 M, the CuO deposition on it might not be sufficient enough to enhance the heat transfer coefficient. Due to all the aforementioned factors, the boiling surface of

Fig. 7. XRD patterns of coated boiling surfaces corresponding to: (a) Run 5, (b) Run4, (c) Run 14, (d) Run 6, (e) Run 12 and (f) bare surface. Symbols Δ and Φ represents the peaks of CuO and bare substrate respectively.

angle. The static contact angle for run no. 4 was 127° which was close to that of run no. 5. Further, the contact angle for run no. 14 was 98° which was intermediary to the highest value (corresponding to run no.5) and the lowest value (corresponding to run no. 6). The static contact angle of run no. 12 was 79° which was a close match to that of run no. 6. The above results indicated the hydrophobic nature of the coating corresponding to run no. 5. Since boiling surface of the experimental run no. 5 was rough and hydrophobic, the droplet placed on it would have the least possibility of capillary wicking action in the pores, and hence attained a cassie state [11]. 3.2. Mechanism of enhancement of boiling heat transfer using CuO thin film Based on the above findings, the reason behind the enhancement in pool boiling heat transfer coefficient value due to the additional coating can be explained as follows: As the surface becomes more nano sized and rough, it promotes fin action and there will be an increase in the effective boiling surface area 45

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Fig. 8. SEM images of boiling surface corresponding to: (a) run 5, (b) run 6, (c) run12, (d) run 4 and (e) run 14.

Table 7 Estimated coating porosity, average roughness, static contact angle, predicted active nucleation sites, HTC, surface area enhancement ratio and coating layer thickness of critical runs and bare surface. Sample identity

Coating porosity (%)

Contact angle ( o)

Average roughness (µm)

Predicted active nucleation site (/m2)

Heat transfer coefficient (kW/m2°C)

Surface area enhancement ratio

Coating layer thickness (µm)

Run 4 Run 5 Run 6 Run 12 Run 14 Bare surface

55 59 44 46 51 –

127 131 71 79 98 61

2.2 2.26 0.98 1.02 1.81 1.03

4.21e11 4.34e11 3.63e10 4.70e10 1.78e11 –

6.33 6.50 5.03 5.09 5.83 4.99

1.187 1.201 1.051 1.039 1.099 –

98 102 158 77 89 –

46

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Fig. 9. Estimation of homogeneity in pore distribution for run 5.

Fig. 11. Static image of boiling surface: (a) Run no. 5, (b) Run no. 6 and (c) plain boiling surface at 35 kW/m2 applied heat flux.

Variance (ANOVA), the result of which is presented in Table 8. In the above table, the insignificant terms (p > 0.05), which would not affect the hierarchy of the model, were excluded [16]. The final reduced quadratic model was found to be significant with a probability (P) less than 0.0001 and no lack of fit (P = 0.0684, larger than the reference limit of P of 0.05). This indicated that the model could adequately predict the heat transfer coefficient [30]. The goodness of fit (R2 = 0.9990) and the goodness of prediction (Pred R2 = 0.9931) confirmed that the levels were within acceptable limits [31]. Further, from the Table 8, it was evident that that molar concentration was the most significant parameter (F = 2509.34) while, the sintering temperature was the least (F = 31.48) significant parameter influencing the pool boiling heat transfer coefficient. Similarly, the most significant interaction effect was between molar concentration and dipping time (F = 257.83) while the least significant interaction effect was between molar concentration and sintering temperature. Fig. 12 shows the normal probability plot of response residuals which gives information about the fitness of the quadratic model selected in the present study. The points in the graph indicate the experimental runs and the color is indicative of the value of HTC of each experimental run (higher the HTC values shown in red, lowest in blue 1 and so on). The closeness of these points to the fit line is a manifestation of the fitness of the model.

Fig. 10. Static contact angle for: (a) run no. 5, (b) plain boiling surface.

the uncoated specimen behaves more or less the same as that of the experimental runs 6 and 12 resulting in minimal variation in their HTC values. 3.3. Stability of the model To predict the heat transfer coefficient, the Design Expert Software (Version 8.0.4) employing a hierarchical quadratic model was used [15]. The stability of the model was evaluated from the Analysis of

1 For interpretation of color in Fig. 12, the reader is referred to the web version of this article.

47

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Table 8 ANOVA table. Source

Sum of squares

Df

Mean square

F-value

p-value

Model A-molar concentration B-dipping time C-sintering temperature AB AC BC A2 B2 C2 Residual Lack of fit Pure error Corrected total S.D Mean C.V% Press

9.35 2.32 0.25 0.029 0.24 8.450E−003 0.026 6.36 0.021 0.32 9.233E−003 7.483E−003 1.750E−03 9.36 0.03 5.77 0.53 0.60

9 1 1 1 1 1 1 1 1 1 10 5 5 19

1.04 2.32 0.25 0.029 0.24 8.45E−003 0.026 6.36 0.021 0.32 9.223E−004 1.497E−003 3.500E−004

1124.63 2509.34 269.70 31.48 257.83 9.15 28.65 6885.18 22.98 349.97 4.28

< 0.0001 < 0.0001 < 0.0001 0.0002 < 0.0001 0.0128 0.0003 < 0.0001 0.0007 < 0.0001 0.0684 – Not significant

R2 Adj R2 Pred R2 Adeq. Precision

0.9990 0.9981 0.9931 125.190

A good agreement between the experimental and model predicted values was observed from the Fig. 13, which guaranteed the accuracy of the model.

The data presented in Fig. 12 was found converging which is an indication of minimal deviation from the fit. The analysis also helped to obtain a regression model to predict the boiling heat transfer coefficient values within the workable range of chosen process parameters. A similar approach was reported in the work of Sivapirakasam et al. [15]. The regression equation is given below:

3.4. Interaction effects of process parameters In Figs. 14–16, the 2D contour plots showing the interaction effect of two process parameters at a time on the boiling heat transfer coefficient keeping the third parameter at a constant intermediate level is presented. From Fig. 14, it could be evidenced that, for all values of dipping time, the heat transfer coefficient increased with an increase in molar concentration from 0.50 M to 1 M. This was followed by a decrease in heat transfer coefficient up to a molar concentration of 1.5 M. The reason behind this behavior might be explained as follows: With an increase in molar concentration from 0.50 M (corresponding to run no. 12) to 1 M (corresponding to run no. 5), there was a consequent increase in the porosity, roughness and hydrophobicity of the boiling surface (Table 7). Thus, there occurred an increase in the number of active nucleation sites and hence the enhancement in heat transfer coefficient. Further increase in molar concentration from 1 M to 1.5 M

Heat transfer coefficient (kW/m2 °C) = −2.503 + (11.474 × M) − (0.051 × D) + (0.019 × S) − (2.357 E− 003 × M× D) − (4.667 E− 003 × M× S) + (9.546 E− 005 × D× S) − (4.795 × M2) − (1.015 E− 006 × D2) − (2.0366 E− 005 × S2)

Significant

(7)

Using the above equation, the boiling heat transfer coefficient values for all the runs were predicted and compared with that of the experimentally determined values as shown in Fig. 13. The straight line in the figure represents the heat transfer coefficient of the plain boiling surface for the sake of comparison.

Fig. 12. Normal probability plot of response residues for heat transfer coefficient. 48

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Fig. 13. Experimental and predicted heat transfer coefficient values.

Fig. 15. 2-Dimensional contour plot showing the effect of interaction of sintering temperature and molar concentration on pool boiling heat transfer coefficient.

Fig. 14. 2-Dimensional contour plot showing the effect of interaction of dipping time and molar concentration on pool boiling heat transfer coefficient.

(corresponding to run no. 6) had decreased the porosity, roughness and hydrophobicity of the boiling surface (Table 7) leading to a reduction in the number of active nucleation sites and the heat transfer. To further strengthen the above argument, the static contact angles were measured at two process conditions, one is at the process condition between 0.5 M and 1 M (ie at 0.7 M) and the other process condition is between 1 M and 1.5 M (ie at 1.3 M). The static solid-liquid contact angle was increased to 79°, 97°, 131° with the increase in molar concentration of 0.5 M, 0.7 M and 1 M respectively. Whereas, the contact angle decreased from 131° to 99° and 71° with further increase in molar concentration from 1 M to 1.3 M and 1.5 M respectively. Further from the Fig. 14, the optimum value of molar concentration yielding higher heat transfer coefficients was found to lie between 0.67 M and 1.17 M. Similarly, Fig. 15 shows the interaction effect of sintering temperature and molar concentration on the heat transfer coefficient. It is apparent that, at both lower and higher molar concentrations, there was a negligible variation in heat transfer coefficient with the sintering temperature. This might be attributed to the negligible variation in porosity, roughness and the wettability of the surface with the variation in sintering temperature. A close observation of Fig. 16 indicates an increase in boiling heat transfer coefficient with an increase in sintering temperature at higher dipping times. The variation was however negligible at lower dipping

times. 4. Conclusions This paper demonstrates the favourable influence of sol-gel derived nano structured CuO film on augmenting the pool boiling heat transfer coefficient. For the first time, the variation of influential process parameters and its effect on the boiling heat transfer rate were systematically studied using the statistical concept of design of experiments and a regression model which could predict the boiling heat transfer rate as a function of these parameters was developed. The major findings of this study are summarized as follows:

• The pool boiling heat transfer coefficient enhanced with the nano

•

49

structured CuO coating. The maximum enhancement in heat transfer coefficient of 30% and the maximum reduction in the wall superheat of 27% were noted for the coating corresponding to a combination of 1 M molar concentration, 180 min dipping time and 550 °C sintering temperature (run no. 5). A decrease in grain size together with an increase in porosity, roughness and hydrophobicity was found to enhance the boiling heat transfer rate. The experimental run which yielded the highest heat transfer coefficient was found to have an average grain size of

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[2] [3]

[4] [5] [6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

Fig. 16. 2-Dimensional contour plot showing the effect of interaction of sintering temperature and dipping time on pool boiling heat transfer coefficient.

•

•

[14]

25 nm, porosity of 59%, roughness of 2.26 µm and a static contact angle of 131°. The number of active nucleation sites was found increasing with an increase in the porosity, roughness and hydrophobicity of the boiling surface. A 15% increase in porosity, and 53% increase in contact angle of the nano-CuO coated boiling surface resulted in a tenfold increase in nucleation site density which in turn favoured higher heat transfer coefficient values. The stability of the model evaluated using ANOVA revealed that the molar concentration had the highest influence in enhancing the heat transfer coefficient followed by dipping time and sintering temperature. The most significant interaction effect was between molar concentration and dipping time while the least significant interaction was amidst molar concentration and sintering temperature.

[15]

[16] [17]

[18] [19] [20] [21] [22]

[23]

Acknowledgements

[24]

The authors are grateful to the Chairman and Principal, Vimal Jyothi Engineering College, Chemperi, Kannur, Kerala, India for providing the necessary facilities for carrying out this work. The authors profusely thank the department of physics, Nirmalagiri College, Kannur, Kerala, India for providing XRD facility. The authors also thank Prof. K. Sasikumar (Chairman), Dr. S. Suresh Babu (Principal), Dr. Saji Varghese (Head of Mechanical Engineering Department) and Prof. Anil Kumar A V of Sree Buddha College of Engineering, Pattoor, Alappuzha, Kerala, India for their support and motivation. The authors also acknowledge the Department of Material Science and Metallurgy, National Institute of Technology, Tiruchirappalli for providing SEM and surface roughness measurement facility.

[25] [26] [27] [28]

[29]

[30]

[31]

Appendix A. Supplementary material

[32]

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.expthermflusci.2018.12.033.

[33]

[34]

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