Experimental investigation of a two-phase closed thermosyphon with Al 2 O 3 /R134a nanorefrigerant

Abstract

Nanorefrigerants are mixtures of refrigerant and nanoparticles, which enhance the heat transfer in both air-conditioning and refrigeration systems. In the present work, the nanorefrigerant Al₂O₃/R134a is applied in a two-phase closed thermosyphon in order to investigate its thermal performance. More specifically, the influence of the Al₂O₃/R134a on the thermal resistance and heat transfer coefficient for various heat inputs in a two-phase closed thermosyphon were investigated experimentally. The two-phase closed thermosyphon was tested by varying the concentration of Al₂O₃ nanoparticles from 0.5% up to 1.5% by weight basis in the usual refrigerant R134a. The results of the experimental investigations indicate that there is a significant enhancement in heat transfer by 93.2% when the nanoparticle concentration of 1.0% was used in R134a when compared to the thermosyphon tested with pure R134a. The nanorefrigerant in thermosyphon has reduced its thermal resistance significantly by 59%. The heat transfer coefficient obtained for nanorefrigerants in two-phase closed thermosyphon is validated by Cooper correlation. It is inferred from the study that the depositions of nanoparticles in the thermosyphon enhance heat transfer. A new correlation to predict the rate of heat transfer is also proposed for employing the nanorefrigerants in a two-phase closed thermosyphon. The predicted correlation, heat transfer coefficient and thermal resistance are optimized by using Grey Relation analysis by computing the grade between the minimum and maximum values obtained from the experimental results.

Introduction

Refrigerants are liquid or gaseous substances, which are categorized with their boiling point, ¹ which are commonly used in vapor compression refrigeration and air conditioning systems. ² Refrigerants in Two-Phase Closed Thermosyphon (TPCT) as working fluid have been utilized for low-temperature heat transfer (−73 °C to 277°C) applications in the past. ³ Therefore, it is necessary to improve the thermosyphon's heat transfer capacity and its performance using refrigerants. Nanoparticles suspended and dispersed in refrigerants lead to the development of nanorefrigerants. They have been widely used by many researchers for numerous applications due to their improved thermo-physical properties than pure refrigerants. ⁴

The nanoparticle in refrigerant enhances the Heat Transfer Coefficient (HTC) and the thermal conductivity compared to the pure refrigerants irrespective of the type of nanoparticles. Mahbubul et al. ⁵ examined the thermal properties such as viscosity, thermal conductivity, specific heat and density of nanorefrigerant Al₂O₃/R134a in a smooth tube. The analysis shows that the pressure drop, viscosity, and HTC of the nanorefrigerant increase with the increase in the concentration of Al₂O₃ in the refrigerant. It is also found that the addition of Al₂O₃ nanoparticles in R134a improves the thermal conductivity by 28.58%. The study also indicates that an appropriate nanoparticle volume fraction in refrigerants can improve its thermal performance. Mahbubul et al. ⁶ also analyzed the thermal conductivity on the volumetric effect of Al₂O₃ nanoparticles in R141b refrigerant. They concluded that the thermal conductivity of the nanoparticle in refrigerant increases when increasing the volume concentration of nanoparticles. In addition to this, Mahbubul et al. ⁷ conducted experimental studies to determine the viscosity and thermal conductivity of Al₂O₃/R141b nanorefrigerant. They investigated by varying the nanoparticle concentration from 0.5% to 2% and temperature between 5 °C and 20 °C. The experimental result shows that the thermal conductivity of Al₂O₃/R141b nanorefrigerant is found to increase with the increase in the concentration of nanoparticles.

Maheshwary et al. ⁸ investigated the shape effect on the thermal conductivity for spherical and cubical shaped ZnO nanoparticles in R134a refrigerant. They observed that the thermal conductivity increased in spherical and cubical shape nanoparticles by 25.26% and 42.5% respectively. In a plane tube, Park and Jung ⁹ investigated the impact of carbon nanotubes on nucleate boiling heat transfer. The carbon nanotubes of 1.0% volume concentration were added in R134a and R123 to estimate the boiling HTC. The result identifies that the boiling HTC increases up to 36.6% for low heat flux with the addition of carbon nanotubes in refrigerants. In their other investigation, Park and Jung ¹⁰ explored the effect of HTC in a plain tube with carbon nanotube nanoparticles (1% volume concentration) in refrigerant R22. The investigation proves that the carbon nanotube in the R22 refrigerant increases the boiling HTC by 28.7%. Sun and Yang ¹¹ studied the flow boiling HTC of the nanorefrigerant Al₂O₃/R141b in a copper tube of the internal thread. It is inferred from the investigations that the addition of nanoparticles in the R141b refrigerant increases the boiling HTC between 17% and 25%. Sun et al. ¹² used multi-walled carbon nanotube nanoparticles with R141b in order to analyze the flow boiling heat transfer inside the smooth tube. The result shows that the heat transfer for multiwalled carbon nanotube in R141b improved by 23.4% than pure R141b. Flow boiling heat transfer in the horizontal tube was experimentally investigated by Akhavan-Behabadi et al. ¹³ using copper oxide nanoparticles and R600a refrigerant. They found that R600a with nanoparticles increase the HTC by 63% when compared to pure R600a.

Anish et al. ¹⁴ evaluated the performance of a refrigerator in which CuO and Al₂O₃nanoparticles were used with R22 refrigerant. It was concluded by them that nanorefrigerant improves the rate of heat transfer and decreases energy consumption. Domestic refrigerator efficiency was measured by Jatinder et al. ¹⁵ with refrigerant 600a by varying the concentrations of TiO₂ nanolubricant (0 g/L, 0.2 g/L 0.4 g/L and 0.6 g/L). The result indicated that the COP reached a value of 0.62 for R600a with nanolubricant, compared with liquified petroleum gas-based refrigerant. It was also suggested that the refrigerator's overall performance was better at 0.2g/L concentration. Naphon et al. ¹⁶ performed parametric studies in a heat pipe with titanium oxide/R11 refrigerant and concluded that the volume concentration of 0.1% nanoparticle in refrigerant R11 enhances the efficiency of the heat pipe by 1.4 times compared to pure R11 refrigerant. It is also inferred from the literature^14–16 that the utilization of nanorefrigerant increased the performance of refrigerators and heat pipes.

TPCT employed with refrigerants are mainly used in low-temperature applications such as solar applications, heating ventilation and air conditioning system as well as in cooling applications.^17,18 In order to identify the best refrigerant in TPCT, several researchers have carried out experimental studies. Gorecki ¹⁹ introduced refrigerants such as R134a, R404A, and R404C in TPCT and concluded that R134a and R404A have the capability of high heat transfer rate. Ma et al. ²⁰ tested TPCT using refrigerants such as R134a, R245fa, R601, R1234ze, R152a, R600a, R601/R245fa, and R245fa/R152a. The experimental result shows that the refrigerant R134a performed better than the other refrigerants.

Jia-qiang et al. ²¹ performed grey relation analysis in an oscillating heat pipe with the chain neural network to determine the relationship between the output heat transfer and the predicted heat transfer utilizing parameters such as charging ratio, inner diameter, inclination angle, number of turns, and so on. The results of the investigation show that the variance between heat transfer obtained is only 4%, and suggest that this method can be used to predict heat transfer performance in oscillating heat pipes.

Aside from heat pipes, literatures include the Grey relation analysis in the effect of heat transfer in concentric pipe heat exchangers, ²² optimization of heat pipe heat exchanger devices, ²³ heat dissipation character in lithium ion batteries ²⁴ and other aspects.

As the nanorefrigerant possesses better thermal conductivity and HTC than other refrigerants, it can be used to enhance the performance of TPCT. Furthermore, it is clear that there is a dearth of information in the literature on the use of nanorefrigerants in TPCT, hence our work is intended to fill that scientific gap. Henceforth, the proposed research will focus on experimental examinations of the TPCT utilizing Al₂O₃/R134a nanorefrigerant at various concentrations and comparisons with refrigerant R134a making it a unique contribution to science. Moreover, the experimental results of heat transfer with the refrigerants and different concentrations of Al₂O₃/R134a nanorefrigerant in cylindrical TPCT are validated with the Grey Relation analysis to prove its efficiency.

Experimental procedures

Preparation and mixing of working fluid

In this experimental process, the refrigerant R134a is chosen as the base fluid, because it has high heat transfer capability among other refrigerants in thermosyphon.^19,20 To increase the heat transfer, Al₂O₃ nanoparticles are added in R134a by a two-step process. Al₂O₃ nanoparticle is chosen due to its improved thermal conductivity at an affordable price. ²⁵ In this investigation, the nanorefrigerant is dispersed continuously for 3–4 h using the ultrasonic bath sonicator similar to the procedure adopted by Jiang et al. ²⁶ Then, the nanorefrigerant is kept inside the ultra-low reaction bath apparatus to observe its stability. The stability of the nanorefrigerant is found to be appreciable for a minimum of 4 h inside the ultralow bath apparatus. This stability is sufficient for the present study as the maximum experimentation time is less than 4 h.

Fabrication of TPCT

A copper pipe of diameter 12.7 mm and length 350 mm is chosen for the fabrication of TPCT where the length of pipe is divided into three different sections namely, Evaporator-100 mm; Adiabatic-100 mm and Condenser-150 mm. End caps are made by machining a copper rod of 11.3 mm diameter and 3 mm thickness to close the ends of the copper pipe. Cleansing of copper pipe and the end cap is mandatory to achieve accuracy in the results and therefore, it is accomplished by deoxidizing solution and deionized water to remove the dust and other foreign particles. The ends of copper pipes are sealed by brazing with end caps. There are seven T-type thermocouples fixed to monitor the wall temperature variation in TPCT, out of which two thermocouples each for the evaporator, adiabatic section and three thermocouples for the condenser section. Through a charging tube attached to the condenser section, 30% of the total volume of the TPCT is charged with nanoparticles and refrigerant R134a working fluid. A flat nichrome wire is winded over the evaporator section, which acts as a heating element. To reduce the heat loss to the surroundings, TPCT is insulated with fiberglass wool.

Experimental test procedure of TPCT

The experimental setup of TPCT is depicted in Figure 1. It consists of a wattmeter, variable transformer, flow meter, chilling unit, and a data acquisition unit. The chilling unit supplies the cold water, which is circulated around the condenser of TPCT. Thermocouples (T₁ and T₂) are used to measure the cold-water temperature at the inlet and outlet of the condenser. The flow rate of the water from the chilling unit to the condenser is measured by a flow meter.

Figure 1.

(a) Experimental arrangement of thermosyphon. (b) Position of the thermocouples.

Supplementary Table 1 show the specifications of the instruments used in the experiment. The thermocouple associated with the data logger has an uncertainty of  ±0.2 °C. The uncertainty analysis estimated parameters are mentioned in Supplementary Table 2.

The equations that have been used in order to determine HTC, thermal resistance and uncertainty are furnished below. The basic equation to find the thermal resistance of TPCT is shown in equation (1):

$$R={\displaystyle \frac{\mathrm{\Delta }T}{Q}}$$

(1)

The evaporator thermal resistance in TPCT is obtained by the difference between the average evaporator temperature and vapor temperature ( $\mathrm{\Delta }{T}_e=T_{e,avg}-T_v$ ) to the heat input (Q _in ). It's difficult to determine the vapor temperature in a refrigerant-charged thermosyphon using sensor tubes. As a result, for the temperature difference, the adiabatic temperature next to the evaporator is assumed to be the vapor temperature.

The heat input is given product of voltmeter reading (V) and ammeter reading (I) as below:

$$Q_{in}=V\cdot I$$

(2)

The condenser thermal resistance ( $\mathrm{\Delta }{R}_c$ ) is calculated by the difference between vapor and average condenser temperature ( $\mathrm{\Delta }{T}_C=T_v-T_{c,avg}$ ) to the heat output (Q _out ). The vapor temperature is assumed to be the adiabatic temperature close to the condenser section. The heat output of the TPCT is calculated by mass flow rate and the difference between output and input temperature of the chilling water around the condenser [Q _out  = ṁ C _p (T _out −T _in )].

HTC of the TPCT is calculated by the heat flux to the temperature difference as shown in equation (3):

$$h={\displaystyle \frac{Q}{\pi d{l}_e(T_{e,avg}-T_{v)}}}$$

(3)

The experimental uncertainty of heat flux, HTC and thermal resistance in TPCT is measured using the following equations:

$${\displaystyle \frac{\mathrm{\Delta }\mathrm{q}}{\mathrm{q}}=\sqrt{{\left({\displaystyle \frac{\mathrm{\Delta }\mathrm{Q}}{\mathrm{Q}}}\right)}^2+{\left({\displaystyle \frac{\mathrm{\Delta }\mathrm{A}}{\mathrm{A}}}\right)}^2}}$$

(4)

$${\displaystyle \frac{\mathrm{\Delta }\mathrm{h}}{\mathrm{h}}=\sqrt{{\left({\displaystyle \frac{\mathrm{\Delta }\mathrm{q}}{\mathrm{q}}}\right)}^2+{\left({\displaystyle \frac{\mathrm{\Delta }(\mathrm{\Delta }\mathrm{T})}{\mathrm{\Delta }\mathrm{T}}}\right)}^2}}$$

(5)

$${\displaystyle \frac{\mathrm{\Delta }\mathrm{R}}{\mathrm{R}}=\sqrt{{\left({\displaystyle \frac{\mathrm{\Delta }\mathrm{Q}}{\mathrm{Q}}}\right)}^2+{\left({\displaystyle \frac{\mathrm{\Delta }(\mathrm{\Delta }\mathrm{T})}{\mathrm{\Delta }\mathrm{T}}}\right)}^2}}$$

(6)

Grey relation analysis

The Grey Relation Analysis (GRA) is done based on the heat transfer coefficient and thermal resistance. This is to compute the normalized outcome of the input values through which we can compute the grade and prove its efficiency.

The experimental results are based on the input parameters such as heat input, nanoparticle concentration, the ratio between adiabatic and condenser area, the ratio between length to the diameter of TPCT, specific heat capacity, thermal conductivity, the viscosity of the working fluid and cooling water temperature around the condenser. The temperature is considered as the experimental output, from that the thermal resistance, heat transfer coefficient and heat transfer ratio between R134a and different nanoparticle concentrations (0.5, 1.0 and 1.5) in TPCT are derived.

The outcome of the gray relation analysis in terms of grade from the normalized output data will provide the minimum (resistance) and maximum (HTC) values to be chosen as the response from the output data. A comparability sequence is generated to compare the minimum and maximum values from the normalization factor. The comparability sequence can be found using the measure of deviation obtained from the highest and lowest values computed during the normalization. The comparability sequence can be computed with the response sequences, which are termed as the deviation sequence. The Grey relation coefficient is the ratio between the minimum value and maximum value used in the reference sequence. For every trial of comparison between the experimental results, the grey relation grade is computed to prioritize the efficient result with that of the other set of results.

The Grey relation analysis can be performed by the following four steps:

Step 1. Normalizing of the output data

$$x_i^{*}(k)={\displaystyle \frac{a_i(k)-\min (a_i(k))}{max(a_i(k))-\min (a_i(k))}}$$

(7)

$$x_i^{*}(k)={\displaystyle \frac{\max (a_i(k))-a_i(k)}{max(a_i(k))-\min (a_i(k))}}$$

(8)

Where, $x_i^{*}(k)$ are the normalized values for the obtained output response $a_i(k)$ ; i is the experimental number; k is the comparability sequence; $\max (a_i(k))$ maximum values within the output response $a_i(k)$ and $\min (a_i(k))$ is the minimum values within the output response $a_i(k)$ .

Step 2. Deviation sequence

$${\mathrm{\Delta }}_{oi}(k)=|x_o^{*}(k)-x_i^{*}(k)|$$

(9)

Where, ${\mathrm{\Delta }}_{oi}(k)$ is the deviation sequence from the reference sequence $x_o^{*}(k)$ and $x_i^{*}(k)$ is the comparability sequence.

Step 3. Grey relation coefficient (GRC)

$${\xi }_i(k)={\displaystyle \frac{{\mathrm{\Delta }}_{min}+\zeta {\mathrm{\Delta }}_{max}}{{\mathrm{\Delta }}_{oi}(k)+\zeta {\mathrm{\Delta }}_{max}}}$$

(10)

Where, ${\xi }_i(k)$ is the Grey relation coefficient (GRC); ${\mathrm{\Delta }}_{min}$ is the minimum value of the obtained sequence; ${\mathrm{\Delta }}_{max}$ is the maximum value of the obtained sequence and $\xi$ is the identification coefficient, normally taken as 0.5.

Step 4. Grey relation grade (GRG)

$${\gamma }_i={\displaystyle \frac{1}{n}\sum _{k=1}^n{\xi }_i(k)}$$

(11)

Where, ${\gamma }_i$ Grey Relation Grade (GRG) for $i^{th}$ trial and n is the number of trials

Results and discussion

Figure 2 presents the temperature difference between the evaporator and the adiabatic section of the TPCT's. It is observed that the temperature difference is less at lower heat input and increases linearly with higher heat inputs. The average temperature difference between evaporator and adiabatic section attained by the experiments are 7.12 °C, 6.55 °C, 3.34 °C and 5.04 °C corresponding to R134a, R134 with 0.5% Al₂O₃, R134 with 1.0% Al₂O₃ and R134 with 1.5% Al₂O₃ by weight basis respectively. It is evident that the temperature difference of TPCT using R134a is higher than that of the nanorefrigerants. This reduction of temperature in nanorefrigerant in TPCT is due to the addition of nanoparticles in the working fluid and a similar change was observed and reported by ²⁷ using Al₂O₃ nanoparticles in water.

Figure 2.

Variation of the temperature difference between evaporator and adiabatic to the heat input.

Figure 3 depicts the temperature difference between the evaporator and condenser of TPCT for varying heat input. From Figure 3, it is inferred that the temperature difference of TPCT with nanorefrigerant is less at lower heat inputs and more for higher heat inputs compared to the refrigerant. The maximum reduction in a temperature difference of 5.95 °C is obtained corresponding to 1.0% Al₂O₃ in R134a at higher heat input. As the concentration of nanoparticles increases the temperature difference decreases in the case of R134a up to a concentration of 1.0% and after that, it again increases. This is due to the reason that is similar to the trend observed in Figure 2, and also the past experimental works on thermosyphon with refrigerant shows a similar variation with respect to heat input as shown in Figure 3.^28,29 The mean temperature difference attained corresponding to concentrations of 1.0% and 1.5% is 7.2 °C, and for concentrations 1.0% and 0.5%is about 15.7 °C respectively. The experimental result shows that effective heat transfer has taken place from evaporator to condenser corresponding to a 1.0% concentration of Al₂O₃ in R134a.

Figure 3.

Variation of the temperature difference between evaporator and condenser at different heat inputs.

The thermal resistance in the evaporator is calculated using the temperature difference and the heat input as mentioned in equation (1). Figure 4 shows the reduction in thermal resistance with the increase of nanoparticle concentration up to 1.0% nanoparticle in R134a for the applied heat input. However, above the concentration of 1.0%, the thermal resistance increases in thermosyphon, which can be noticed in Figure 4. A similar observation of heat transfer enhancement in the heat pipe using nanorefrigerant was reported. ²⁷ The average difference between the thermal resistance of pure R134a and R134a with Al₂O₃ of 1.0% concentration is 0.088 °C/W.

Figure 4.

Variation of thermal resistance of the evaporator for various heat inputs.

Figure 5 shows the thermal resistance at the condenser section with respect to heat input. Similar to the resistance in the evaporator, the condenser section also decreases with the heat input. The thermal resistance reduces with an increase in heat input for all combinations of nanorefrigerants and refrigerants. Also, Figure 5 reveals that the thermal resistance in the condenser of TPCT charged R134a with Al₂O₃ shows less significance with pure R134a.

Figure 5.

Variation of thermal resistance of the condenser for different heat inputs.

After the completion of experimentation, the nanoparticle charged TPCT's was mechanically opened, to analyze the nanoparticle configuration in the evaporator and condenser section as shown in Figure 6 and Figure 7. A thin layer of nanoparticle deposition was found in the evaporator, but no deposition was found in the condenser section. This layer is formed due to the boiling characteristics of nanorefrigerant. When the heat input is increased, the liquid pool gets heated up and increases the temperature along with the increase in pressure. ³⁰ An increase in pressure leads to the rigorous scattering of nanoparticles in refrigerant and adheres to the surface of the evaporator wall in TPCT and the condition in which only pure refrigerant moves to the condenser section as vapor state. The nanoparticle-formed layer inside the TPCT results in the enhancement of boiling HTC, ³¹ which reduces the thermal resistance of TPCT. Thus, the enhancement of HTC is found due to the deposition of nanoparticles which improves nucleation sites, and vapor bubble formation at the evaporator.^32–34

Figure 6.

Photographic view of evaporator sections of thermosyphon after completing the experiment (a) R134a + 0.5% Al₂O₃; (b) R134a + 1.0% Al₂O₃; (c) R134a + 1.5% Al₂O₃.

Figure 7.

Sem image of evaporator sections of the thermosyphon (a) R134a + 0.5% Al₂O₃; (b) R134a + 1.0% Al₂O₃; (c) R134a + 1.5% Al₂O₃.

It is observed that the thermosyphon charged with a concentration of 0.5% and 1.5% of Al₂O₃/R134a has less cluster formation than the concentration of 1.0% of Al₂O₃/R134a. The formation of a thin nanolayer corresponding to a 0.5% concentration of Al₂O₃/R134a was less due to a lower concentration of nanoparticles in the refrigerant. As the concentration of the nanoparticle is increased from 0.5% to 1% in the refrigerant, the thickness of the deposition layer also increases to a level at which maximum heat transfer is attained. Further, an increase in the concentration of nanoparticles resulted in an additional layer of coating over the existing layer, closing the active nucleation sites and decreasing the heat transfer. ³⁵ Moreover, a careful analysis of both photograph and Scanning Electron Microscope (SEM) image reveals some of the fall-off spots in the evaporator section, which may be resulted due to the collapse of deposited nanoparticles for 1.5% nanoparticle concentration, as shown in Figures 6(c) and 7(c).

This phenomenon again leads to the reduction of heat transfer since it decreases the thickness of the coating required for the enhancement of performance. Whenever the nanoparticle concentration increases above the particular level, the thermal performance decreases ¹⁶ which can also increase the settlement of nanoparticles at the bottom of the TPCT.^6,10,36 These findings suggest that the optimal range of concentration of nanoparticles in a refrigerant or the optimum thickness of deposition in the thermosyphon surface is necessary for increasing the performance. In general, the deposition of nanoparticles alters the surface roughness and enhances the HTC. Therefore, Cooper correlation ³⁷ incorporated the deposition effect by including the roughness parameter for HTC as shown in equation (12):

$$h=55P^{*(0.12-0.2{\log }_{10}{R}_p)}[{\log }_{10}{P}^{*}{]}^{-0.55}{q}^{0.67}{M}^{-0.5}$$

(12)

The variation in HTC concerning the thickness of deposition varies from 1 to 90 nm is estimated and compared with the experimental results, as shown in Figure 8. It is seen that the experimental and theoretical results follow the same trend except for low heat inputs. As the roughness increases, the HTC also increases exponentially with heat flux. Moreover, the experimental HTC is found to be in good agreement with theoretical results as shown in Figure 9. The TPCT charged with 1.0% Al₂O₃ in R134a shows an improvement in HTC of 93.2% over pure R134a and confirms that the presence of Al₂O₃ nanoparticles in R134a raises the HTC significantly.

Figure 8.

Variation on heat transfer coefficient of the evaporator for different heat flux.

Figure 9.

Variation on experimented and predicted heat transfer coefficient.

Since the Cooper correlation ³⁷ under-predicted the HTC at low heat inputs, hence, a new correlation is proposed for different concentrations of nanoparticles in the TPCT. Variables such as HTC, evaporation temperature and thermophysical properties of nanorefrigerants are considered for the formulation of correlation. The refrigerant properties of R134a ³⁸ and the nanoparticle^31,39 are obtained at various saturation temperatures corresponding to the heat inputs. The nanorefrigerant properties are taken for the mean evaporator temperature of TPCT charged with refrigerants and are highlighted in Supplementary Table 3.

The thermal conductivity and dynamic viscosity of the nanorefrigerants are estimated using the correlation proposed by Syam Sundar et al. ⁴⁰ as given below:

$$k_{nr}=k_{br}(1+10.5\varphi {)}^{0.1051}$$

(13)

$${\mu }_{nr}={\mu }_{br}{\left(1+{\displaystyle \frac{\varphi }{12.5}}\right)}^{6.356}$$

(14)

The nanorefrigerant's specific heat capacity was obtained from the Pak and Cho model ⁴¹ :

$$C_{P,nr}=(1-\varphi )\cdot C_{p,br}+\varphi \cdot C_{p,np}$$

(15)

The suggested correlations are presented in equation (16) ⁴¹ :

$$\begin{array}{rl}{\displaystyle \frac{h_{br}}{h_{nr}}=}& 5.915{\left({\displaystyle \frac{C_{P,nr}}{C_{P,br}}}\right)}^{0.452}{\left({\displaystyle \frac{{\mu }_{nr}}{{\mu }_{br}}}\right)}^{-2.031}{\left({\displaystyle \frac{T_e}{T_{am}}}\right)}^{-0.158}\\ & {\left[{\displaystyle \frac{q{l}_e}{k_{nr}(T_e-T_{adi})}}\right]}^{0.055}{\left({\displaystyle \frac{k_{br}}{k_{nr}}}\right)}^{13.86}\end{array}$$

(16)

The predicted and experimental HTC values are compared and found that the deviation is less than 10% as shown in Figure 9.

For the correlation (mentioned in equation no.16), the experimental variables such as HTC, temperature and the theoretical variables of nanorefrigerants are taken from the temperature of the normal thermosyphon with refrigerants. Hence, it is simple to apprehend that the HTC of nanorefrigerant charged thermosyphon with the help of predicted correlation alongside the normal thermosyphon and properties of nanorefrigerant. The proposed work has its impact in terms of nanorefrigerant Al₂O₃/R134a in thermosyphon which is considered to be a novel approach and henceforth validation becomes obsolete.

The grey analysis was conducted for the experimental outcome and the variations on Grey grade with the inputs which are included in Table 1. It shows the varying combination of Al₂O₃ with R134a with that of the grade computed for the experimental output. The rank is computed with the output parameters such as heat transfer coefficient; thermal resistance and ratio between heat transfer base-refrigerant and nanorefrigerant (h_br/h_nr) as mentioned in correlation are considered. Three different concentration of Al₂O₃ is taken into consideration and aligned with the normalized output to predict the optimized value from the results. The comparative analysis shows that the grade obtained for the varying heat inputs stands efficient. Among the nanorefrigerant charged thermosyphon, 1.0% concentration of Al₂O₃/R134a obtained the grey relation grade of 0.387 which is maximum than another concentration level (0.5% and 1.5%) experimented. The Grey analysis helped the process to predict the rate of heat transfer from the given inputs and to find the output based on the varying heat inputs.

Table 1.

Grey relation coefficient and grade for the experimental outcome results HTC, resistance and ratio between the pure refrigerant and nanorefrigetant in TPCT.

No. of input	Nanoparticle Concentration	Normalizing response			Grey relation coefficient			Grey relation Grade
		HTC	Resistance	hbr/hnr	HTC	Resistance	hbr/hnr
1	0.5	0.000	0.000	0.636	0.333	0.333	0.579	0.208
	1	0.221	0.486	0.121	0.391	0.493	0.363	0.208
	1.5	0.082	0.231	0.392	0.353	0.394	0.451	0.200
2	0.5	0.224	0.491	0.850	0.392	0.496	0.769	0.276
	1	0.584	0.819	0.238	0.546	0.734	0.396	0.279
	1.5	0.172	0.410	1.000	0.377	0.459	1.000	0.306
3	0.5	0.311	0.600	0.706	0.420	0.555	0.630	0.268
	1	0.779	0.914	0.097	0.694	0.854	0.356	0.317
	1.5	0.330	0.620	0.667	0.427	0.568	0.600	0.266
4	0.5	0.355	0.630	0.744	0.437	0.575	0.661	0.279
	1	0.880	0.952	0.098	0.806	0.913	0.357	0.346
	1.5	0.323	0.613	0.813	0.425	0.563	0.728	0.286
5	0.5	0.383	0.672	0.730	0.448	0.604	0.649	0.283
	1	0.968	0.969	0.062	0.940	0.941	0.348	0.371
	1.5	0.366	0.656	0.765	0.441	0.592	0.680	0.286
6	0.5	0.400	0.688	0.671	0.455	0.615	0.603	0.279
	1	0.984	1.000	0.037	0.968	1.000	0.342	0.385
	1.5	0.402	0.689	0.669	0.455	0.616	0.601	0.279
7	0.5	0.398	0.686	0.717	0.454	0.614	0.639	0.284
	1	1.000	0.991	0.051	1.000	0.982	0.345	0.388
	1.5	0.388	0.677	0.736	0.450	0.608	0.655	0.285
8	0.5	0.394	0.682	0.722	0.452	0.611	0.642	0.284
	1	0.980	0.969	0.061	0.962	0.942	0.348	0.375
	1.5	0.378	0.668	0.753	0.446	0.601	0.669	0.286
9	0.5	0.360	0.650	0.683	0.438	0.588	0.612	0.273
	1	0.982	0.985	0.001	0.966	0.972	0.333	0.379
	1.5	0.409	0.695	0.592	0.458	0.621	0.551	0.272
10	0.5	0.362	0.652	0.651	0.439	0.590	0.589	0.270
	1	0.957	0.978	0.000	0.921	0.957	0.333	0.369
	1.5	0.398	0.685	0.585	0.454	0.614	0.546	0.269

Thus, the TPCT charged with refrigerant is suitable for applications such as electrical cooling, permafrost de-icing and solar heating.^18,42–46 As a result of the experiments, it appears that by replacing refrigerants with nanorefrigerants in TPCT, the performance of the aforementioned applications can be improved, which will have a significant influence on low-temperature applications. This fact leads to significant energy savings, something that has a positive environmental impact because it leads to CO₂ emissions avoidance. Furthermore, due to the environmental impact of the refrigerant R134a (relatively high GWP), it is preferable to investigate fluids with lower GWP in the future (e.g. natural refrigerants).

Conclusions

In this work, the performance of the TPCT utilizing the Al₂O₃/R134a nanorefrigerant was examined and compared with the performance of a TPCT charged with pure R134a. It is inferred that the addition of an even small amount of Al₂O₃ nanoparticle increases the performance of the TPCT significantly. The evaporator temperature of TPCT decreases with the addition of nanoparticles in the refrigerant when compared to the pure refrigerant. The mean temperature difference for all heat inputs (10 to 120W) between the evaporator and condenser section is 16.7 °C, 15.9 °C, 14.5 °C and 14.9 °C for refrigerant R134a, R134 with 0.5% Al₂O₃, R134 with 1% Al₂O₃ and R134 with 1.5% Al₂O₃ concentration respectively. The thermal resistance in the evaporator section corresponding to 1.0% Al₂O₃/R134a decreases by 59.01%, 50.2% and 34.2% with R134a, 0.5% Al₂O₃/R134a and 1.5% Al₂O₃/R134a respectively. Also, the SEM analysis result shows that the deposition of nanoparticles in the TPCT surface plays a vital role in the enhancement of HTC. The grey analysis also shows that the concentration of 0.5%, 1.0% and 1.5% Al₂O₃/R134aproduces 26, 8.5 and 19.7 respectively. The Grey analysis on the experimental outcome with nanorefrigerant in TPCT, the working fluid of 1.0% Al₂O₃/R134a concentration in TPCT provides a better rank among other concentrations. Thus, this also confirms that the nanoparticle in working fluid has an effect on performance in TPCT.

Supplemental Material

sj-doc-1-pie-10.1177_09544089221093975 - Supplemental material for Experimental investigation of a two-phase closed thermosyphon with Al₂O₃/R134a nanorefrigerant

Supplemental material, sj-doc-1-pie-10.1177_09544089221093975 for Experimental investigation of a two-phase closed thermosyphon with Al₂O₃/R134a nanorefrigerant by R.S. Anand, C.P. Jawahar, A. Brusly Solomon, Evangelos Bellos and X. Ajay Vasanth in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering