# Investigation on the Performance of Domestic Refrigerator with Zirconium Oxide-R134a Nanorefrigerant

Authors: A. Baskaran; N. Manikandan; JuleLeta Tesfaye; N. Nagaprasad; R. Krishnaraj
Journal: Journal of Nanomaterials
 (2022)

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## Abstract

This study investigates the performance of nanorefrigerants (R134a-ZrO2) in a domestic refrigerator at a concentration of 0.2 g/l without changing the components. Nanoparticles of ZrO2 of 0.2 g/L concentration with particle size 1-10 nm and 140 g of R134a have been charged, and investigations were carried out. Energy consumption and pull-down tests were conducted to investigate the performance of the refrigerator. The performance parameters like refrigeration capacity, compressor power, discharge temperature, coefficient of performance, and energy consumption were investigated for the nanorefrigerant (R134a-ZrO2), and the results were compared with base refrigerant R134a. The pull downtime, energy consumption, and discharge temperature are reduced with increased COP and compressor power when the system is operated with R134a-ZrO2 nanorefrigerant. Also, the thermophysical properties of the nanorefrigerant (R134a-ZrO2) are calculated and analyzed for the various volume fraction of nanoparticles.


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## Body

## 1. Introduction

In this present scenario, nanoparticles and nanofluids have attracted attention from several research works. Nanoparticles are in the order of 15-nanometer particles. Nanofluids are designed colloids consisting of a primary fluid suspended from nanosize particles (1-15 nm). Nanofluids have the special advantage of having enhanced characteristics as compared to base fluids. They have many attractive characteristics, such as improving thermal physical properties, higher thermal conductivity, and higher surface area. Metal oxides, carbides, nitrides, carbon nanotubes, diamonds, and other nanoparticles may be dispersed in base fluids. Given the higher thermal conductivity of solids than fluids, it was first proposed by Maxwell [1] to disperse metal particles into fields to improve fluid thermal conductivity. Earlier studies have used solid particles with various sizes have subject to major problems such as clogging tendencies, erosion, surface abrasion, and high-pressure decreases. The use of nanofluids ensures that these problems can be overcome. Choi and Eastman [2] measured thermal conductivity of 13 nm Al2O3–water and 27 nm TiO2–water. It was observed that when 4.3 percent of the volume fractions of Al2O3 and TiO2 nanoparticles were incorporated in water, the thermal fluid conductivity increased in comparison to pure water by 32% and 11%, respectively. This is the first report on nanopowder and was the basis of further thermal fluid conductivity studies. In order to improve conductivity, they have been introduced the concept by mixing 1-15 nm large nanoparticles into liquids and called nanofluids. This concept has been extended to refrigerants in recent times. The development of nanofluids is aimed at increasing the thermal flow performance of different heat transfer fluids.The performance of the cooling system with Al2O3-PAG oil was researched by Kumar and Elansezhian [3]. The performance of the cooling system was better than the pure lubricant of working fluid R134a, and energy consumption was reduced by 10.32% at 0.2% volumetric. The results show that the coefficient of heat transfer increases with nano-Al2O3. Therefore, it is found possible to use Al2O3 nanorefrigerant in the cooling system. Sreejith [4] studied the performance of the refrigerator with air and a water-cooled condenser with a 0.06 percent mass fraction of CuO mixture and various compressor oils. Compared with the HFC134a/POE oil system and between 9 and 14% while using a water-cooled condenser under various load conditions, the energy consumption was reduced between 12% and 19% with SUNISO 3GS mineral oil system. The performance of domestic refrigerators with different nanorefrigerants was studied by Subramani et al. [5]. Desai and Patil [6] conducted an experiment to investigate the performance of a refrigerator compressor in which SiO2 nano-oil is offered as a viable lubricant. Energy consumption tests were used to assess the VCRS performance using nanoparticles. When nano-oil was utilized instead of pure oil, the COP of the system was improved. An experimental investigation was performed by Kushwaha et al. [7] on a nanorefrigerant (R134a+Al2O3)-based refrigeration system. The results indicate that the coefficient of performance increases with the usage of nano-Al2O3. Veera et al. [8] experimented with nanorefrigerants (Al2O3-ethylene glycol oil and TiO2-ethylene glycol oil) in an R134a vapour compression refrigeration system. The system was subjected to a cooling capacity test. The results show that using nano-Al2O3 and TiO2 increases the heat transfer coefficient. Thus, it has been discovered that using Al2O3 and TiO2 nanorefrigerants in refrigeration systems is feasible.Haque et al. [9] studied the performance of a household refrigerator by the addition of nanoparticles into this lubricant. The polyester (POE) oil has been added at two different volume concentrations to different sizes of nanoparticles of Al2O3 and TiO2 (0.05 and 0.1%). The refrigerator is tested for energy consumption and freezer capacity. Kumar et al. [10] investigated the performance of a VCRS with ZrO2 nanoparticle in the working liquid. The concentration of nano-ZrO2 with the particle size of 20 nm ranging from 0.01% to 0.06% with R134a and R152a is studied. Krishnan et al. [11] analyzed the performance of nanocoolant (Al2O3–R290/R600a) in a VCRS. The simulation programme based on the stable state mathematical models of the VCR system performed this analysis. The results show that the coefficient of performance, mass flow rate, and actual compressor piston displacement is heavier while heat rejection and compressor power are slightly lower in the nanocoolant mix. Baskar and Karikalan [12] did the study to increase the functioning of a vapour compression cooler compressor, and ZrO2 nano-oil was used as a potential lubricant. The usage of nano-oil with precise concentrations of 0.1%, 0.2%, and 0.3% was added to the compressor oil (by a mass fraction). The study demonstrates that using nano-oil instead of pure oil enhanced the system’s COP by 7.61 percent, 14.05 percent, and 11.90 percent, respectively. Singh and Ansari [13] present experimental work on the cooling system using nanorefrigerant (R600a/R290). This cooling system was made up of three different volumetric (0.15, 0.25, and 35 g) concentrations of CuO particles of size (20-30 nm). An improved performance coefficient was also observed during the study (3.18%-11.57%). Jatinder et al. [14] study the energetic and energetic behaviours of a household cooler using several lubricants, such as R134a and LPG coolants, polyol-ester, mineral oils (MO), and TiO2, SiO2, and Al2O3 nanoparticles distributed in mineral oil. The refrigerator with a 40 g charge of LPG/TiO2–MO lubrication (0.2 g/L TiO2) had the highest COP efficiency and 2nd efficiency, with the minimum compressor energy usage and 100% irreversibility. Adelekan et al. [15] present an experimental analysis of energy consumption and heat transmission performance of the safe mass charge of LPG refrigerant by enhanced nanoparticles 0.2 g/l, 0.4 g/l, and 0.6 g/l in a home refrigerator with varying concentrations. All nanolubricants based on TiO2 have been observed to reduce average power consumption. Baskaran et al. [16–27] evaluated the performance of a vapour compression refrigeration device using a variety of different refrigerants (including refrigerant mixtures) and nanorefrigerants, and their findings were compared to those obtained by using R134a as a potential alternative replacement. An investigative test rig is indigenously created and manufactured to conduct the examinations. The system effectiveness was evaluated with and without nanoparticles. Several studies have been conducted to address the problem of global warming and the depletion of the ozone layer using alternative refrigerants in the refrigeration system, therefore considered to be a useful attempt to study the effect of a surfactant and examine detailed possibilities of exploring a new alternative refrigerant and incorporating nanoparticles into the refrigerant.Shaheed [28] studied the surface tension of nanofluids which increases with increasing particle concentration but decreases with increasing temperature. The density of nanofluids increases with the increasing volume concentration of nanoparticles and decreases with increasing temperature. Specific heat of nanofluids increases with increasing temperature and volume concentration. Comparison of various nanorefrigerants for the performance enhancement of VCRS are listed in Table 1.Table 1
Comparison of various nanorefrigerants for the performance enhancement of VCRS.
AuthorRef systemRef. typeOil in compressorNanoparticlesTest rigCOPSystem power consumptionTypeConcentrationWeight in g per ml of oilSyed Shaheed [28]VCRSCARE 30Al2O3TiO2Shaik Imaduddin [29]VCRSR134aPOETiO22.5 g/350 mlYES22.5% higherSendil Kumar et al. [30]VCRSR134aPAG (poly alkylene glycol)Al2O30.2%VPAG-150 ml, 180 ml, and 200 mlYES3.510.32% reducedFadhilah et al. [31]Copper tube of an evaporator coilR134aCuO1%VCoumaressin et al. [32]VCRSR134aCuO0.05 to 1%YESSanukrishna et al. [33]VCRSR134aPAG (poly alkylene glycol)CuO0.06%, 0.08%, 0.1% by volumeYesSendil Kumar et al. [34]VCRSR152aZnO0.1%V, 0.3%V, 0.5%VYes21% less@0.5%VPresent studyVCRSPAG (poly alkylene glycol)ZrO20.2 g/LYes2.1% higherEnergy consumption is reduced by 6.25%Refrigeration capacity increased by 38.7%%Pull downtime decreased by 23.3%Imaduddin [29] used the refrigerant R134a and POE oil (300 ml) suspended with TiO2 nanoparticles (2.5 g). The author found that the COP increased by 22.5 percent when compared to R134a VCRS without nanoparticle. Kumar et al. [30] tested the VCRS performance using refrigerant R134a and PAG oil (150, 180, and 200 ml) suspended with Al2O3 nanoparticles (0.2 percent concentration by volume) and found that the COP increased by 3.5 percent. At the same time, the power consumption decreased by 10.32 percent compared to R134a VCRS without nanoparticle. Various experimental analyses were conducted to study the performance of VCR system with nanorefrigerants which reveals the enhancement of performance.In today’s scenario, air conditioning and refrigeration are essential equipment for people’s comfort. The researchers are focused on increasing cooling capacity and developing a cooling system that is energy efficient. Nanotechnological goods, methods, and applications contribute to environmental and climate conservation by conserving raw materials, energy, and water, as well as lowering greenhouse gas emissions and hazardous waste. Nanoparticles improve the thermal, chemical, and other properties of the refrigerant in the refrigeration system. The use of nanorefrigerants in small amount in vapour compression refrigeration system aided in improving system performance. This paper examines the thermophysical properties of nanorefrigerant (R134a-ZrO2) and the thermal performance of the system used in a residential refrigerator.


## 2. Materials and Methods

### 2.1. Zirconium Dioxide (ZrO2) Nanoparticles

Polyvinyl pyrrolidone was employed as the capping agent, and deionized water was utilized as the solvent in the thermal treatment technique, which used zirconium (IV) acetate hydroxide to produce zirconium dioxide nanoparticles with monoclinic composition as a substrate and deionized water as the solvent. It was necessary to combine the chemicals and eliminate them in accordance to generate a standardised solution, which was then immediately calcinated in order to create the pure nanocrystalline powder that was validated by the results of the FTIR, EDX, and SEM tests. Thermal calcination at temperatures ranging from 600 to 900 degrees Celsius allowed for precise control of the dimensions and optical characteristics of nanoparticles. It was discovered by XRD and transmission electron microscope images that the mean particle sizes grew larger as the calcination temperature rises. The optical characteristics of the material were investigated using a UV-Vis spectrophotometer, which revealed that the bandgap energy decreased as the calcination temperature increased. UV-Vis absorption spectroscopy is a nondestructive technique for determining the optical characteristics of semiconductor nanostructures. By dissolving the nanopowder in ethanol in the wavelength range of 700 nm to 200 nm, the absorption spectra of samples were recorded using a JASCO V-670 UV-Vis absorption spectrophotometer. These results show that size-controlled zirconium nanoparticles with reduced time and energy consumption of the synthesis were manufactured in a convenient way with the elimination of the drying method (24 h) in the current thermal treatment technique. Figure1 illustrates the SEM image of zirconium oxide, and Table 2 shows the properties of zirconium oxide.Figure 1
Zirconium oxide. (a) Zirconium oxide nanoparticle; (b) SEM image of zirconium oxide.
(a)(b)Table 2
Properties of zirconium oxide.
Chemical composition (%)Zirconium74.03Oxygen24.34Physical propertiesDensity5680 kg/m3Molar mass231.891 g/molThermal propertiesMelting point2715°CBoiling point4300°C


### 2.2. Thermophysical Properties of Nanofluid

Thermophysical parameters of nanofluids are extremely important in predicting their heat transfer behaviour. It is critical in terms of industrial and energy-saving control. Nanofluids have sparked the interest of the industry. When compared to traditional particles such as fluid suspension, millimetre, and micrometre-sized particles, nanoparticles have a lot of potentials to improve thermal transport qualities. The transport properties of nanofluid are influenced by a variety of elements, including particle volume fraction, particle material, particle size, particle shape, base fluid material, temperature, mixture combinations, slide mechanisms, and surfactant. When compared to a base fluid, studies have shown that using nanofluid increases both thermal conductivity and viscosity.


### 2.3. Thermal Conductivity

To model the thermal conductivity of nanofluid, a variety of experimental and theoretical researches have been undertaken in the literature. The preceding results were mostly based on the definition of a two-component mixture’s effective thermal conductivity. One of the first models developed for solid-liquid mixtures with relatively sizable particles was Maxwell [1] model. It was based on a solution of the heat conduction equation.The effective thermal conductivity (Equation (1)) is given by
(1)Keff=KbfKp+2Kbf+2ΦKp−KbfKp+2Kbf−ΦKp−Kbf,where kp is the thermal conductivity of the particles, keff is the effective thermal conductivity of nanofluid, kbf is the base fluid thermal conductivity, and Φ is the volume fraction of the suspended particles.The thermal conductivity of nanofluids rises with decreasing particle size, according to the experimental evidence. Two processes of thermal conductivity enhancement are theoretically suggested by this trend: Brownian motion of nanoparticles and liquid layering around nanoparticles (Ozerinc et al. [35]).


### 2.4. Viscosity

Researchers have utilized various viscosity models to model the effective viscosity of nanofluid as a function of volume fraction. Using empirical hydrodynamic equations, Einstein [36] calculated the effective viscosity of a suspension of spherical solids as a function of volume fraction (volume concentration less than 5%) (Equation (2)).This equation was expressed by the following:(2)μeff=1+2.5ϕμbf,where μeff is the effective viscosity of nanofluid, μbf is the base fluid viscosity, and ϕ is the volume fraction of the suspended particles.Brinkman [37] proposed a viscosity correlation (Equation (3)) that extended Einstein’s equation to suspensions with a moderate particle volume fraction, usually less than 4%.
(3)μeff=μbf11−ϕ2.5.Batchelor [38] investigated the influence of Brownian motion on the effective viscosity in a suspension of stiff spherical particles. The effective viscosity of an isotropic suspension was calculated using Equation (4).
(4)μeff=1+2.5ϕ+6.2ϕμbf.


### 2.5. Specific Heat and Density

The specific heat capacity (Pak and Cho [39]) and density (Xuan and Roetzel [40]) of the nanofluid as a function of particle volume concentration and individual properties can be computed using the following equations (Equations (5) and (6)), respectively, using classical formulas derived for a two-phase mixture:
(5)ρeff=1−ϕρbf+ϕρp,(6)ρcpeff=1−ϕρcpbf+ϕρcpp,where ρeff is the effective density and ρcp is the specific heat capacity.


### 2.6. Experimental Setup

Figure2 illustrates the photographic view of the domestic refrigerator of Kelvinator made with 185-liter capacity with a testing facility designed to work with R134a. It is comprised of an evaporator, a wire meshes wind cooled condenser, and a reciprocating compressor that is hermetically sealed. A total of four pressure gauges have been mounted at the compressor air intake and outlet as well as the condenser outlet and the evaporator inlet. All these pressure gauges were fitted on a wooden panel to avoid vibration during testing. The thermocouple wire was linked to the thermocouple analyzer at all 10 of its locations, which was a first. A thermocouple analyzer is a device that reads the temperatures that have been measured. It was decided to install ten calibrated temperature sensors at the evaporator inlet and outlet, compressor intake and outlet, compressor outlet, and condenser inlet, as well as the freezer section and refrigerator cabin. Furthermore, the voltage and current that were spent were recorded. Additionally, the flow meter, which also has been connected to the tubing running between both the condenser and the capillary tube, was permanently attached to the wooden panel. All of the data was captured using digital storage equipment for the Human Machine Interface (HMI), which was set to record data periodically every 10 seconds. To check the quality of condensed liquid flow, a sight glass is provided.Figure 2
Photographic view of the experimental setup.An energy meter of the Select MFM384 model with a capability of 100-500 V was connected to the compressor in order to assess the power and electricity consumption. Service ports were built on the upper face of the compressor to allow for the charging and recovery of refrigerant while the compressor was in operation. Initially, the service port was used to facilitate the removal of moisture from the system. The device was cleaned with nitrogen gas in order to remove any air, contaminants, moisture, or other things that may have accumulated inside the system and could have negatively impacted its operation. The system was charged by using a charging system. The system was vacuumed with the assistance of just a vacuum pump to pressure with 30 millimetres of mercury. The layout of the measurement device that was employed in the research setup is depicted schematically in Figure3. Table 3 lists the technical parameters of the residential refrigerator test unit, whereas Table 4 lists the measured quantities, as well as their range and precision, for the unit.Figure 3
Schematic diagram of the measurement system in an experimental setup.Table 3
The technical characteristics of the residential refrigerator test unit.
DetailsSpecificationsStorage volume169 LCurrent rating1.1 maxVoltage220-240 VFrequency50 HzNo. of doors1Refrigerant typeR134aDefrost systemAuto defrostRefrigerant charged0.140 kgCapillary tube length3.35 mCapillary tube inner diameter0.00078 mCooling capacity182 WTable 4
Quantities that have been evaluated, along with respective range and precision.
QuantityRangeAccuracyTemperature-40°C to 110°C+0.1°CPower consumption0 to 1000 W1 WVoltage0 to 240 V0.1 VCurrent0 to 10 A0.1 APressure0 to 150 MPa+0.7 kPaFlow rate0 to 100 cc/s0.1 cc/s


### 2.7. Uncertainty Analysis for Measuring Devices and Calculated Items

#### 2.7.1. Temperature Measurement

RTD type sensors are used to measure the temperatures at various state points of the system. The maximum possible error in the case of temperature measurement is calculated from the minimum value of the thermocouple output and the accuracy of the instrument. The error in temperature measurement for thermocouples is(7)∂tt=0.118=0.55%.


#### 2.7.2. Pressure Measurement

The pressure transducer is used to measure the pressure at various points in the system. The maximum error in the measurement of pressure for the condenser and evaporator side is(8)∂PP=0.120=±0.5%,(9)∂PP=0.1220=±0.04%.


#### 2.7.3. Flow Rate Measurement

A refrigerant flow meter was used to measure the refrigerant flow rate. The maximum error in the measurement of refrigerant flow rate is(10)∂mm=0.1100=±0.1%.


#### 2.7.4. Power Measurement

A multifunction meter was used to measure the power consumption by the compressor. The maximum error in the measurement of power is(11)∂CPCP=11000=±0.1%.


#### 2.7.5. Refrigerating Capacity

(12)∂RCRC=∂mm2+2∂TT2=±0.784%.


#### 2.7.6. Coefficient of Performance

(13)∂COPCOP=∂RCRC2+2∂CPCP2=±0.7903%.


#### 2.7.7. Test Procedure

As per the guidelines given by ASHRAE handbook, 2010, the energy consumption test and no-load pull-down test were conducted with the following system parameters.Freezer compartment: -18°C to -15°CFood compartment: 3°C to 5°CSteady ambient temperature: 25°C to 32°CFurthermore, certain randomized experiments were conducted to establish the reproducibility of the data. When the system was left to function, measurements were taken of the temperatures; pressure, energy consumed, and the amount of refrigerant flow were collected once in every ten seconds till the steady-state operating conditions were reached. The observed temperatures, pressures, mass flow rate, electric power, and energy were utilized to calculate the operation characteristics of the refrigeration system.


#### 2.7.8. Pull Downtime

Generally speaking, pull downtime is described as the amount of time it takes to cool air within a refrigerator from its ambient temperature of 32 degrees Celsius to the ideal freezer and cabin air temperatures of 16 degrees Celsius and 4 degrees Celsius, respectively (ISO8187).


#### 2.7.9. Refrigerating Capacity

Qeva is the amount of cooling potential which may be represented as
(14)Qeva=mrqeva,where mr is the measured refrigerant mass flow rate and qeva is the cooling effect of the evaporator.


#### 2.7.10. Coefficient of Performance

The relationship between theoretical coefficients of effectiveness (COPth) and real coefficients of performance (COPact) could be represented as
(15)COPth=QevaPcom,(16)COPact=QevaPelec,where Pelec is the measured electric power supplied to the compressor and Pcom is the theoretical power consumed by the compressor. Pcom can be written as follows:
(17)Pcom=mrwcom,where wcom is the specific work of compressor, which is equal to the enthalpy difference through the compressor.


### 2.8. Experimental Analysis Procedure

#### 2.8.1. Pull-Down Test

In this test, the cooling capacity of the system is evaluated. Refrigeration cycle off time will increase with the increase of cooling rate. During the test, doors were closed, and the refrigerator was under no-load condition. Temperatures at several places were measured every 10 seconds throughout the pull-down experiment until the temperature reached the targeted freezing threshold.


#### 2.8.2. Compressor Energy Utilization, Cooling Effect, and Exact COP

The compressor energy consumption experiment is carried out at an ambient temperature of 32°C. Most assessments were made when the level of stability was attained. The energy usage of the energy meter was recorded. To calculate the actual COP and cooling effect, various freezer temperatures (-6°C to -16°C) were selected. At a particular freezer temperature, the observed mass flow rate and power are used to find COP and refrigerating effect.


## 2.1. Zirconium Dioxide (ZrO2) Nanoparticles

Polyvinyl pyrrolidone was employed as the capping agent, and deionized water was utilized as the solvent in the thermal treatment technique, which used zirconium (IV) acetate hydroxide to produce zirconium dioxide nanoparticles with monoclinic composition as a substrate and deionized water as the solvent. It was necessary to combine the chemicals and eliminate them in accordance to generate a standardised solution, which was then immediately calcinated in order to create the pure nanocrystalline powder that was validated by the results of the FTIR, EDX, and SEM tests. Thermal calcination at temperatures ranging from 600 to 900 degrees Celsius allowed for precise control of the dimensions and optical characteristics of nanoparticles. It was discovered by XRD and transmission electron microscope images that the mean particle sizes grew larger as the calcination temperature rises. The optical characteristics of the material were investigated using a UV-Vis spectrophotometer, which revealed that the bandgap energy decreased as the calcination temperature increased. UV-Vis absorption spectroscopy is a nondestructive technique for determining the optical characteristics of semiconductor nanostructures. By dissolving the nanopowder in ethanol in the wavelength range of 700 nm to 200 nm, the absorption spectra of samples were recorded using a JASCO V-670 UV-Vis absorption spectrophotometer. These results show that size-controlled zirconium nanoparticles with reduced time and energy consumption of the synthesis were manufactured in a convenient way with the elimination of the drying method (24 h) in the current thermal treatment technique. Figure1 illustrates the SEM image of zirconium oxide, and Table 2 shows the properties of zirconium oxide.Figure 1
Zirconium oxide. (a) Zirconium oxide nanoparticle; (b) SEM image of zirconium oxide.
(a)(b)Table 2
Properties of zirconium oxide.
Chemical composition (%)Zirconium74.03Oxygen24.34Physical propertiesDensity5680 kg/m3Molar mass231.891 g/molThermal propertiesMelting point2715°CBoiling point4300°C


## 2.2. Thermophysical Properties of Nanofluid

Thermophysical parameters of nanofluids are extremely important in predicting their heat transfer behaviour. It is critical in terms of industrial and energy-saving control. Nanofluids have sparked the interest of the industry. When compared to traditional particles such as fluid suspension, millimetre, and micrometre-sized particles, nanoparticles have a lot of potentials to improve thermal transport qualities. The transport properties of nanofluid are influenced by a variety of elements, including particle volume fraction, particle material, particle size, particle shape, base fluid material, temperature, mixture combinations, slide mechanisms, and surfactant. When compared to a base fluid, studies have shown that using nanofluid increases both thermal conductivity and viscosity.


## 2.3. Thermal Conductivity

To model the thermal conductivity of nanofluid, a variety of experimental and theoretical researches have been undertaken in the literature. The preceding results were mostly based on the definition of a two-component mixture’s effective thermal conductivity. One of the first models developed for solid-liquid mixtures with relatively sizable particles was Maxwell [1] model. It was based on a solution of the heat conduction equation.The effective thermal conductivity (Equation (1)) is given by
(1)Keff=KbfKp+2Kbf+2ΦKp−KbfKp+2Kbf−ΦKp−Kbf,where kp is the thermal conductivity of the particles, keff is the effective thermal conductivity of nanofluid, kbf is the base fluid thermal conductivity, and Φ is the volume fraction of the suspended particles.The thermal conductivity of nanofluids rises with decreasing particle size, according to the experimental evidence. Two processes of thermal conductivity enhancement are theoretically suggested by this trend: Brownian motion of nanoparticles and liquid layering around nanoparticles (Ozerinc et al. [35]).


## 2.4. Viscosity

Researchers have utilized various viscosity models to model the effective viscosity of nanofluid as a function of volume fraction. Using empirical hydrodynamic equations, Einstein [36] calculated the effective viscosity of a suspension of spherical solids as a function of volume fraction (volume concentration less than 5%) (Equation (2)).This equation was expressed by the following:(2)μeff=1+2.5ϕμbf,where μeff is the effective viscosity of nanofluid, μbf is the base fluid viscosity, and ϕ is the volume fraction of the suspended particles.Brinkman [37] proposed a viscosity correlation (Equation (3)) that extended Einstein’s equation to suspensions with a moderate particle volume fraction, usually less than 4%.
(3)μeff=μbf11−ϕ2.5.Batchelor [38] investigated the influence of Brownian motion on the effective viscosity in a suspension of stiff spherical particles. The effective viscosity of an isotropic suspension was calculated using Equation (4).
(4)μeff=1+2.5ϕ+6.2ϕμbf.


## 2.5. Specific Heat and Density

The specific heat capacity (Pak and Cho [39]) and density (Xuan and Roetzel [40]) of the nanofluid as a function of particle volume concentration and individual properties can be computed using the following equations (Equations (5) and (6)), respectively, using classical formulas derived for a two-phase mixture:
(5)ρeff=1−ϕρbf+ϕρp,(6)ρcpeff=1−ϕρcpbf+ϕρcpp,where ρeff is the effective density and ρcp is the specific heat capacity.


## 2.6. Experimental Setup

Figure2 illustrates the photographic view of the domestic refrigerator of Kelvinator made with 185-liter capacity with a testing facility designed to work with R134a. It is comprised of an evaporator, a wire meshes wind cooled condenser, and a reciprocating compressor that is hermetically sealed. A total of four pressure gauges have been mounted at the compressor air intake and outlet as well as the condenser outlet and the evaporator inlet. All these pressure gauges were fitted on a wooden panel to avoid vibration during testing. The thermocouple wire was linked to the thermocouple analyzer at all 10 of its locations, which was a first. A thermocouple analyzer is a device that reads the temperatures that have been measured. It was decided to install ten calibrated temperature sensors at the evaporator inlet and outlet, compressor intake and outlet, compressor outlet, and condenser inlet, as well as the freezer section and refrigerator cabin. Furthermore, the voltage and current that were spent were recorded. Additionally, the flow meter, which also has been connected to the tubing running between both the condenser and the capillary tube, was permanently attached to the wooden panel. All of the data was captured using digital storage equipment for the Human Machine Interface (HMI), which was set to record data periodically every 10 seconds. To check the quality of condensed liquid flow, a sight glass is provided.Figure 2
Photographic view of the experimental setup.An energy meter of the Select MFM384 model with a capability of 100-500 V was connected to the compressor in order to assess the power and electricity consumption. Service ports were built on the upper face of the compressor to allow for the charging and recovery of refrigerant while the compressor was in operation. Initially, the service port was used to facilitate the removal of moisture from the system. The device was cleaned with nitrogen gas in order to remove any air, contaminants, moisture, or other things that may have accumulated inside the system and could have negatively impacted its operation. The system was charged by using a charging system. The system was vacuumed with the assistance of just a vacuum pump to pressure with 30 millimetres of mercury. The layout of the measurement device that was employed in the research setup is depicted schematically in Figure3. Table 3 lists the technical parameters of the residential refrigerator test unit, whereas Table 4 lists the measured quantities, as well as their range and precision, for the unit.Figure 3
Schematic diagram of the measurement system in an experimental setup.Table 3
The technical characteristics of the residential refrigerator test unit.
DetailsSpecificationsStorage volume169 LCurrent rating1.1 maxVoltage220-240 VFrequency50 HzNo. of doors1Refrigerant typeR134aDefrost systemAuto defrostRefrigerant charged0.140 kgCapillary tube length3.35 mCapillary tube inner diameter0.00078 mCooling capacity182 WTable 4
Quantities that have been evaluated, along with respective range and precision.
QuantityRangeAccuracyTemperature-40°C to 110°C+0.1°CPower consumption0 to 1000 W1 WVoltage0 to 240 V0.1 VCurrent0 to 10 A0.1 APressure0 to 150 MPa+0.7 kPaFlow rate0 to 100 cc/s0.1 cc/s


## 2.7. Uncertainty Analysis for Measuring Devices and Calculated Items

### 2.7.1. Temperature Measurement

RTD type sensors are used to measure the temperatures at various state points of the system. The maximum possible error in the case of temperature measurement is calculated from the minimum value of the thermocouple output and the accuracy of the instrument. The error in temperature measurement for thermocouples is(7)∂tt=0.118=0.55%.


### 2.7.2. Pressure Measurement

The pressure transducer is used to measure the pressure at various points in the system. The maximum error in the measurement of pressure for the condenser and evaporator side is(8)∂PP=0.120=±0.5%,(9)∂PP=0.1220=±0.04%.


### 2.7.3. Flow Rate Measurement

A refrigerant flow meter was used to measure the refrigerant flow rate. The maximum error in the measurement of refrigerant flow rate is(10)∂mm=0.1100=±0.1%.


### 2.7.4. Power Measurement

A multifunction meter was used to measure the power consumption by the compressor. The maximum error in the measurement of power is(11)∂CPCP=11000=±0.1%.


### 2.7.5. Refrigerating Capacity

(12)∂RCRC=∂mm2+2∂TT2=±0.784%.


### 2.7.6. Coefficient of Performance

(13)∂COPCOP=∂RCRC2+2∂CPCP2=±0.7903%.


### 2.7.7. Test Procedure

As per the guidelines given by ASHRAE handbook, 2010, the energy consumption test and no-load pull-down test were conducted with the following system parameters.Freezer compartment: -18°C to -15°CFood compartment: 3°C to 5°CSteady ambient temperature: 25°C to 32°CFurthermore, certain randomized experiments were conducted to establish the reproducibility of the data. When the system was left to function, measurements were taken of the temperatures; pressure, energy consumed, and the amount of refrigerant flow were collected once in every ten seconds till the steady-state operating conditions were reached. The observed temperatures, pressures, mass flow rate, electric power, and energy were utilized to calculate the operation characteristics of the refrigeration system.


### 2.7.8. Pull Downtime

Generally speaking, pull downtime is described as the amount of time it takes to cool air within a refrigerator from its ambient temperature of 32 degrees Celsius to the ideal freezer and cabin air temperatures of 16 degrees Celsius and 4 degrees Celsius, respectively (ISO8187).


### 2.7.9. Refrigerating Capacity

Qeva is the amount of cooling potential which may be represented as
(14)Qeva=mrqeva,where mr is the measured refrigerant mass flow rate and qeva is the cooling effect of the evaporator.


### 2.7.10. Coefficient of Performance

The relationship between theoretical coefficients of effectiveness (COPth) and real coefficients of performance (COPact) could be represented as
(15)COPth=QevaPcom,(16)COPact=QevaPelec,where Pelec is the measured electric power supplied to the compressor and Pcom is the theoretical power consumed by the compressor. Pcom can be written as follows:
(17)Pcom=mrwcom,where wcom is the specific work of compressor, which is equal to the enthalpy difference through the compressor.


## 2.7.1. Temperature Measurement

RTD type sensors are used to measure the temperatures at various state points of the system. The maximum possible error in the case of temperature measurement is calculated from the minimum value of the thermocouple output and the accuracy of the instrument. The error in temperature measurement for thermocouples is(7)∂tt=0.118=0.55%.


## 2.7.2. Pressure Measurement

The pressure transducer is used to measure the pressure at various points in the system. The maximum error in the measurement of pressure for the condenser and evaporator side is(8)∂PP=0.120=±0.5%,(9)∂PP=0.1220=±0.04%.


## 2.7.3. Flow Rate Measurement

A refrigerant flow meter was used to measure the refrigerant flow rate. The maximum error in the measurement of refrigerant flow rate is(10)∂mm=0.1100=±0.1%.


## 2.7.4. Power Measurement

A multifunction meter was used to measure the power consumption by the compressor. The maximum error in the measurement of power is(11)∂CPCP=11000=±0.1%.


## 2.7.5. Refrigerating Capacity

(12)∂RCRC=∂mm2+2∂TT2=±0.784%.


## 2.7.6. Coefficient of Performance

(13)∂COPCOP=∂RCRC2+2∂CPCP2=±0.7903%.


## 2.7.7. Test Procedure

As per the guidelines given by ASHRAE handbook, 2010, the energy consumption test and no-load pull-down test were conducted with the following system parameters.Freezer compartment: -18°C to -15°CFood compartment: 3°C to 5°CSteady ambient temperature: 25°C to 32°CFurthermore, certain randomized experiments were conducted to establish the reproducibility of the data. When the system was left to function, measurements were taken of the temperatures; pressure, energy consumed, and the amount of refrigerant flow were collected once in every ten seconds till the steady-state operating conditions were reached. The observed temperatures, pressures, mass flow rate, electric power, and energy were utilized to calculate the operation characteristics of the refrigeration system.


## 2.7.8. Pull Downtime

Generally speaking, pull downtime is described as the amount of time it takes to cool air within a refrigerator from its ambient temperature of 32 degrees Celsius to the ideal freezer and cabin air temperatures of 16 degrees Celsius and 4 degrees Celsius, respectively (ISO8187).


## 2.7.9. Refrigerating Capacity

Qeva is the amount of cooling potential which may be represented as
(14)Qeva=mrqeva,where mr is the measured refrigerant mass flow rate and qeva is the cooling effect of the evaporator.


## 2.7.10. Coefficient of Performance

The relationship between theoretical coefficients of effectiveness (COPth) and real coefficients of performance (COPact) could be represented as
(15)COPth=QevaPcom,(16)COPact=QevaPelec,where Pelec is the measured electric power supplied to the compressor and Pcom is the theoretical power consumed by the compressor. Pcom can be written as follows:
(17)Pcom=mrwcom,where wcom is the specific work of compressor, which is equal to the enthalpy difference through the compressor.


## 2.8. Experimental Analysis Procedure

### 2.8.1. Pull-Down Test

In this test, the cooling capacity of the system is evaluated. Refrigeration cycle off time will increase with the increase of cooling rate. During the test, doors were closed, and the refrigerator was under no-load condition. Temperatures at several places were measured every 10 seconds throughout the pull-down experiment until the temperature reached the targeted freezing threshold.


### 2.8.2. Compressor Energy Utilization, Cooling Effect, and Exact COP

The compressor energy consumption experiment is carried out at an ambient temperature of 32°C. Most assessments were made when the level of stability was attained. The energy usage of the energy meter was recorded. To calculate the actual COP and cooling effect, various freezer temperatures (-6°C to -16°C) were selected. At a particular freezer temperature, the observed mass flow rate and power are used to find COP and refrigerating effect.


## 2.8.1. Pull-Down Test

In this test, the cooling capacity of the system is evaluated. Refrigeration cycle off time will increase with the increase of cooling rate. During the test, doors were closed, and the refrigerator was under no-load condition. Temperatures at several places were measured every 10 seconds throughout the pull-down experiment until the temperature reached the targeted freezing threshold.


## 2.8.2. Compressor Energy Utilization, Cooling Effect, and Exact COP

The compressor energy consumption experiment is carried out at an ambient temperature of 32°C. Most assessments were made when the level of stability was attained. The energy usage of the energy meter was recorded. To calculate the actual COP and cooling effect, various freezer temperatures (-6°C to -16°C) were selected. At a particular freezer temperature, the observed mass flow rate and power are used to find COP and refrigerating effect.


## 3. Result and Discussions

### 3.1. Variation of Thermophysical Properties of Nanorefrigerant ZrO2/R134a

The thermophysical properties of the ZrO2/R134a nanorefrigerant are studied in relation to various volume fractions of nanoparticles. The thermal conductivity of ZrO2/R134a nanorefrigerant increased with increasing particle concentration in this investigation. The improvement in thermal conductivity of nanorefrigerants is evident from experimental results. The increased surface area of nanoparticles and the Brownian motion of nanoparticles dispersed in the base fluid are responsible for the rise in conductivity.Furthermore, the results of the nanorefrigerant’s viscosity show a significant increase with increasing volume fractions, whereas specific heat and density change in the opposite direction. As a result, the optimal particle volume fraction of 0.02 percent is taken into account when producing nanorefrigerants that can improve the performance of the refrigeration system.Figure4 shows the variation of thermophysical properties of nanorefrigerant (R134a+ZrO2) for various volume fractions of nanoparticles.Figure 4
Variation of thermophysical properties of nanorefrigerant (R134a+ZrO2) for various volume fraction of nanoparticle in %.


### 3.2. Pull Downtime

Figure5 shows the plot of pull downtime for ZrO2+R134a nanorefrigerant and pure R134a refrigerant. The pull downtime for nanorefrigerant at -14°C freezer temperature was reduced by 23.53% when compared to pure R134a refrigerant. Table 5 indicates the variation of evaporator inlet temperature and freezer temperature with time.Figure 5
Variation of pull downtime for R134a and R134a+ZrO2.Table 5
Variation of evaporator inlet temperature with time.
Time (min)Evaporator inlet temperature (°C)Freezer temperature (°C)R134aR134a+ZrO2R134aR134a+ZrO202931303256-31822105-5134153-85-520-6-100-725-8-11-2-830-10-12-6-935-11-13-8-1040-12-14-8-1145-12-14-9-1150-13-15-11-1255-14-16-12-1360-14-16-12-1365-14-17-12-1470-15-17-13-1475-15-18-13-1580-15-18-13-1585-15-18-14-1590-16-19-14-1695-16-14100-16-14


### 3.3. Coefficient of Performance

Figure6 shows the variation of coefficient of performance with evaporating temperature varies from -6°C to -16°C. It is observed from the figure that the COP of the system decreases when the evaporating temperature decreases. The COP of the system with ZrO2+R134a nanorefrigerant is higher than that of pure R134a refrigerant.Figure 6
Variation of coefficient of performance for R134a and R134a+ZrO2.


### 3.4. Compressor Power

Figure7 shows the variation of compressor power with evaporating temperature varies from -6°C to -16°C. It is seen from the figure that the compressor power of the system decreases when the evaporating temperature decreases. The compressor takes slightly more power when the system is charged with R134a+ZrO2 nanorefrigerant.Figure 7
Variation of compressor power for R134a and R134a+ZrO2.


### 3.5. Discharge Temperature

Figure8 shows the variation of discharge temperature with evaporating temperature varies from -6°C to -16°C. It is observed from the figure that the discharge temperature of the system increases when the evaporating temperature decreases. The discharge temperature of the system with ZrO2+R134a nanorefrigerant is 6.5% lower than that of pure R134a refrigerant.Figure 8
Variation of discharge temperature for R134a and R134a+ZrO2.


### 3.6. Refrigeration Capacity

Figure9 shows the variation of refrigeration capacity with evaporating temperature varies from -6°C to -16°C. It was seen from the figure the refrigeration capacity of the system decreases when the evaporating temperature decreases. It was observed that the average refrigeration capacity of the system with ZrO2+R134a nanorefrigerant is 38.70% higher than that of pure R134a refrigerant.Figure 9
Variation of refrigeration capacity for R134a and R134a+ZrO2.


### 3.7. Energy Consumption

Figure10 shows the variation of energy consumption for one working cycle with ZrO2+R134a nanorefrigerant and pure R134a refrigerant. The energy consumption of the nanorefrigerant was 6.25% lower than that of pure refrigerant.Figure 10
Variation of energy consumption for R134a and R134a+ZrO2.


### 3.8. Variation of Evaporator Inlet Temperature and Freezer Temperature with Time

## 3.1. Variation of Thermophysical Properties of Nanorefrigerant ZrO2/R134a

The thermophysical properties of the ZrO2/R134a nanorefrigerant are studied in relation to various volume fractions of nanoparticles. The thermal conductivity of ZrO2/R134a nanorefrigerant increased with increasing particle concentration in this investigation. The improvement in thermal conductivity of nanorefrigerants is evident from experimental results. The increased surface area of nanoparticles and the Brownian motion of nanoparticles dispersed in the base fluid are responsible for the rise in conductivity.Furthermore, the results of the nanorefrigerant’s viscosity show a significant increase with increasing volume fractions, whereas specific heat and density change in the opposite direction. As a result, the optimal particle volume fraction of 0.02 percent is taken into account when producing nanorefrigerants that can improve the performance of the refrigeration system.Figure4 shows the variation of thermophysical properties of nanorefrigerant (R134a+ZrO2) for various volume fractions of nanoparticles.Figure 4
Variation of thermophysical properties of nanorefrigerant (R134a+ZrO2) for various volume fraction of nanoparticle in %.


## 3.2. Pull Downtime

Figure5 shows the plot of pull downtime for ZrO2+R134a nanorefrigerant and pure R134a refrigerant. The pull downtime for nanorefrigerant at -14°C freezer temperature was reduced by 23.53% when compared to pure R134a refrigerant. Table 5 indicates the variation of evaporator inlet temperature and freezer temperature with time.Figure 5
Variation of pull downtime for R134a and R134a+ZrO2.Table 5
Variation of evaporator inlet temperature with time.
Time (min)Evaporator inlet temperature (°C)Freezer temperature (°C)R134aR134a+ZrO2R134aR134a+ZrO202931303256-31822105-5134153-85-520-6-100-725-8-11-2-830-10-12-6-935-11-13-8-1040-12-14-8-1145-12-14-9-1150-13-15-11-1255-14-16-12-1360-14-16-12-1365-14-17-12-1470-15-17-13-1475-15-18-13-1580-15-18-13-1585-15-18-14-1590-16-19-14-1695-16-14100-16-14


## 3.3. Coefficient of Performance

Figure6 shows the variation of coefficient of performance with evaporating temperature varies from -6°C to -16°C. It is observed from the figure that the COP of the system decreases when the evaporating temperature decreases. The COP of the system with ZrO2+R134a nanorefrigerant is higher than that of pure R134a refrigerant.Figure 6
Variation of coefficient of performance for R134a and R134a+ZrO2.


## 3.4. Compressor Power

Figure7 shows the variation of compressor power with evaporating temperature varies from -6°C to -16°C. It is seen from the figure that the compressor power of the system decreases when the evaporating temperature decreases. The compressor takes slightly more power when the system is charged with R134a+ZrO2 nanorefrigerant.Figure 7
Variation of compressor power for R134a and R134a+ZrO2.


## 3.5. Discharge Temperature

Figure8 shows the variation of discharge temperature with evaporating temperature varies from -6°C to -16°C. It is observed from the figure that the discharge temperature of the system increases when the evaporating temperature decreases. The discharge temperature of the system with ZrO2+R134a nanorefrigerant is 6.5% lower than that of pure R134a refrigerant.Figure 8
Variation of discharge temperature for R134a and R134a+ZrO2.


## 3.6. Refrigeration Capacity

Figure9 shows the variation of refrigeration capacity with evaporating temperature varies from -6°C to -16°C. It was seen from the figure the refrigeration capacity of the system decreases when the evaporating temperature decreases. It was observed that the average refrigeration capacity of the system with ZrO2+R134a nanorefrigerant is 38.70% higher than that of pure R134a refrigerant.Figure 9
Variation of refrigeration capacity for R134a and R134a+ZrO2.


## 3.7. Energy Consumption

Figure10 shows the variation of energy consumption for one working cycle with ZrO2+R134a nanorefrigerant and pure R134a refrigerant. The energy consumption of the nanorefrigerant was 6.25% lower than that of pure refrigerant.Figure 10
Variation of energy consumption for R134a and R134a+ZrO2.


## 3.8. Variation of Evaporator Inlet Temperature and Freezer Temperature with Time

## 4. Conclusion

This experimental work was conducted in the domestic refrigerator with a blend of ZrO2 nanoparticles in the R134a refrigerant. Based on the experimental calculation, the following specific conclusion could be drawn.The results indicate that the refrigeration capacity of the system was increased to 38.7% by adding nanoparticles in pure refrigerant using a 0.2 g/L nanoconcentration.The pull downtime was reduced by 23.53% when using nanorefrigerant at -14°C freezer temperature. It proves that the thermodynamic behaviour of fluid is improved for 0.02%-ZrO2.The refrigerator energy consumption is reduced by 6.25% while using nanorefrigerant (ZrO2+R134a).The compressor discharge temperature is reduced by 6.5% when the system is charged with R134a+ZrO2 nanorefrigerant.The compressor power was slightly increased by adding ZrO2 nanoparticles to the R134a refrigerant. It has been found that the electricity consumption of the refrigerator was 6.25% lower than that of the base fluid (R134a) when 0.02%-ZrO2 nanoparticle was added to the system.Finally, it can be concluded that using nanoparticles in a refrigeration system can improve thermodynamic characteristics and decrease the energy consumption of a domestic refrigerator.



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*Source: 3668458-2022-03-18.xml*