Introduction

Energy is a very important quantitative property that must be transferred before any system can perform work. The transfer of energy can be done by either work or heat [1]. Heat is transferred from one system to another when there exists a temperature difference between the two systems and travels from high to low temperatures [2]. The science that describes the means and rate in which thermal (heat) energy is transferred is known as heat transfer. Heat transfer applications are experienced in our daily life; the human body, for instance, is constantly emitting heat, and humans adjust their body temperature to suit environmental conditions using clothing. Heat transfer is also used in our buildings to regulate temperature [3] and is necessary for cooking, refrigeration and drying. It is also directly applied in car radiators [4] and for temperature control in electronic devices [5]. Heat transfer is used in solar thermal collectors to convert solar energy to heat and power [6, 7] and used in thermal control elements in spacecraft [8]. In many of these devices, heat needs to be dissipated at a rapid rate to ensure effective operation and maximum efficiency within the system [9]. As technology evolves, devices have become smaller and thus require better thermal management. Essentially, the more compact the size, the larger the requirement for effective cooling technology [10]. Therefore, heat transfer enhancement is a very important area in thermal engineering.

Several techniques have been considered to improve the heat transfer coefficient between the working fluids and the fluid contact surfaces [11, 12]. Conventional heat transfer fluids such as water, thermal oils and ethylene glycol/water have some limitations as their thermal properties are quite low when compared to those of solids, as shown in Fig. 1. The improvement in the thermal properties of these fluids through the addition of nanoscaled particles has led to an evolution in the study of heat transfer fluids. The suspension of these solid particles in the base fluid enhances the energy transmission in the fluid leading to improved thermal conductivity properties and better heat transfer characteristics [13]. The resultant fluids have been seen to possess higher values of thermal conductivity [14, 15]. Choi and Eastman [15] were the first to name such fluids as nanofluids. Nanofluids are the engineered colloidal suspension of nanoscaled particles (10–100 nm) in a base fluid [16]. These particles are generally metals, metallic oxides or other carbon-based elements. Over a century ago, Maxwell [17] was the first to discuss the suspension of micro-scaled particles into a fluid. However, microparticles settled rapidly in the fluid leading to abrasion and clogging in the flow channel, limiting further research into suspensions in fluids. Furthermore, these fluids did not exhibit the significant enhancement witnessed today with the use of nanofluids. The introduction of nanoparticles has allowed for further investigation into colloidal dispersion in fluids. Nanoparticles are more stable when dispersed in fluids and tend to improve on the thermal properties of the fluids. Some other properties of nanofluids which make them adequate heat transfer fluids include the Brownian motion of particles, particle/fluid nanolayers and their reduced pump power when compared to pure liquids to achieve intensified heat transfer.

Fig. 1
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Bulk material thermal conductivity difference between a commonly used base fluids and b commonly used nanoparticles

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Despite these benefits, nanofluids still possess some application-based limitations. Issues of sedimentation and aggregation in the fluid have been raised, although the use of ultra-sonication, pH modulation, magnetic stirring and the addition of surfactants has been recorded to improve the stability of the nanofluids [18]. Also, increasing the fluid circulation rate in the device reduces the possibilities of sedimentation, although this can lead to an erosion of heat transfer in the device or flow stream. Particles of larger sizes also tend to clog the flow channel, and there have been cases of pressure loss recorded in some devices due to the marginal increase in viscosity. Nanofluids are also expensive to prepare and toxic due to the reactive nature of the nanoparticles [19]. Over the past decade, emphasis on nanofluids research has been more apparent as shown in Fig. 2, which illustrates the number of publications involving nanofluids since 2010. These studies include those related to their preparation, characterisation, measurement of their physical properties and their utilisation in various applications. The data presented in Fig. 2 were obtained by searching the word “nanofluids” in the Scopus database against titles, abstracts and keywords over the period presented. The search illustrates that approximately 3165 papers were published in 2019 alone, and this trend is expected to increase in the coming years. Incidentally, several review papers related to the heat transfer properties and application of nanofluids were published in 2019. The reviews include solar collector applications [20, 21], a review of nanofluids in heat exchangers [22, 23], review of nanofluids in heat pipes [24], radiator cooling [25], electronic cooling [5] and also a review on various thermophysical properties of nanofluids [26, 27]. A list of some of the review papers published in 2019 is listed in Table 1.

Fig. 2
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Nanofluids-related publication in the past decade

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Table 1 Reviews on heat transfer-related application of nanofluids published in 2019
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Owing to the increasing number of studies relating to nanofluids, there is a need for a holistic review of the progress and steps taken in 2019 concerning their application in heat transfer devices. This study adopts a retrospective look at the year 2019 by reviewing the progress made in the area of nanofluids preparation, nanofluid thermophysical properties measurements and the applications of nanofluids in various heat transfer devices including solar collectors, heat exchangers, refrigeration systems, radiators, thermal storage systems and electronic cooling. The study aims to update readers on recent progress in nanofluid synthesis and application. The study also seeks to highlight the challenges and prospects of nanofluids as the next-generation heat transfer material.

Preparation of nanofluids

The method used in the preparation of nanofluids is important in the study of the stability and thermophysical behaviour of nanofluids [57]. The preparation steps are also vital in estimating the degree to which the nanofluids are employed in heat transfer systems [58], [59]. In this section, the studies related to nanofluid stability and their synthesis techniques are discussed. Nanofluids are produced by suspending particles of nanosize dimensions in the traditional heat transfer fluids such as water, oils, acetone and glycols [60]. A wide range of nanoparticles have been utilised in the formation of nanofluids, some of these include:

  1. (1)

    Carbon nanoparticles (such as MWCNT, SWCNT, Gn, GO, graphite, diamond and fullerene).

  2. (2)

    Metal nanoparticles (such as Ag, Al, Au, Co, Cu and Fe).

  3. (3)

    Metal oxide nanoparticles (such as Al2O3, CeO2, CuO, Fe3O4, TiO2 and ZnO).

  4. (4)

    Others (such as Si, AlN-C, CoFe2O4, SiC, Field’s alloy nanoparticles, ZnBr2 and SiO2).

Nanofluids can be unstable due to the strong Van der Waals interactions and cohesive forces between nanoparticles. Therefore, the preparation technique used is extremely important in others to break down these forces and produce stable nanofluids. Different methods have been used to avoid nanoparticle agglomeration and improve the stability of nanofluids, such as pH control, surfactant addition, ultrasonic agitation, magnetic stirring, functionalisation and high-pressure homogenisation [114] experimentally measured the thermal conductivity of MWCNT–TiO2/ethylene glycol nanofluid and obtained that an increase in volume concentration of the nanofluid tends to increase the thermal conductivity of the hybrid nanofluid. The study considered volume concentration between 0.05 and 1% and observed that at a volume concentration of 1%, maximum enhancement (40.1%) in thermal conductivity was obtained. The study also developed an artificial neural network (ANN) model to predict the thermal conductivity values obtained from the experiment. Also, Alarifi et al. [115] developed an ANN model from their experimental results to predict the thermal conductivity of MWCNT–TiO2/thermal oil nanofluid.

As shown in Table 4, while many studies have begun to use the artificial neural network (ANN) models in thermal conductivity prediction, some others have proposed regression-based correlation equations to fit results obtained from their experiments. Moldoveanu et al. [116] conducted an experimental study on the thermal conductivity variation of Al2O3–TiO2/water nanofluid at volume concentration between 0.25 and 1% and proposed a correlation model to predict the thermal conductivity.

Table 4 Thermal conductivity studies of hybrid nanofluids in 2019
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In terms of unique conventional nanofluids, Essajai et al. [117] studied the effect of particle shape on the thermal conductivity of nanofluids. The study was performed using a one-dimensional (1-D) network of interconnected gold nanoparticles (IAuNPs) and spherical Au nanoparticles. It was observed that IAuNPs in base fluids were more effective in improving the thermal conductivity of nanofluids than spherical Au particles suspended in a base fluid. Applying the one-step synthesis technique, the stability and the thermal conductivity measurement of MWCNTs/Jatropha seed oil nanofluid were investigated [118]; this environment-friendly nanofluid showed a thermal conductivity enchantment of 6.76% at a mass concentration of 0.8%.

ANN has also been used to predict the effect of particle aggregation on the thermal conductivity of nanofluids [119]. Mirsaeidi and Yousef [120] used ANN to predict the thermal conductivity, density and viscosity of carbon quantum dots nanofluids using water, ethylene glycol and EG–water (60:40) as base fluids. Motlagh et al. [121] used gene expression programming to propose a correlation that estimates the thermal conductivity of Al2O3 and CuO–water-based nanofluid based on experimental data from the literature. Going forward, it is expected that there will be an increase in research and development exploring the possible advantages of using ternary hybrid nanofluids. In this regard, the study conducted by Mousavi et al. [122] has already demonstrated that the thermal conductivity of CuO–MgO–TiO2/water nanofluid is enhanced by 78.6% at a mass fraction of 0.1. For the reader’s convenience, the authors have summarised the work on thermal conductivity studies for hybrid nanofluids in the year 2019 in Table 4.

Viscosity of nanofluids

The viscosity of a fluid is important in understanding both the heat transfer and the flow behaviour of the fluid. Several experimental studies have been carried out to understand the behaviour of nanofluids. The available research on the topic is not limited to experiments alone as molecular dynamics simulations have also been used to explain the viscosity of nanofluids [136]. Dehghani et al. [137] analysed the effect of temperature and mass fraction of Al2O3 and WO3 nanoparticles in water and liquid paraffin. Their findings showed that the viscosity of both nanofluids is increased only by adding a certain number of nanoparticles to both fluids. Regarding the shear rates, the viscosity of water-based nanofluids is constant, which indicates a Newtonian behaviour, while that of paraffin does not remain constant at different shear rates, and at a low amount of shear rate the viscosity achieves higher value, indicating a non-Newtonian behaviour for liquid paraffin-based nanofluids. Finally, they presented a correlation based on temperature, nanoparticle concentration and the physical properties of both the nanoparticle and base fluid for predicting the viscosity of aqueous and non-aqueous nanofluids. Ye et al. [138] extensively covered the viscosity of nanofluids that predate 2019. In 2019, as with thermal conductivity studies, there has been a trend towards hybrid nanofluids. The significance of viscosity in lubrication applications has been seen in many investigations related to oil-based nanofluids. Using the ultrasonic-assisted process, Barai et al. [85] synthesised graphene oxide–Fe3O4/water nanofluid at volume concentrations between 0.01 and 0.2%. The study obtained a maximum viscosity enhancement of 41%. Studying Fe–CuO/EG–water nanofluid, Bahrami et al. [139] obtained that the backward propagation methods presented the least error in predicting dynamic viscosity. Bahrami et al. [139] deduced that when the hybrid nanofluids volume concentration is below 0.1%, the Fe–CuO/EG–water nanofluid exhibited Newtonian behaviour. However, when Fe–CuO/EG–water nanofluid volume concentration is above 0.25% the behaviour of the fluid changed.

As shown in Table 5, while many studies have proposed various correlation models to predict the viscosity behaviour of nanofluids, the more accurate models proposed are the artificial neural network (ANN) models. Ruhani et al. [140] investigated the effects of volume concentration and fluid temperature on the viscosity of hybrid nanofluids. The correlation model proposed in this study demonstrated a 1.8% margin of deviation between experimental values and correlation results. Viscosity enhancement was about 80% when the volume fraction was 2%.

Table 5 Viscosity studies of hybrid nanofluids in 2019
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Other types of conventional nanofluids were also studied; Mousavi et al. [141] conducted an experimental investigation into the viscosity measurements of MoS2/diesel oil nanofluid at particle concentration between 0.1 and 0.7%. The study observed that the viscosity increased by 7.04% when volume concentration was 0.7%. Hameed et al. [142] synthesised an eco-friendly MWCNTs-Kapok seed oil nanofluid using a one-step method, at a constant nanoparticle concentration of 0.1%.

Considering all of the experimental viscosity measurements conducted, the relationship between viscosity and both temperature and particle concentration is apparent. Naturally, the viscosity of nanofluids increases with an increase in particle concentration, and this is observed in virtually all measured experiments. The viscosity of nanofluids decreases with an increase in temperature, and this is also observed in all measured experiments; this behaviour is expected as entropy is increased as particles gain thermal energy. However, the relationship between particle concentration and rheology is not as apparent. Considering the sample size alone as illustrated in Table 5, it can be observed that there exists no clear pattern between rheological behaviour and particle concentration in nanofluids. Rheological behaviour appears to vary from material to material.

Specific heat of nanofluids

The specific heat capacity of fluids is important in understanding both the heat transfer and the energy content of thermal systems. While significant research has focused on both viscosity and thermal conductivity, studies relating to the specific heat capacity of nanofluids are not as advanced. However, the specific heat capacity of fluid bears significance in thermal storage applications. Therefore, many studies regarding specific heat capacity often use molten salt as their base nanofluid. Moldoveanu and Minea [153] experimentally measured the specific heat of both Al2O3–TiO2/water nanofluids and Al2O3–SiO2/water nanofluids at volume concentration between 1 and 3.0%. A correlation model was determined from the measured specific heat capacity values. It is important to note that the correlation model had an average deviation of 11% when compared to experimental specific heat values. However, when the mixture theory model was used to predict the nanofluids’ specific heat capacity values, the deviation was as high as 19%.

The effect of particle size and volume fraction on the specific heat of SiO2 molten salt nanofluid was investigated by Li et al. [154]. Using SiO2 nanoparticle with sizes of 10, 20, 30 and 60 nm, SiO2 molten salt nanofluid was synthesised at particle concentration between 0.5  and 2%. Addition of particles to molten salt increases the specific heat capacity for all of the volume concentrations and particle sizes considered. An important point to note is that the particle concentration and particle size with the most specific heat enhancements were 1% and 20 nm, respectively.

Using SiO2, Al2O3 and TiO2 nanoparticles, three conventional nanofluids were synthesised by Hassan and Banerjee [155]. The study aimed to predict the specific heat capacity of metal oxide molten nitrate salt nanofluids using a multilayer perceptron neural network (MLP-ANN). The ANN model proposed was more accurate when compared to classical prediction methods [155]. Alade et al. [156] also considered a machine learning approach by applying a support vector regression model optimised with a Bayesian algorithm to predict the specific heat capacity of Al2O3 ethylene glycol nanofluids. The proposed model also exhibited a high degree of accuracy with the root-mean-square error (RMSE) equivalent to 0.0047.

From Table 6, while the specific heat of molten salts increases with the addition nanoparticles, in experiments involving MWCNTs PEG 400 nanofluid, Al2O3–water nanofluid, Fe–water nanofluid and Al2O3–Fe nanofluid the specific heat of the base fluid exceeds that of the nanofluids.

Table 6 Some specific heat studies of nanofluids in 2019
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Factors affecting nanofluids stability and thermophysical properties

The main factors affecting the thermophysical properties of nanofluids includes the morphology and concentration of nanoparticles, aggregation in the nanofluids and the sonication time used in its preparation [158]. The stability of nanoparticles suspended in a fluid is a very important parameter that affects both the rheological and thermophysical behaviours of the resultant nanofluids. Brownian motion causes the particles to collide with one another leading to cluster formation in the base fluid. These cluster formations or aggregation are controlled by a variety of internal forces between the base fluid and the nanoparticles such as the Van der Waals forces of attraction between the particles [159]. The aggregates begin to crystallise as their density exceeds that of the base fluid and affects the stability of the nanofluids over time [152]. Some of the factors that affect the stability of the nanofluids include the method of preparation of the nanofluids [66], agitation and sonication time [160,161,162], pH of the nanofluids [152], the addition of surfactants [163, 164] and surface charge density of the nanoparticles [158]. Asadi et al. [165] reviewed the effect of sonication on the stability and thermophysical properties of nanofluids. The study concluded that while there exists an optimum sonication time where thermal conductivity is maximum, and viscosity is least, more research is required to determine this optimum value, as it appears to differ for different nanofluids. Khan and Arasu [166] also reviewed the effects of nanoparticle synthesis techniques on the stability and thermophysical behaviour of nanofluids. The study importantly noted that there appears to be no standard method for stability measurements; this makes it difficult to compare stability across different papers. This is a problem because of the significant differences in reported fluid stability; this can range from days in some studies to months in others.

The values of the thermophysical properties of nanofluids are sensitive to the volume and size of nanoparticles used, the temperature of the mixture and the use of surfactants [167]. Yang et al. [168] investigated the thermal conductivity of graphene oxide/water nanofluids with a mass concentration range of 0–1.5%. Their result showed that as the mass fraction of nanoparticles increased, the thermal conductivity enhancement increased. Also, at a pH of 8, the nanofluids showed maximum stability with a maximum thermal conductivity enhancement of 48.1%. This indicated that the pH was a significant parameter in both its stability and thermal conductivity. The authors attributed the thermal enhancement observed to the increased Brownian motion of particles and molecules of the base fluid as temperature increased. Yang et al. [169] also studied the thermal conductivity behaviour of zinc nanopowder in SAE 50 engine oil and recorded an increase in the thermal conductivity of the nanolubricant as the volume concentration of nanoparticles was increased. They recorded a maximum thermal conductivity enhancement of 8.74% and attributed this to the effects of increased Brownian motion of particles in the lubricant as temperature raises. The thermophoresis effect was another factor they highlighted that affected the thermal conductivity enhancement.

Rostami et al. [71] examined the thermal conductivity of GO–CuO water/EG (50:50) hybrid nanofluid at a temperature of 25–50 °C and particle volume concentration of 0.1–1.6%. Their investigation observes a 46% enhancement in thermal conductivity, which is higher than the enhancement of using single nanomaterial. Mahyari et al. [73] investigated the thermal conductivity GO/SiC (50:50)/water hybrid nanofluid at volume concentrations between 0.05 and 1%. Their investigation reveals that the effect of the volume concentration of nanoparticles was more significant than the effect of increasing temperature. Importantly, the studies observed that the enhancement in thermal conductivity of their hybrid nanofluid was more than the reported thermal conductivity enhancement using GO or SiC individually. Hybrid nanofluids not only affect the thermal conductivity, but also enhance the stability of nanofluids.

Heat transfer mechanisms of nanofluids

The main benefit of using nanofluids is their enhanced thermal transport which results in improvements in the thermal conductivity of traditional heat transfer fluids. As previously outlined, several parameters influence the thermal conductivity enhancement and include nanoparticle type, nanoparticles size, nanoparticles concentration, temperature, type of base fluid and the thermophysical properties of both the base fluid and the nanoparticles. Over the last three decades, since the introduction of nanofluids in 1995, the explanations behind the enhanced heat transfer of nanofluids have been attributed to several mechanisms. The size and the large number of particles interacting with the base fluid present a challenge to properly understanding the nanoscale effects that support the improved thermal properties observed in the literature. Mahian et al. [108, 170] studied the mechanisms that would aid the simulation of nanofluids flow. They highlighted that forces such as drag, lift, Brownian motion, thermophoresis, Van der Waals and electrostatic double-layer forces had a significant effect on the thermal and rheological behaviours of nanofluids.

Brownian motion is defined as the uncontrollable random motion of particles within the fluid due to the collision between slow moving and higher velocity particles. Brownian motion occurs as a result of thermal diffusion, and this phenomenon is increased at higher temperatures, low viscosity and smaller particle size. As promoted by the scientific community, the random collision of particles within the fluid remains the primary reason for the thermal conductivity enhancement observed with nanofluids [73, 79, 92]. However, Jang and Choi [171] provided three types of collisions that occur due to the rising temperature of nanofluids: collisions between the molecules of the base fluid, collisions between base fluid molecules and the nanoparticles, and the collisions between nanoparticles due to Brownian motion. They concluded that the effect of Brownian motion on thermal conductivity enhancement had the least effect among the three types of collisions.

Keblinski et al. [172] was the first to introduce the idea of nanolayers and their effect in nanofluid thermophysical behaviour. The nanolayer is known as the solid-like structure or the interfacial layer between the solid surface and the first layer of the fluid in contact with the solid surface. A structured, layered arrangement of the fluid molecules around the surface of the nanoparticles was observed. These layers behaved like solids and act as a thermal bridge for the heat transfer process enhancing the overall thermal conductivity of the fluid. In the solid–solid interface, this layer acts as a barrier of heat transfer due to incomplete contact between solid surfaces. However, it is not the case for the solid–liquid interface as the aligned interfacial shell in the nanoparticle suspension would make heat transfer across the interface effective. Yu and Choi [173] presented a modified Maxwell model to account for the effect of nanolayers on the thermal conductivity of nanofluids. Their results proved that the thermal model is enhanced as a result of accounting for this factor. **ing [317] all show an increase in the dimensionless heat transfer parameter with the addition of nanoparticles. These studies prove the tremendous potentials of nanofluids in the electronics and data storage industries. Also, the heat transfer behaviour of nanofluids in magnetic fields has shown promising potentials [318].

Vishnuprasad et al. [319] experimentally evaluated the cooling performance of microwave-assisted acid-functionalised graphene (MAAFG) in water. The characterisation of the nanofluid showed that the MAAFG nanofluid had a 55.38% enhancement in thermal conductivity. The effect of varying the flow rate and nanoparticle volume concentration on the heat transfer coefficient and processor temperature was studied, and the results show that at 0.2 Vol%, there was an increase in the convective heat transfer coefficient by 78.5%. The processor temperature was also decreased by 15%, although a 5% pressure drop was recorded at 0.2 Vol% and a flow rate of 10 mL s−1. Joy et al. [320] investigated the use of Cu–water and Al–water to increase the critical heat flux (CHF) limit in a heat pipe for electronic cooling. The result of the study demonstrated that nanofluids increased the CHF by 140% at a mass concentration of 0.01%. Both nanoparticle concentrations represented the optimum value of CHF for both nanofluids without preheating. Zing and Mahjoob [321] theoretically investigated the use of single- and multijet im**ements through a porous channel for electronics cooling applications. The study evaluated the effect of two different coolants in their system: water and TiO2–water nanofluids at a volume concentration of 5%. Results demonstrate that the use of TiO2 nanofluid decreased the base temperature of the device more effectively than using water. For enhanced heat transfer in electronic cooling, Bezaatpour and Goharkhah [322] designed a mini heat sink with porous fins operating with a magnetite nanofluid of Fe3O4–water at volume concentrations up to 3%. The study recorded an increase in heat transfer of 32% with the use of the ferrofluids at 3 Vol% and Re of 1040. The pressure drop also recorded a decrease of 33% with the use of the ferrofluids.

Al-Rashed et al. [323] evaluated the first and second law performance of a non-Newtonian nanofluid of CuO and 0.5% carboxymethyl cellulose (CMC) in water for use in a microchannel heat sink (MCHS). Figure 16 illustrates an offset strip-fin MCHS with a description of its geometric parameters and imposed boundary conditions. By varying the nanoparticle concentration and Reynolds number, the effect of the nanofluids on the surface temperature of the CPU was observed. The results demonstrate that increasing Reynolds number adversely affected the frictional entropy generation and pressure drop. The nanofluid also reduced the surface temperature of the CPU and entropy generation rate in the system. A 2.7% decrease in the entropy generation rate of the CPU was attained at 1 Vol% and Re of 300. At 1 Vol% and Re of 700, the CMC/CuO water had an optimal ratio of heat transfer to the pressure drop of 2.29. Qui et al. [324] investigated the interfacial transport between vertically aligned carbon nanotube and electronic heat sinks. Their results show that CNT reduced the thermal contact resistances from 10 mm2K/W to 0.3 mm2K/W. Other studies related to the use of nanofluid in improving heat transfer in electronic devices are detailed in Table 9.

Fig. 16
figure 16

The schematic of (a) offset strip-fin microchannels, and b the computational domain of the one unit of microchannels [323], [325]

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Table 9 Studies related to the application of nanofluids in electronic cooling devices
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Nanofluids in automobile radiators

The thermal management of automobile engines is necessary for the effective and efficient operation of the automobile. Figure 17 illustrates a schematic diagram of a car radiator which functions as a heat exchanger that disperses the heat generated from the operation of the engines. Recently, the use of nanofluids as alternative coolants in radiators have been investigated. Elsaid [341] experimentally investigated the performance of an engine radiator using nanofluids in the hot arid climate of Cairo, Egypt. Two nanoparticles Al2O3 and Co3O4 are used in varying concentrations in a base fluid of EG/water at 0:100%, 10:90% and 20:80%. A schematic of his experimental set-up for investigating nanofluids effectiveness in radiators is illustrated in Fig. 18. The study confirms that the use of Co3O4/EG–water results in a more favourable thermal performance than that of Al2O3/EG–water. The cobalt oxide also contributed to larger energy savings when compared to alumina. The nanoparticles enhanced the Nusselt number by 31.8%; however, this was at the expense of an increase of 16% in friction factor. This reduction in friction factor resulted in the need for additional pump power for the nanofluids. It is essential to note that pump power was also intensified with the use of EG–water as the base fluid. The performance of a hybrid of Al2O3 nanocellulose dispersed in EG/water in a radiator was investigated by Naiman et al. [342], who recorded a maximum thermal conductivity at 0.9 Vol% and concluded that the nanofluids were more efficient than the use of EG–water. Al Rafi et al. [343] studied the heat transfer potential of Al2O3/EG–water and CuO/EG–water in a car radiator, revealing that the addition of EG into the water decreased the overall heat conductance by 20–25%. Moreover, experimental results demonstrate that Al2O3/EG–water at 0.1 Vol% and CuO/EG–water at 0.2 Vol% improved the heat transfer potential of the radiator by 30–35% and 40–45%, respectively.

Fig. 17
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Schematic diagram of a car radiator

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Fig. 18
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Schematic diagram of the experimentation system used by Elsaid [341]

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Kumar and Sahoo [344] analysed the energy and exergy performance of a wavy fin radiator using Al2O3–water nanofluid as a coolant. The effect of various nanoparticle shapes (spherical, brick and platelet) on the radiator’s effectiveness, pump power and heat transfer was also investigated; results show that the shape of the nanoparticles affected their performance in the radiator. Furthermore, it was observed that the spherical nanofluids had a 21.98% enhancement in heat transfer when compared to the platelet nanofluid. A 13% enhancement in the exergy efficiency of the spherical nanofluids determined that the use of spherical nanofluids performed better in comparison with nanofluids of other shapes. Contreras et al. [345] experimentally investigated the thermo-hydraulic performance of silver/EG–water and graphene/EG–water for use in a radiator. The study showed that silver/EG–water had an improved heat transfer rate of 4.7% when compared to EG–water, while the heat transfer using graphene nanofluid decreased by 11% and 3% at concentrations for 0.01 Vol% and 0.05 Vol%, respectively, when compared to water. The thermo-hydraulic performance coefficient of all nanofluids showed that nanographene at 0.1 Vol% and silver nanofluids at 0.05 Vol% had values of 1.5% and 2.5%, respectively, while graphene nanofluids at concentrations of 0.01 Vol% and 0.05 Vol% were not suitable for use in the radiator as they performed below EG–water. Other studies on the use of nanofluid in improving the performance of automobile radiators are detailed in Table 10.

Table 10 Studies related to the application of nanofluids in automobile radiators
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Nanofluids in thermal storage

Thermal energy storage (TES) is a very important part of the utilisation, conservation and development of new and existing energy sources. The three forms of TES are chemical energy storage, sensible heat storage and latent heat storage. The difference between sensible and latent heat storage types is related to the phase transition of the thermal material used for storage. There is a phase transition before energy is released or stored in the Latent TES, while sensible TES does not require a phase change and operates mainly with the changing temperature of the material. Phase change materials (PCMs) can be used in both cases and is essential to the operation of the latent TES unit. The drawbacks of PCMs are their low thermal properties.

A classification of the various materials used in thermal energy storage is presented in Fig. 19. Highlighting the studies that investigate the effects of nanoparticles on the thermal performance of PCM, Bondareva et al. [357] investigated the heat transfer performance of the nano-enhanced phase change material system under the inclination influence. Studying the performance of paraffin enhanced with Al2O3 nanoparticles, they discovered that; for small inclinations of the cavity, when convective heat transfer dominates, an increase in the nanoparticles volume fraction leads to an increase in the melting time. Navarrete et al. [358] proposed the use of molten salt-based nanofluid for both sensible and latent energy storage. The molten salt nitrate would serve as the base fluid for the nano-encapsulated phase change materials (nePCM) consisting of Al-Cu alloy nuclei. Oxidation that occurs as a result of the metals been exposed to air would serve as an encapsulation over the nanoparticles. The study tested the resistance of the oxide shell to temperatures up to 570 °C, demonstrating that although the specific heat and by extension the sensible heat storage decreased with the presence of solid content, the phase change enthalpy and latent storage capacity increased by 17.8% at constant volume bases. Furthermore, the thermal conductivity of the salt nitrates increased with the addition of nanoparticles enhancing the heat transfer performance of the PCM nanofluid. Martin et al. [359] developed a novel nePCM from two fatty acids of capric acid (CA) and capric–myristic (CA-MA) using nSiO2 for thermal energy management in a building. The addition of the 1.5% nSiO2 significantly improved both the thermal conductivity and specific heat of nePCM. The thermal stability test after 2000 thermal cycles indicated that the addition of nanoparticles did not affect the thermal stability of CA, but slightly improved that of CA-MA. The sensible heat storage capacity of both fatty acids improved due to a 20% improvement in specific heat capacity at a volume concentration of 1%; however, the latent energy storage capacity of both fatty acids was lowered. The use of the nSiO2 nanoparticles strengthens on the initial weaknesses of the fatty acids as heat storage fluids as Fig. 20 illustrates.

Fig. 19
figure 19

Classification of the various thermal energy storage materials (modified from [362])

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Fig. 20
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Organic PCMs that plot latent heat of fusion vs thermal conductivity [359]

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Ding et al. [360] studied the use of two crystal forms of TiO2 nanoparticles (anatase referred to as A and rutile referred to as R) dispersed in water operating in a microchannel inside a PCM used to enhance the thermal storage in miniatured devices. The two nanofluids R-TiO2–water and A-TiO2–water were thermally tested, and both nanofluids confirmed to be stable. R-TiO2–water was more stable than A-TiO2, and the thermal conductivity of R-TiO2 was found to be higher than that of A-TiO2. The addition of TiO2–water in the microchannel at a volume concentration of 0.5%, 0.7% and 1.0% decreased the complete melting time of paraffin by 7.78%, 16.51% and 32.90% while increasing the complete solidification time by 7.42%, 15.65% and 22.57% in the solidification process. The use of nanofluids increased the melting and solidification pressure by less than 8% in both cases. Harikrishnan et al. [361] investigated the effect of Ni–ZnO nanocomposite dispersed in oleic acid on the thermal conductivity and phase change properties of the resulting nePCM. The thermal reliability along with the freezing and melting characteristics of the nePCM was studied, and the thermal conductivity of the nanofluids was confirmed to be higher than that of oleic acid. For the mass fraction considered, 0.3, 0.6, 0.9 and 1.2% of Ni–ZnO, the complete melting and solidification processes were enhanced by 7.03%, 14.06% 24.21%, 29.69% and 7.58%, 13%, 19.13%, 28.52%, respectively. The trend confirms that the time required in melting and freezing was lowered with the use of the nano-PCMs. Other studies related to the use of nanoparticles in thermal storage units are detailed in Table 11.

Table 11 Studies related to the application of nanofluids in thermal energy storage
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Nanofluids in refrigeration

Nanofluids can also be used in air conditioning and refrigeration systems. The negative environmental effect of using chlorofluorocarbons along with hydrofluorocarbons has propelled research into alternative refrigerants. Traditionally, vapour compression refrigeration systems (VCRSs) are used in the cooling industry; however, the major drawback to this system is the large compressor power requirement. An alternative heat-powered absorption refrigeration system (VARS) has been developed, although the coefficient of performance (COP) of these systems is still below those of the VCRS. Nanoparticles have been used to create new refrigerants known as nanorefrigerants which can improve the COP of both the VARS and VCRS and decrease the compression work of the VCRS.

Rahman et al. [376] analysed the effect of using nanoparticles in a refrigerant. The effect of the nanorefrigerant on the compression work and COP of the air conditioning system is observed. They observed that the addition of 5% SWCNT to R407c refrigerant at temperatures between 283 K and 308 K resulted in a reduction in the energy consumption of the compressor by 4%. Moreover, the nanorefrigerant had improved the thermal conductivity and specific heat values by 17.02% and 10.06%, respectively. The nanorefrigerant also enhanced the COP by 4.59% and reduced the compressor work by 34% when compared to conventional vapour compression refrigeration systems.

Jiang et al. [377] investigated the effect of 0.5% TiO2 and 0.02% SDBS on the COP of ammonia absorption refrigeration system (AARS). The experimental set-up of the test rig used in their investigations is illustrated in Fig. 21. Outcomes of the experiment were compared to that of 0.1%, 0.3% and 0.5% of TiO2 dispersed in ammonia water as a refrigerant. The results demonstrate that the addition of TiO2 to any of the concentrations studied significantly improved the COP of the AARS. It was observed that the further addition of 0.02% of SDBS improved the stability of the mixture and enhanced the COP by 27% as shown in Fig. 22. In conclusion, the improvement in COP of the AARS was strongly dependent not only on nanoparticle concentration but also on the number of nanoparticles stably dispersed in the base fluid. Jeyakumar et al. [378] investigated the use of three nanoparticles CuO, ZnO and Al2O3 in the refrigerant of a vapour compression system. The nanoparticles were added to refrigerant R134 at concentrations of 0.06%, 0.08% and 0.1% with 0.1% polyester oil as a lubricant. The results demonstrate an improvement in COP of 12.2% and 3.42% using the nanorefrigerant of CuO and Al2O3, respectively. Also, a reduction in the power consumption of 1.39% and 0.6% with CuO and Al2O3, respectively, was observed. Other studies related to the use of nanoparticles in compression and absorption refrigeration systems are given in Table 12.

Fig. 21
figure 21

Test rig for investigating the influence of TiO2 nanoparticles on AARS [377]

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Fig. 22
figure 22

The COP of AARS with different mass fractions of TiO2 [377]

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Table 12 Studies related to the application of nanofluids refrigeration systems
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The use of nanofluids in many other devices has also been studied, and some of these include the application of nanofluids in solar still [389, 390] and also in mineral oil to enhance the insulating properties of high-voltage AC and DC transformers as proposed by Rafiq et al. [391].

Challenges and future prospects

Due to stability concerns with nanofluids, exponential improvements are required for nanofluids to reach their full potential as heat transfer fluids. The problems with stability are more obvious in liquids with low viscosity than liquids with high viscosity. Most of the current methods used to increase fluid stability appear to fall short in certain regards. pH modulation has demonstrated promising signs of improving the stability of nanofluids; however, acidic and basic solutions exponentially increase corrosion in metals and would thus render heat transfer system untenable. The addition of surfactants has the potential to improve nanofluids stability, however, at high-temperature surfactants tend to foam and decrease the overall efficiency of the system. The most promising technique for increasing fluid stability is by improving the synthesis techniques used. Incidentally, the most common method for synthesising nanofluid is the worst performing method for ensuring fluid stability. Green synthesis techniques demonstrate sufficient promise in improving stability; however, the thermal performance of the green-synthesised nanofluids is not normally as high as nanofluids synthesised by the two-step technique. Furthermore, there appears no standard for reporting the stability of nanofluids. Therefore, a generic standard for measuring nanofluid stability must be developed so that easy comparisons can be made across nanofluid types.

Another significant challenge is the theoretical unpredictability of the thermophysical behaviour of nanofluids. While many studies settle for regression-based correlation models to predict thermophysical properties, intelligent computing has also been widely used in the predictions. It is the opinion of the authors that because of the almost infinite variables that affect the thermophysical behaviour of nanofluids, intelligent computing would be the most accurate predicting the thermophysical behaviour of nanofluids. Therefore, a generic standard must be developed for labelling data obtained from the experiments measuring thermophysical properties of nanofluids. Develo** a global data bank will drastically improve the prediction accuracy of artificial neural network and machine learning models, saving unlimited research costs in conducting thermophysical behaviour measurements.

To improve numerical analysis models, further nanofluid heat transfer correlation studies are required for determining the Nusselt number correction equation. Many studies adopt the Nusselt number correlation equation proposed by Pak and Cho [392]; however, this model was developed for water, Al2O3–water and TiO2–water nanofluids and may not be particularly accurate for other nanofluids. More experiments using other nanofluids, especially for hybrid nanofluids, will further enlighten the field and improve the accuracy of numerical studies.

Finally, the classification of nanofluids must be improved. As nanofluid research increases, several unique types of fluids are synthesised. Previously, conventional fluid often implies fluids with a single-particle material, while hybrid nanofluid refers to a fluid with more than one nanoparticle material. However, it appears that further classifications are required as nanofluid have the potential to have an nth number of significant nanomaterials types present in the fluid. Some authors have sought to classify nanofluids with two significant nanomaterials type as “binary hybrid nanofluid” and nanofluids with three significant nanomaterials type as “ternary hybrid nanofluid”. It may be beneficial if classifications are conducted along these lines.

Conclusions and recommendations

The use of nanofluids as coolants in heat transfer devices has gained attention over the years. This study presents a detailed review of studies relating to the preparation, thermophysical property measurements and application of nanofluids in a range of thermal devices requiring efficient heat transfer published in 2019. Some of the areas reviewed include thermophysical models used in determining the properties of the nanofluids, mechanisms that support the enhanced thermal behaviours of nanofluids, and the application of different nanofluids in devices such as solar collectors, heat exchangers, electronics cooling and thermal storage. Based on the articles reviewed in this study, the following recommendations are made:

On the preparation of nanofluids;

  • Few studies on the preparation of nanofluids based on the one-step method are available, and this method has been proven to have better stability than the two-step method. More studies on the production of nanofluids using the one-step method are needed, as this could help in the development of more cost-effective means for the large-scale production of nanofluids.

Regarding the thermophysical properties of nanofluids:

  • An increase in the nanoparticle volume concentration leads to a decrease in the specific heat capacity of nanofluids in cases where the heat capacity of base fluids is higher than those of nanoparticles. Since a higher heat capacity is needed in coolants, further studies are required to assess how this phenomenon can be improved.

  • Many studies on the thermal behaviour of nanofluids were conducted for temperatures between 10 and 100 °C. The interaction mechanism of nanoparticles in base fluids for heat transfer at higher temperatures (greater than 100 °C) and cryogenic conditions requires further investigation.

  • There exist huge differences between the heat transfer predicted by the single-phase homogenous model and those obtained from experiments. More studies related to the development of other models (two-phase models) are required which allude to defining the conditions where the single-phase models can be applied to provide more accurate results.

  • There has been an increase in both the number and methods for develo** correlation models that predict the thermophysical properties of nanofluids. However, more correlation equations that predict the heat transfer (Nusselt number) and friction factor behaviours of many nanofluids are needed.

On studying the mechanisms that influenced the properties of nanofluids:

  • Knowledge of the dominant forces responsible for the behaviour of nanorefrigerants in various flow configurations requires further development.

  • An understanding of the impact of nanoparticle morphology (size and shape), nanoparticle mixture ratio (for hybrid nanofluids) on heat transfer augmentation is limited. More studies are needed to understand the impact of these on the performance of nanofluids in heat transfer devices.

Investigation on the various heat transfer devices:

  • Further studies are required, as there are contrasting reports on the effect of nanoparticle loading on the pressure drop and additional pump power requirement. While some studies claim that the effect of particle loading increases the pressure drop and consequently the pump power requirement of the system, others argue that when the heat transfer rate obtained using nanofluids is compared with that of conventional fluids, the nanofluids lowers the pump power requirements.

  • In heat exchangers and car radiators, the constant rate of heat transfer from the use of nanofluids leads to a reduction in the heat transfer surface. This can result in an improvement in the size and volume of these devices. Such improvements would lead to a reduction in the drag forces witnessed in vehicles and increase the performance of the engine.

  • The most common model used in the literature for the simulation of nanofluids remains the finite volume method. Further studies using other methods are needed for the comparison of the different numerical approaches.

  • Further studies on the effects of erosion of heat transfer and corrosion of flow channels resulting from the use of nanofluids, especially in high temperatures, are required. Both the short- and long-term impacts of sedimentation and nanoparticle deposition on the efficiency of heat transfer devices require investigation.

  • Few studies are available on the production cost and environmental impact of nanofluids. Such factors present huge hurdles to the commercialisation of nanofluids.

  • Further information on the effect of oxidisation of metallic nanoparticles used with phase change materials on the thermal performance of the thermal storage unit is required, especially during the melting phase.