Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment
© Ramesh and Prabhu; licensee Springer. 2011
Received: 4 November 2010
Accepted: 14 April 2011
Published: 14 April 2011
The success of quenching process during industrial heat treatment mainly depends on the heat transfer characteristics of the quenching medium. In the case of quenching, the scope for redesigning the system or operational parameters for enhancing the heat transfer is very much limited and the emphasis should be on designing quench media with enhanced heat transfer characteristics. Recent studies on nanofluids have shown that these fluids offer improved wetting and heat transfer characteristics. Further water-based nanofluids are environment friendly as compared to mineral oil quench media. These potential advantages have led to the development of nanofluid-based quench media for heat treatment practices. In this article, thermo-physical properties, wetting and boiling heat transfer characteristics of nanofluids are reviewed and discussed. The unique thermal and heat transfer characteristics of nanofluids would be extremely useful for exploiting them as quench media for industrial heat treatment.
Water, brine solution, oil, polymer etc. are used as conventional quenching media. Water and brine solution are restricted to quenching simple shapes and steels of comparatively low hardenability because of the occurrence of intolerable distortion, warpage and quench cracks . On the other hand, convective cooling in oil is less intensive due to relatively high viscosity and lower heat capacity. A variety of different quenching oils tend to show a prolonged vapour blanket stage, a short nucleate boiling stage with a much lower cooling rate, and finally a prolonged convective cooling stage with a very modest cooling rate . Polymer quenchants show low cooling rate and it cannot be used with some common additives and anti oxidants. Continuous monitoring of polymer quenchant is required for optimal performance and it is not suitable for steels requiring high temperature quenching . Therefore, it is necessary to develop new type of quenchants capable of producing desired property distribution, acceptable microstructure and residual stress distribution in section thicknesses of interest with avoidance of cracking and reduced distortion.
Modern nanotechnology provides new opportunities to process and produce materials with average crystallite sizes below 50 nm . The unique properties of these nanoparticles are (i) size dependent physical properties, (ii) large surface area, (iii) large number density and (iv) surface structure . Fluids with nanoparticles suspended in them are called nanofluids . Commonly used materials for nanoparticles are oxide ceramics (Al2O3, CuO), metal carbides (SiC), nitrides (AlN, SiN), metals (Al, Cu), nonmetals (graphite, carbon nanotubes), layered (Al+Al2O3, Cu+C), PCM and functionalized nanoparticles and the base fluids includes are water, Ethylene or tri-ethylene glycols, oil, polymer solutions, bio-fluids and other common fluids . There are mainly two techniques used to produce nanofluid: the single-step and two-step method. Latter method is extensively used in the synthesis of nanofluids in which nanoparticles was first produced and then dispersed in the base fluids . The properly prepared nanofluids are expected to give the benefits of (i) higher heat conduction, (ii) more stability, (iii) microchannel cooling without clogging, (iv) reduced chances of erosion and (v) reduction in pumping power . The addition nanoparticles to the conventional fluids result in anomalous change in thermo-physical properties of the fluid. Apart from that, the addition of nanoparticles affect the boiling behaviour at the surfaces as they fill up the discontinuity at the surfaces and probably affect the critical heat flux. Nanofluids can be considered to be the next generation heat transfer fluids as they offer exciting new possibilities to enhance heat transfer performance compared to pure liquids. They are expected to have different properties related to heat transfer as compared to conventional fluids . Nanofluids offer completely different behaviour of wetting kinetics and heat removal characteristics and these characteristics could be exploited in industrial heat treatment for quenching. The present article reviews important thermo-physical properties, wetting and boiling heat transfer characteristics of the nanofluids. The importance of using nanofluids as effective quench media for hardening process during heat treatment is highlighted.
Thermophysical properties of nanofluids
Particle size (nm)
Thermal conductivity ratio (Keff/Kf)
Laurate salt Surfactant
Spherical and square
Spherical and square
Spherical and square
Spherical and square
Spherical, square, and needle
200 × 500
Spherical, square, and needle
30°C (citerate reduced)
40°C (citerate reduced)
30°C (citerate reduced)
60°C (citerate reduced)
pH = 3
pH = 6
Sodium dodeculbenzene sulfonate
10 × 40
15 × 30000
100 × >50000
Sodium dodecyl sulfate
Sodium dodecyl benzene 20°C
Sodium dodecyl benzene 45°C
130 × >10000
Gum Arabic 20°C
Gum Arabic 25°C
Gum Arabic 30°C
SDS 0.1 mass%
SDS 0.5 m ass%
SDS 2 mass%
SDS 3 mass%
CTAB 0.1 mass%
CTAB 1 mass%
CTAB 3 mass%
CTAB 6 mass%
Triton 0.17 mass%
Triton 0.35 mass%
Triton 0.5 mass%
Triton 1 mass%
Nanosperse 0.7 mass%
CTAB 1 mass%
CTAB 3 mass%
CTAB 5.5 mass%
Brownian motion of nanoparticles: The Brownian motion of nanoparticles at the molecular and nanoscale level was a key mechanism governing the thermal behaviour of nanoparticle-fluid suspensions . The random motion of nanoparticles suspended in the fluid results in continuous collisions between the particles and molecules of bulk liquid thereby transport energy directly by nanoparticles. The impact of Brownian motion was more effective at higher temperatures . The micro convection/mixing effect of the base fluid in the immediate vicinity of the nanoparticles caused by the Brownian motion was an important reason for the large thermal conductivity enhancement of nanofluids . However, the Brownian motion contribution to the thermal conductivity of nanofluid was very small and cannot be responsible for extraordinary thermal transport properties of nanofluids [43, 48–50].
Liquid layering around nanoparticles: The ordered layering of liquid molecules at the solid particle surface forms solid-like nanolayer. This layer acts as a thermal bridge between the solid nanoparticles and the base liquid and plays an important role in the enhanced thermal conductivity of nanofluids [51–54]. The effective thermal conductivity increases with increase in nanolayer thickness. Especially in small particle size range, the effects of particle size and nanolayer thickness become much more obvious, which implies that manipulating nanolayer structure might be an effective method to produce highly thermally conductive nanofluids . Although the presence of an interfacial layer may play a role in heat transport, it is not likely to be solely responsible for enhancement of thermal conductivity . By using molecular dynamics simulations, Xue et al. demonstrated that the layering of the liquid atoms at the liquid-solid interface does not have any significant effect on thermal transport properties .
Nature of the heat transport in the nanoparticles: When the nanoparticle size becomes very small, the mean free path of phonon is comparable to the size of the particle. In that case diffusive thermal transport in nanoparticles is not valid and ballistic transport is more realistic. Keblinski et al. indicated that inside the solid particles, heat moves in a ballistic manner that involves multiple scattering from the solid/liquid interface, which plays a key role in translating fast thermal transport in particles into high overall conductivity of the nanofluids. They also suggested that particles may be much closer due to Brownian motion and thus enhance coherent phonon heat flow among the particles . The estimated mean free path and the transition speed of phonons in nanofluids through density functional theory indicated that the speed of phonon transport will not be affected due to the existence of nanoparticles in the low volume fraction limit .
Clustering of nanoparticles: Since nanoparticles in the fluid are in Brownian motion and the Van der Waals force against gravity results in clustering of nanoparticles into percolating patterns with lower thermal resistance paths. With decreasing packing fraction, the effective volume of the cluster increases thus enhancing the thermal conductivity. Clustering may also exert a negative effect on the heat transfer enhancement particularly at low volume fraction, by settling small particles out of the liquid and creating large regions of particle free liquid with high thermal resistance . Using non-equilibrium molecular dynamics simulations, Eapen et al. showed that the thermal conductivity of a well-dispersed nanofluid was enhanced beyond the 3φ Maxwell limit through a percolating amorphous-like fluid structure at the cluster interface . Studies on clustering of nanoparticles in the fluids suggest varying values of thermal conductivities, i.e. enhanced, reduce and unchanged thermal conductivity of nanofluids [61–63]. Ozerinc et al. mentioned that there should be an optimum level of clustering for maximum thermal conductivity enhancement .
The experimentally measured thermal conductivities of nanofluids deviate from conventional models such as Maxwell, Hamilton-Crosser, Jeffery, Davis, Bruggeman, Lu and Lin model. The important factors, which control the thermal conductivity of nanofluids, are particle volume concentration, particle material, particle size, particle shape, base fluid material, temperature, additive and acidity [17, 44]. Due to these complex variables and different mechanisms, the exact model for effective thermal conductivity of nanofluid is difficult. Yu and Choi have modified the Maxwell equation for the effective thermal conductivity of solid/liquid suspensions to include the effect of this ordered nanolayer . Wang et al. proposed fractal model for liquid with dilute suspensions of nonmetallic nanoparticles, which involves the effective medium theory. The proposed model describes the nanoparticle clusters and their size distribution . Xue presented a novel model considering the interface effect between the solid particles and the base fluid in nanofluids based on Maxwell theory and average polarization theory . Jang and Choi devised a theoretical model that accounts for the role of Brownian motion of nanoparticles in nanofluid. This model also includes the concentration, temperature and size dependent conductivity . By considering the particle dynamics (Brownian motion), Koo and Kleinstreuer expressed a model which consists of particle volume fraction, particle size, particle material and temperature dependence as well as properties of base liquid . A comprehensive theoretical model has been developed by Kumar et al. which explains the enhancement in thermal conductivity of a nanofluid with respect to variation in particle size, particle volume fraction, and temperature . Xue and Xu derived a model which consists of the thermal conductivity of the solid and liquid, their relative volume fraction, the particle size and interfacial properties . Patel et al. introduced a concept of micro-convection into Kumar et al. model for predicting the thermal conductivity accurately over a wide range of particle sizes (10 to 100 nm), particle concentrations (1 to 8%), particle materials (metal particles as well as metal oxides), different base fluids (water, ethylene glycol) and temperature (20 to 50°C) . By considering the effect of the interfacial layer at the solid particle/liquid interface, Leong et al. proposed a model which accounts for the effects of particle size, interfacial layer thickness, volume fraction and thermal conductivity . For carbon nanotube (CNT) nanofluids, Patel et al. presented a simple model which shows linear variation of the thermal conductivity of CNT nanofluid with volume concentration . Feng et al. expressed a model as a function of the thermal conductivities of the base fluid and the nanoparticles, the volume fraction, fractal dimension for particles, the size of nanoparticles, and the temperature, as well as random number. Monte Carlo technique combined with fractal geometry theory is applied to predict the thermal conductivity of nanofluids . Shukla and Dhir developed a microscopic model based on the theory of Brownian motion of nanoparticles in a fluid which account size of the particle and temperature . Moghadassi et al. presented a novel model based on dimensionless groups which included the thermal conductivity of the solid and liquid, their volume fractions, particle size and interfacial shell properties. The proposed model creates a non-linear relation between the effective thermal conductivity and nanoparticle volume fraction . Wang et al. proposed a Novel Statistical Clustering Model to determine the macroscopic characteristics of clusters, and then, the thermal conductivity of a nanofluid . Sitprasert et al. modified the Leong model inorder to predict both the temperature and the volume fraction dependence of the thermal conductivity of nanofluids for both non-flowing and flowing fluids . Murugesan and Sivan developed lower and upper limits for thermal conductivity of nanofluids. The upper limit is estimated by coupling heat transfer mechanisms like particle shape, Brownian motion and nanolayer while the lower limit is based on Maxwell's equation . Teng et al. proposed an empirical equation incorporating the nanoparticle size, temperature and lower weight fraction of Al2O3/water nanofluid . By considering nanoparticles as liquid-like particles, Meibodi et al. expressed a model for estimation of upper and lower limits of nanofluid thermal conductivity .
Viscosity is an intrinsic property of a fluid that influences flow and heat transfer phenomena. The addition of nanoparticles to the base fluid shows Newtonian and/or Non-Newtonian behaviour depending on the volume percentage of particles, temperature and methods used to disperse and stabilize the nanoparticle suspension [41, 77–79]. The effective viscosity of nanofluid increases by increasing concentration of particles and decreases with increase in temperature [14, 41, 78, 80–82]. The effective viscosity of fluid containing a dilute suspension of small particles is given by Einstein's equation. Mooney extended Einstein equation to apply to a suspension of finite concentration . Later Brinkman modified the Einstein equation to more generalized form . However, the experimentally measured nanofluids viscosities deviate from the classical model because these models relate viscosity as a function of volume concentration only and there is no consideration of temperature dependence and particle aggregation . Pak and Cho measured viscosities of the dispersed fluids with γ-Al2O3 and TiO2 particles at a 10% volume concentration and were approximately 200 and 3 times greater than that of water . Wang et al. observed 20 to 30% increase in viscosity of water when 3 vol.% Al2O3 nanoparticles is added to water . Das et al. measured the viscosity of water-based Al2O3 nanofluids at 1 and 4 vol.%. They found that the increase of viscosity with particles concentration but the fluid remains Newtonian in nature . Experimental studies on CNT nanofluid by Ding et al.  found the shear thinning behaviour at low shear rates but slight shear thickening at shear rates greater than 200s-1. Kulkarni et al. investigated the rheological behaviour of copper oxide (CuO) nanoparticles of 29 nm average diameter dispersed in deionized (DI) water over a range of volumetric solids concentrations of 5 to 15% and temperatures varying from 278 to 323 K. These experiments showed that nanofluids exhibited time-independent pseudoplastic and shear-thinning behaviour. The suspension viscosities of nanofluids decrease exponentially with respect to the shear rate . Similarly Namburu et al. showed the non-Newtonian behaviour at sub-zero temperatures below -10°C and Newtonian behaviour above -10°C in SiO2 nanofluid . Chen et al. categorized the rheological behaviour of nanofluids into four groups as dilute nanofluids, semi-dilute nanofluids, semi-concentrated nanofluids, concentrated nanofluids . Xinfang et al. measured the viscosity of Cu-H2O nanofluid by using capillary viscometers and results showed that the temperature and sodium dodecylbenzenesulfonate (SDBS) concentration are the major factors affecting the viscosity of the nano-copper suspensions, while the effect of the mass fraction of Cu on the viscosity is not as obvious as that of the temperature and SDBS dispersant for the mass fraction chosen in the experiment . Recently Masoumi et al. introduces a new theoretical model for the prediction of the effective viscosity of nanofluids based on Brownian motion. This model could calculate the effective viscosity as a function of the temperature, the mean particle diameter, the nanoparticle volume fraction, the nanoparticle density and the base fluid physical properties .
Research work on the specific heat of nanofluids is limited compared to that on thermal conductivity and viscosity. The specific heat of nanofluid depends on the specific heat of base fluid and nanoparticle, volume concentration of nanoparticles, temperature of the fluids and the literature suggests that the specific heat of nanofluid decreases with an increase in the volume concentration and increases with temperature [88–90].
where ϕ is the volume fraction of the nanoparticles, ρp is the density of the nanoparticles and ρw is the density of the base fluid. Sundar et al. estimated the densities of nanofluids at different temperatures. The density was found to decrease with increase in temperature . Similarly Harkirat measured the density of Al2O3 nanoparticles dispersed in water using specific gravity bottles at different ranges of temperature (30 to 90°C) and different concentrations of nanofluids (1 to 4%). He observed that density of nanofluids is higher than the base fluids and increase with increase in volume fraction of nanoparticles from 1 to 4%. The density of nanofluids decreases with increase in temperature upto about 80°C. Beyond this value, densities of 1 to 4% nanofluids remained nearly constant but still were more than that of water .
Surface tension is defined as the force acting over the surface of the liquid per unit length of the surface perpendicular to the force. Surface tension has a significant influence on the boiling process since bubble departure and interfacial equilibrium depends on it . Surface tension of nanofluids prepared by without addition of any surfactant was found to differ minimally whereas addition of surfactant during preparation of nanofluids affect significantly [78, 95, 96]. The surfactant behaves like an interfacial shell between the nanoparticles and base fluids and modifies the surface tension of nanofluids . Surface tension decreases with increases in concentration of nanoparticle and temperature [98–100].
Wetting characteristics of Nanofluids
The presence of nanoparticles affects the spreading and wettability of base fluids because of additional particle-particle, particle-solid and particle-fluid interactions . Two important phenomena for the enhancement of wetting behaviour of nanofluid are (i) solid like ordering of nanoparticles in the vicinity of three-phase contact region and (ii) deposition of nanoparticles during boiling. Simulations study by Boda et al. on hard spheres in a wedge-shaped cell reported formation of new layers of hard spheres between the walls of the wedge . Wasan and Nikolov directly observed the particle-structuring phenomenon in the liquid film-meniscus region by using reflected-light digital video microscopy . The layering arrangement of the particles gives rise to an excess pressure in the film, the structural disjoining pressure which has an oscillatory decay profile with the film thickness. A result of such a structure force is that nano-dispersions could exhibit improved spreading/wetting capabilities at a confined space . The pool boiling studies on nanofluid shows deposition of porous layer of nanoparticle on the heater surface. The reason for this porous layer formation could be microlayer evaporation with subsequent settlement of the nanoparticles initially contained in it. The nanoparticles deposition improves the wettability of the surface considerably .
Boiling heat transfer characteristics of nanofluids
The alteration of thermophysical properties, especially the enhancement of the thermal conductivity, of the nanofluid and different heat transfer mechanisms are expected to have a significant effect on heat transfer characteristics. Xuan and Li  listed the following five reasons for improved heat transfer performance of the fluid by suspending nanophase particles in heating or cooling fluids: (i) the suspended nanoparticles increase the surface area and the heat capacity of the fluid, (ii) the suspended nanoparticles increase the effective (or apparent) thermal conductivity of the fluid, (iii) the interaction and collision among particles, fluid and the flow passage surface are intensified, (iv) the mixing fluctuation and turbulence of the fluid are intensified and (v) the dispersion of nanoparticles flattens the transverse temperature gradient of the fluid. Experiments on two phase (boiling) heat transfer of nanofluid shows different behaviour. Das et al. conducted experiments to study the pool boiling in water-Al2O3 nanofluid with different particle concentration, heater diameter and surface roughness. The results indicate that the nanoparticles have pronounced and significant influence on the boiling process deteriorating the boiling characteristics of the fluid. The deterioration in boiling performance was observed to be more drastic at a higher surface roughness. It has been observed that the shift of the curve to the right is not proportional to the particle concentration and it is strongly dependent on the tube diameter even for the similar values of surface roughness [78, 113]. Zhou observed a reduction in pool boiling heat transfer of nanofluids . Similarly Bang and Chang also observed that the addition of alumina nanoparticles caused a decrease of the pool nucleate boiling heat transfer. The heat transfer coefficient was decreased by increasing the particle concentration. On the other hand, CHF performance has been enhanced to 32 and 13%, respectively, for both horizontal flat surface and vertical flat surface in the pool . You et al. observed the addition of nanoparticles to the water have no significant effect on nucleate pool boiling heat transfer. However, the measured pool boiling curves of nanofluids saturated at 60°C have demonstrated that the CHF increases dramatically (approx. 200% increase) compared to pure water . Similarly pool boiling experiment on water-silica nanofluids by Vassallo et al. observed that no improvement in pool boiling heat transfer but the CHF increased by about three times. They observed the formation of a silica coating over the heater surface . Wen and Ding observed a significant enhancement in the pool boiling heat transfer of alumina nanofluids. The enhancement increases with increasing particle concentration and reaches approximately 40% at a particle loading of 1.25% by weight . Kim et al. showed 200% enhancement of CHF of nanofluids on a bare heater compared to that of pure water by increasing nanoparticle concentration. SEM images of the heater surface taken after pool boiling CHF tests revealed that CHF enhancement of nanofluids was closely related to the surface microstructure and enhanced topography resulting from the deposition of nanoparticles . Kim et al. reported that the formation of the porous nanoparticle layer during the nucleate boiling is a plausible mechanism for enhancement of CHF . The nucleate boiling heat transfer experiments of water-CuO nanoparticles by Liu et al. showed that the both boiling heat transfer coefficient and CHF of the nanofluids increase with the increase of the mass concentration. However, when the concentration (optimum mass concentration) is over 1 wt%, the CHF is basically close to a constant value, and the heat transfer deteriorates gradually. They also found that the boiling heat transfer of the nanofluid on the smooth surface is almost the same with that of water on the smooth surface at atmospheric pressure whereas boiling heat transfer of the nanofluids on the grooved surface increases remarkably . Kathiravan et al. observed the enhancement of heat transfer coefficient during the pool boiling of water-CNT nanofluids of 0.25, 0.5 and 1.0% concentration by volume of CNT by 1.76, 1.203 and 1.20 times greater than that of heat transfer coefficient of water, respectively, at the critical heat flux. They also observed that there is no fouling over the test-section . Another study by Park et al. shows that the pool boiling heat transfer coefficients of the aqueous solutions with CNTs are lower than those of pure water in the entire nucleate boiling regime but the CHF increased up to 200% as compared to that of pure water. They observed the deposition of a thin film of CNTs on the surface and decrease in the contact angle . So, it is clear that the CHF during pool boiling of nanofluids increased even when the pool boiling heat transfer of nanofluid may decrease or remain unchanged.
During quench hardening process, the surface heat transfer conditions between the steel part and the quenchant are the most important factors controlling the microstructural evolution, generation of stresses and distortion . Kobasko showed that very fast and uniform part cooling within the martensitic range actually reduces the probability of part cracking and distortion, while improving the surface hardness and durability of steel parts . The enhanced CHF of the nanofluids during pool boiling revealed that nanofluids may be suitable for cooling at high heat flux applications . According to Kim et al. the use of nanofluids can afford a significant acceleration of quenching by means of premature destabilization of film boiling due to nanoparticle deposition . The quenching of 304 stainless steel probe into different concentration of nanofluids yielded varying peak heat transfer coefficient (HTC) and Grossmann severity of quenching . Jagannath and Prabhu measured the interfacial peak HTC of water was 1280 W/m2 K and the peak HTC decreased from 1400 to 965 W/m2 K with increases in Al2O3 nanoparticle concentration from 0.01 to 4 wt% when copper is quenched . Similarly Babu and Kumar observed that the peak heat flux during quenching in CNT nanofluids increases with an increase in the CNT concentration until 0.50 wt.% and starts decreasing with further increase in the CNT concentration . These results suggest that for the same base fluid there is an optimum level of nanoparticle concentration to enhance/decrease the heat transfer characteristics of nanofluids. The enhancement and deterioration of pool boiling heat transfer of nanofluids could be utilized in quenching heat treatment in two ways either to promote or decrease the rate of heat transfer depending upon the section thickness of the part to be heat treated and the desired microstructure. Hence there is a need for development of nanofluids having (i) high quench severity for enhancement of heat transfer for thick sections with low quench sensitivity and (ii) low cooling severity for thin sections with high quench sensitivity .
Effect of addition of nanoparticles on microstructure and mechanical properties of components
Heat transfer and wetting kinematics are the two important phenomena during quenching that controls the final metallurgical and mechanical properties of the components. Judicious selection of quench medium is critical for obtaining optimum mechanical properties, avoiding quench cracks, minimizing distortion and improving reproducibility in hardening. The addition of nanoparticles to the conventional quenching fluid results in anomalous change in thermo-physical properties of the fluid, enhanced critical heat flux during boiling heat transfer, improved wetting characteristics and improved metallurgical and mechanical properties. By exploiting these potential advantages of nanofluids, preparation of a spectrum of quench media, known as nanoquenchants, with varying cooling severity would be extremely useful for industrial heat treatment.
heat transfer coefficient
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