# A new heat propagation velocity prevails over Brownian particle velocities in determining the thermal conductivities of nanofluids

- Kenneth D Kihm
^{1, 3}Email author, - Chan Hee Chon
^{2}, - Joon Sik Lee
^{3}and - Stephen US Choi
^{4}

**6**:361

https://doi.org/10.1186/1556-276X-6-361

© Kihm et al; licensee Springer. 2011

**Received: **4 December 2010

**Accepted: **27 April 2011

**Published: **27 April 2011

## Abstract

An alternative insight is presented concerning heat propagation velocity scales in predicting the effective thermal conductivities of nanofluids. The widely applied Brownian particle velocities in published literature are often found too slow to describe the relatively higher nanofluid conductivities. In contrast, the present model proposes a faster heat transfer velocity at the same order as the speed of sound, rooted in a modified kinetic principle. In addition, this model accounts for both nanoparticle heat dissipation as well as coagulation effects. This novel model of effective thermal conductivities of nanofluids agrees well with an extended range of experimental data.

## Findings on nanofluid thermal conductivity

A nanofluid [1] is defined as a mixture of nanosized particles suspended in liquid as the base fluid. The nanofluid is perceived as an extended scope of earlier efforts to study the effective thermal conductivity of multiphase systems containing microscale particle-embedded solid materials [2–4] and a solid dispersion in liquid [5].

Since the first article on measurements of the enhanced thermal conductivity of nanofluids (suspension of Al_{2}O_{3} and CuO nanoparticles in either water or ethylene glycol) using the transient hot-wire technique was published in 1999 [6], a number of successive measurement studies have supplemented the original findings and extended the parametric variations affecting the level of conductivity enhancement [7–23]. These experimental examinations have revealed the parametric importance of thermal conductivity enhancement, including the volume concentration of nanoparticles and their sizes, clustering or aggregation effect, pH effect, surfactant effect, and the base fluid temperature. As a systematic approach, Chon et al. [24] have constructed an experimentally extrapolated equation that predicts the nanofluid conductivity in terms of the related parameters.

*k*

_{p}embedded in a continuous solid phase with thermal conductivity

*k*

_{BF}. The volume concentration

*f*of the embedded spheres is taken to be sufficiently small, such that the spheres do not interact thermally and the effect of the particle size is assumed negligible. In 1962, Hamilton and Crosser [3] extended Maxwell's model and incorporated a modification for non-spherical particles by the empirical shape factor

*n*.

A number of alternative models have been proposed with the use of the Brownian motion-induced micro-convection in a nanofluid. By adding the second term to the Maxwell model, Xuan et al. [25] proposed a model incorporating the Brownian motion of nanoparticles in 2003. A year later, Jang and Choi [26] introduced the Brownian-motion-driven convection model and attempted to describe the temperature-dependency of nanofluid thermal conductivity. They assumed the Nusselt number (*Nu*) to be the product of Reynolds number (*Re*) and Prandtl number (*Pr*), i.e., *Nu* = *Re*
^{2}
*Pr*
^{2}, based on the postulation of Reynolds number of the order of unity. However, this assumption is invalid because it is incorrect to neglect the first two terms, i.e., lower degree terms of *Re·Pr*, in the expression for the Nusselt number that Acrivos and Taylor [31] have derived for heat transfer from a spherical particle at low values of the Reynolds number.

Kumar et al. [27] also attempted to incorporate the nanoparticle thermal conductivity based on the Brownian velocity. However, their model failed as Keblinski et al. [32] asserted that "the Brownian motion mean free path of a nanoparticle in fluid (by Kumar et al.) is on the order of 1 cm, which is unphysical."

In 2005, Prasher et al. [28] developed a model combining the Maxwell-Garnett model [33] incorporating both the Kapitza resistance effect of particles with the surrounding medium and the effect of the Brownian motion-induced convection. Later, they expanded their theoretical prediction for nanofluid thermal conductivity by adding aggregation conductivity contributions for the convection enhancements [29]. However, they assumed a less justifiable Brownian velocity of nanoparticles as
based on the kinetic theory of *gas*, which is valid just for fine particles suspended in a dilute gas (Boltzmann constant *k*
_{
b
} = 1.3807 × 10^{-23}J/K, the base fluid temperature *T*, the nanoparticle densify *ρ*
_{p}, and its diameter *d*
_{
p
} )--but not quite valid for nanoparticles suspended in liquid. Quite possibly because of this conflict, their model fits only to a subset of experimental data, e.g., agrees fairly well with Al_{2}O_{3} nanofluid data, but fails to fit to CuO nanofluid data.

The effect of the Brownian motion-induced microconvection remains controversial among different research groups. Eapen et al. [34] strongly argued that microconvection around randomly moving nanoparticles does not influence the thermal conductivity of the nanofluid. In 2007, Das group proposed a nanofluid thermal conductivity model based on a cell model [30]. Their cell model tried to explain the nonlinear dependence of thermal conductivity of nanofluids on particle volume fraction. However, their empirical constants were defined only to fit to their experimental data. In fact, their model constants did not show consistency for an identical Al_{2}O_{3} nanofluid.

The kinetic principle well describes the thermal conductivity of gas, as the gas molecules are assumed to be freely moving due to their relatively lean distributions [35]. For liquids, however, their stronger intermolecular forces, primarily because of the higher packing density, make it necessary to modify the kinetic theory. In addition, the molecular collision velocities of gases are too low to explain liquid thermal conductivities that are at least one order of magnitude higher than the gas conductivities. Hence, the thermal conductivities of denser liquids are conjectured to be more properly expressed by the faster sound propagation in the case of liquids, and by the phonon velocity in the case of solids.

In this article, a novel theoretical model describing the nanofluid thermal conductivities, considering all major effective parameters including the size, density and volume concentration of nanoparticles, the fluid temperature and viscosity, and relevant thermal parameters such as thermal conductivity of base fluid and heat capacity of nanoparticles, is proposed and examined for its validity against available experimental data.

## Introduction of heat propagation velocity

_{2}O

_{3}, or CuO, is believed to be attributed to the interaction of nanoparticles with the base fluid molecules. The thermal conductivity of a liquid is given by [36]:

*c*

_{v}are the liquid density and specific heat, respectively,

*u*is sonic velocity in liquid, and

*a*is the molecular travel distance between two successive collisions. Likewise, the thermal conductivity enhancement of a nanofluid can include the thermal properties of nanoparticles (

*ρ*

_{p},

*c*

_{p}), the heat propagation velocity

*V*

_{ht}, which substitutes the sonic velocity, the heat travel distance

*l*

_{ht}, which replaces the collision travel distance

*a*, and additional consideration of the volume fraction of nanoparticles

*f*[14, 35]:

Note that the combined term *V*
_{ht}·*l*
_{ht} relates to the increase of thermal diffusivity of nanofluid as
.

*l*

_{ht}, which is defined as the freely traveled distance of heat energy during the interaction of base fluid molecules and nanoparticles, is shown to be equivalent to the root-mean-square displacement of nanoparticles [25] as:

where *μ* is the dynamic viscosity of the base fluid and *c*
_{1} is a dimensionless proportional constant. In the case of nanofluid, if *l*
_{ht} is assumed to have the same order of magnitude as the mean free path of water molecules, one can estimate *l*
_{ht} ~ 0.170 nm.

The heat propagation velocity can be estimated by examining the order-of-magnitudes of the involved parameters in Equation 2. For example, for 47-nm Al_{2}O_{3} at 1 vol.% concentration (*f*·*ρ*
_{p}·*c*
_{p} ~ 3.2 × 10^{4}), the thermal conductivity enhancement Δ*k*
_{enh} is found to range from 0.025 to 0.100 W/K m [24]. Thus, the heat propagation velocity *V*
_{ht} is estimated to be on the order of 10^{3} m/s. While a more rigorous analysis to determine the heat transfer velocity is yet to be discussed, this estimation is consistent with the conjectures of the characterisitc heat propagation velocity being on the scale of the sound propagation velocity of an order 10^{3} m/s of both in a liquid medium [22] and in a colloidal medium [37, 38].

*V*

_{ht}represents the heat propagation rate by the vibration of base fluid molecules. In a stationary liquid, individual molecules are constantly moving, and their motions are largely confined within a "cage" formed by the closely packed neighboring molecules [36]. This virtual cage is conceived by the energy barrier of height where represents the molar free energy of activation for escaping the cage and denotes the molar Avogadro number. The molecular vibrational frequency

*ν*is given by:

where *k*
_{b} denotes the Boltzmann constant, *h* and *R* are the Planck constant and the specific gas constant, respectively, and *T* is the fluid temperature. The free energy of activation,
, is assumed to be constant for a specified fluid and also assumed to be directly related to the internal energy of vaporization at the normal boiling point [39].

*T*

_{b}. Substituting this into Equation 4, and then multiplied by the the heat propagation length scale λ

_{ ht }, gives an expression for the heat propagation velocity

*V*

_{ ht }as:

The propagation length scale λ_{
ht
}, is calculated based on the assumption that the base fluid moledules and nanoparticles are arranged in a cubic lattice, with a center-to-center spacing given by
, where
is molar mass of the base fluid.

## New model for nanofluid thermal conductivity

*k*

_{eff}as:

Two additional modifications of Equation 6 are implemented. First, the volume fraction *f* is modified to a reduced volume fraction *f*
^{
a
} (*a* < 1) to account for the coagulation of nanoparticles that effectively reduce the original volume fraction [38]. The coagulation becomes more severe to require a smaller exponent *a* with increasing particle concentration because of the decreased inter-particle distance. For example, The surface-to-surface distance of nanoparticles is twice the particle size at 1 vol.%; however, it can decrease to half the particle size at 5 vol.%. Secondly, the effective thermal conductivity of Equation 6 is modified by multiplying the heat capacity ratio of the base fluid to nanoparticles,
. It is known that shorter heat dissipation time from nanoparticles into the base fluid enhances the effective thermal conductivity of nanofluid [41, 42]. The heat dissipation time decreases with increasing heat capacity of the base fluid and decreasing heat capacity of the nanoparticles. In other words, nanoparticles with a smaller heat capacity require shorter heat dissipation time to the base fluid, and this results in greater thermal diffusion and higher effective thermal conductivity. The effective conductivity increases in consistency with the heat capacity ratio
.

where *C* is a modified constant and *c*
_{BF} is the base fluid specific heat. The heat transfer length scale *λ*
_{ht} is difficult to be calculated directly, but may be determined by order analysis and merged into the constant *C*. The exponents *a* and *b* are empirical constants that represent the effect of nanoparticle coagulation and of nanoparticle heat dissipation, respectively. A regression analysis of published experimental data by the authors [24] provides *a* = 0.70, *b* = 1.5, and *C* = 3.58 × 10^{-14} m for the case of Al_{2}O_{3} nanoparticles of three different sizes (11 nm, 47 nm, 150 nm diameters) suspended in water under various experimental conditions of a volume concentration range of 1 to 4 vol.% and a tested temperature range of 21 to 71°C.

_{2}O

_{3}nanoparticles [26–28], (2) the Brownian velocity of the base fluid (water) molecules [26], (3) the heat propagation velocity based on the currently proposed model (Equation 5), (4) the sound velocity in water [43], and (5) phonon velocities for selected solid mediums of αalpha-Fe and silicons [44, 45]. The phonon velocities are expected to be faster than the heat propagation velocity in liquid because of the relatively higher heat conductivities in solid mediums.

Differently defined Brownian velocities and heat propagation velocities, and their magnitudes calculated for the range from 20 to 71°C

Author | Velocity model | Calculated velocity (m/s) |
---|---|---|

Brownian velocity of nanoparticles [26] | 0.055-0.160 | |

Brownian velocity of nanoparticles [27] | 0.0012-0.0035 | |

Brownian velocity of nanoparticles [26] | 0.249-0.270 | |

Brownian velocity of water molecules [26] | 6.710-19.534 | |

Sound propagation velocity in water [45] | 1480-1555 | |

Heat propagation velocity [Present model, Equation 5] | 950-2250 |

Nevertheless, we do not mean that the Brownian motion is not related to the thermal conductivity enhancement. Nor do we mean that Brownian convection is not significant. What we imply is that the assumption in [26], i.e., the Nusselt number can be expressed as *Nu* = *Re*
^{2}
*Pr*
^{2}, is invalid because it is incorrect to neglect the first two terms, i.e., lower degree terms of *Re·Pr*, in the expression for the Nusselt number that Acrivos and Taylor [31] have derived.

In addition, in order to have significant convection effect by wavelength mode of long molecular motion, the bulk fluid needs externally imposed gradients such as pressure, gravity or temperature. However, a nanofluid has quiescent condition, which cannot support any convection [34, 46]. The Brownian velocity, as shown in Figure 1, is several orders of magnitude lower than the required velocity scale of 10^{3} in modeling nanofluid conductivity enhancement.

_{2}O

_{3}[24] and CuO [present work]. For all three nanofluids with 47-nm Al

_{2}O

_{3}at 1 and 4%, and 30-nm CuO at 1%, Xuan et al. [25] overestimated the Maxwell's model [2] for nanofluids. Jang and Choi's model [26] shows proximity with experimental data for up to about 50°C for 1 vol.% Al

_{2}O

_{3}and 40°C for 4 vol.% Al

_{2}O

_{3}, but substantially deviates thereafter. This deviation beyond a certain temperature is believed to be attributed to their incorrect postulation implied in determining the Nusselt number, as previously noted. For the CuO nanofluid, their model shows large discrepancies throughout the tested temperature range. Additionally, the model by Kumar et al. [27] wrongly postulates the mean free path of the base fluid, as pointed out by Keblinski et al. [32], and completely fails to predict nanofuidic thermal conductivities for all presently tested conditions.

Prasher et al. [28, 29] show fairly good agreement with the experiments for the Al_{2}O_{3} nanofluid, as shown in Figure 2a, c. However, for the CuO nanofluid (Figure 2b), their model underestimates the corresponding experimental data [24]. When completely different model parameters were imposed for CuO from that of Al_{2}O_{3}, the model agrees well with the data; however, the model then lacks comprehensiveness because different model parameters need to be determined for different types of nanofluids. Finally, Patel et al. [30] agrees fairly well with the experimental data at higher concentrations (Figure 2c) but overestimate the thermal conductivities for low volume concentrations (Figure 2a, b).

_{2}O

_{3}and CuO nanofluids from different leading groups [6, 10, 24].

## Concluding remarks

can more accurately and comprehensively describe the effective thermal conductivities of nanofluids with different types (Al_{2}O_{3} and CuO nanofluids) and sizes of nanoparticles (ranging from 10 to 150 nm), for a relatively wider range of temperatures in comparison with the most popular range of up to 50°C of published studies.

As similar conceptual studies, the recent thermal-wave [47] and the dual-phase lagging heat conductions [48] are attracted by researchers because both models can explain the high-rate heat flux in microscale and also can be applied to the thermal conductivity of nanofluid. Thermal-wave and dual-phase lagging heat conduction are developed analytically, however the new model is approached by physical manner and it considers more practical factors such as particle coagulation effect and heat dissipation effect. Therefore our new model will be bridging the practical thermal conductivity enhancement of nanofluid and theoretical concept of the high-rate heat flux of nanofluid such as thermal-wave dual-phase lagging heat conduction of nanofluid.

## Declarations

### Acknowledgements

This work was supported by the U.S. Department of Energy, Office of Basic Energy Science under Contract No. DE-FG02-05ER46182 to the University of Tennessee (KK, CC, SC) as well as by the WCU (World Class University) Program at Seoul National University through the Korea Research Foundation funded by the Ministry of Education, Science and Technology under Contract No. R31-2008-000-10083-0 (KK, JL).

## Authors’ Affiliations

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