A Review of Thermal Conductivity Models for Nanofluids. (original) (raw)
Related papers
2012
Nanofluids, containing nanometric metallic or oxide particles, exhibit extraordinarily high thermal conductivity that can be used for enhancing heat transfer performance of conventional systems. This work presents a proposed method for calculating the effective thermal conductivity of nanofluids. The thermal conductivity of nanofluids primarily depends on the properties of base fluids and nanoparticles, the volume fraction of nanoparticles, the interfacial layer, non-uniform sizes of nanoparticles, fractal dimension of particles, Brownian motion and temperature. In the resent study, the impact of non-uniform sizes of nanoparticles and interfacial layer is investigated simultaneously. Hence, this model has the capability of offering both analytical and numerical Predictions. Results of the present model show reasonably good and better agreements with available experimental data.
The present paper discusses the various effects of parameters like particle volume fraction, particle material, particle size, particle shape, particle material and base fluid, temperature, effect of acidity(PH) on thermal conductivity of nanofluids. And also discusses the different mechanisms of heat transfer enhancement like improvement in the thermal conductivity, effect of Brownian motion, thermophoresis, intensification of turbulence, clustering of nano particles. In order to put the nanofluid heat transfer technologies into reality, fundamental studies are greatly needed to understand the physical mechanisms.
Effective thermal conductivity of nanofluids – A new model taking into consideration Brownian motion
In this study, a new analytical model for the effective thermal conductivity of liquids containing dispersed spherical and non-spherical nanometer particles was developed. In addition to heat conduction in the base fluid and the nanoparticles, we also consider convective heat transfer caused by the Brownian motion of the particles. For nanoparticle suspensions, the latter mechanism has significant influence on the effective thermal conductivity, which is reduced compared to a system in which only conduction is considered. The simple model developed allows for the prediction of the effective thermal conductivity of nanofluids as a function of volume fraction, diameter, and shape of the nanoparticles as well as temperature. Due to the inconsistency of experimental data in the literature, the model has been compared with the established Hamilton–Crosser model and other empirical models for the systems aluminum oxide (Al 2 O 3) and titanium dioxide (TiO 2) suspended in water and ethylene glycol. The theoretical estimates show no anomalous enhancement of the effective thermal conductivity and agree very well with the Hamilton–Crosser model within relative deviations of less than 8% for volume fractions of spherical particles up to 0.25. In accordance with the Hamilton–Crosser model for non-spherical particles, our model reveals that a more distinct increase in the enhancement of the effective thermal conductivity can be achieved using non-spherical nanoparticles having a larger volume-specific surface area.
A model for thermal conductivity of nanofluids
Materials Chemistry and Physics, 2010
Various mechanisms and correlations have been developed for prediction of thermal conductivity of nanofluids. In this work, a simple equation has been proposed for prediction of thermal conductivity of all suspensions, including suspensions containing microparticles and nanoparticles. This equation relates thermal conductivity of suspensions to the mean distance between the particles. For ordinary suspensions -suspensions containing micro-or millimeter sized particles -this distance can be equated to the effective diameter; however, for nanofluids, the Brownian motion approach should be used to estimate it. Nevertheless, since all required assumptions for deriving Brownian motion relations do not hold for nanofluids, we assumed this distance can be evaluated using an adjustable coefficient multiplying the mean free path -or distance between particles -calculated by Brownian motion approach. The applicability of the proposed model has been proven by comparing the results with our experimental data.
indian journal of natural sciences,issue 77,april, 2023
A numerical model for The improvement in thermal conductivity in nanofluids was generated by integrating the following: the development of nanoparticles into nanoclusters, the thickness of the nanolayer of liquid, Brownian movement, and the volume component of nanoclusters. The articulation that was made passed muster when measured against the exploratory data obtained from the writing. The model offered the possibility of comprehensively making sense of the enhanced conductivity of nanofluids. [Note: According to the findings of this study, nanoparticles will typically take the form of nanoclusters, and both the volume component of the nanoclusters and the captured liquid within the nanocluster contributed to the overall conductivity of the material. Several variations of cluster formation were investigated, and in general, it was discovered that the utilisation of circular nanocluster models was more successful in accurately predicting the thermal conductivity of nanofluids. When compared to the bunch effect, the contribution of Brownian mobility of nanoparticles to the overall conductivity of nanofluids was seen as extremely important yet relatively minor. The formation of nanoclusters and the thickness of the nanolayer are the most important factors to take into account while attempting to improve the thermal conductivity of nanofluids. When compared to standard cooling solutions, a mixture of the base liquid and nanoparticles that have been formed from nanoclusters is known to provide a more effective cooling arrangement.
Review on Thermal Conductivity of Nanofluids
Among the thermo physical properties of the nano fluids thermal conductivity is the key property which depends on the pertinent parameters of nanoparticles material, volume fraction, size, aspect ratio, base fluid thermo physical properties, temperature, and surfactant. Over last decades, numerous works have reported the higher thermal conductivity of nano fluids than that of the conventional heat transfer fluids with the reason of random motion of nanoparticles and a number of numerical and theoretical models have been proposed. In this article, the current state of knowledge on the several thermal conductivity measurement techniques employed by the researchers along with the factors affecting the thermal conductivity of nano fluids have been presented. This review leads to some directions for future research in nano fluids.
A benchmark study on the thermal conductivity of nanofluids
Journal of Applied Physics, 2009
This article reports on the International Nanofluid Property Benchmark Exercise, or INPBE, in which the thermal conductivity of identical samples of colloidally stable dispersions of nanoparticles or "nanofluids," was measured by over 30 organizations worldwide, using a variety of experimental approaches, including the transient hot wire method, steady-state methods, and optical methods. The nanofluids tested in the exercise were comprised of aqueous and nonaqueous basefluids, metal and metal oxide particles, near-spherical and elongated particles, at low and high particle concentrations. The data analysis reveals that the data from most organizations lie within a relatively narrow band ͑Ϯ10% or less͒ about the sample average with only few outliers. The thermal conductivity of the nanofluids was found to increase with particle concentration and aspect ratio, as expected from classical theory. There are ͑small͒ systematic differences in the absolute values of the nanofluid thermal conductivity among the various experimental approaches; however, such differences tend to disappear when the data are normalized to the measured thermal conductivity of the basefluid. The effective medium theory developed for dispersed particles by Maxwell in 1881 and recently generalized by Nan et al. ͓J. Appl. Phys. 81, 6692 ͑1997͔͒, was found to be in good agreement with the experimental data, suggesting that no anomalous enhancement of thermal conductivity was achieved in the nanofluids tested in this exercise.
Pramana
The effect of diffusive heat conduction and Brownian motion on the enhancement of thermal conductivity in nanofluids is presented here. Al 2 O 3 and TiO 2 nanofluids were prepared at four different wt. fractions of 1%, 0.5%, 0.1% and 0.05% and their thermal conductivity values were measured over temperatures ranging from 25 to 55 • C for every 10 • C interval. The thermal conductivity of nanofluids increased with the increase in concentration and temperature. Diffusive thermal conduction and Brownian motion contribute to thermal conductivity enhancement. However, diffusive heat conduction has major contribution to thermal conductivity enhancement in nanofluids. The thermal boundary resistance was found to be increasing with wt. fraction and decreasing with temperature elevation. Finally, a correlation is presented using group method of data handling (GMDH) neural network to predict the thermal conductivity of nanofluids.