Using molecular dynamics simulations to investigate the effect of the interfacial nanolayer structure on enhancing the viscosity and thermal conductivity of nanofluids

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Abstract

To understand the remarkable enhancements in viscosity and thermal conductivity of nanofluids, a molecular dynamics (MD) simulation approach was used to investigate the structure and thickness of the interfacial layer and obtain values for radial distribution function, g(r), viscosity, and thermal conductivity of Cu/water nanofluids with volume fractions and temperatures ranging from 0.5–2 vol% and 293–333 K, respectively. The results revealed that value of g(r) was much higher in the first molecular nanolayer compared to the second layer, indicating that greater numbers of water molecules are present in the first layer, which reduces contact thermal resistance and improves thermal conductivity of nanofluids. Moreover, while value of g(r) increased with increasing Cu volume fraction and decreased with temperature, nanolayer thickness was unchanged with temperature at each volume fraction, indicating that intermolecular forces intensify in the first nanolayer. Macroscopically, viscosity and thermal conductivity of the nanofluids increased at higher volume fractions, showing increases of 71.7% and 75% for viscosity, and 41.7% and 53.3% for thermal conductivity at 0.5 and 2 vol%, respectively, compared to water at 333 K. These results, although obtained for Cu/water systems, can provide useful insights into structural and transport properties of nanofluids in general.

Introduction

Owing to their outstanding thermophysical properties, nanofluids have attracted significant research interest in recent years, and are widely used in diverse applications such as biomedical engineering, heat exchangers, photocatalysts, photovoltaics, energy conversion systems, and electronic cooling [1,2]. Compared to conventional fluids such as water, ethylene glycol, and mineral oils, nanofluids typically exhibit better heat transfer performance, enabling them to meet the requirements for high heat dissipation in applications such as integrated circuits and modern microprocessor chips [3]. As an example, adding 0.1 wt% of GnPs and TiO2 nanoparticles into water and ethylene glycol can increase the thermal conductivity up to 23.74% [2]. Generally, nanofluids not only effectively improve the thermal conductivity of systems, but also significantly increase the viscosity. The thermal conductivity of a material governs the rate of heat transfer, while the viscosity is directly associated with the friction factor and pressure drop, and consequently determines the pumping power in a system [4]. Thus, the practical use of nanofluids in any thermal management system involves a tradeoff between their high thermal conductivity on the one hand, and increased viscosity on the other; hence, gaining an understanding of the mechanisms of viscosity and thermal conductivity enhancement in nanofluids is a prerequisite to their use in real-world applications.

The density, specific heat, viscosity, and thermal conductivity of nanofluids are essential transport parameters. Of these, the first two are relatively straightforward to calculate analytically, using conventional mixing theory based on the mass balance and thermal equilibrium. However, the viscosity and thermal conductivity are more complicated to calculate, being affected by factors such as the volume fraction, temperature, particle shape, type and size [5], which makes them difficult to predict using a unified theoretical model or empirical correlations. Moreover, the microscopic interactions between the nanoparticles and base fluid molecules (such as the Van der Waals force, Brownian motion, and micro-convection) cannot be neglected in a nanofluidic system, although such micro-forces are not usually measured or observed in macro-scale experiments.

Investigating the mechanisms of the viscosity and thermal conductivity enhancement at the nanoscale can be difficult due to the lack of proper visual equipment. Computer simulations are currently a useful tool for understanding such physical phenomena. Of the many molecular-level simulation methods, such as molecular dynamics (MD), the thermal diffusion equation, and the non-equilibrium Green's function, the first is the most commonly-used method to simulate the thermal behavior of nanofluids [6]. The MD simulations can enable precise prediction of a set of atomic positions and motion at any time through the application of Newton's equations of motion.

In recent years, researchers have increasingly applied MD simulations to investigate the mechanisms by which nanofluids work. Keblinski et al. [7] pointed out that the theromphysical properties of nanofluids are mainly determined by four microscopic mechanisms, namely the Brownian motion, nature of heat transport in nanoparticles, nanolayer at the solid-liquid interface, and nanoparticle clusters. Subsequently, Jiang et al. [8] pointed out that the thermal conductivity of nanofluids not only depends on the ratio of the thermal conductivities of nanoparticles and base fluids, but is also affected by the thickness of the interfacial nanolayer. Heyhat et al. [9] also asserted that the interfacial nanolayer is one of the most important microscopic factors in enhancing thermophysical properties. In their simulations, the effect of the nanolayer on the density and viscosity of Ag/water nanofluids was investigated, with the results showing that the enhancement in the density and viscosity of nanofluids are caused by the contraction of the base fluid in the ordered liquid nanolayer. Essajai et al. [10] investigated the effect of the gold nanoparticle type on the thermal conductivity of Au/Ar nanofluids. Their results revealed that the thermal conductivity of 1-D networks of Au nanoparticles is much higher than that of spherical Au nanoparticles. However, the underlying phenomenon behind this was not discussed. Mitiche et al. [11] investigated the effect of the interfacial nanolayer in enhancing the thermal conductivity of Cu/Ar nanofluids, asserting that the increase in vibrational mean-free paths is the main reason for the thermal conductivity enhancement. Chen et al. [12] also investigated the enhanced thermal conductivity of Cu/Ar nanofluids using a reverse non-equilibrium MD simulation method, finding that the crystal-like microstructure, high energy transfer, and temperature gradient were the main contributing factors towards the nanofluid properties. The viscosity of Cu/Ar nanofluids was investigated by Zeroual et al. [13], with the higher density of the ordered liquid layer found to be the main cause in enhancing the viscosity compared to the base fluid.

The aforementioned studies featured investigations into the effects of the nanolayer structure and its thickness on the both thermal conductivity and viscosity of nanofluids, as the viscosity is equally important to the performance of thermal management systems. Moreover, in many of these studies, liquid argon was chosen as base fluid due to its simple interatomic potential, which reduces the computation time of MD simulations [14]. However, as liquid argon is typically not used as a base fluid in industry, more research is needed into water-based nanofluids as these are directly relevant to industrial applications.

Against this backdrop, therefore, the present study investigated the effect of the interfacial structure and its thickness on the viscosity and thermal conductivity of Cu/water nanofluids using a MD simulation approach. The study is structured into three parts: first, thermodynamic equilibrium of the nanofluid system and model validation were conducted; second, the thermal conductivity and viscosity were investigated as functions of volume fraction and temperature in comparison with existing models; finally, the nanoparticle trajectory and the structure and thickness of the nanolayer were analyzed to explain the mechanisms behind the remarkable enhancement in thermophysical properties. The results from this study are expected to be applicable not just to Cu/water systems but to other types of nanofluids as well.

Section snippets

Mathematical model of nanofluid

The MD simulation domain was cubical, with dimensions of 10 nm × 10 nm × 10 nm. Cu atoms were arranged in the center, packed in a face-centered cubic (FCC) lattice with a lattice constant of 0.36 nm to form spherical Cu nanoparticles with a diameter of 2 nm. The SPC/E model, in which both hydrogen and oxygen atoms are considered rigid particles, was used to calculate the water-water potentials [15]. The parameters used in the SPC/E model are listed in Table 1.

Four different scenarios were

Thermodynamic equilibrium and validation of model

Prior to the determination of μ and λ for the Cu/water nanofluids system, thermodynamic equilibrium of the nanofluid system was conducted to guarantee stable simulation conditions and the simulation model was validated using reference data. The achievement of system equilibrium can be monitored from the kinetic energy, potential energy, total energy, and temperature.

Fig. 2 shows the time-evolution of the kinetic, potential, and total energy of the system. The total energy is the sum of the

Conclusions

In this study, the underlying mechanisms behind the increased viscosity and thermal conductivity of Cu/water nanofluids compared to the base fluid were investigated at the molecular level using an equilibrium molecular dynamics simulation approach. To this end, the nanolayer structure and thickness and the radial distribution function, g(r), were obtained. The main conclusions which may be drawn from the results of this study are as follows:

  • 1.

    The strength of the first molecular nanolayer is the

Author contributions section

Yanhua Li contributed to methodology

Yuling Zhai contributed to the formal analysis and writing

Mingyan Ma and Zihao Xuan contributed to validation

Hua Wang contributed to writing - review & editing.

Declaration of Competing Interest

None.

Acknowledgments

This project is supported by the National Natural Science Foundation of China (No. 51806090), and the Basic Research Project of Yunnan Province (No. 202001AT070081).

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