Elsevier

Advanced Powder Technology

Volume 29, Issue 10, October 2018, Pages 2434-2439
Advanced Powder Technology

Original Research Paper
Modeling and simulations of nanofluids using classical molecular dynamics: Particle size and temperature effects on thermal conductivity

https://doi.org/10.1016/j.apt.2018.06.023Get rights and content

Highlights

  • The efficiency of nanofluids is improved when the particle size and temperature increase.

  • The thermal conductivity of nanofluid increases with the increases of the volume fraction.

  • The thermal conductivity of nanofluid increases with the augmentation of temperature.

  • Thermal conductivity enhancement due to the fluid condensation around the nanoparticle.

  • Thermal conductivity enhancement due to the decreasing specific surface of the solid–liquid.

Abstract

We use molecular dynamics simulations to investigate the thermal conductivity of argon-based nanofluid with copper nanoparticles through the Green-Kubo formalism. To describe the interaction between argon-argon atoms, we used the well-known Lennard-Jones (L-J) potential, while the copper–copper interactions are modeled using the embedded atom method (EAM) potential that takes the metallic bonding into account. The thermal conductivity of the pure argon liquid obtained in the present simulation agreed with available experimental results. In the case of nanofluid, our simulation predicted thermal conductivity values larger than those found by the existing analytical models, but in a good accordance with experimental results. This implies that our simulation is more adequate, to describe the thermal conductivity of nanofluids than the previous analytical models. The efficiency of nanofluids is improved and the thermal conductivity enhancement is appeared when the particle size and temperature increase.

Introduction

Nanofluids are a new class of Nano technological materials, which are prepared by suspension of nanoparticles, nanotubes, or nanofibers with length on the order of 1–50 nm in conventional fluids [1], [2], [3]. Furthermore, many works [4], [5] have shown that nanofluids exhibit very high thermal conductivity even for low concentrations of suspended nanoparticles. In this context, many experimental and theoretical studies on the thermal conductivity of solid-particles suspensions have been carried out since the classic works of Maxwell [6]. However, these works have been largely restricted to suspensions with millimeter- or micronized particles. The proposed approach in engineering fluids with heat transfer properties, based on the emerging field of nanotechnology, has recently been the subject of many researchers. Lee et al. [4] have measured thermal conductivity of nanofluids containing (CuO) and (Al2O) nanoparticles in ethylene glycol and water, using the volume fractions of 1 to 6% to obtain a significant enhancement of thermal conductivity, they found that not only particle shape, but size is deemed to be controlling in improving the thermal conductivity of nanofluids. Another, work carried out by Choi et al. [7], [8], has reported that a small amount of copper nanoparticles or carbon nanotubes dispersed in oil and/or ethylene glycol increases the inherently poor thermal conductivity of the liquid.

So, to understand the mechanisms responsible for the enhancement of thermal conductivity in nanofluids, Keblinski et al. [13] have suggested four potential mechanisms: effects of nanoparticles clustering, molecular-level layering of the liquid at the nanoparticle surface, nature of heat transport in nanoparticles, and Brownian motion of nanoparticles. To specify these mechanisms, the modeling and simulation techniques can be investigated to understand many properties at the microscopic scale. Molecular dynamics (MD) simulations are generally applied in many areas to obtain structural and thermal properties with very good accuracy [9], [10], [11], [12]. In this context, Brownian dynamics simulations, based on equilibrium Green–Kubo approach [14], have been interested in calculating the effective thermal conductivity of nanofluids and confirmed the success of this approach to reproduce the experimental results [15]. Barrat and Vladkov [16] have used molecular dynamics simulation to calculate thermal conductivity of nanofluids through the classical Maxwell Garnet equation model [17], they found that there is an absence of collective effects. Xue et al. [18] have studied the effect of the liquid–solid interface on the interfacial thermal resistance using non-equilibrium molecular dyanmics simulations and they concluded that the simple monoatomic liquid around the solid particle do not have any influence on the thermal transport neither normal nor parallel to the surface.

In this work, we have investigated the thermal conductivity of a nanofluid (Ar-Cu) consisting of argon-based fluid and suspended copper nanoparticles using the Green–Kubo formalism to compute the thermal conductivity of Ar-Cu Nanofluid using equilibrium molecular dynamics simulation. So, to show the volume fraction and temperature effect on thermal conductivity, we investigated the thermal conductivity corresponding to several volume fractions at the same temperature around 86 K, while, the effect of temperature has been shown within a temperature range from 86 K to 102 K. The obtained results show that thermal conductivity of nanofluids depends on both temperature and volume fraction of nanoparticles.

The remainder of this paper is structured as follows. Section 2 presents a brief description of the interatomic potential used together with the simulation technique and explains the methods for preparing the nanofluids and for determining the thermal conductivity. Section 3, presents and discusses the detailed results about the fraction volume and the temperature effects on the thermal conductivity. The last section summarizes the findings of this work.

Section snippets

Interatomic potentials

In the present work, we have used MD simulations to investigate Argon-Copper nanofluids, where the nanoparticles are suspended in the liquid argon. The interatomic potentials between (Ar-Cu) and (Ar-Ar) are modeled by the L-J potential Eq. (1), which is sufficient and physically meaningful for many relevant applications [19], [20], [21].Urij=4εσrij12-σrij6where ε is the depth of the L-J potential well,σ is the finite distance at which the inter-particle L-J potential is zero, and rij is the

Results and discussions

Nanofluids exhibit an alluring challenge, because it is hard from any theoretical point of view to formulate a theory that allows predicting the performance of a nanofluid. Therefore, we have treated this issue like a multi-component fluid [38]. A nanofluid is a dynamic system because the nanoparticles are in motion even if the fluid is stationary. Previous works [6], [39], have investigated the thermal conductivity of nanofluids using effective medium theory models, it has been found that the

Conclusions

Thermal conductivity of a copper-argon nanofluid has been studied by means of molecular dynamics simulations. We computed thermal conductivities using the Green–Kubo formalism. To model the Ar-Ar and Ar-Cu interactions, we used a Lennard–Jones potential, whereas the Cu-Cu interaction is modeled using the embedded atom method (EAM). The results showed that the relative thermal conductivity enhancement of the nanofluid increases with the augmentation of the volume fraction from 0.19% to 7.66% due

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