Heat Conduction In Nanofluids Research Articles

Micro-mechanism Analysis of Heat conductivity Enhancement of Argon-based Copper Nanofluids

Enhancing heat transfer is the consistent goal in current industrial production. Nanofluids have greater advantages than traditional fluids.In this in the passage, MDS was used to research the mechanism of improving the heat conductivity of Cu-Ar nanofluids compared with conventional fluids. The effects of nanoparticles with various volume fractions (1% - 3%) and different sizes (r = 1.03 nm, 1.26 nm, 1.50 nm) on the heat conductivity of nanofluids were mainly discussed. The results showed that the insertion of Cu nanoparticles increased the heat conductivity of the base fluid in studied range. The heat conductivity of the nanofluid was proportional to the volume fraction of the nanoparticles, and the maximum increase was 29.1%. Inversely ratio to the particle size, the maximum increase was 15.3%. The microscopic mechanism of the systems was statistically analyzed by the radial distribution function. It was found that the addition of nanoparticles caused the change in the atomic arrangement of the nanofluids, which made the nanofluids exhibit a microstructure similar to that of the solid. This is why the heat transfer efficiency of nanofluids is higher.

Heat Conduction and Microconvection in Nanofluids: Comparison between Theoretical Models and Experimental Results

A nanofluid is a suspension consisting of a uniform distribution of nanoparticles in a base fluid, generally a liquid. Nanofluid can be used as a working fluid in heat exchangers to dissipate heat in the automotive, solar, aviation, aerospace industries. There are numerous physical phenomena that affect heat conduction in nanofluids: clusters, the formation of adsorbate nanolayers, scattering of phonons at the solid–liquid interface, Brownian motion of the base fluid and thermophoresis in the nanofluids. The predominance of one physical phenomenon over another depends on various parameters, such as temperature, size and volume fraction of the nanoparticles. Therefore, it is very difficult to develop a theoretical model for estimating the effective thermal conductivity of nanofluids that considers all these phenomena and is accurate for each value of the influencing parameters. The aim of this study is to promote a way to find the conditions (temperature, volume fraction) under which certain phenomena prevail over others in order to obtain a quantitative tool for the selection of the theoretical model to be used. For this purpose, two sets (SET-I, SET-II) of experimental data were analyzed; one was obtained from the literature, and the other was obtained through experimental tests. Different theoretical models, each considering some physical phenomena and neglecting others, were used to explain the experimental results. The results of the paper show that clusters, the formation of the adsorbate nanolayer and the scattering of phonons at the solid–liquid interface are the main phenomena to be considered when φ = 1 ÷ 3%. Instead, at a temperature of 50 °C and in the volume fraction range (0.04–0.22%), microconvection prevails over other phenomena.

The Role of Particle Interaction in the Cluster Model of Heat Conductivity of Nanofluids

Analytical dependences of the effective heat conductivity coefficient of an individual cluster and cluster nanofluid are obtained on the basis of classical equations of continuum mechanics with allowance for the interaction of temperature fields of spherical particles inside a cluster. The calculated dependences of the heat-conductivity coefficient of the cluster nanofluid agree with the corresponding experimental data.

An investigation into the effect of nanoclusters growth on perikinetic heat conduction mechanism in an oxide based nanofluid

Thermal conductivity enhancement of nanofluids primarily depends upon the factors affecting their heat transport mechanisms. More insight into the phenomenon of thermal heat conduction in nanofluids has been presented in this paper. The growth of nanoclusters in the water (DI) based nanofluid containing γ-Al2O3 (size 25–30nm), has been investigated and studied. A comprehensive report on the suspension size distribution of the nanoparticles at different pH values, their zeta potential along with the stability ratio of suspensions, has been presented. A quantitative analysis on the thermal conductivity enhancement, along with an investigation on the role of nanoparticles present in the form of dead ends and backbone chains in an aggregate has been put forward. Their individual effect under perikinetic heat conduction conditions has been incorporated to address the thermal conductivity enhancements of Al2O3-H2O nanofluid. Moreover, the effect of the basefluid layering around the nanoparticles in an aggregate has been highlighted. The possible and the different governing static/structural models which are capable to predict the nanocluster based thermal conductivity enhancements in nanofluids, have been investigated and modified. This has been done to include the effect of liquid layering on the thermal conductivity of the water present in an aggregate compared to the bulk water present outside to a nanocluster or an aggregate. While, modifying the equations and models, the focus has been laid down to the average hydrodynamic size of the nanoparticles in a suspension than the individual particle size. The modified model gives a fairly good prediction of the thermal conductivity enhancement of the nanofluid under investigation. The experimental and theoretical results of thermal conductivity of Al2O3 nanofluid have been correlated and found to be within the accuracy level of ±1 to ±2.3% at 0.05% volume fraction. The error analysis shows that the developed model is enough capable to predict the thermal conductivity enhancement with an average error of ±2.6% compared to the predictions made from the other existing theoretical models, where the error involved is found to be varying from ±3 to ±9% at volume fraction ranging from 0.01 to 0.12%.

Evaluation of clustering role versus Brownian motion effect on the heat conduction in nanofluids: A novel approach

In this study, the temperature and viscosity-dependent methods were used to identify the main heat conduction mechanism in nanofluids. Three sets of experiments were conducted to investigate the effects of Brownian motion and aggregation. Image processing approach was used to identify detailed configurations of different nanofluids microstructures. The thermal conductivity of the nanofluids was measured with respect to the dynamic viscosity in the temperature range between 0 and 55°C. The results clearly indicated that the nanoparticle Brownian motion did not play a significant role in heat conduction of nanofluids, which was also supported by the observation that a more viscous sample rendered a higher thermal conductivity. Moreover, the microscopic pictures and the differences in the viscosity between theoretical and experimental values suggested the major role of particle aggregation and clustering.

Percolating micro-structures as a key-role of heat conduction mechanism in nanofluids

The primary working mechanism behind nanofluids thermal conductivity can be distinguished as one of the most controversial issues of nanofluids. Between several theories proposed in literature, nanoparticles’ clustering is the sole mechanism that can be observed even at the micro-scale level. Several parameters including nanofluid preparation method, particles concentration, particles morphology, temperature, and elapsed time are experimentally altered to investigate the role of nanoparticles micro-clusters in nanofluids thermal conductivity. It is observed that a minor variation in the altering parameters, would lead a remarkable change in the micro-clusters configuration and subsequently, in the thermal conductivity of nanofluids. In this paper, it is focused on this phenomenon by introducing a new application of image processing as a tool to study the morphological characteristics of micron-sized clusters. Through this approach, it is demonstrated that higher thermal conductivity is obtained when particles micro-clusters are well percolated all over the base fluid. As a result, well-diffused percolating micro-structures are known to have a significant role in heat conduction of nanofluids.

An analytical model for the determination of effective heat conduction of nanofluids

In this study, we use Maxwell’s homogenization method followed by mesoscopic analysis to study heat transfer in nanofluids. It is explored how microconvection resulting from the Brownian motion of nanoparticles and the nanolayer formation around nanoparticles are impacting the effective thermal conductivity of nanofluids. The heat transfer within the nanolayer is addressed by direct solution of the Boltzmann transport equation. The results obtained from experimental observations agree with model predictions.

Heat conduction mechanism in nanofluids

Nanofluids are produced by dispersing nanoparticles in basefluid. Given its superior thermo-physical properties, nanofluids are gaining increasing attention and are showing promising potential in various applications. Numerous studies have been conducted in the past decade to experimentally and theoretically investigate thermal conductivity. The experimental finding is briefly summarized in this study; however, we do not intend to present a systematic summary of the available references from the literature. The primary objective of this study is to review and summarize the most debated mechanisms for heat conduction in nanofluids, such as the effects of a nanolayer, the Brownian motion of nanoparticles and aggregation, as well as induced convection. Finally, at a low concentration of nanoparticles, nanoconvection is the leading contributor to thermal conductivity enhancement, whereas at a higher concentration, the natural thermal transport along the backbone would aggregate, and the effects of the nanolayer would become significant and become ineligible.

An overview on nanofluids and applications

The purpose of this review paper is to introduce some progresses in nanofluid technology and applications. The enhancement mechanism of heat conduction of nanofluids and heat and mass heat transport inside nanofluids as well as controlling methods are outlined. Also, a series of possible applications of nanofluids are discussed. The effects of properties, fractions and sizes of nanoparticles on flow and heat transport processes of nanofluids are discussed. The roles of the suspension stability and temperature in affecting thermal conductivity of nanofluids are analyzed. Furthermore, challenges of improving nanofluid technology and putting it in more and more applications are involved.

Results of experimental investigations on the heat conductivity of nanofluids based on diathermic oil for high temperature applications

The work reported in this paper shows the experimental results from a study on diathermic oil based nanofluids. Diathermic oil finds application in renewable energy, cogeneration and cooling systems. For example, it is used in solar thermodynamic or biomass plants, where high efficiency, compact volumes and high energy fluxes are required. Besides diathermic oil is very important in those applications where high temperatures are reached or where the use of water or vapor is not suitable. Therefore an improvement of diathermic oil thermo-physical properties, by using of nanoparticles, can increase the performance of the systems.In literature there are not many experimental data on diathermic oil based nanofluids because many experimental campaigns are focused on water nanofluids. Samples of nanofluids, with nanoparticles of CuO, Al2O3, ZnO and Cu, having different shapes and concentrations varying from 0.0% up to 3.0%, have been produced and their thermal conductivity has been measured by means of hot-wire technique, according to the standard ASTM D 2717-95. Measurements were carried out to investigate the effects of volume fraction, particle size of nanoparticles on the thermal conductivity of the nanofluid. The effect of temperature has been also investigated in the range 20–60°C. A dependence was observed on the measured parameters and the results showed that the heat transfer performance of diathermic oil enhances more than water with the same nanoparticles.

Erratum: “Review of Heat Conduction in Nanofluids”[Journal of Heat Transfer, 2011, 133(4), p. 040801

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Heat conduction in nanofluids: Structure–property correlation

We examine numerically the effects of particle-fluid thermal conductivity ratio, particle volume fraction, and particle morphology on nanofluids effective thermal conductivity and phase lags of heat flux and temperature gradient, for six types of nanofluids containing sphere, cube, hollow sphere, hollow cube, slab-cross and column-cross nanoparticles, respectively. The particle’s radius of gyration and the non-dimensional particle-fluid interfacial area are found to be two characteristic parameters for the effect of particles’ geometrical structure on the effective thermal conductivity. The nanoparticles with larger values of these two parameters can change fluid conductivity more significantly. Due to the enhanced particle-fluid interfacial heat transfer, the nanofluid effective thermal conductivity can practically reach the Hashin–Shtrikman bounds when the particle-phase connects to form a network and separates the base fluid into a dispersed phase. The particle aggregation can effectively affect the effective thermal conductivity when the separation distance among particles is smaller than about one fifth of the particles’ dimension. For the nanofluids considered in the present work, the phase lags of heat flux and temperature gradient scale with the square of particle dimension and range from 10 −11 s to 10 −7 s; the effect of cross-coupling between the heat conduction in the fluid and particle phases is weak; the phase lag of temperature gradient is larger than that of heat flux such that the heat conduction in them is diffusion-dominant and their effective thermal conductivity can be well predicted by the predictive models developed in the present work based on the classical diffusion theory for two-phase systems.

Toward nanofluids of ultra-high thermal conductivity

The assessment of proposed origins for thermal conductivity enhancement in nanofluids signifies the importance of particle morphology and coupled transport in determining nanofluid heat conduction and thermal conductivity. The success of developing nanofluids of superior conductivity depends thus very much on our understanding and manipulation of the morphology and the coupled transport. Nanofluids with conductivity of upper Hashin-Shtrikman (H-S) bound can be obtained by manipulating particles into an interconnected configuration that disperses the base fluid and thus significantly enhancing the particle-fluid interfacial energy transport. Nanofluids with conductivity higher than the upper H-S bound could also be developed by manipulating the coupled transport among various transport processes, and thus the nature of heat conduction in nanofluids. While the direct contributions of ordered liquid layer and particle Brownian motion to the nanofluid conductivity are negligible, their indirect effects can be significant via their influence on the particle morphology and/or the coupled transport.

Review of Heat Conduction in Nanofluids

Abstract Nanofluids—fluid suspensions of nanometer-sized particles—are a very important area of emerging technology and are playing an increasingly important role in the continuing advances of nanotechnology and biotechnology worldwide. They have enormously exciting potential applications and may revolutionize the field of heat transfer. This review is on the advances in our understanding of heat-conduction process in nanofluids. The emphasis centers on the thermal conductivity of nanofluids: its experimental data, proposed mechanisms responsible for its enhancement, and its predicting models. A relatively intensified effort has been made on determining thermal conductivity of nanofluids from experiments. While the detailed microstructure-conductivity relationship is still unknown, the data from these experiments have enabled some trends to be identified. Suggested microscopic reasons for the experimental finding of significant conductivity enhancement include the nanoparticle Brownian motion, the Brownian-motion-induced convection, the liquid layering at the liquid-particle interface, and the nanoparticle cluster/aggregate. Although there is a lack of agreement regarding the role of the first three effects, the last effect is generally accepted to be responsible for the reported conductivity enhancement. The available models of predicting conductivity of nanofluids all involve some empirical parameters that negate their predicting ability and application. The recently developed first-principles theory of thermal waves offers not only a macroscopic reason for experimental observations but also a model governing the microstructure-conductivity relationship without involving any empirical parameter.

Erratum to: Heat Conductivity of Nanofluids Based on Al2O3, SiO2, and TiO2

Erratum to: Heat Conductivity of Nanofluids Based on Al2O3, SiO2, and TiO2

Heat conductivity of nanofluids based on Al2O3, SiO2, and TiO2

Heat conductivity of nanofluids based on Al2O3, SiO2, and TiO2

MICROSTRUCTURAL EFFECTS ON MACROSCALE THERMAL PROPERTIES IN NANOFLUIDS

The recent first-principle model shows that heat conduction in nanofluids can be diffusion-dominant or thermal-wave-dominant depending on their microscale physics (structures, properties and activities). As the first attempt of quantifying when and to what extent thermal waves become important, we numerically examine effects of particle–fluid conductivity ratio, particle shape, volume fraction and nondimensional particle–fluid interfacial area in the unit-cell on macroscale thermal properties for nanofluids consisting of in-line arrays of perfectly dispersed two-dimensional circular, square and hollow particles, respectively. In simple and perfectly dispersed nanofluids, the heat conduction is diffusion-dominant so the effective thermal conductivity can be predicted adequately by the mixture rule with the effect of particle shape and particle–fluid conductivity ratio incorporated into its empirical parameter. Thermal waves appear more likely at smaller particle–fluid conductivity ratio (< 1) and lower particle-volume-fraction, which agrees with the experimentally observed significant conductivity enhancement in the oil-in-water emulsion. The computed thermal conductivity predicts some experimental data in the literature very well and shows the sensitivity to the nondimensional particle–fluid interfacial area in the unit-cell.

The mechanism of heat transfer in nanofluids: state of the art (review). Part 1. Synthesis and properties of nanofluids

Here is the review of experimental and theoretical results on the mechanism of heat transfer in nanofluids. A wide scope of problems related to the technology of nanofluid production, experimental equipment, and features of measurement methods is considered. Experimental data on heat conductivity of nanofluids with different concentrations, sizes, and material of nanoparticles are presented. Results on forced and free convection in laminar, and turbulent flows are analyzed. The available models of physical mechanisms of heat transfer intensification and suppression in nanofluids are presented. There are significant divergences in data of different researchers; possible reasons for this divergence are analyzed.

Nanofluids: Synthesis, Heat Conduction, and Extension

We synthesize eight kinds of nanofluids with controllable microstructures by a chemical solution method (CSM) and develop a theory of macroscale heat conduction in nanofluids. By the CSM, we can easily vary and manipulate nanofluid microstructures through adjusting synthesis parameters. Our theory shows that heat conduction in nanofluids is of a dual-phase-lagging type instead of the postulated and commonly used Fourier heat conduction. Due to the coupled conduction of the two phases, thermal waves and possibly resonance may appear in nanofluid heat conduction. Such waves and resonance are responsible for the conductivity enhancement. Our theory also generalizes nanofluids into thermal-wave fluids in which heat conduction can support thermal waves. We emulsify olive oil into distilled water to form a new type of thermal-wave fluids that can support much stronger thermal waves and resonance than all reported nanofluids, and consequently extraordinary water conductivity enhancement (up to 153.3%) by adding some olive oil that has a much lower conductivity than water.

Self-consistent fluctuating hydrodynamics simulations of thermal transport in nanoparticle suspensions

We report on the mesoscopic simulation of heat conduction in nanoparticle suspensions (nanofluids) by using the energy-conserving dissipative particle dynamics (DPD) method. Through coarse graining, our simulations probe the thermal and momentum transport in nanofluids at a length scale much greater than that in atomistic methods. We show that our simulations model the fluctuating hydrodynamics in nanofluids in a thermodynamically self-consistent manner, which is critical for resolving the current controversies on mechanisms of heat conduction in nanofluids. Simulation results indicate that the Brownian motion of nanoparticles plays a negligible role in determining the thermal conductivity of nanofluids at least within the framework of fluctuating hydrodynamics at mesoscales.