Effects of Experimental Method on Aggregation State and Thermal Conductivity of Carbon Nanotube-Based Fluids

The thermal conductivity of liquids with carbon nanotubes (CNTs) is higher than that of liquids with spherical nanoparticles which results from low-resistance heat flow paths formed by CNT–CNT contact. Since CNTs easily precipitate or cluster in base liquids, path formation depends on the dispersion state of CNTs. A model of the thermal-conductivity enhancement of liquids with CNTs is presented by incorporating the aggregate state where such paths are formed. This model concludes that the anomalously wide range of enhancement values that have been observed recently is attributed to aggregate concentration. CNT clustering and sedimentation in base liquids are the causes of the decreased thermal-conductivity enhancement of such liquids due to the increase of the volume fraction of CNTs in aggregates. Predictions based on our model also show that experimental methods of obtaining liquids with uniformly dispersed CNTs can change the CNT geometry and aggregate concentration related to the thermal-conductivity enhancement. Surfactant addition, CNT surface treatment with acid, and sonication have characteristic effects on the CNT state and thermal conduction. The model from this study can prove helpful in explaining the magnitude of such effects quantitatively.     

Thermal Conductivity and Viscosity Measurements of Water-Based TiO<Subscript>2</Subscript> Nanofluids

In this study, the thermal conductivity and viscosity of TiO₂ nanoparticles in deionized water were investigated up to a volume fraction of 3% of particles. The nanofluid was prepared by dispersing TiO₂ nanoparticles in deionized water by using ultrasonic equipment. The mean diameter of TiO₂ nanoparticles was 21 nm. While the thermal conductivity of nanofluids has been measured in general using conventional techniques such as the transient hot-wire method, this work presents the application of the 3ω method for measuring the thermal conductivity. The 3ω method was validated by measuring the thermal conductivity of pure fluids (water, methanol, ethanol, and ethylene glycol), yielding accurate values within 2%. Following this validation, the effective thermal conductivity of TiO₂ nanoparticles in deionized water was measured at temperatures of 13 °C, 23 °C, 40 °C, and 55 °C. The experimental results showed that the thermal conductivity increases with an increase of particle volume fraction, and the enhancement was observed to be 7.4% over the base fluid for a nanofluid with 3% volume fraction of TiO₂ nanoparticles at 13 °C. The increase in viscosity with the increase of particle volume fraction was much more than predicted by the Einstein model. From this research, it seems that the increase in the nanofluid viscosity is larger than the enhancement in the thermal conductivity.     

Evidence for enhanced thermal conduction through percolating structures in nanofluids

The unusually large enhancement of thermal conductivity (k/k(f)～4.0, where k and k(f) are the thermal conductivities of the nanofluid and the base fluid, respectively) observed in a nanofluid containing linear chain-like aggregates provides direct evidence for efficient transport of heat through percolating paths. The nanofluid used was a stable colloidal suspension of magnetite (Fe(3)O(4)) nanoparticles of average diameter 6.7?nm, coated with oleic acid and dispersed in kerosene. The maximum enhancement under magnetic field was about 48φ (where φ is the volume fraction). The maximum enhancement is observed when chain-like aggregates are uniformly dispersed without clumping. These results also suggest that nanofluids containing well-dispersed nanoparticles (without aggregates) do not exhibit significant enhancement of thermal conductivity. Our findings offer promising applications for developing a new generation of nanofluids with tunable thermal conductivity.     

Effect of aggregation kinetics on the thermal conductivity of nanoscale colloidal solutions (nanofluid)

The thermal conductivity, k, of nanoscale colloidal suspensions (also known as nanofluid), consisting of nanoparticles suspended in a base liquid, is much higher than the thermal conductivity of the base liquid at very small volume fractions of the nanoparticles. However, experimental results from various groups all across the world have shown various anomalies such as a peak in the enhancement of k with respect to nanoparticle size, an increase as well as a decrease in the ratio of k of these colloidal solutions with the k of the base fluid with increasing temperature, and a dependence of k on pH and time. In this paper, the aggregation kinetics of nanoscale colloidal solutions are combined with the physics of thermal transport to capture the effects of aggregation on k. Results show that the observed anomalies reported in experimental work can be well described by taking aggregation kinetics into account. Finally, we show that colloidal chemistry plays a significant role in deciding the k of colloidal nanosuspensions.     

A molecular dynamics-stochastic model for thermal conductivity of nanofluids and its experimental validation

A model to predict the enhanced thermal conductivity of water based copper nanofluid on the basis of molecular dynamics simulation coupled with stochastic simulation shows for the first time that the temperature of a copper nanoparticle colliding with a heat source can rise rapidly within the short collision period (e.g., 10-50 ps) estimated by impact dynamics due to phonon transfer. Thereafter the particles undergo Brownian movement in the base fluid and transfer the excess heat in about 2 to 3 ms to the surrounding fluid resulting in an appreciable enhancement of the thermal conductivity of the fluid. Microconvection has minor contribution to the enhanced thermal conductivity of nanofluids. The predicted thermal conductivity of nanofluid and its variation with the volume fraction of the nanoparticles agree well with the present experiments, as well as, with the data reported in the literature.     

MgO nanofluids: higher thermal conductivity and lower viscosity among ethylene glycol-based nanofluids containing oxide nanoparticles

Five kinds of oxides, including MgO, TiO₂, ZnO, Al₂O₃ and SiO₂ nanoparticles were selected as additives and ethylene glycol (EG) was used as base fluid to prepare stable nanofluids. Thermal transport property investigation demonstrated substantial increments in the thermal conductivity and viscosity of all these nanofluids with oxide nanoparticle addition in EG. Among all the studied nanofluids, MgO–EG nanofluid was found to have superior features, with the highest thermal conductivity and lowest viscosity. The thermal conductivity enhancement ratio of MgO–EG nanofluid increases nonlinearly with the volume fraction of nanoparticles. In the experimental temperature range of 10–60°C, thermal conductivity enhancement ratio of MgO–EG nanofluids appears to have a weak dependence on the temperature. Viscosity measurements showed that MgO–EG nanofluids demonstrated Newtonian rheological behaviour, and the viscosity significantly decreases with the temperature. The thermal conductivity and viscosity increments of the nanofluids are much higher than the corresponding values predicted by the existing classical models for the solid–liquid mixture.     

A study on the effects of temperature and volume fraction on thermal conductivity of copper oxide nanofluid

By using copper oxide nanofluid fabricated by the self-made Submerged Arc Nanofluid Synthesis System (SANSS), this paper measures the thermal conductivity under different volume fractions and different temperatures by thermal properties analyzer, and analyzes the correlation among the thermal conductivity, volume fraction, and temperature of nanofluid. The CuO nanoparticles used in the experiment are needle-like, with a mean particle size of about 30 nm. They can be stably suspended in deionized water for a long time. The experimental results show that under the condition that the temperature is 40 degrees C, when the volume fraction of nanofluid increases from 0.2% to 0.8%, the thermal conductivity increment of the prepared nanofluid towards deionized water can be increased from 14.7% to 38.2%. Under the condition that the volume fraction is 0.8%, as the temperature of nanofluid rises from 5 degrees C to 40 degrees C, the thermal conductivity increment of the prepared nanofluid towards deionized water increases from 5.9% to 38.2%. Besides, the effects of temperature change are greater than the effects of volume fraction on the thermal conductivity of nanofluid. Therefore, when the self-made copper oxide nanofluid is applied to the heat exchange device under medium and high temperature, an optimal radiation effect can be acquired.     

Stability and thermal conductivity enhancement of carbon nanotube nanofluid using gum arabic

This experimental study reports on the stability and thermal conductivity enhancement of carbon nanotubes (CNTs) nanofluids with and without gum arabic (GA). The stability of CNT in the presence of GA dispersant in water is systematically investigated by taking into account the combined effect of various parameters, such as sonication time, temperature, dispersant and particle concentration. The concentrations of CNT and GA have been varied from 0.01 to 0.1?wt% and from 0.25 to 5?wt%, respectively, and the sonication time has been varied in between 1 and 24?h. The stability of nanofluid is measured in terms of CNT concentration as a function of sediment time using UV-Vis spectrophotometer. Thermal conductivity of CNT nanofluids is measured using KD-2 prothermal conductivity meter from 25 to 60°C. Optimum GA concentration is obtained for the entire range of CNT concentration and 1–2.5?wt% of GA is found to be sufficient to stabilise all CNT range in water. Rapid sedimentation of CNTs is observed at higher GA concentration and sonication time. CNT in aqueous suspensions show strong tendency to aggregation and networking into clusters. Stability and thermal conductivity enhancement of CNT nanofluids have been presented to provide a heat transport medium capable of achieving high heat conductivity. Increase in CNT concentrations resulted in the non-linear thermal conductivity enhancement. More than 100–250% enhancement in thermal conductivity is observed for the range of CNT concentration and temperature.     

Investigation of turbulent heat transfer performance of aviation turbine fuel multi-wall carbon nanotube nanofluid

Nanofluid is a novel heat transfer fluid prepared by suspending high thermal conductivity nano-sized particles in conventional fluids (water, engine oil and ethylene glycol). Thermo-physical properties (Thermal conductivity, dynamic viscosity and specific heat) and turbulent heat transfer performance of Aviation Turbine Fuel (ATF) based Multiwall Carbon Nanotube (MWCNT) nanofluid are investigated experimentally for particle volume concentrations of 0–1% and at mean fluid temperatures of 30οC and 50οC for a potential regenerative heat transfer application in semi-cryogenic liquid propellant rocket engine. The experimental results show that the heat transfer coefficient of the nanofluid increases with particle volume concentration, with a maximum enhancement at 1% particle volume concentration of approximately 23% and 50% observed at 30οC and 50οC respectively. Two different numerical modelling approaches (a single phase fluid model with enhanced thermo-physical properties and an Eulerian-Lagrangian model called the “discrete phase model”) are employed to simulate the experimental conditions. The predictions from both numerical modelling approaches are found to compare reasonably well with the experimental data. The enhanced heat transfer performance is expressed on an equal power penalty basis to clearely show the advantage of the nanofluid.     

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

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.     

An effective thermal conductivity model of nanofluids with a cubical arrangement of spherical particles

The theoretical investigation of the effective thermal conductivities of nanofluids, a new class of solid-liquid suspensions, is important in both predicting and designing nanofluids with effective thermal conductivities. We have developed a new thermal conductivity model for nanofluids that is based on the assumption that monosized spherical particles are uniformly dispersed in the liquid and are located at the vertexes of a simple cubic lattice, with each particle surrounded by a liquid layer having a thermal conductivity that differs from that of the bulk liquid. This model nanofluid with a cubical arrangement of nanoparticles gives a more practical upper limit of thermal conduction than a model nanofluid with a parallel arrangement of nanoparticles. The new model unexpectedly shows a nonlinear relationship of thermal conductivity with particle concentration, whereas the conductivity-concentration curve changes from convex upward to concave upward with increasing volume concentration. The effects of particle and layer parameters on the effective thermal conductivities are also analyzed. A comparison of predicted thermal conductivity values and experimental data shows that the predicted values are much higher than the experimental data, a finding that indicates that there is a potential to further improve the effective thermal conductivities of nanofluids with more uniformly dispersed particles.     

基于径向基神经网络模型预测CuO-ZnO/(乙二醇-水)混合纳米流体导热系数

采用两步法制备CuO-ZnO质量分数为0~3 wt%的CuO-ZnO/(乙二醇(EG)-水)混合纳米流体。其中,纳米颗粒CuO和ZnO质量比为50∶50不变,基液混合比(EG与去离子水的质量比)变化范围为20∶80~80∶20,分析其导热系数随温度(25~60℃)及基液比的变化规律。然后,以质量分数、温度及基液混合比为自变量,导热系数为因变量,采用径向基神经网络(RBFNN)模型预测导热系数,并与反向传播神经网络(BPNN)模型和多元线性回归(MLR)模型的预测值对比。结果表明,CuO-ZnO/(EG-水)纳米流体导热系数随温度的升高呈非线性增大,当CuO-ZnO质量分数为3 wt%及基液混合比为20∶80时,其导热系数与纯基液相比增大了14.03%~23.47%;但随着基液中的EG含量增大,导热系数非线性下降。总之,CuO-ZnO/(EG-水)纳米流体的导热系数受粒子随机运动和温度变化呈非线性变化。采用RBFNN模型预测CuO-ZnO/(EG-水)纳米流体的导热系数,其结果与BPNN模型和MLR模型对比,RBFNN模型性能评价指标均方根误差(RMSE)、平均相对百分比误差(MRPE)及误差平方和(SSE)更接近于0,多元统计系数R2更接近于1,说明RBFNN模型预测导热系数的精度更高,能够较好地表征不同参数对导热系数的影响,为CuO-ZnO/(EG-水)纳米流体的热物理性能参数的预测提供了一种有效的数据驱动建模方法。      

Electrical and thermal property enhancement of fiber-reinforced polymer laminate composites through controlled implementation of multi-walled carbon nanotubes

Aligned carbon nanotubes (CNTs) are implemented into alumina-fiber reinforced laminates, and enhanced mass-specific thermal and electrical conductivities are observed. Electrical conductivity enhancement is useful for electrostatic discharge and sensing applications, and is used here for both electromagnetic interference (EMI) shielding and deicing. CNTs were grown directly on individual fibers in woven cloth plies, and maintained their alignment during the polymer (epoxy) infiltration used to create laminates. Using multiple complementary methods, non-isotropic electrical and thermal conductivities of these hybrid composites were thoroughly characterized as a function of CNT volume/mass fraction. DC and AC electrical conductivity measurements demonstrate high electrical conductivity of >100 S/m (at 3% volume fraction, ∼1.5% weight fraction, of CNTs) that can be used for multifunctional applications such as de-icing and electromagnetic shielding. The thermal conductivity enhancement (∼1 W/m K) suggests that carbon-fiber based laminates can significantly benefit from aligned CNTs. Application of such new nano-engineered, multi-scale, multi-functional CNT composites can be extended to system health monitoring with electrical or thermal resistance change induced by damage, fire-resistant structures among other multifunctional attributes.     

Investigation of turbulent heat transfer and nanofluid flow in a double pipe heat exchanger

In present study, heat transfer and turbulent flow of water/alumina nanofluid in a parallel as well as counter flow double pipe heat exchanger have been investigated. The governing equations have been solved using an in-house FORTRAN code, based on finite volume method. Single-phase and standard k-ε models have been used for nanofluid and turbulent modeling, respectively. The internal fluid has been considered as hot fluid (nanofluid) and the external fluid, cold fluid (base fluid). The effects of nanoparticles volume fraction, flow direction and Reynolds number on base fluid, nanofluid and wall temperatures, thermal efficiency, Nusselt number and convection heat transfer coefficient have been studied. The results indicated that increasing the nanoparticles volume fraction or Reynolds number causes enhancement of Nusselt number and convection heat transfer coefficient. Maximum rate of average Nusselt number and thermal efficiency enhancement are 32.7% and 30%, respectively. Also, by nanoparticles volume fraction increment, the outlet temperature of fluid and wall temperature increase. Study the minimum temperature in the solid wall of heat exchangers, it can be observed that the minimum temperature in counter flow has significantly reduced, compared to parallel flow. However, by increasing Reynolds number, the slope of thermal efficiency enhancement of heat exchanger gradually tends to a constant amount. This behavior is more obvious in parallel flow heat exchangers. Therefore, using of counter flow heat exchangers is recommended in higher Reynolds numbers.     

Application of CNT nanofluids in a turbulent flow heat exchanger

Nanofluids have received much attention since its discovery owing to its enhanced thermal conductivity and heat transfer characteristics which makes them a promising coolant in heat transfer application. In this study, the enhancement in heat transfer of carbon nanotube (CNT) nanofluids under turbulent flow conditions is investigated experimentally. The CNT concentration was varied from 0.051 to 0.085 wt%, respectively. The nanofluid suspension was stabilised by gum arabic through a process of homogenisation and water bath sonication at 25 °C. The flow rate of cold fluid (water) is varied from 1.7 to 3 L/min, while flow rate of the hot fluid is varied between 2 and 3.5 L/min. Thermal conductivity, density, and viscosity of the nanofluids are also measured as a function of temperature and CNT concentration. The experimental results were validated with theoretical correlations for turbulent flow available in the literature. Results showed an enhancement in heat transfer between 9% and 67% as a function of temperature and CNT concentration.     

A nonlinear effective thermal conductivity model for carbon nanotube and nanofiber suspensions

It has been experimentally demonstrated that suspensions of carbon nanotubes (CNTs) and nanofibers (CNFs) significantly increase the thermal conductivity of nanofluids; however, a physically sound theory of the underlying phenomenon is still missing. In this study, the nonlinear nature of the effective thermal conductivity enhancement with the particle concentration of CNT and CNF nanofluids is explained physically using the excluded volume concept. Specifically, the number of contacting CNTs and CNFs could be calculated by using the excluded volume concept, where the distance for heat to travel in a cylinder between the contacting cylinders in the thermal network of percolating CNTs and CNFs increased with the excluded volume. In contrast to the effective thermal conductivity model of Sastry et al (2008 Nanotechnology 19 055704) the present revised model could reproduce the nonlinear increase of the thermal conductivity with particle concentration, as well as the dependence on the diameter and aspect ratio of the CNTs and CNFs. It was found that the alignment of CNTs and CNFs due to the long range repulsion force decreases the excluded volume, leading to both the convex and concave nonlinear as well as linear increase of the thermal conductivity with particle concentration. The difference between various carrier fluids of the suspensions could be explained as the result of the change in the excluded volume in different base fluids.     

Effects of curvature ratio on forced convection and entropy generation of nanofluid in helical coil using two-phase approach

Applying nanofluid and helical coils are two effective methods for thermal performance enhancement. Combination of these techniques could improve the energy efficiency of thermal equipment dramatically. In this study, a numerical analysis of nanofluid flowing in helical coil with constant wall temperature boundary condition was performed to evaluate nanofluid superiority over the base fluid. Forced convective heat transfer and entropy generation of aqueous Al₂O₃ nanofluid with temperature dependent properties were investigated. Eulerian two-phase mixture model was employed for nanofluid modeling and governing mass, momentum, energy, and volume fraction equations were solved using finite volume method. Simulations covered a range of nanoparticle volume fraction of 1–3%, Reynolds number from 200 to 2000, and curvature ratio of 0.05–0.2. In order to evaluate the heat transfer performance, a parameter referred as thermo-hydrodynamic performance index was applied. Also, entropy generation analysis was performed to examine the efficiency of the helical coil and nanofluid. The results demonstrate that performance index enhances by decreasing the Reynolds number and the increasing nanoparticle concentration. The best thermo-hydrodynamic performance can be obtained at low Reynolds number, high nanoparticle volume fraction, and large curvature ratio. Increasing curvature ratio decreases the ratio of local entropy generation by nanofluid to the base fluid. So, utilization of water based Al₂O₃ nanofluid in higher curvature ratio is more efficient from irreversibility point of view.     

Model for thermal conductivity of CNT-nanofluids

This work presents a simple model for predicting the thermal conductivity of carbon nanotube (CNT) nanofluids. Effects due to the high thermal conductivity of CNTs and the percolation of heat through it are considered to be the most important reasons for their anomalously high thermal conductivity enhancement. A new approach is taken for the modeling, the novelty of which lies in the prediction of the thermal behaviour of oil based as well as water based CNT nanofluids, which are quite different from each other in thermal characteristics. The model is found to correctly predict the trends observed in experimental data for different combinations of CNT nanofluids with varying concentrations.     

Thermal Conductivity of Suspensions Containing Nanosized SiC Particles

Nanosized SiC suspensions were prepared, and their thermal conductivities were measured using a transient hot-wire method. The experimental results showed that the thermal conductivities of the studied suspensions were increased as expected, and the enhancement was proportional to the volume fraction of the solid phase, but the increasing ratio of the thermal conductivity was not significantly related to the base fluid. The effects of the morphologies (size and shape) of the added solid phase on the enhancement of the thermal conductivity of the nanoparticle suspension are reported for the first time.