Al2O3/TiO2 hybrid nanofluids thermal conductivity

Journal of Thermal Analysis and Calorimetry - This research deals with experimental studies on thermal conductivity variation of Al2O3 and TiO2 hybrid nanofluids with water as the base fluid. In...     

An intelligent approach to predicting the effect of nanoparticle mixture ratio,concentration and temperature on thermal conductivity of hybrid nanofluids

Journal of Thermal Analysis and Calorimetry - Hybrid nanofluids are better heat transfer fluids than conventional nanofluids because of the combined properties of two or more nanoparticles. In this...     

The effect of silver ions electrolytically introduced into colloidal nanodiamond solution on its viscosity and thermal conductivity

Experimental data have been presented on the influence of silver on the viscosity and thermal conductivity of a dispersion of diamond nanoparticles. A stable dispersion (5 wt %) of detonation nanodiamond particles has been used in the experiments. Silver ions have been introduced electrolytically into the dispersion of diamond nanoparticles. Silver concentration was not higher than 0.05 wt %. It has been shown that the introduction of silver ions significantly affects the thermal conductivity and viscosity of the dispersion.     

The noble effect of aging on the thermal conductivity of modified CNTs-ethylene glycol nanofluids

In order to improve the heat transfer process by using nanofluids, different nanoparticles and base fluids have been studied. In this work, stability and effect of aging and temperature on the thermal conductivity of CNTs-ethylene glycol (EG) nanofluids were investigated. Chemical functionalisation was used to oxidise the surface of CNTs. The functionalised CNTs were used to prepare the nanofluids by a two-step method. The stability of nanofluids was measured by UV-vis spectroscopy and the results showed that the nanofluids had a good stability over several days. Immediately after nanofluid preparation not too much increase was observed for thermal conductivity but the nanofluid aging had a great influence on the improvement of the thermal conductivity, as after 65 days, about 50% increase was observed. The increase has been attributed to forming an ordered nanolayer of EG molecules around the CNTs. Also no significant temperature dependence of thermal conductivity was observed up to 50°C possibly due to the lack of temperature dependence of CNTs Brownian motions.     

Determination of nanolayer thickness and effective thermal conductivity of nanofluids

Nanofluids having high thermal conductivity enhancement relative to conventional pure fluids are fluids engineered by suspending solid nanoparticles into base fluids. In the present study, calculating the Van der Waals interaction energy between a nanoparticle and an ordered liquid nanolayer around it, the nanolayer thickness was determined, the average velocity of the Brownian motion of nanoparticles in a fluid was estimated, and by taking into account both the aggregation of nanoparticles and the presence of a nanolayer a new thermal conductivity model for nanofluids was proposed. It has been shown that the nanolayer thickness in nanofluids is independent on the radius of nanoparticles when the radius of the nanoparticles is much greater than the nanolayer thickness and determines by the specific interaction of the given liquid and solid nanoparticle through the Hamaker constant, the surface tension and the wetting angle. It was proved that the frequency of heat exchange by fluid molecules is two orders of magnitude higher than the frequency of heat transfer by nanoparticles, so that the contribution due to the Brownian motion of nanoparticles in the thermal conductivity of nanofluids can be neglected. The predictions of the proposed model of thermal conductivity were compared with the experimental data and a good correlation was achieved.     

Experimental investigation and prediction of the thermal conductivity of water-based oxide nanofluids with low volume fractions

Journal of Thermal Analysis and Calorimetry - Although there are already many models for prediction of the thermal conductivity of nanoparticle suspension, most of them only consider the cases for...     

Phenomenological formula for thermal conductivity coefficient of water-based nanofluids

A phenomenological formula has been proposed to describe the thermal conductivity of waterbased nanofluids. The formula has been derived based on available experimental data on nanofluids containing Al₂O₃ particles. It takes into account the dependence of the thermal conductivity coefficients of the nanofluids on both volume concentration and sizes of the particles. The formula has also been shown to describe with an accuracy of about 3% the thermal conductivity coefficients of nanofluids containing TiO₂, SiO₂, ZrO₂, and CuO particles with sizes of 8–150 nm and volume concentrations as high as 8%.     

Effect of copper nanoparticle aggregation on the thermal conductivity of nanofluids

The thermal conductivity of water and glycerol is investigated via the transient hot wire method by adding small amounts of copper nanoparticles to solutions. At a 0.2% copper nanoparticle concentration, the thermal conductivity coefficient rises to 25% for the Cu + glycerol system, and to 35% for Cu + water system. A mechanism and mathematical model for describing the nanoparticle aggregation effect on the thermal properties of nanofluids are proposed, based on an analysis of the accumulated experimental data. It is shown that the enhancement of nanofluid thermal conductivity at low nanoparticle concentrations is directly proportional to their volume fraction and thermal conductivity coefficient, and (in accordance with the literature data) is inversely proportional to the radius and the aggregation ratio. The proposed model describes the existing experimental data quite well. The results from this work can be applied to the rapid cooling of electronic components, in the power engineering for ensuring the rapid and effective transfer of thermal energy in a nuclear reactor, and in the oil industry for thermal stimulation.     

A model of nanofluids effective thermal conductivity based on dimensionless groups

Thermal conductivity is an important parameter in the field of nanofluid heat transfer. This article presents a novel model for the prediction of the effective thermal conductivity of nanofluids based on dimensionless groups. The model expresses the thermal conductivity of a nanofluid as a function of the thermal conductivity of the solid and liquid, their volume fractions, particle size and interfacial shell properties. According to this model, thermal conductivity changes nonlinearly with nanoparticle loading. The results are in good agreement with the experimental data of alumina-water and alumina-ethylene glycol based nanofluids.     

Toward a modeling study of thermal conductivity of nanofluids using LSSVM strategy

Journal of Thermal Analysis and Calorimetry - In the present study, a comprehensive model based on least square support vector machine algorithm (LSSVM) was developed to estimate thermal...     

Performance improvement of solar still by using hybrid nanofluids

Journal of Thermal Analysis and Calorimetry - The biggest shortcoming of conventional solar still is its limited productivity. In this work, improvement of solar still performance using two hybrid...     

Influence of graphene oxide nanosheets on the stability and thermal conductivity of nanofluids

Journal of Thermal Analysis and Calorimetry - Many theoretical and experimental studies on heat transfer and flow behavior of nanofluids have been conducted, and the results show that nanofluids...     

A new semi-analytical model for effective thermal conductivity of nanofluids

A new theoretical model for thermal conductivity of nanofluids is developed incorporating effective medium theory, interfacial layer, particle aggregation and Brownian motion-induced convection from multiple nanoparticles/aggregates. The predicated result using aggregate size, which represents the particle size in the actual condition of nanofluids, fits well with the experimental data for water-, R113- and ethylene glycol (EG)-based nanofluids. The present model also gives much better predictions compared to the existing models. A parametric analysis, particularly particle aggregation, is conducted to investigate the dependence of effective thermal conductivity of nanofluids on the properties of nanoparticles and fluid. Aggregation is the main factor responsible for thermal conductivity enhancement. The dynamic contribution of Brownian motion on thermal conductivity enhancement is surpassed by that of static mechanisms, particularly at high volume fraction. Predication also indicated that the viscosity increases faster than the thermal conductivity, causing the highly aggregated nanofluids to become unfavourable, especially for d_f = 1.8.     

Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles

Journal of Thermal Analysis and Calorimetry - Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids...     

Application of ANN technique to predict the thermal conductivity of nanofluids: a review

Journal of Thermal Analysis and Calorimetry - A thorough analysis of the uses of “intelligence” approaches to model the nanofluid thermal conductivity is carried out in this study....