ANN modelling and experimental investigation on effective thermal conductivity of ethylene glycol:water nanofluids

Journal of Thermal Analysis and Calorimetry - In this study, ethylene glycol (EG)–water (35:65 %v)-based nanofluids have been prepared to study enhancement in thermal conductivity....     

Experimental determination of thermal conductivity and viscosity of different nanofluids and its effect on a hybrid solar collector

In this research, three different volume concentrations (??=?0.05, 0.1 and 0.2%) of Al₂O₃/water, CuO/water and Al₂O₃–CuO/water (50:50) nanofluids are prepared by adopting a two-step nanofluid preparation method. Al₂O₃ and CuO nanoparticles with the average diameter of 50 nm and 27 nm were dispersed in distilled water. The thermal conductivity and viscosity of prepared nanofluids are measured for different temperatures by using KD2 Pro thermal property analyzed and Brookfield viscometer, respectively. The effects of nanofluids on the thermal, electrical and overall efficiency of photovoltaic thermal (PVT) solar collector are also studied. The experimental results revealed that the thermal conductivity and viscosity increase with the increase in percentage volume concentration and viscosity decreases with the increase in temperature. Furthermore, the obtained maximum thermal and electrical efficiencies of a PVT solar collector for 0.2% volume concentration of hybrid nanofluids are 82% and 15%, respectively, at peak solar radiation. The highest overall efficiency of a PVT collector with .2% volume concentration of hybrid nanofluid was 97% at peak solar radiation. Results recommend that nanofluids can be used as a heat transfer in PVT solar collector.

     

Controlled synthesis and size-dependent thermal conductivity of Fe3O4 magnetic nanofluids

The effect of nanoparticle size (4~44 nm) on the thermal conductivities of heat transfer oils has been systematically examined using iron oxide nanoparticles. Such Fe(3)O(4) nanoparticles were synthesized by a simple one-pot pyrolysis method. The size (16~44 nm), shape and assembly patterns of monodisperse Fe(3)O(4) nanoparticles were modulated by only controlling the amount of Fe(acac)(3). After the as-prepared Fe(3)O(4) NPs were dispersed in heat transfer oils, the prepared magnetic nanofluids exhibit higher thermal conductivity than heat transfer oils, and the enhanced values increase with a decrease in particle size. In addition, the viscosities of all nanofliuids are remarkably lower than that of the base fluid, which has been found for the first time in the nanofluid field. The promising features offer potential application in thermal energy engineering.     

Synthesis and characterization of graphene oxide-based nanofluids and study of their thermal conductivity

Journal of Thermal Analysis and Calorimetry - In the present research work, we investigated the effect of loading mass concentrations (0.05%, 0.15%, and 0.25 mass %) and temperature...     

Thermal conductivity of Al2O3/water nanofluids

A considerable number of studies can be found on the thermal conductivity of nanofluids in which Al₂O₃ nanoparticles are used as additives. In the present study, the aim is to measure the thermal conductivity of very narrow Al₂O₃ nanoparticles with the size of 5 nm suspended in water. The thermal conductivity of nanofluids with concentrations up to 5 % is measured in a temperature range between 26 and 55 °C. Using the experimental data, a correlation is presented as a function of the temperature and volume fraction of nanoparticles. Finally, a sensitivity analysis is performed to assess the sensitivity of thermal conductivity of nanofluids to increase the particle loading at different temperatures. The sensitivity analysis reveals that at a given concentration, the sensitivity of thermal conductivity to particle loading increases when the temperature increases.     

The effect of capillary column diameter on the performance of thermal conductivity detectors

The concentration sensitivity of a thermal conductivity detector (TCD) depends, among other factors, on the amount of sample mixture in the detector's sensing cell. Since the cell volume has to be appropriately matched with column diameter, it makes the concentration sensitivity of a TCD dependent on column diameter and, therefore, on the speed of gas chromatography. Through reduction of column diameter, higher speed tends to lead to a reduction in the concentration sensitivity of the cell. The factor which the most directly affects the concentration sensitivity of a TCD cell is the heat power conducted through the cell. The higher the power, the greater the sensitivity. The limit of detection of a TCD depends on the concentration-sensitivity of its cell and on the level of statistical errors in the measurement. The errors increase with increasing analysis speed. As the column diameter is reduced, the errors cause additional worsening (on top of the decrease in concentration sensitivity) of the detection limit, dynamic range, and other performance characteristics of the TCD.     

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...     

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.     

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...     

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 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.     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Nonequilibrium versus equilibrium molecular dynamics for calculating the thermal conductivity of nanofluids

Journal of Thermal Analysis and Calorimetry - Both equilibrium and nonequilibrium molecular dynamics (EMD and NEMD, respectively) methods have been used to predict the thermal conductivity of...     

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.     

An experimental study on the air refinement and heat recovery of hybrid TiO2-Ag nanofluids

Journal of Thermal Analysis and Calorimetry - Most of the research on nanofluids focus on the heat/mass transfer or solar collection, while little consideration is given to the synergistic use of...     

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....     

An experimental study on thermal decomposition behavior of magnesite

Thermal decomposition of magnesite is investigated by using a TG–MS. Different kinetic methods including Coats–Redfern, Flynn–Wall–Ozawa, and Kissinger–Akahira–Sunose are used to investigate the thermal decomposition kinetics of magnesite. It was observed that the activation energy values obtained by these methods are similar. The average apparent activation energy is found to be about 203 kJ mol^?1. The raw magnesite and its decomposition products obtained at different temperatures are analyzed by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscope (SEM). The concentration of functional groups, crystal structure and composition, and apparent morphology of decomposition products were studied in detail. The FTIR, XRD, and SEM analyses showed that magnesite was completely decomposed at 973 K to form MgO.